Mercurial > repos > davidvanzessen > shm_csr
changeset 80:a4617f1d1d89 draft
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--- a/.gitattributes Tue Sep 01 16:03:44 2020 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,2 +0,0 @@ -# Auto detect text files and perform LF normalization -* text=auto
--- a/.gitignore Tue Sep 01 16:03:44 2020 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,4 +0,0 @@ - -shm_csr\.tar\.gz - -\.vscode/settings\.json
--- a/LICENSE Tue Sep 01 16:03:44 2020 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,21 +0,0 @@ -MIT License - -Copyright (c) 2019 david - -Permission is hereby granted, free of charge, to any person obtaining a copy -of this software and associated documentation files (the "Software"), to deal -in the Software without restriction, including without limitation the rights -to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -copies of the Software, and to permit persons to whom the Software is -furnished to do so, subject to the following conditions: - -The above copyright notice and this permission notice shall be included in all -copies or substantial portions of the Software. - -THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -SOFTWARE. \ No newline at end of file
--- a/README.md Tue Sep 01 16:03:44 2020 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,13 +0,0 @@ -# SHM CSR - -Somatic hypermutation and class switch recombination pipeline. -The docker version can be found [here](https://github.com/ErasmusMC-Bioinformatics/ARGalaxy-docker). - -# Dependencies --------------------- -[Python 2.7](https://www.python.org/) -[Change-O](https://changeo.readthedocs.io/en/version-0.4.4/) -[Baseline](http://selection.med.yale.edu/baseline/) -[R data.table](https://cran.r-project.org/web/packages/data.table/data.table.pdf) -[R ggplot2](https://cran.r-project.org/web/packages/ggplot2/ggplot2.pdf) -[R reshape2](https://cran.r-project.org/web/packages/reshape/reshape.pdf)
--- a/aa_histogram.r Tue Sep 01 16:03:44 2020 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,69 +0,0 @@ -library(ggplot2) - -args <- commandArgs(trailingOnly = TRUE) - -mutations.by.id.file = args[1] -absent.aa.by.id.file = args[2] -genes = strsplit(args[3], ",")[[1]] -genes = c(genes, "") -outdir = args[4] - - -print("---------------- read input ----------------") - -mutations.by.id = read.table(mutations.by.id.file, sep="\t", fill=T, header=T, quote="") -absent.aa.by.id = read.table(absent.aa.by.id.file, sep="\t", fill=T, header=T, quote="") - -for(gene in genes){ - graph.title = paste(gene, "AA mutation frequency") - if(gene == ""){ - mutations.by.id.gene = mutations.by.id[!grepl("unmatched", mutations.by.id$best_match),] - absent.aa.by.id.gene = absent.aa.by.id[!grepl("unmatched", absent.aa.by.id$best_match),] - - graph.title = "AA mutation frequency all" - } else { - mutations.by.id.gene = mutations.by.id[grepl(paste("^", gene, sep=""), mutations.by.id$best_match),] - absent.aa.by.id.gene = absent.aa.by.id[grepl(paste("^", gene, sep=""), absent.aa.by.id$best_match),] - } - print(paste("nrow", gene, nrow(absent.aa.by.id.gene))) - if(nrow(mutations.by.id.gene) == 0){ - next - } - - mutations.at.position = colSums(mutations.by.id.gene[,-c(1,2)]) - aa.at.position = colSums(absent.aa.by.id.gene[,-c(1,2,3,4)]) - - dat_freq = mutations.at.position / aa.at.position - dat_freq[is.na(dat_freq)] = 0 - dat_dt = data.frame(i=1:length(dat_freq), freq=dat_freq) - - - print("---------------- plot ----------------") - - m = ggplot(dat_dt, aes(x=i, y=freq)) + theme(axis.text.x = element_text(angle = 90, hjust = 1), text = element_text(size=13, colour="black")) - m = m + geom_bar(stat="identity", colour = "black", fill = "darkgrey", alpha=0.8) + scale_x_continuous(breaks=dat_dt$i, labels=dat_dt$i) - m = m + annotate("segment", x = 0.5, y = -0.05, xend=26.5, yend=-0.05, colour="darkgreen", size=1) + annotate("text", x = 13, y = -0.1, label="FR1") - m = m + annotate("segment", x = 26.5, y = -0.07, xend=38.5, yend=-0.07, colour="darkblue", size=1) + annotate("text", x = 32.5, y = -0.15, label="CDR1") - m = m + annotate("segment", x = 38.5, y = -0.05, xend=55.5, yend=-0.05, colour="darkgreen", size=1) + annotate("text", x = 47, y = -0.1, label="FR2") - m = m + annotate("segment", x = 55.5, y = -0.07, xend=65.5, yend=-0.07, colour="darkblue", size=1) + annotate("text", x = 60.5, y = -0.15, label="CDR2") - m = m + annotate("segment", x = 65.5, y = -0.05, xend=104.5, yend=-0.05, colour="darkgreen", size=1) + annotate("text", x = 85, y = -0.1, label="FR3") - m = m + expand_limits(y=c(-0.1,1)) + xlab("AA position") + ylab("Frequency") + ggtitle(graph.title) - m = m + theme(panel.background = element_rect(fill = "white", colour="black"), panel.grid.major.y = element_line(colour = "black"), panel.grid.major.x = element_blank()) - #m = m + scale_colour_manual(values=c("black")) - - print("---------------- write/print ----------------") - - - dat.sums = data.frame(index=1:length(mutations.at.position), mutations.at.position=mutations.at.position, aa.at.position=aa.at.position) - - write.table(dat.sums, paste(outdir, "/aa_histogram_sum_", gene, ".txt", sep=""), sep="\t",quote=F,row.names=F,col.names=T) - write.table(mutations.by.id.gene, paste(outdir, "/aa_histogram_count_", gene, ".txt", sep=""), sep="\t",quote=F,row.names=F,col.names=T) - write.table(absent.aa.by.id.gene, paste(outdir, "/aa_histogram_absent_", gene, ".txt", sep=""), sep="\t",quote=F,row.names=F,col.names=T) - write.table(dat_dt, paste(outdir, "/aa_histogram_", gene, ".txt", sep=""), sep="\t",quote=F,row.names=F,col.names=T) - - png(filename=paste(outdir, "/aa_histogram_", gene, ".png", sep=""), width=1280, height=720) - print(m) - dev.off() - - ggsave(paste(outdir, "/aa_histogram_", gene, ".pdf", sep=""), m, width=14, height=7) -}
--- a/baseline/Baseline_Functions.r Tue Sep 01 16:03:44 2020 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,2287 +0,0 @@ -######################################################################################### -# License Agreement -# -# THIS WORK IS PROVIDED UNDER THE TERMS OF THIS CREATIVE COMMONS PUBLIC LICENSE -# ("CCPL" OR "LICENSE"). THE WORK IS PROTECTED BY COPYRIGHT AND/OR OTHER -# APPLICABLE LAW. ANY USE OF THE WORK OTHER THAN AS AUTHORIZED UNDER THIS LICENSE -# OR COPYRIGHT LAW IS PROHIBITED. -# -# BY EXERCISING ANY RIGHTS TO THE WORK PROVIDED HERE, YOU ACCEPT AND AGREE TO BE -# BOUND BY THE TERMS OF THIS LICENSE. TO THE EXTENT THIS LICENSE MAY BE CONSIDERED -# TO BE A CONTRACT, THE LICENSOR GRANTS YOU THE RIGHTS CONTAINED HERE IN -# CONSIDERATION OF YOUR ACCEPTANCE OF SUCH TERMS AND CONDITIONS. -# -# BASELIne: Bayesian Estimation of Antigen-Driven Selection in Immunoglobulin Sequences -# Coded by: Mohamed Uduman & Gur Yaari -# Copyright 2012 Kleinstein Lab -# Version: 1.3 (01/23/2014) -######################################################################################### - -# Global variables - - FILTER_BY_MUTATIONS = 1000 - - # Nucleotides - NUCLEOTIDES = c("A","C","G","T") - - # Amino Acids - AMINO_ACIDS <- c("F", "F", "L", "L", "S", "S", "S", "S", "Y", "Y", "*", "*", "C", "C", "*", "W", "L", "L", "L", "L", "P", "P", "P", "P", "H", "H", "Q", "Q", "R", "R", "R", "R", "I", "I", "I", "M", "T", "T", "T", "T", "N", "N", "K", "K", "S", "S", "R", "R", "V", "V", "V", "V", "A", "A", "A", "A", "D", "D", "E", "E", "G", "G", "G", "G") - names(AMINO_ACIDS) <- c("TTT", "TTC", "TTA", "TTG", "TCT", "TCC", "TCA", "TCG", "TAT", "TAC", "TAA", "TAG", "TGT", "TGC", "TGA", "TGG", "CTT", "CTC", "CTA", "CTG", "CCT", "CCC", "CCA", "CCG", "CAT", "CAC", "CAA", "CAG", "CGT", "CGC", "CGA", "CGG", "ATT", "ATC", "ATA", "ATG", "ACT", "ACC", "ACA", "ACG", "AAT", "AAC", "AAA", "AAG", "AGT", "AGC", "AGA", "AGG", "GTT", "GTC", "GTA", "GTG", "GCT", "GCC", "GCA", "GCG", "GAT", "GAC", "GAA", "GAG", "GGT", "GGC", "GGA", "GGG") - names(AMINO_ACIDS) <- names(AMINO_ACIDS) - - #Amino Acid Traits - #"*" "A" "C" "D" "E" "F" "G" "H" "I" "K" "L" "M" "N" "P" "Q" "R" "S" "T" "V" "W" "Y" - #B = "Hydrophobic/Burried" N = "Intermediate/Neutral" S="Hydrophilic/Surface") - TRAITS_AMINO_ACIDS_CHOTHIA98 <- c("*","N","B","S","S","B","N","N","B","S","B","B","S","N","S","S","N","N","B","B","N") - names(TRAITS_AMINO_ACIDS_CHOTHIA98) <- sort(unique(AMINO_ACIDS)) - TRAITS_AMINO_ACIDS <- array(NA,21) - - # Codon Table - CODON_TABLE <- as.data.frame(matrix(NA,ncol=64,nrow=12)) - - # Substitution Model: Smith DS et al. 1996 - substitution_Literature_Mouse <- matrix(c(0, 0.156222928, 0.601501588, 0.242275484, 0.172506739, 0, 0.241239892, 0.586253369, 0.54636291, 0.255795364, 0, 0.197841727, 0.290240811, 0.467680608, 0.24207858, 0),nrow=4,byrow=T,dimnames=list(NUCLEOTIDES,NUCLEOTIDES)) - substitution_Flu_Human <- matrix(c(0,0.2795596,0.5026927,0.2177477,0.1693210,0,0.3264723,0.5042067,0.4983549,0.3328321,0,0.1688130,0.2021079,0.4696077,0.3282844,0),4,4,byrow=T,dimnames=list(NUCLEOTIDES,NUCLEOTIDES)) - substitution_Flu25_Human <- matrix(c(0,0.2580641,0.5163685,0.2255674,0.1541125,0,0.3210224,0.5248651,0.5239281,0.3101292,0,0.1659427,0.1997207,0.4579444,0.3423350,0),4,4,byrow=T,dimnames=list(NUCLEOTIDES,NUCLEOTIDES)) - load("FiveS_Substitution.RData") - - # Mutability Models: Shapiro GS et al. 2002 - triMutability_Literature_Human <- matrix(c(0.24, 1.2, 0.96, 0.43, 2.14, 2, 1.11, 1.9, 0.85, 1.83, 2.36, 1.31, 0.82, 0.52, 0.89, 1.33, 1.4, 0.82, 1.83, 0.73, 1.83, 1.62, 1.53, 0.57, 0.92, 0.42, 0.42, 1.47, 3.44, 2.58, 1.18, 0.47, 0.39, 1.12, 1.8, 0.68, 0.47, 2.19, 2.35, 2.19, 1.05, 1.84, 1.26, 0.28, 0.98, 2.37, 0.66, 1.58, 0.67, 0.92, 1.76, 0.83, 0.97, 0.56, 0.75, 0.62, 2.26, 0.62, 0.74, 1.11, 1.16, 0.61, 0.88, 0.67, 0.37, 0.07, 1.08, 0.46, 0.31, 0.94, 0.62, 0.57, 0.29, NA, 1.44, 0.46, 0.69, 0.57, 0.24, 0.37, 1.1, 0.99, 1.39, 0.6, 2.26, 1.24, 1.36, 0.52, 0.33, 0.26, 1.25, 0.37, 0.58, 1.03, 1.2, 0.34, 0.49, 0.33, 2.62, 0.16, 0.4, 0.16, 0.35, 0.75, 1.85, 0.94, 1.61, 0.85, 2.09, 1.39, 0.3, 0.52, 1.33, 0.29, 0.51, 0.26, 0.51, 3.83, 2.01, 0.71, 0.58, 0.62, 1.07, 0.28, 1.2, 0.74, 0.25, 0.59, 1.09, 0.91, 1.36, 0.45, 2.89, 1.27, 3.7, 0.69, 0.28, 0.41, 1.17, 0.56, 0.93, 3.41, 1, 1, NA, 5.9, 0.74, 2.51, 2.24, 2.24, 1.95, 3.32, 2.34, 1.3, 2.3, 1, 0.66, 0.73, 0.93, 0.41, 0.65, 0.89, 0.65, 0.32, NA, 0.43, 0.85, 0.43, 0.31, 0.31, 0.23, 0.29, 0.57, 0.71, 0.48, 0.44, 0.76, 0.51, 1.7, 0.85, 0.74, 2.23, 2.08, 1.16, 0.51, 0.51, 1, 0.5, NA, NA, 0.71, 2.14), nrow=64,byrow=T) - triMutability_Literature_Mouse <- matrix(c(1.31, 1.35, 1.42, 1.18, 2.02, 2.02, 1.02, 1.61, 1.99, 1.42, 2.01, 1.03, 2.02, 0.97, 0.53, 0.71, 1.19, 0.83, 0.96, 0.96, 0, 1.7, 2.22, 0.59, 1.24, 1.07, 0.51, 1.68, 3.36, 3.36, 1.14, 0.29, 0.33, 0.9, 1.11, 0.63, 1.08, 2.07, 2.27, 1.74, 0.22, 1.19, 2.37, 1.15, 1.15, 1.56, 0.81, 0.34, 0.87, 0.79, 2.13, 0.49, 0.85, 0.97, 0.36, 0.82, 0.66, 0.63, 1.15, 0.94, 0.85, 0.25, 0.93, 1.19, 0.4, 0.2, 0.44, 0.44, 0.88, 1.06, 0.77, 0.39, 0, 0, 0, 0, 0, 0, 0.43, 0.43, 0.86, 0.59, 0.59, 0, 1.18, 0.86, 2.9, 1.66, 0.4, 0.2, 1.54, 0.43, 0.69, 1.71, 0.68, 0.55, 0.91, 0.7, 1.71, 0.09, 0.27, 0.63, 0.2, 0.45, 1.01, 1.63, 0.96, 1.48, 2.18, 1.2, 1.31, 0.66, 2.13, 0.49, 0, 0, 0, 2.97, 2.8, 0.79, 0.4, 0.5, 0.4, 0.11, 1.68, 0.42, 0.13, 0.44, 0.93, 0.71, 1.11, 1.19, 2.71, 1.08, 3.43, 0.4, 0.67, 0.47, 1.02, 0.14, 1.56, 1.98, 0.53, 0.33, 0.63, 2.06, 1.77, 1.46, 3.74, 2.93, 2.1, 2.18, 0.78, 0.73, 2.93, 0.63, 0.57, 0.17, 0.85, 0.52, 0.31, 0.31, 0, 0, 0.51, 0.29, 0.83, 0.54, 0.28, 0.47, 0.9, 0.99, 1.24, 2.47, 0.73, 0.23, 1.13, 0.24, 2.12, 0.24, 0.33, 0.83, 1.41, 0.62, 0.28, 0.35, 0.77, 0.17, 0.72, 0.58, 0.45, 0.41), nrow=64,byrow=T) - triMutability_Names <- c("AAA", "AAC", "AAG", "AAT", "ACA", "ACC", "ACG", "ACT", "AGA", "AGC", "AGG", "AGT", "ATA", "ATC", "ATG", "ATT", "CAA", "CAC", "CAG", "CAT", "CCA", "CCC", "CCG", "CCT", "CGA", "CGC", "CGG", "CGT", "CTA", "CTC", "CTG", "CTT", "GAA", "GAC", "GAG", "GAT", "GCA", "GCC", "GCG", "GCT", "GGA", "GGC", "GGG", "GGT", "GTA", "GTC", "GTG", "GTT", "TAA", "TAC", "TAG", "TAT", "TCA", "TCC", "TCG", "TCT", "TGA", "TGC", "TGG", "TGT", "TTA", "TTC", "TTG", "TTT") - load("FiveS_Mutability.RData") - -# Functions - - # Translate codon to amino acid - translateCodonToAminoAcid<-function(Codon){ - return(AMINO_ACIDS[Codon]) - } - - # Translate amino acid to trait change - translateAminoAcidToTraitChange<-function(AminoAcid){ - return(TRAITS_AMINO_ACIDS[AminoAcid]) - } - - # Initialize Amino Acid Trait Changes - initializeTraitChange <- function(traitChangeModel=1,species=1,traitChangeFileName=NULL){ - if(!is.null(traitChangeFileName)){ - tryCatch( - traitChange <- read.delim(traitChangeFileName,sep="\t",header=T) - , error = function(ex){ - cat("Error|Error reading trait changes. Please check file name/path and format.\n") - q() - } - ) - }else{ - traitChange <- TRAITS_AMINO_ACIDS_CHOTHIA98 - } - TRAITS_AMINO_ACIDS <<- traitChange - } - - # Read in formatted nucleotide substitution matrix - initializeSubstitutionMatrix <- function(substitutionModel,species,subsMatFileName=NULL){ - if(!is.null(subsMatFileName)){ - tryCatch( - subsMat <- read.delim(subsMatFileName,sep="\t",header=T) - , error = function(ex){ - cat("Error|Error reading substitution matrix. Please check file name/path and format.\n") - q() - } - ) - if(sum(apply(subsMat,1,sum)==1)!=4) subsMat = t(apply(subsMat,1,function(x)x/sum(x))) - }else{ - if(substitutionModel==1)subsMat <- substitution_Literature_Mouse - if(substitutionModel==2)subsMat <- substitution_Flu_Human - if(substitutionModel==3)subsMat <- substitution_Flu25_Human - - } - - if(substitutionModel==0){ - subsMat <- matrix(1,4,4) - subsMat[,] = 1/3 - subsMat[1,1] = 0 - subsMat[2,2] = 0 - subsMat[3,3] = 0 - subsMat[4,4] = 0 - } - - - NUCLEOTIDESN = c(NUCLEOTIDES,"N", "-") - if(substitutionModel==5){ - subsMat <- FiveS_Substitution - return(subsMat) - }else{ - subsMat <- rbind(subsMat,rep(NA,4),rep(NA,4)) - return( matrix(data.matrix(subsMat),6,4,dimnames=list(NUCLEOTIDESN,NUCLEOTIDES) ) ) - } - } - - - # Read in formatted Mutability file - initializeMutabilityMatrix <- function(mutabilityModel=1, species=1,mutabilityMatFileName=NULL){ - if(!is.null(mutabilityMatFileName)){ - tryCatch( - mutabilityMat <- read.delim(mutabilityMatFileName,sep="\t",header=T) - , error = function(ex){ - cat("Error|Error reading mutability matrix. Please check file name/path and format.\n") - q() - } - ) - }else{ - mutabilityMat <- triMutability_Literature_Human - if(species==2) mutabilityMat <- triMutability_Literature_Mouse - } - - if(mutabilityModel==0){ mutabilityMat <- matrix(1,64,3)} - - if(mutabilityModel==5){ - mutabilityMat <- FiveS_Mutability - return(mutabilityMat) - }else{ - return( matrix( data.matrix(mutabilityMat), 64, 3, dimnames=list(triMutability_Names,1:3)) ) - } - } - - # Read FASTA file formats - # Modified from read.fasta from the seqinR package - baseline.read.fasta <- - function (file = system.file("sequences/sample.fasta", package = "seqinr"), - seqtype = c("DNA", "AA"), as.string = FALSE, forceDNAtolower = TRUE, - set.attributes = TRUE, legacy.mode = TRUE, seqonly = FALSE, - strip.desc = FALSE, sizeof.longlong = .Machine$sizeof.longlong, - endian = .Platform$endian, apply.mask = TRUE) - { - seqtype <- match.arg(seqtype) - - lines <- readLines(file) - - if (legacy.mode) { - comments <- grep("^;", lines) - if (length(comments) > 0) - lines <- lines[-comments] - } - - - ind_groups<-which(substr(lines, 1L, 3L) == ">>>") - lines_mod<-lines - - if(!length(ind_groups)){ - lines_mod<-c(">>>All sequences combined",lines) - } - - ind_groups<-which(substr(lines_mod, 1L, 3L) == ">>>") - - lines <- array("BLA",dim=(length(ind_groups)+length(lines_mod))) - id<-sapply(1:length(ind_groups),function(i)ind_groups[i]+i-1)+1 - lines[id] <- "THIS IS A FAKE SEQUENCE" - lines[-id] <- lines_mod - rm(lines_mod) - - ind <- which(substr(lines, 1L, 1L) == ">") - nseq <- length(ind) - if (nseq == 0) { - stop("no line starting with a > character found") - } - start <- ind + 1 - end <- ind - 1 - - while( any(which(ind%in%end)) ){ - ind=ind[-which(ind%in%end)] - nseq <- length(ind) - if (nseq == 0) { - stop("no line starting with a > character found") - } - start <- ind + 1 - end <- ind - 1 - } - - end <- c(end[-1], length(lines)) - sequences <- lapply(seq_len(nseq), function(i) paste(lines[start[i]:end[i]], collapse = "")) - if (seqonly) - return(sequences) - nomseq <- lapply(seq_len(nseq), function(i) { - - #firstword <- strsplit(lines[ind[i]], " ")[[1]][1] - substr(lines[ind[i]], 2, nchar(lines[ind[i]])) - - }) - if (seqtype == "DNA") { - if (forceDNAtolower) { - sequences <- as.list(tolower(chartr(".","-",sequences))) - }else{ - sequences <- as.list(toupper(chartr(".","-",sequences))) - } - } - if (as.string == FALSE) - sequences <- lapply(sequences, s2c) - if (set.attributes) { - for (i in seq_len(nseq)) { - Annot <- lines[ind[i]] - if (strip.desc) - Annot <- substr(Annot, 2L, nchar(Annot)) - attributes(sequences[[i]]) <- list(name = nomseq[[i]], - Annot = Annot, class = switch(seqtype, AA = "SeqFastaAA", - DNA = "SeqFastadna")) - } - } - names(sequences) <- nomseq - return(sequences) - } - - - # Replaces non FASTA characters in input files with N - replaceNonFASTAChars <-function(inSeq="ACGTN-AApA"){ - gsub('[^ACGTNacgt[:punct:]-[:punct:].]','N',inSeq,perl=TRUE) - } - - # Find the germlines in the FASTA list - germlinesInFile <- function(seqIDs){ - firstChar = sapply(seqIDs,function(x){substr(x,1,1)}) - secondChar = sapply(seqIDs,function(x){substr(x,2,2)}) - return(firstChar==">" & secondChar!=">") - } - - # Find the groups in the FASTA list - groupsInFile <- function(seqIDs){ - sapply(seqIDs,function(x){substr(x,1,2)})==">>" - } - - # In the process of finding germlines/groups, expand from the start to end of the group - expandTillNext <- function(vecPosToID){ - IDs = names(vecPosToID) - posOfInterests = which(vecPosToID) - - expandedID = rep(NA,length(IDs)) - expandedIDNames = gsub(">","",IDs[posOfInterests]) - startIndexes = c(1,posOfInterests[-1]) - stopIndexes = c(posOfInterests[-1]-1,length(IDs)) - expandedID = unlist(sapply(1:length(startIndexes),function(i){ - rep(i,stopIndexes[i]-startIndexes[i]+1) - })) - names(expandedID) = unlist(sapply(1:length(startIndexes),function(i){ - rep(expandedIDNames[i],stopIndexes[i]-startIndexes[i]+1) - })) - return(expandedID) - } - - # Process FASTA (list) to return a matrix[input, germline) - processInputAdvanced <- function(inputFASTA){ - - seqIDs = names(inputFASTA) - numbSeqs = length(seqIDs) - posGermlines1 = germlinesInFile(seqIDs) - numbGermlines = sum(posGermlines1) - posGroups1 = groupsInFile(seqIDs) - numbGroups = sum(posGroups1) - consDef = NA - - if(numbGermlines==0){ - posGermlines = 2 - numbGermlines = 1 - } - - glPositionsSum = cumsum(posGermlines1) - glPositions = table(glPositionsSum) - #Find the position of the conservation row - consDefPos = as.numeric(names(glPositions[names(glPositions)!=0 & glPositions==1]))+1 - if( length(consDefPos)> 0 ){ - consDefID = match(consDefPos, glPositionsSum) - #The coservation rows need to be pulled out and stores seperately - consDef = inputFASTA[consDefID] - inputFASTA = inputFASTA[-consDefID] - - seqIDs = names(inputFASTA) - numbSeqs = length(seqIDs) - posGermlines1 = germlinesInFile(seqIDs) - numbGermlines = sum(posGermlines1) - posGroups1 = groupsInFile(seqIDs) - numbGroups = sum(posGroups1) - if(numbGermlines==0){ - posGermlines = 2 - numbGermlines = 1 - } - } - - posGroups <- expandTillNext(posGroups1) - posGermlines <- expandTillNext(posGermlines1) - posGermlines[posGroups1] = 0 - names(posGermlines)[posGroups1] = names(posGroups)[posGroups1] - posInput = rep(TRUE,numbSeqs) - posInput[posGroups1 | posGermlines1] = FALSE - - matInput = matrix(NA, nrow=sum(posInput), ncol=2) - rownames(matInput) = seqIDs[posInput] - colnames(matInput) = c("Input","Germline") - - vecInputFASTA = unlist(inputFASTA) - matInput[,1] = vecInputFASTA[posInput] - matInput[,2] = vecInputFASTA[ which( names(inputFASTA)%in%paste(">",names(posGermlines)[posInput],sep="") )[ posGermlines[posInput]] ] - - germlines = posGermlines[posInput] - groups = posGroups[posInput] - - return( list("matInput"=matInput, "germlines"=germlines, "groups"=groups, "conservationDefinition"=consDef )) - } - - - # Replace leading and trailing dashes in the sequence - replaceLeadingTrailingDashes <- function(x,readEnd){ - iiGap = unlist(gregexpr("-",x[1])) - ggGap = unlist(gregexpr("-",x[2])) - #posToChange = intersect(iiGap,ggGap) - - - seqIn = replaceLeadingTrailingDashesHelper(x[1]) - seqGL = replaceLeadingTrailingDashesHelper(x[2]) - seqTemplate = rep('N',readEnd) - seqIn <- c(seqIn,seqTemplate[(length(seqIn)+1):readEnd]) - seqGL <- c(seqGL,seqTemplate[(length(seqGL)+1):readEnd]) -# if(posToChange!=-1){ -# seqIn[posToChange] = "-" -# seqGL[posToChange] = "-" -# } - - seqIn = c2s(seqIn[1:readEnd]) - seqGL = c2s(seqGL[1:readEnd]) - - lenGL = nchar(seqGL) - if(lenGL<readEnd){ - seqGL = paste(seqGL,c2s(rep("N",readEnd-lenGL)),sep="") - } - - lenInput = nchar(seqIn) - if(lenInput<readEnd){ - seqIn = paste(seqIn,c2s(rep("N",readEnd-lenInput)),sep="") - } - return( c(seqIn,seqGL) ) - } - - replaceLeadingTrailingDashesHelper <- function(x){ - grepResults = gregexpr("-*",x) - grepResultsPos = unlist(grepResults) - grepResultsLen = attr(grepResults[[1]],"match.length") - #print(paste("x = '", x, "'", sep="")) - x = s2c(x) - if(x[1]=="-"){ - x[1:grepResultsLen[1]] = "N" - } - if(x[length(x)]=="-"){ - x[(length(x)-grepResultsLen[length(grepResultsLen)]+1):length(x)] = "N" - } - return(x) - } - - - - - # Check sequences for indels - checkForInDels <- function(matInputP){ - insPos <- checkInsertion(matInputP) - delPos <- checkDeletions(matInputP) - return(list("Insertions"=insPos, "Deletions"=delPos)) - } - - # Check sequences for insertions - checkInsertion <- function(matInputP){ - insertionCheck = apply( matInputP,1, function(x){ - inputGaps <- as.vector( gregexpr("-",x[1])[[1]] ) - glGaps <- as.vector( gregexpr("-",x[2])[[1]] ) - return( is.finite( match(FALSE, glGaps%in%inputGaps ) ) ) - }) - return(as.vector(insertionCheck)) - } - # Fix inserstions - fixInsertions <- function(matInputP){ - insPos <- checkInsertion(matInputP) - sapply((1:nrow(matInputP))[insPos],function(rowIndex){ - x <- matInputP[rowIndex,] - inputGaps <- gregexpr("-",x[1])[[1]] - glGaps <- gregexpr("-",x[2])[[1]] - posInsertions <- glGaps[!(glGaps%in%inputGaps)] - inputInsertionToN <- s2c(x[2]) - inputInsertionToN[posInsertions]!="-" - inputInsertionToN[posInsertions] <- "N" - inputInsertionToN <- c2s(inputInsertionToN) - matInput[rowIndex,2] <<- inputInsertionToN - }) - return(insPos) - } - - # Check sequences for deletions - checkDeletions <-function(matInputP){ - deletionCheck = apply( matInputP,1, function(x){ - inputGaps <- as.vector( gregexpr("-",x[1])[[1]] ) - glGaps <- as.vector( gregexpr("-",x[2])[[1]] ) - return( is.finite( match(FALSE, inputGaps%in%glGaps ) ) ) - }) - return(as.vector(deletionCheck)) - } - # Fix sequences with deletions - fixDeletions <- function(matInputP){ - delPos <- checkDeletions(matInputP) - sapply((1:nrow(matInputP))[delPos],function(rowIndex){ - x <- matInputP[rowIndex,] - inputGaps <- gregexpr("-",x[1])[[1]] - glGaps <- gregexpr("-",x[2])[[1]] - posDeletions <- inputGaps[!(inputGaps%in%glGaps)] - inputDeletionToN <- s2c(x[1]) - inputDeletionToN[posDeletions] <- "N" - inputDeletionToN <- c2s(inputDeletionToN) - matInput[rowIndex,1] <<- inputDeletionToN - }) - return(delPos) - } - - - # Trim DNA sequence to the last codon - trimToLastCodon <- function(seqToTrim){ - seqLen = nchar(seqToTrim) - trimmedSeq = s2c(seqToTrim) - poi = seqLen - tailLen = 0 - - while(trimmedSeq[poi]=="-" || trimmedSeq[poi]=="."){ - tailLen = tailLen + 1 - poi = poi - 1 - } - - trimmedSeq = c2s(trimmedSeq[1:(seqLen-tailLen)]) - seqLen = nchar(trimmedSeq) - # Trim sequence to last codon - if( getCodonPos(seqLen)[3] > seqLen ) - trimmedSeq = substr(seqToTrim,1, ( (getCodonPos(seqLen)[1])-1 ) ) - - return(trimmedSeq) - } - - # Given a nuclotide position, returns the pos of the 3 nucs that made the codon - # e.g. nuc 86 is part of nucs 85,86,87 - getCodonPos <- function(nucPos){ - codonNum = (ceiling(nucPos/3))*3 - return( (codonNum-2):codonNum) - } - - # Given a nuclotide position, returns the codon number - # e.g. nuc 86 = codon 29 - getCodonNumb <- function(nucPos){ - return( ceiling(nucPos/3) ) - } - - # Given a codon, returns all the nuc positions that make the codon - getCodonNucs <- function(codonNumb){ - getCodonPos(codonNumb*3) - } - - computeCodonTable <- function(testID=1){ - - if(testID<=4){ - # Pre-compute every codons - intCounter = 1 - for(pOne in NUCLEOTIDES){ - for(pTwo in NUCLEOTIDES){ - for(pThree in NUCLEOTIDES){ - codon = paste(pOne,pTwo,pThree,sep="") - colnames(CODON_TABLE)[intCounter] = codon - intCounter = intCounter + 1 - CODON_TABLE[,codon] = mutationTypeOptimized(cbind(permutateAllCodon(codon),rep(codon,12))) - } - } - } - chars = c("N","A","C","G","T", "-") - for(a in chars){ - for(b in chars){ - for(c in chars){ - if(a=="N" | b=="N" | c=="N"){ - #cat(paste(a,b,c),sep="","\n") - CODON_TABLE[,paste(a,b,c,sep="")] = rep(NA,12) - } - } - } - } - - chars = c("-","A","C","G","T") - for(a in chars){ - for(b in chars){ - for(c in chars){ - if(a=="-" | b=="-" | c=="-"){ - #cat(paste(a,b,c),sep="","\n") - CODON_TABLE[,paste(a,b,c,sep="")] = rep(NA,12) - } - } - } - } - CODON_TABLE <<- as.matrix(CODON_TABLE) - } - } - - collapseClone <- function(vecInputSeqs,glSeq,readEnd,nonTerminalOnly=0){ - #print(length(vecInputSeqs)) - vecInputSeqs = unique(vecInputSeqs) - if(length(vecInputSeqs)==1){ - return( list( c(vecInputSeqs,glSeq), F) ) - }else{ - charInputSeqs <- sapply(vecInputSeqs, function(x){ - s2c(x)[1:readEnd] - }) - charGLSeq <- s2c(glSeq) - matClone <- sapply(1:readEnd, function(i){ - posNucs = unique(charInputSeqs[i,]) - posGL = charGLSeq[i] - error = FALSE - if(posGL=="-" & sum(!(posNucs%in%c("-","N")))==0 ){ - return(c("-",error)) - } - if(length(posNucs)==1) - return(c(posNucs[1],error)) - else{ - if("N"%in%posNucs){ - error=TRUE - } - if(sum(!posNucs[posNucs!="N"]%in%posGL)==0){ - return( c(posGL,error) ) - }else{ - #return( c(sample(posNucs[posNucs!="N"],1),error) ) - if(nonTerminalOnly==0){ - return( c(sample(charInputSeqs[i,charInputSeqs[i,]!="N" & charInputSeqs[i,]!=posGL],1),error) ) - }else{ - posNucs = charInputSeqs[i,charInputSeqs[i,]!="N" & charInputSeqs[i,]!=posGL] - posNucsTable = table(posNucs) - if(sum(posNucsTable>1)==0){ - return( c(posGL,error) ) - }else{ - return( c(sample( posNucs[posNucs%in%names(posNucsTable)[posNucsTable>1]],1),error) ) - } - } - - } - } - }) - - - #print(length(vecInputSeqs)) - return(list(c(c2s(matClone[1,]),glSeq),"TRUE"%in%matClone[2,])) - } - } - - # Compute the expected for each sequence-germline pair - getExpectedIndividual <- function(matInput){ - if( any(grep("multicore",search())) ){ - facGL <- factor(matInput[,2]) - facLevels = levels(facGL) - LisGLs_MutabilityU = mclapply(1:length(facLevels), function(x){ - computeMutabilities(facLevels[x]) - }) - facIndex = match(facGL,facLevels) - - LisGLs_Mutability = mclapply(1:nrow(matInput), function(x){ - cInput = rep(NA,nchar(matInput[x,1])) - cInput[s2c(matInput[x,1])!="N"] = 1 - LisGLs_MutabilityU[[facIndex[x]]] * cInput - }) - - LisGLs_Targeting = mclapply(1:dim(matInput)[1], function(x){ - computeTargeting(matInput[x,2],LisGLs_Mutability[[x]]) - }) - - LisGLs_MutationTypes = mclapply(1:length(matInput[,2]),function(x){ - #print(x) - computeMutationTypes(matInput[x,2]) - }) - - LisGLs_Exp = mclapply(1:dim(matInput)[1], function(x){ - computeExpected(LisGLs_Targeting[[x]],LisGLs_MutationTypes[[x]]) - }) - - ul_LisGLs_Exp = unlist(LisGLs_Exp) - return(matrix(ul_LisGLs_Exp,ncol=4,nrow=(length(ul_LisGLs_Exp)/4),byrow=T)) - }else{ - facGL <- factor(matInput[,2]) - facLevels = levels(facGL) - LisGLs_MutabilityU = lapply(1:length(facLevels), function(x){ - computeMutabilities(facLevels[x]) - }) - facIndex = match(facGL,facLevels) - - LisGLs_Mutability = lapply(1:nrow(matInput), function(x){ - cInput = rep(NA,nchar(matInput[x,1])) - cInput[s2c(matInput[x,1])!="N"] = 1 - LisGLs_MutabilityU[[facIndex[x]]] * cInput - }) - - LisGLs_Targeting = lapply(1:dim(matInput)[1], function(x){ - computeTargeting(matInput[x,2],LisGLs_Mutability[[x]]) - }) - - LisGLs_MutationTypes = lapply(1:length(matInput[,2]),function(x){ - #print(x) - computeMutationTypes(matInput[x,2]) - }) - - LisGLs_Exp = lapply(1:dim(matInput)[1], function(x){ - computeExpected(LisGLs_Targeting[[x]],LisGLs_MutationTypes[[x]]) - }) - - ul_LisGLs_Exp = unlist(LisGLs_Exp) - return(matrix(ul_LisGLs_Exp,ncol=4,nrow=(length(ul_LisGLs_Exp)/4),byrow=T)) - - } - } - - # Compute mutabilities of sequence based on the tri-nucleotide model - computeMutabilities <- function(paramSeq){ - seqLen = nchar(paramSeq) - seqMutabilites = rep(NA,seqLen) - - gaplessSeq = gsub("-", "", paramSeq) - gaplessSeqLen = nchar(gaplessSeq) - gaplessSeqMutabilites = rep(NA,gaplessSeqLen) - - if(mutabilityModel!=5){ - pos<- 3:(gaplessSeqLen) - subSeq = substr(rep(gaplessSeq,gaplessSeqLen-2),(pos-2),(pos+2)) - gaplessSeqMutabilites[pos] = - tapply( c( - getMutability( substr(subSeq,1,3), 3) , - getMutability( substr(subSeq,2,4), 2), - getMutability( substr(subSeq,3,5), 1) - ),rep(1:(gaplessSeqLen-2),3),mean,na.rm=TRUE - ) - #Pos 1 - subSeq = substr(gaplessSeq,1,3) - gaplessSeqMutabilites[1] = getMutability(subSeq , 1) - #Pos 2 - subSeq = substr(gaplessSeq,1,4) - gaplessSeqMutabilites[2] = mean( c( - getMutability( substr(subSeq,1,3), 2) , - getMutability( substr(subSeq,2,4), 1) - ),na.rm=T - ) - seqMutabilites[which(s2c(paramSeq)!="-")]<- gaplessSeqMutabilites - return(seqMutabilites) - }else{ - - pos<- 3:(gaplessSeqLen) - subSeq = substr(rep(gaplessSeq,gaplessSeqLen-2),(pos-2),(pos+2)) - gaplessSeqMutabilites[pos] = sapply(subSeq,function(x){ getMutability5(x) }, simplify=T) - seqMutabilites[which(s2c(paramSeq)!="-")]<- gaplessSeqMutabilites - return(seqMutabilites) - } - - } - - # Returns the mutability of a triplet at a given position - getMutability <- function(codon, pos=1:3){ - triplets <- rownames(mutability) - mutability[ match(codon,triplets) ,pos] - } - - getMutability5 <- function(fivemer){ - return(mutability[fivemer]) - } - - # Returns the substitution probabilty - getTransistionProb <- function(nuc){ - substitution[nuc,] - } - - getTransistionProb5 <- function(fivemer){ - if(any(which(fivemer==colnames(substitution)))){ - return(substitution[,fivemer]) - }else{ - return(array(NA,4)) - } - } - - # Given a nuc, returns the other 3 nucs it can mutate to - canMutateTo <- function(nuc){ - NUCLEOTIDES[- which(NUCLEOTIDES==nuc)] - } - - # Given a nucleotide, returns the probabilty of other nucleotide it can mutate to - canMutateToProb <- function(nuc){ - substitution[nuc,canMutateTo(nuc)] - } - - # Compute targeting, based on precomputed mutatbility & substitution - computeTargeting <- function(param_strSeq,param_vecMutabilities){ - - if(substitutionModel!=5){ - vecSeq = s2c(param_strSeq) - matTargeting = sapply( 1:length(vecSeq), function(x) { param_vecMutabilities[x] * getTransistionProb(vecSeq[x]) } ) - #matTargeting = apply( rbind(vecSeq,param_vecMutabilities),2, function(x) { as.vector(as.numeric(x[2]) * getTransistionProb(x[1])) } ) - dimnames( matTargeting ) = list(NUCLEOTIDES,1:(length(vecSeq))) - return (matTargeting) - }else{ - - seqLen = nchar(param_strSeq) - seqsubstitution = matrix(NA,ncol=seqLen,nrow=4) - paramSeq <- param_strSeq - gaplessSeq = gsub("-", "", paramSeq) - gaplessSeqLen = nchar(gaplessSeq) - gaplessSeqSubstitution = matrix(NA,ncol=gaplessSeqLen,nrow=4) - - pos<- 3:(gaplessSeqLen) - subSeq = substr(rep(gaplessSeq,gaplessSeqLen-2),(pos-2),(pos+2)) - gaplessSeqSubstitution[,pos] = sapply(subSeq,function(x){ getTransistionProb5(x) }, simplify=T) - seqsubstitution[,which(s2c(paramSeq)!="-")]<- gaplessSeqSubstitution - #matTargeting <- param_vecMutabilities %*% seqsubstitution - matTargeting <- sweep(seqsubstitution,2,param_vecMutabilities,`*`) - dimnames( matTargeting ) = list(NUCLEOTIDES,1:(seqLen)) - return (matTargeting) - } - } - - # Compute the mutations types - computeMutationTypes <- function(param_strSeq){ - #cat(param_strSeq,"\n") - #vecSeq = trimToLastCodon(param_strSeq) - lenSeq = nchar(param_strSeq) - vecCodons = sapply({1:(lenSeq/3)}*3-2,function(x){substr(param_strSeq,x,x+2)}) - matMutationTypes = matrix( unlist(CODON_TABLE[,vecCodons]) ,ncol=lenSeq,nrow=4, byrow=F) - dimnames( matMutationTypes ) = list(NUCLEOTIDES,1:(ncol(matMutationTypes))) - return(matMutationTypes) - } - computeMutationTypesFast <- function(param_strSeq){ - matMutationTypes = matrix( CODON_TABLE[,param_strSeq] ,ncol=3,nrow=4, byrow=F) - #dimnames( matMutationTypes ) = list(NUCLEOTIDES,1:(length(vecSeq))) - return(matMutationTypes) - } - mutationTypeOptimized <- function( matOfCodons ){ - apply( matOfCodons,1,function(x){ mutationType(x[2],x[1]) } ) - } - - # Returns a vector of codons 1 mutation away from the given codon - permutateAllCodon <- function(codon){ - cCodon = s2c(codon) - matCodons = t(array(cCodon,dim=c(3,12))) - matCodons[1:4,1] = NUCLEOTIDES - matCodons[5:8,2] = NUCLEOTIDES - matCodons[9:12,3] = NUCLEOTIDES - apply(matCodons,1,c2s) - } - - # Given two codons, tells you if the mutation is R or S (based on your definition) - mutationType <- function(codonFrom,codonTo){ - if(testID==4){ - if( is.na(codonFrom) | is.na(codonTo) | is.na(translateCodonToAminoAcid(codonFrom)) | is.na(translateCodonToAminoAcid(codonTo)) ){ - return(NA) - }else{ - mutationType = "S" - if( translateAminoAcidToTraitChange(translateCodonToAminoAcid(codonFrom)) != translateAminoAcidToTraitChange(translateCodonToAminoAcid(codonTo)) ){ - mutationType = "R" - } - if(translateCodonToAminoAcid(codonTo)=="*" | translateCodonToAminoAcid(codonFrom)=="*"){ - mutationType = "Stop" - } - return(mutationType) - } - }else if(testID==5){ - if( is.na(codonFrom) | is.na(codonTo) | is.na(translateCodonToAminoAcid(codonFrom)) | is.na(translateCodonToAminoAcid(codonTo)) ){ - return(NA) - }else{ - if(codonFrom==codonTo){ - mutationType = "S" - }else{ - codonFrom = s2c(codonFrom) - codonTo = s2c(codonTo) - mutationType = "Stop" - nucOfI = codonFrom[which(codonTo!=codonFrom)] - if(nucOfI=="C"){ - mutationType = "R" - }else if(nucOfI=="G"){ - mutationType = "S" - } - } - return(mutationType) - } - }else{ - if( is.na(codonFrom) | is.na(codonTo) | is.na(translateCodonToAminoAcid(codonFrom)) | is.na(translateCodonToAminoAcid(codonTo)) ){ - return(NA) - }else{ - mutationType = "S" - if( translateCodonToAminoAcid(codonFrom) != translateCodonToAminoAcid(codonTo) ){ - mutationType = "R" - } - if(translateCodonToAminoAcid(codonTo)=="*" | translateCodonToAminoAcid(codonFrom)=="*"){ - mutationType = "Stop" - } - return(mutationType) - } - } - } - - - #given a mat of targeting & it's corresponding mutationtypes returns - #a vector of Exp_RCDR,Exp_SCDR,Exp_RFWR,Exp_RFWR - computeExpected <- function(paramTargeting,paramMutationTypes){ - # Replacements - RPos = which(paramMutationTypes=="R") - #FWR - Exp_R_FWR = sum(paramTargeting[ RPos[which(FWR_Nuc_Mat[RPos]==T)] ],na.rm=T) - #CDR - Exp_R_CDR = sum(paramTargeting[ RPos[which(CDR_Nuc_Mat[RPos]==T)] ],na.rm=T) - # Silents - SPos = which(paramMutationTypes=="S") - #FWR - Exp_S_FWR = sum(paramTargeting[ SPos[which(FWR_Nuc_Mat[SPos]==T)] ],na.rm=T) - #CDR - Exp_S_CDR = sum(paramTargeting[ SPos[which(CDR_Nuc_Mat[SPos]==T)] ],na.rm=T) - - return(c(Exp_R_CDR,Exp_S_CDR,Exp_R_FWR,Exp_S_FWR)) - } - - # Count the mutations in a sequence - # each mutation is treated independently - analyzeMutations2NucUri_website <- function( rev_in_matrix ){ - paramGL = rev_in_matrix[2,] - paramSeq = rev_in_matrix[1,] - - #Fill seq with GL seq if gapped - #if( any(paramSeq=="-") ){ - # gapPos_Seq = which(paramSeq=="-") - # gapPos_Seq_ToReplace = gapPos_Seq[paramGL[gapPos_Seq] != "-"] - # paramSeq[gapPos_Seq_ToReplace] = paramGL[gapPos_Seq_ToReplace] - #} - - - #if( any(paramSeq=="N") ){ - # gapPos_Seq = which(paramSeq=="N") - # gapPos_Seq_ToReplace = gapPos_Seq[paramGL[gapPos_Seq] != "N"] - # paramSeq[gapPos_Seq_ToReplace] = paramGL[gapPos_Seq_ToReplace] - #} - - analyzeMutations2NucUri( matrix(c( paramGL, paramSeq ),2,length(paramGL),byrow=T) ) - - } - - #1 = GL - #2 = Seq - analyzeMutations2NucUri <- function( in_matrix=matrix(c(c("A","A","A","C","C","C"),c("A","G","G","C","C","A")),2,6,byrow=T) ){ - paramGL = in_matrix[2,] - paramSeq = in_matrix[1,] - paramSeqUri = paramGL - #mutations = apply(rbind(paramGL,paramSeq), 2, function(x){!x[1]==x[2]}) - mutations_val = paramGL != paramSeq - if(any(mutations_val)){ - mutationPos = {1:length(mutations_val)}[mutations_val] - mutationPos = mutationPos[sapply(mutationPos, function(x){!any(paramSeq[getCodonPos(x)]=="N")})] - length_mutations =length(mutationPos) - mutationInfo = rep(NA,length_mutations) - if(any(mutationPos)){ - - pos<- mutationPos - pos_array<-array(sapply(pos,getCodonPos)) - codonGL = paramGL[pos_array] - - codonSeq = sapply(pos,function(x){ - seqP = paramGL[getCodonPos(x)] - muCodonPos = {x-1}%%3+1 - seqP[muCodonPos] = paramSeq[x] - return(seqP) - }) - GLcodons = apply(matrix(codonGL,length_mutations,3,byrow=TRUE),1,c2s) - Seqcodons = apply(codonSeq,2,c2s) - mutationInfo = apply(rbind(GLcodons , Seqcodons),2,function(x){mutationType(c2s(x[1]),c2s(x[2]))}) - names(mutationInfo) = mutationPos - } - if(any(!is.na(mutationInfo))){ - return(mutationInfo[!is.na(mutationInfo)]) - }else{ - return(NA) - } - - - }else{ - return (NA) - } - } - - processNucMutations2 <- function(mu){ - if(!is.na(mu)){ - #R - if(any(mu=="R")){ - Rs = mu[mu=="R"] - nucNumbs = as.numeric(names(Rs)) - R_CDR = sum(as.integer(CDR_Nuc[nucNumbs]),na.rm=T) - R_FWR = sum(as.integer(FWR_Nuc[nucNumbs]),na.rm=T) - }else{ - R_CDR = 0 - R_FWR = 0 - } - - #S - if(any(mu=="S")){ - Ss = mu[mu=="S"] - nucNumbs = as.numeric(names(Ss)) - S_CDR = sum(as.integer(CDR_Nuc[nucNumbs]),na.rm=T) - S_FWR = sum(as.integer(FWR_Nuc[nucNumbs]),na.rm=T) - }else{ - S_CDR = 0 - S_FWR = 0 - } - - - retVec = c(R_CDR,S_CDR,R_FWR,S_FWR) - retVec[is.na(retVec)]=0 - return(retVec) - }else{ - return(rep(0,4)) - } - } - - - ## Z-score Test - computeZScore <- function(mat, test="Focused"){ - matRes <- matrix(NA,ncol=2,nrow=(nrow(mat))) - if(test=="Focused"){ - #Z_Focused_CDR - #P_Denom = sum( mat[1,c(5,6,8)], na.rm=T ) - P = apply(mat[,c(5,6,8)],1,function(x){(x[1]/sum(x))}) - R_mean = apply(cbind(mat[,c(1,2,4)],P),1,function(x){x[4]*(sum(x[1:3]))}) - R_sd=sqrt(R_mean*(1-P)) - matRes[,1] = (mat[,1]-R_mean)/R_sd - - #Z_Focused_FWR - #P_Denom = sum( mat[1,c(7,6,8)], na.rm=T ) - P = apply(mat[,c(7,6,8)],1,function(x){(x[1]/sum(x))}) - R_mean = apply(cbind(mat[,c(3,2,4)],P),1,function(x){x[4]*(sum(x[1:3]))}) - R_sd=sqrt(R_mean*(1-P)) - matRes[,2] = (mat[,3]-R_mean)/R_sd - } - - if(test=="Local"){ - #Z_Focused_CDR - #P_Denom = sum( mat[1,c(5,6,8)], na.rm=T ) - P = apply(mat[,c(5,6)],1,function(x){(x[1]/sum(x))}) - R_mean = apply(cbind(mat[,c(1,2)],P),1,function(x){x[3]*(sum(x[1:2]))}) - R_sd=sqrt(R_mean*(1-P)) - matRes[,1] = (mat[,1]-R_mean)/R_sd - - #Z_Focused_FWR - #P_Denom = sum( mat[1,c(7,6,8)], na.rm=T ) - P = apply(mat[,c(7,8)],1,function(x){(x[1]/sum(x))}) - R_mean = apply(cbind(mat[,c(3,4)],P),1,function(x){x[3]*(sum(x[1:2]))}) - R_sd=sqrt(R_mean*(1-P)) - matRes[,2] = (mat[,3]-R_mean)/R_sd - } - - if(test=="Imbalanced"){ - #Z_Focused_CDR - #P_Denom = sum( mat[1,c(5,6,8)], na.rm=T ) - P = apply(mat[,5:8],1,function(x){((x[1]+x[2])/sum(x))}) - R_mean = apply(cbind(mat[,1:4],P),1,function(x){x[5]*(sum(x[1:4]))}) - R_sd=sqrt(R_mean*(1-P)) - matRes[,1] = (mat[,1]-R_mean)/R_sd - - #Z_Focused_FWR - #P_Denom = sum( mat[1,c(7,6,8)], na.rm=T ) - P = apply(mat[,5:8],1,function(x){((x[3]+x[4])/sum(x))}) - R_mean = apply(cbind(mat[,1:4],P),1,function(x){x[5]*(sum(x[1:4]))}) - R_sd=sqrt(R_mean*(1-P)) - matRes[,2] = (mat[,3]-R_mean)/R_sd - } - - matRes[is.nan(matRes)] = NA - return(matRes) - } - - # Return a p-value for a z-score - z2p <- function(z){ - p=NA - if( !is.nan(z) && !is.na(z)){ - if(z>0){ - p = (1 - pnorm(z,0,1)) - } else if(z<0){ - p = (-1 * pnorm(z,0,1)) - } else{ - p = 0.5 - } - }else{ - p = NA - } - return(p) - } - - - ## Bayesian Test - - # Fitted parameter for the bayesian framework -BAYESIAN_FITTED<-c(0.407277142798302, 0.554007336744485, 0.63777155771234, 0.693989162719009, 0.735450014674917, 0.767972534429806, 0.794557287143399, 0.816906816601605, 0.83606796225341, 0.852729446430296, 0.867370424541641, 0.880339760590323, 0.891900995024999, 0.902259181289864, 0.911577919359,0.919990301665853, 0.927606458124537, 0.934518806350661, 0.940805863754375, 0.946534836475715, 0.951763691199255, 0.95654428191308, 0.960920179487397, 0.964930893680829, 0.968611312149038, 0.971992459313836, 0.975102110004818, 0.977964943023096, 0.980603428208439, 0.983037660179428, 0.985285800977406, 0.987364285326685, 0.989288037855441, 0.991070478823525, 0.992723699729969, 0.994259575477392, 0.995687688867975, 0.997017365051493, 0.998257085153047, 0.999414558305388, 1.00049681357804, 1.00151036237481, 1.00246080204981, 1.00335370751909, 1.0041939329768, 1.0049859393417, 1.00573382091263, 1.00644127217376, 1.00711179729107, 1.00774845526417, 1.00835412715854, 1.00893143010366, 1.00948275846309, 1.01001030293661, 1.01051606798079, 1.01100188771288, 1.01146944044216, 1.01192026195449, 1.01235575766094, 1.01277721370986) - CONST_i <- sort(c(((2^(seq(-39,0,length.out=201)))/2)[1:200],(c(0:11,13:99)+0.5)/100,1-(2^(seq(-39,0,length.out=201)))/2)) - - # Given x, M & p, returns a pdf - calculate_bayes <- function ( x=3, N=10, p=0.33, - i=CONST_i, - max_sigma=20,length_sigma=4001 - ){ - if(!0%in%N){ - G <- max(length(x),length(N),length(p)) - x=array(x,dim=G) - N=array(N,dim=G) - p=array(p,dim=G) - sigma_s<-seq(-max_sigma,max_sigma,length.out=length_sigma) - sigma_1<-log({i/{1-i}}/{p/{1-p}}) - index<-min(N,60) - y<-dbeta(i,x+BAYESIAN_FITTED[index],N+BAYESIAN_FITTED[index]-x)*(1-p)*p*exp(sigma_1)/({1-p}^2+2*p*{1-p}*exp(sigma_1)+{p^2}*exp(2*sigma_1)) - if(!sum(is.na(y))){ - tmp<-approx(sigma_1,y,sigma_s)$y - tmp/sum(tmp)/{2*max_sigma/{length_sigma-1}} - }else{ - return(NA) - } - }else{ - return(NA) - } - } - # Given a mat of observed & expected, return a list of CDR & FWR pdf for selection - computeBayesianScore <- function(mat, test="Focused", max_sigma=20,length_sigma=4001){ - flagOneSeq = F - if(nrow(mat)==1){ - mat=rbind(mat,mat) - flagOneSeq = T - } - if(test=="Focused"){ - #CDR - P = c(apply(mat[,c(5,6,8)],1,function(x){(x[1]/sum(x))}),0.5) - N = c(apply(mat[,c(1,2,4)],1,function(x){(sum(x))}),0) - X = c(mat[,1],0) - bayesCDR = apply(cbind(X,N,P),1,function(x){calculate_bayes(x=x[1],N=x[2],p=x[3],max_sigma=max_sigma,length_sigma=length_sigma)}) - bayesCDR = bayesCDR[-length(bayesCDR)] - - #FWR - P = c(apply(mat[,c(7,6,8)],1,function(x){(x[1]/sum(x))}),0.5) - N = c(apply(mat[,c(3,2,4)],1,function(x){(sum(x))}),0) - X = c(mat[,3],0) - bayesFWR = apply(cbind(X,N,P),1,function(x){calculate_bayes(x=x[1],N=x[2],p=x[3],max_sigma=max_sigma,length_sigma=length_sigma)}) - bayesFWR = bayesFWR[-length(bayesFWR)] - } - - if(test=="Local"){ - #CDR - P = c(apply(mat[,c(5,6)],1,function(x){(x[1]/sum(x))}),0.5) - N = c(apply(mat[,c(1,2)],1,function(x){(sum(x))}),0) - X = c(mat[,1],0) - bayesCDR = apply(cbind(X,N,P),1,function(x){calculate_bayes(x=x[1],N=x[2],p=x[3],max_sigma=max_sigma,length_sigma=length_sigma)}) - bayesCDR = bayesCDR[-length(bayesCDR)] - - #FWR - P = c(apply(mat[,c(7,8)],1,function(x){(x[1]/sum(x))}),0.5) - N = c(apply(mat[,c(3,4)],1,function(x){(sum(x))}),0) - X = c(mat[,3],0) - bayesFWR = apply(cbind(X,N,P),1,function(x){calculate_bayes(x=x[1],N=x[2],p=x[3],max_sigma=max_sigma,length_sigma=length_sigma)}) - bayesFWR = bayesFWR[-length(bayesFWR)] - } - - if(test=="Imbalanced"){ - #CDR - P = c(apply(mat[,c(5:8)],1,function(x){((x[1]+x[2])/sum(x))}),0.5) - N = c(apply(mat[,c(1:4)],1,function(x){(sum(x))}),0) - X = c(apply(mat[,c(1:2)],1,function(x){(sum(x))}),0) - bayesCDR = apply(cbind(X,N,P),1,function(x){calculate_bayes(x=x[1],N=x[2],p=x[3],max_sigma=max_sigma,length_sigma=length_sigma)}) - bayesCDR = bayesCDR[-length(bayesCDR)] - - #FWR - P = c(apply(mat[,c(5:8)],1,function(x){((x[3]+x[4])/sum(x))}),0.5) - N = c(apply(mat[,c(1:4)],1,function(x){(sum(x))}),0) - X = c(apply(mat[,c(3:4)],1,function(x){(sum(x))}),0) - bayesFWR = apply(cbind(X,N,P),1,function(x){calculate_bayes(x=x[1],N=x[2],p=x[3],max_sigma=max_sigma,length_sigma=length_sigma)}) - bayesFWR = bayesFWR[-length(bayesFWR)] - } - - if(test=="ImbalancedSilent"){ - #CDR - P = c(apply(mat[,c(6,8)],1,function(x){((x[1])/sum(x))}),0.5) - N = c(apply(mat[,c(2,4)],1,function(x){(sum(x))}),0) - X = c(apply(mat[,c(2,4)],1,function(x){(x[1])}),0) - bayesCDR = apply(cbind(X,N,P),1,function(x){calculate_bayes(x=x[1],N=x[2],p=x[3],max_sigma=max_sigma,length_sigma=length_sigma)}) - bayesCDR = bayesCDR[-length(bayesCDR)] - - #FWR - P = c(apply(mat[,c(6,8)],1,function(x){((x[2])/sum(x))}),0.5) - N = c(apply(mat[,c(2,4)],1,function(x){(sum(x))}),0) - X = c(apply(mat[,c(2,4)],1,function(x){(x[2])}),0) - bayesFWR = apply(cbind(X,N,P),1,function(x){calculate_bayes(x=x[1],N=x[2],p=x[3],max_sigma=max_sigma,length_sigma=length_sigma)}) - bayesFWR = bayesFWR[-length(bayesFWR)] - } - - if(flagOneSeq==T){ - bayesCDR = bayesCDR[1] - bayesFWR = bayesFWR[1] - } - return( list("CDR"=bayesCDR, "FWR"=bayesFWR) ) - } - - ##Covolution - break2chunks<-function(G=1000){ - base<-2^round(log(sqrt(G),2),0) - return(c(rep(base,floor(G/base)-1),base+G-(floor(G/base)*base))) - } - - PowersOfTwo <- function(G=100){ - exponents <- array() - i = 0 - while(G > 0){ - i=i+1 - exponents[i] <- floor( log2(G) ) - G <- G-2^exponents[i] - } - return(exponents) - } - - convolutionPowersOfTwo <- function( cons, length_sigma=4001 ){ - G = ncol(cons) - if(G>1){ - for(gen in log(G,2):1){ - ll<-seq(from=2,to=2^gen,by=2) - sapply(ll,function(l){cons[,l/2]<<-weighted_conv(cons[,l],cons[,l-1],length_sigma=length_sigma)}) - } - } - return( cons[,1] ) - } - - convolutionPowersOfTwoByTwos <- function( cons, length_sigma=4001,G=1 ){ - if(length(ncol(cons))) G<-ncol(cons) - groups <- PowersOfTwo(G) - matG <- matrix(NA, ncol=length(groups), nrow=length(cons)/G ) - startIndex = 1 - for( i in 1:length(groups) ){ - stopIndex <- 2^groups[i] + startIndex - 1 - if(stopIndex!=startIndex){ - matG[,i] <- convolutionPowersOfTwo( cons[,startIndex:stopIndex], length_sigma=length_sigma ) - startIndex = stopIndex + 1 - } - else { - if(G>1) matG[,i] <- cons[,startIndex:stopIndex] - else matG[,i] <- cons - #startIndex = stopIndex + 1 - } - } - return( list( matG, groups ) ) - } - - weighted_conv<-function(x,y,w=1,m=100,length_sigma=4001){ - lx<-length(x) - ly<-length(y) - if({lx<m}| {{lx*w}<m}| {{ly}<m}| {{ly*w}<m}){ - if(w<1){ - y1<-approx(1:ly,y,seq(1,ly,length.out=m))$y - x1<-approx(1:lx,x,seq(1,lx,length.out=m/w))$y - lx<-length(x1) - ly<-length(y1) - } - else { - y1<-approx(1:ly,y,seq(1,ly,length.out=m*w))$y - x1<-approx(1:lx,x,seq(1,lx,length.out=m))$y - lx<-length(x1) - ly<-length(y1) - } - } - else{ - x1<-x - y1<-approx(1:ly,y,seq(1,ly,length.out=floor(lx*w)))$y - ly<-length(y1) - } - tmp<-approx(x=1:(lx+ly-1),y=convolve(x1,rev(y1),type="open"),xout=seq(1,lx+ly-1,length.out=length_sigma))$y - tmp[tmp<=0] = 0 - return(tmp/sum(tmp)) - } - - calculate_bayesGHelper <- function( listMatG,length_sigma=4001 ){ - matG <- listMatG[[1]] - groups <- listMatG[[2]] - i = 1 - resConv <- matG[,i] - denom <- 2^groups[i] - if(length(groups)>1){ - while( i<length(groups) ){ - i = i + 1 - resConv <- weighted_conv(resConv, matG[,i], w= {{2^groups[i]}/denom} ,length_sigma=length_sigma) - #cat({{2^groups[i]}/denom},"\n") - denom <- denom + 2^groups[i] - } - } - return(resConv) - } - - # Given a list of PDFs, returns a convoluted PDF - groupPosteriors <- function( listPosteriors, max_sigma=20, length_sigma=4001 ,Threshold=2 ){ - listPosteriors = listPosteriors[ !is.na(listPosteriors) ] - Length_Postrior<-length(listPosteriors) - if(Length_Postrior>1 & Length_Postrior<=Threshold){ - cons = matrix(unlist(listPosteriors),length(listPosteriors[[1]]),length(listPosteriors)) - listMatG <- convolutionPowersOfTwoByTwos(cons,length_sigma=length_sigma) - y<-calculate_bayesGHelper(listMatG,length_sigma=length_sigma) - return( y/sum(y)/(2*max_sigma/(length_sigma-1)) ) - }else if(Length_Postrior==1) return(listPosteriors[[1]]) - else if(Length_Postrior==0) return(NA) - else { - cons = matrix(unlist(listPosteriors),length(listPosteriors[[1]]),length(listPosteriors)) - y = fastConv(cons,max_sigma=max_sigma, length_sigma=length_sigma ) - return( y/sum(y)/(2*max_sigma/(length_sigma-1)) ) - } - } - - fastConv<-function(cons, max_sigma=20, length_sigma=4001){ - chunks<-break2chunks(G=ncol(cons)) - if(ncol(cons)==3) chunks<-2:1 - index_chunks_end <- cumsum(chunks) - index_chunks_start <- c(1,index_chunks_end[-length(index_chunks_end)]+1) - index_chunks <- cbind(index_chunks_start,index_chunks_end) - - case <- sum(chunks!=chunks[1]) - if(case==1) End <- max(1,((length(index_chunks)/2)-1)) - else End <- max(1,((length(index_chunks)/2))) - - firsts <- sapply(1:End,function(i){ - indexes<-index_chunks[i,1]:index_chunks[i,2] - convolutionPowersOfTwoByTwos(cons[ ,indexes])[[1]] - }) - if(case==0){ - result<-calculate_bayesGHelper( convolutionPowersOfTwoByTwos(firsts) ) - }else if(case==1){ - last<-list(calculate_bayesGHelper( - convolutionPowersOfTwoByTwos( cons[ ,index_chunks[length(index_chunks)/2,1]:index_chunks[length(index_chunks)/2,2]] ) - ),0) - result_first<-calculate_bayesGHelper(convolutionPowersOfTwoByTwos(firsts)) - result<-calculate_bayesGHelper( - list( - cbind( - result_first,last[[1]]), - c(log(index_chunks_end[length(index_chunks)/2-1],2),log(index_chunks[length(index_chunks)/2,2]-index_chunks[length(index_chunks)/2,1]+1,2)) - ) - ) - } - return(as.vector(result)) - } - - # Computes the 95% CI for a pdf - calcBayesCI <- function(Pdf,low=0.025,up=0.975,max_sigma=20, length_sigma=4001){ - if(length(Pdf)!=length_sigma) return(NA) - sigma_s=seq(-max_sigma,max_sigma,length.out=length_sigma) - cdf = cumsum(Pdf) - cdf = cdf/cdf[length(cdf)] - return( c(sigma_s[findInterval(low,cdf)-1] , sigma_s[findInterval(up,cdf)]) ) - } - - # Computes a mean for a pdf - calcBayesMean <- function(Pdf,max_sigma=20,length_sigma=4001){ - if(length(Pdf)!=length_sigma) return(NA) - sigma_s=seq(-max_sigma,max_sigma,length.out=length_sigma) - norm = {length_sigma-1}/2/max_sigma - return( (Pdf%*%sigma_s/norm) ) - } - - # Returns the mean, and the 95% CI for a pdf - calcBayesOutputInfo <- function(Pdf,low=0.025,up=0.975,max_sigma=20, length_sigma=4001){ - if(is.na(Pdf)) - return(rep(NA,3)) - bCI = calcBayesCI(Pdf=Pdf,low=low,up=up,max_sigma=max_sigma,length_sigma=length_sigma) - bMean = calcBayesMean(Pdf=Pdf,max_sigma=max_sigma,length_sigma=length_sigma) - return(c(bMean, bCI)) - } - - # Computes the p-value of a pdf - computeSigmaP <- function(Pdf, length_sigma=4001, max_sigma=20){ - if(length(Pdf)>1){ - norm = {length_sigma-1}/2/max_sigma - pVal = {sum(Pdf[1:{{length_sigma-1}/2}]) + Pdf[{{length_sigma+1}/2}]/2}/norm - if(pVal>0.5){ - pVal = pVal-1 - } - return(pVal) - }else{ - return(NA) - } - } - - # Compute p-value of two distributions - compareTwoDistsFaster <-function(sigma_S=seq(-20,20,length.out=4001), N=10000, dens1=runif(4001,0,1), dens2=runif(4001,0,1)){ - #print(c(length(dens1),length(dens2))) - if(length(dens1)>1 & length(dens2)>1 ){ - dens1<-dens1/sum(dens1) - dens2<-dens2/sum(dens2) - cum2 <- cumsum(dens2)-dens2/2 - tmp<- sum(sapply(1:length(dens1),function(i)return(dens1[i]*cum2[i]))) - #print(tmp) - if(tmp>0.5)tmp<-tmp-1 - return( tmp ) - } - else { - return(NA) - } - #return (sum(sapply(1:N,function(i)(sample(sigma_S,1,prob=dens1)>sample(sigma_S,1,prob=dens2))))/N) - } - - # get number of seqeunces contributing to the sigma (i.e. seqeunces with mutations) - numberOfSeqsWithMutations <- function(matMutations,test=1){ - if(test==4)test=2 - cdrSeqs <- 0 - fwrSeqs <- 0 - if(test==1){#focused - cdrMutations <- apply(matMutations, 1, function(x){ sum(x[c(1,2,4)]) }) - fwrMutations <- apply(matMutations, 1, function(x){ sum(x[c(3,4,2)]) }) - if( any(which(cdrMutations>0)) ) cdrSeqs <- sum(cdrMutations>0) - if( any(which(fwrMutations>0)) ) fwrSeqs <- sum(fwrMutations>0) - } - if(test==2){#local - cdrMutations <- apply(matMutations, 1, function(x){ sum(x[c(1,2)]) }) - fwrMutations <- apply(matMutations, 1, function(x){ sum(x[c(3,4)]) }) - if( any(which(cdrMutations>0)) ) cdrSeqs <- sum(cdrMutations>0) - if( any(which(fwrMutations>0)) ) fwrSeqs <- sum(fwrMutations>0) - } - return(c("CDR"=cdrSeqs, "FWR"=fwrSeqs)) -} - - - -shadeColor <- function(sigmaVal=NA,pVal=NA){ - if(is.na(sigmaVal) & is.na(pVal)) return(NA) - if(is.na(sigmaVal) & !is.na(pVal)) sigmaVal=sign(pVal) - if(is.na(pVal) || pVal==1 || pVal==0){ - returnColor = "#FFFFFF"; - }else{ - colVal=abs(pVal); - - if(sigmaVal<0){ - if(colVal>0.1) - returnColor = "#CCFFCC"; - if(colVal<=0.1) - returnColor = "#99FF99"; - if(colVal<=0.050) - returnColor = "#66FF66"; - if(colVal<=0.010) - returnColor = "#33FF33"; - if(colVal<=0.005) - returnColor = "#00FF00"; - - }else{ - if(colVal>0.1) - returnColor = "#FFCCCC"; - if(colVal<=0.1) - returnColor = "#FF9999"; - if(colVal<=0.05) - returnColor = "#FF6666"; - if(colVal<=0.01) - returnColor = "#FF3333"; - if(colVal<0.005) - returnColor = "#FF0000"; - } - } - - return(returnColor) -} - - - -plotHelp <- function(xfrac=0.05,yfrac=0.05,log=FALSE){ - if(!log){ - x = par()$usr[1]-(par()$usr[2]-par()$usr[1])*xfrac - y = par()$usr[4]+(par()$usr[4]-par()$usr[3])*yfrac - }else { - if(log==2){ - x = par()$usr[1]-(par()$usr[2]-par()$usr[1])*xfrac - y = 10^((par()$usr[4])+((par()$usr[4])-(par()$usr[3]))*yfrac) - } - if(log==1){ - x = 10^((par()$usr[1])-((par()$usr[2])-(par()$usr[1]))*xfrac) - y = par()$usr[4]+(par()$usr[4]-par()$usr[3])*yfrac - } - if(log==3){ - x = 10^((par()$usr[1])-((par()$usr[2])-(par()$usr[1]))*xfrac) - y = 10^((par()$usr[4])+((par()$usr[4])-(par()$usr[3]))*yfrac) - } - } - return(c("x"=x,"y"=y)) -} - -# SHMulation - - # Based on targeting, introduce a single mutation & then update the targeting - oneMutation <- function(){ - # Pick a postion + mutation - posMutation = sample(1:(seqGermlineLen*4),1,replace=F,prob=as.vector(seqTargeting)) - posNucNumb = ceiling(posMutation/4) # Nucleotide number - posNucKind = 4 - ( (posNucNumb*4) - posMutation ) # Nuc the position mutates to - - #mutate the simulation sequence - seqSimVec <- s2c(seqSim) - seqSimVec[posNucNumb] <- NUCLEOTIDES[posNucKind] - seqSim <<- c2s(seqSimVec) - - #update Mutability, Targeting & MutationsTypes - updateMutabilityNTargeting(posNucNumb) - - #return(c(posNucNumb,NUCLEOTIDES[posNucKind])) - return(posNucNumb) - } - - updateMutabilityNTargeting <- function(position){ - min_i<-max((position-2),1) - max_i<-min((position+2),nchar(seqSim)) - min_ii<-min(min_i,3) - - #mutability - update locally - seqMutability[(min_i):(max_i)] <<- computeMutabilities(substr(seqSim,position-4,position+4))[(min_ii):(max_i-min_i+min_ii)] - - - #targeting - compute locally - seqTargeting[,min_i:max_i] <<- computeTargeting(substr(seqSim,min_i,max_i),seqMutability[min_i:max_i]) - seqTargeting[is.na(seqTargeting)] <<- 0 - #mutCodonPos = getCodonPos(position) - mutCodonPos = seq(getCodonPos(min_i)[1],getCodonPos(max_i)[3]) - #cat(mutCodonPos,"\n") - mutTypeCodon = getCodonPos(position) - seqMutationTypes[,mutTypeCodon] <<- computeMutationTypesFast( substr(seqSim,mutTypeCodon[1],mutTypeCodon[3]) ) - # Stop = 0 - if(any(seqMutationTypes[,mutCodonPos]=="Stop",na.rm=T )){ - seqTargeting[,mutCodonPos][seqMutationTypes[,mutCodonPos]=="Stop"] <<- 0 - } - - - #Selection - selectedPos = (min_i*4-4)+(which(seqMutationTypes[,min_i:max_i]=="R")) - # CDR - selectedCDR = selectedPos[which(matCDR[selectedPos]==T)] - seqTargeting[selectedCDR] <<- seqTargeting[selectedCDR] * exp(selCDR) - seqTargeting[selectedCDR] <<- seqTargeting[selectedCDR]/baseLineCDR_K - - # FWR - selectedFWR = selectedPos[which(matFWR[selectedPos]==T)] - seqTargeting[selectedFWR] <<- seqTargeting[selectedFWR] * exp(selFWR) - seqTargeting[selectedFWR] <<- seqTargeting[selectedFWR]/baseLineFWR_K - - } - - - - # Validate the mutation: if the mutation has not been sampled before validate it, else discard it. - validateMutation <- function(){ - if( !(mutatedPos%in%mutatedPositions) ){ # if it's a new mutation - uniqueMutationsIntroduced <<- uniqueMutationsIntroduced + 1 - mutatedPositions[uniqueMutationsIntroduced] <<- mutatedPos - }else{ - if(substr(seqSim,mutatedPos,mutatedPos)==substr(seqGermline,mutatedPos,mutatedPos)){ # back to germline mutation - mutatedPositions <<- mutatedPositions[-which(mutatedPositions==mutatedPos)] - uniqueMutationsIntroduced <<- uniqueMutationsIntroduced - 1 - } - } - } - - - - # Places text (labels) at normalized coordinates - myaxis <- function(xfrac=0.05,yfrac=0.05,log=FALSE,w="text",cex=1,adj=1,thecol="black"){ - par(xpd=TRUE) - if(!log) - text(par()$usr[1]-(par()$usr[2]-par()$usr[1])*xfrac,par()$usr[4]+(par()$usr[4]-par()$usr[3])*yfrac,w,cex=cex,adj=adj,col=thecol) - else { - if(log==2) - text( - par()$usr[1]-(par()$usr[2]-par()$usr[1])*xfrac, - 10^((par()$usr[4])+((par()$usr[4])-(par()$usr[3]))*yfrac), - w,cex=cex,adj=adj,col=thecol) - if(log==1) - text( - 10^((par()$usr[1])-((par()$usr[2])-(par()$usr[1]))*xfrac), - par()$usr[4]+(par()$usr[4]-par()$usr[3])*yfrac, - w,cex=cex,adj=adj,col=thecol) - if(log==3) - text( - 10^((par()$usr[1])-((par()$usr[2])-(par()$usr[1]))*xfrac), - 10^((par()$usr[4])+((par()$usr[4])-(par()$usr[3]))*yfrac), - w,cex=cex,adj=adj,col=thecol) - } - par(xpd=FALSE) - } - - - - # Count the mutations in a sequence - analyzeMutations <- function( inputMatrixIndex, model = 0 , multipleMutation=0, seqWithStops=0){ - - paramGL = s2c(matInput[inputMatrixIndex,2]) - paramSeq = s2c(matInput[inputMatrixIndex,1]) - - #if( any(paramSeq=="N") ){ - # gapPos_Seq = which(paramSeq=="N") - # gapPos_Seq_ToReplace = gapPos_Seq[paramGL[gapPos_Seq] != "N"] - # paramSeq[gapPos_Seq_ToReplace] = paramGL[gapPos_Seq_ToReplace] - #} - mutations_val = paramGL != paramSeq - - if(any(mutations_val)){ - mutationPos = which(mutations_val)#{1:length(mutations_val)}[mutations_val] - length_mutations =length(mutationPos) - mutationInfo = rep(NA,length_mutations) - - pos<- mutationPos - pos_array<-array(sapply(pos,getCodonPos)) - codonGL = paramGL[pos_array] - codonSeqWhole = paramSeq[pos_array] - codonSeq = sapply(pos,function(x){ - seqP = paramGL[getCodonPos(x)] - muCodonPos = {x-1}%%3+1 - seqP[muCodonPos] = paramSeq[x] - return(seqP) - }) - GLcodons = apply(matrix(codonGL,length_mutations,3,byrow=TRUE),1,c2s) - SeqcodonsWhole = apply(matrix(codonSeqWhole,length_mutations,3,byrow=TRUE),1,c2s) - Seqcodons = apply(codonSeq,2,c2s) - - mutationInfo = apply(rbind(GLcodons , Seqcodons),2,function(x){mutationType(c2s(x[1]),c2s(x[2]))}) - names(mutationInfo) = mutationPos - - mutationInfoWhole = apply(rbind(GLcodons , SeqcodonsWhole),2,function(x){mutationType(c2s(x[1]),c2s(x[2]))}) - names(mutationInfoWhole) = mutationPos - - mutationInfo <- mutationInfo[!is.na(mutationInfo)] - mutationInfoWhole <- mutationInfoWhole[!is.na(mutationInfoWhole)] - - if(any(!is.na(mutationInfo))){ - - #Filter based on Stop (at the codon level) - if(seqWithStops==1){ - nucleotidesAtStopCodons = names(mutationInfoWhole[mutationInfoWhole!="Stop"]) - mutationInfo = mutationInfo[nucleotidesAtStopCodons] - mutationInfoWhole = mutationInfo[nucleotidesAtStopCodons] - }else{ - countStops = sum(mutationInfoWhole=="Stop") - if(seqWithStops==2 & countStops==0) mutationInfo = NA - if(seqWithStops==3 & countStops>0) mutationInfo = NA - } - - if(any(!is.na(mutationInfo))){ - #Filter mutations based on multipleMutation - if(multipleMutation==1 & !is.na(mutationInfo)){ - mutationCodons = getCodonNumb(as.numeric(names(mutationInfoWhole))) - tableMutationCodons <- table(mutationCodons) - codonsWithMultipleMutations <- as.numeric(names(tableMutationCodons[tableMutationCodons>1])) - if(any(codonsWithMultipleMutations)){ - #remove the nucleotide mutations in the codons with multiple mutations - mutationInfo <- mutationInfo[!(mutationCodons %in% codonsWithMultipleMutations)] - #replace those codons with Ns in the input sequence - paramSeq[unlist(lapply(codonsWithMultipleMutations, getCodonNucs))] = "N" - matInput[inputMatrixIndex,1] <<- c2s(paramSeq) - } - } - - #Filter mutations based on the model - if(any(mutationInfo)==T | is.na(any(mutationInfo))){ - - if(model==1 & !is.na(mutationInfo)){ - mutationInfo <- mutationInfo[mutationInfo=="S"] - } - if(any(mutationInfo)==T | is.na(any(mutationInfo))) return(mutationInfo) - else return(NA) - }else{ - return(NA) - } - }else{ - return(NA) - } - - - }else{ - return(NA) - } - - - }else{ - return (NA) - } - } - - analyzeMutationsFixed <- function( inputArray, model = 0 , multipleMutation=0, seqWithStops=0){ - - paramGL = s2c(inputArray[2]) - paramSeq = s2c(inputArray[1]) - inputSeq <- inputArray[1] - #if( any(paramSeq=="N") ){ - # gapPos_Seq = which(paramSeq=="N") - # gapPos_Seq_ToReplace = gapPos_Seq[paramGL[gapPos_Seq] != "N"] - # paramSeq[gapPos_Seq_ToReplace] = paramGL[gapPos_Seq_ToReplace] - #} - mutations_val = paramGL != paramSeq - - if(any(mutations_val)){ - mutationPos = which(mutations_val)#{1:length(mutations_val)}[mutations_val] - length_mutations =length(mutationPos) - mutationInfo = rep(NA,length_mutations) - - pos<- mutationPos - pos_array<-array(sapply(pos,getCodonPos)) - codonGL = paramGL[pos_array] - codonSeqWhole = paramSeq[pos_array] - codonSeq = sapply(pos,function(x){ - seqP = paramGL[getCodonPos(x)] - muCodonPos = {x-1}%%3+1 - seqP[muCodonPos] = paramSeq[x] - return(seqP) - }) - GLcodons = apply(matrix(codonGL,length_mutations,3,byrow=TRUE),1,c2s) - SeqcodonsWhole = apply(matrix(codonSeqWhole,length_mutations,3,byrow=TRUE),1,c2s) - Seqcodons = apply(codonSeq,2,c2s) - - mutationInfo = apply(rbind(GLcodons , Seqcodons),2,function(x){mutationType(c2s(x[1]),c2s(x[2]))}) - names(mutationInfo) = mutationPos - - mutationInfoWhole = apply(rbind(GLcodons , SeqcodonsWhole),2,function(x){mutationType(c2s(x[1]),c2s(x[2]))}) - names(mutationInfoWhole) = mutationPos - - mutationInfo <- mutationInfo[!is.na(mutationInfo)] - mutationInfoWhole <- mutationInfoWhole[!is.na(mutationInfoWhole)] - - if(any(!is.na(mutationInfo))){ - - #Filter based on Stop (at the codon level) - if(seqWithStops==1){ - nucleotidesAtStopCodons = names(mutationInfoWhole[mutationInfoWhole!="Stop"]) - mutationInfo = mutationInfo[nucleotidesAtStopCodons] - mutationInfoWhole = mutationInfo[nucleotidesAtStopCodons] - }else{ - countStops = sum(mutationInfoWhole=="Stop") - if(seqWithStops==2 & countStops==0) mutationInfo = NA - if(seqWithStops==3 & countStops>0) mutationInfo = NA - } - - if(any(!is.na(mutationInfo))){ - #Filter mutations based on multipleMutation - if(multipleMutation==1 & !is.na(mutationInfo)){ - mutationCodons = getCodonNumb(as.numeric(names(mutationInfoWhole))) - tableMutationCodons <- table(mutationCodons) - codonsWithMultipleMutations <- as.numeric(names(tableMutationCodons[tableMutationCodons>1])) - if(any(codonsWithMultipleMutations)){ - #remove the nucleotide mutations in the codons with multiple mutations - mutationInfo <- mutationInfo[!(mutationCodons %in% codonsWithMultipleMutations)] - #replace those codons with Ns in the input sequence - paramSeq[unlist(lapply(codonsWithMultipleMutations, getCodonNucs))] = "N" - #matInput[inputMatrixIndex,1] <<- c2s(paramSeq) - inputSeq <- c2s(paramSeq) - } - } - - #Filter mutations based on the model - if(any(mutationInfo)==T | is.na(any(mutationInfo))){ - - if(model==1 & !is.na(mutationInfo)){ - mutationInfo <- mutationInfo[mutationInfo=="S"] - } - if(any(mutationInfo)==T | is.na(any(mutationInfo))) return(list(mutationInfo,inputSeq)) - else return(list(NA,inputSeq)) - }else{ - return(list(NA,inputSeq)) - } - }else{ - return(list(NA,inputSeq)) - } - - - }else{ - return(list(NA,inputSeq)) - } - - - }else{ - return (list(NA,inputSeq)) - } - } - - # triMutability Background Count - buildMutabilityModel <- function( inputMatrixIndex, model=0 , multipleMutation=0, seqWithStops=0, stopMutations=0){ - - #rowOrigMatInput = matInput[inputMatrixIndex,] - seqGL = gsub("-", "", matInput[inputMatrixIndex,2]) - seqInput = gsub("-", "", matInput[inputMatrixIndex,1]) - #matInput[inputMatrixIndex,] <<- cbind(seqInput,seqGL) - tempInput <- cbind(seqInput,seqGL) - seqLength = nchar(seqGL) - list_analyzeMutationsFixed<- analyzeMutationsFixed(tempInput, model, multipleMutation, seqWithStops) - mutationCount <- list_analyzeMutationsFixed[[1]] - seqInput <- list_analyzeMutationsFixed[[2]] - BackgroundMatrix = mutabilityMatrix - MutationMatrix = mutabilityMatrix - MutationCountMatrix = mutabilityMatrix - if(!is.na(mutationCount)){ - if((stopMutations==0 & model==0) | (stopMutations==1 & (sum(mutationCount=="Stop")<length(mutationCount))) | (model==1 & (sum(mutationCount=="S")>0)) ){ - - fivermerStartPos = 1:(seqLength-4) - fivemerLength <- length(fivermerStartPos) - fivemerGL <- substr(rep(seqGL,length(fivermerStartPos)),(fivermerStartPos),(fivermerStartPos+4)) - fivemerSeq <- substr(rep(seqInput,length(fivermerStartPos)),(fivermerStartPos),(fivermerStartPos+4)) - - #Background - for(fivemerIndex in 1:fivemerLength){ - fivemer = fivemerGL[fivemerIndex] - if(!any(grep("N",fivemer))){ - fivemerCodonPos = fivemerCodon(fivemerIndex) - fivemerReadingFrameCodon = substr(fivemer,fivemerCodonPos[1],fivemerCodonPos[3]) - fivemerReadingFrameCodonInputSeq = substr(fivemerSeq[fivemerIndex],fivemerCodonPos[1],fivemerCodonPos[3]) - - # All mutations model - #if(!any(grep("N",fivemerReadingFrameCodon))){ - if(model==0){ - if(stopMutations==0){ - if(!any(grep("N",fivemerReadingFrameCodonInputSeq))) - BackgroundMatrix[fivemer] <- (BackgroundMatrix[fivemer] + 1) - }else{ - if( !any(grep("N",fivemerReadingFrameCodonInputSeq)) & translateCodonToAminoAcid(fivemerReadingFrameCodon)!="*" ){ - positionWithinCodon = which(fivemerCodonPos==3)#positionsWithinCodon[(fivemerCodonPos[1]%%3)+1] - BackgroundMatrix[fivemer] <- (BackgroundMatrix[fivemer] + probNonStopMutations[fivemerReadingFrameCodon,positionWithinCodon]) - } - } - }else{ # Only silent mutations - if( !any(grep("N",fivemerReadingFrameCodonInputSeq)) & translateCodonToAminoAcid(fivemerReadingFrameCodon)!="*" & translateCodonToAminoAcid(fivemerReadingFrameCodonInputSeq)==translateCodonToAminoAcid(fivemerReadingFrameCodon)){ - positionWithinCodon = which(fivemerCodonPos==3) - BackgroundMatrix[fivemer] <- (BackgroundMatrix[fivemer] + probSMutations[fivemerReadingFrameCodon,positionWithinCodon]) - } - } - #} - } - } - - #Mutations - if(stopMutations==1) mutationCount = mutationCount[mutationCount!="Stop"] - if(model==1) mutationCount = mutationCount[mutationCount=="S"] - mutationPositions = as.numeric(names(mutationCount)) - mutationCount = mutationCount[mutationPositions>2 & mutationPositions<(seqLength-1)] - mutationPositions = mutationPositions[mutationPositions>2 & mutationPositions<(seqLength-1)] - countMutations = 0 - for(mutationPosition in mutationPositions){ - fivemerIndex = mutationPosition-2 - fivemer = fivemerSeq[fivemerIndex] - GLfivemer = fivemerGL[fivemerIndex] - fivemerCodonPos = fivemerCodon(fivemerIndex) - fivemerReadingFrameCodon = substr(fivemer,fivemerCodonPos[1],fivemerCodonPos[3]) - fivemerReadingFrameCodonGL = substr(GLfivemer,fivemerCodonPos[1],fivemerCodonPos[3]) - if(!any(grep("N",fivemer)) & !any(grep("N",GLfivemer))){ - if(model==0){ - countMutations = countMutations + 1 - MutationMatrix[GLfivemer] <- (MutationMatrix[GLfivemer] + 1) - MutationCountMatrix[GLfivemer] <- (MutationCountMatrix[GLfivemer] + 1) - }else{ - if( translateCodonToAminoAcid(fivemerReadingFrameCodonGL)!="*" ){ - countMutations = countMutations + 1 - positionWithinCodon = which(fivemerCodonPos==3) - glNuc = substr(fivemerReadingFrameCodonGL,positionWithinCodon,positionWithinCodon) - inputNuc = substr(fivemerReadingFrameCodon,positionWithinCodon,positionWithinCodon) - MutationMatrix[GLfivemer] <- (MutationMatrix[GLfivemer] + substitution[glNuc,inputNuc]) - MutationCountMatrix[GLfivemer] <- (MutationCountMatrix[GLfivemer] + 1) - } - } - } - } - - seqMutability = MutationMatrix/BackgroundMatrix - seqMutability = seqMutability/sum(seqMutability,na.rm=TRUE) - #cat(inputMatrixIndex,"\t",countMutations,"\n") - return(list("seqMutability" = seqMutability,"numbMutations" = countMutations,"seqMutabilityCount" = MutationCountMatrix, "BackgroundMatrix"=BackgroundMatrix)) - - } - } - - } - - #Returns the codon position containing the middle nucleotide - fivemerCodon <- function(fivemerIndex){ - codonPos = list(2:4,1:3,3:5) - fivemerType = fivemerIndex%%3 - return(codonPos[[fivemerType+1]]) - } - - #returns probability values for one mutation in codons resulting in R, S or Stop - probMutations <- function(typeOfMutation){ - matMutationProb <- matrix(0,ncol=3,nrow=125,dimnames=list(words(alphabet = c(NUCLEOTIDES,"N"), length=3),c(1:3))) - for(codon in rownames(matMutationProb)){ - if( !any(grep("N",codon)) ){ - for(muPos in 1:3){ - matCodon = matrix(rep(s2c(codon),3),nrow=3,ncol=3,byrow=T) - glNuc = matCodon[1,muPos] - matCodon[,muPos] = canMutateTo(glNuc) - substitutionRate = substitution[glNuc,matCodon[,muPos]] - typeOfMutations = apply(rbind(rep(codon,3),apply(matCodon,1,c2s)),2,function(x){mutationType(c2s(x[1]),c2s(x[2]))}) - matMutationProb[codon,muPos] <- sum(substitutionRate[typeOfMutations==typeOfMutation]) - } - } - } - - return(matMutationProb) - } - - - - -#Mapping Trinucleotides to fivemers -mapTriToFivemer <- function(triMutability=triMutability_Literature_Human){ - rownames(triMutability) <- triMutability_Names - Fivemer<-rep(NA,1024) - names(Fivemer)<-words(alphabet=NUCLEOTIDES,length=5) - Fivemer<-sapply(names(Fivemer),function(Word)return(sum( c(triMutability[substring(Word,3,5),1],triMutability[substring(Word,2,4),2],triMutability[substring(Word,1,3),3]),na.rm=TRUE))) - Fivemer<-Fivemer/sum(Fivemer) - return(Fivemer) -} - -collapseFivemerToTri<-function(Fivemer,Weights=MutabilityWeights,position=1,NUC="A"){ - Indices<-substring(names(Fivemer),3,3)==NUC - Factors<-substring(names(Fivemer[Indices]),(4-position),(6-position)) - tapply(which(Indices),Factors,function(i)weighted.mean(Fivemer[i],Weights[i],na.rm=TRUE)) -} - - - -CountFivemerToTri<-function(Fivemer,Weights=MutabilityWeights,position=1,NUC="A"){ - Indices<-substring(names(Fivemer),3,3)==NUC - Factors<-substring(names(Fivemer[Indices]),(4-position),(6-position)) - tapply(which(Indices),Factors,function(i)sum(Weights[i],na.rm=TRUE)) -} - -#Uses the real counts of the mutated fivemers -CountFivemerToTri2<-function(Fivemer,Counts=MutabilityCounts,position=1,NUC="A"){ - Indices<-substring(names(Fivemer),3,3)==NUC - Factors<-substring(names(Fivemer[Indices]),(4-position),(6-position)) - tapply(which(Indices),Factors,function(i)sum(Counts[i],na.rm=TRUE)) -} - -bootstrap<-function(x=c(33,12,21),M=10000,alpha=0.05){ -N<-sum(x) -if(N){ -p<-x/N -k<-length(x)-1 -tmp<-rmultinom(M, size = N, prob=p) -tmp_p<-apply(tmp,2,function(y)y/N) -(apply(tmp_p,1,function(y)quantile(y,c(alpha/2/k,1-alpha/2/k)))) -} -else return(matrix(0,2,length(x))) -} - - - - -bootstrap2<-function(x=c(33,12,21),n=10,M=10000,alpha=0.05){ - -N<-sum(x) -k<-length(x) -y<-rep(1:k,x) -tmp<-sapply(1:M,function(i)sample(y,n)) -if(n>1)tmp_p<-sapply(1:M,function(j)sapply(1:k,function(i)sum(tmp[,j]==i)))/n -if(n==1)tmp_p<-sapply(1:M,function(j)sapply(1:k,function(i)sum(tmp[j]==i)))/n -(apply(tmp_p,1,function(z)quantile(z,c(alpha/2/(k-1),1-alpha/2/(k-1))))) -} - - - -p_value<-function(x=c(33,12,21),M=100000,x_obs=c(2,5,3)){ -n=sum(x_obs) -N<-sum(x) -k<-length(x) -y<-rep(1:k,x) -tmp<-sapply(1:M,function(i)sample(y,n)) -if(n>1)tmp_p<-sapply(1:M,function(j)sapply(1:k,function(i)sum(tmp[,j]==i))) -if(n==1)tmp_p<-sapply(1:M,function(j)sapply(1:k,function(i)sum(tmp[j]==i))) -tmp<-rbind(sapply(1:3,function(i)sum(tmp_p[i,]>=x_obs[i])/M), -sapply(1:3,function(i)sum(tmp_p[i,]<=x_obs[i])/M)) -sapply(1:3,function(i){if(tmp[1,i]>=tmp[2,i])return(-tmp[2,i])else return(tmp[1,i])}) -} - -#"D:\\Sequences\\IMGT Germlines\\Human_SNPless_IGHJ.FASTA" -# Remove SNPs from IMGT germline segment alleles -generateUnambiguousRepertoire <- function(repertoireInFile,repertoireOutFile){ - repertoireIn <- read.fasta(repertoireInFile, seqtype="DNA",as.string=T,set.attributes=F,forceDNAtolower=F) - alleleNames <- sapply(names(repertoireIn),function(x)strsplit(x,"|",fixed=TRUE)[[1]][2]) - SNPs <- tapply(repertoireIn,sapply(alleleNames,function(x)strsplit(x,"*",fixed=TRUE)[[1]][1]),function(x){ - Indices<-NULL - for(i in 1:length(x)){ - firstSeq = s2c(x[[1]]) - iSeq = s2c(x[[i]]) - Indices<-c(Indices,which(firstSeq[1:320]!=iSeq[1:320] & firstSeq[1:320]!="." & iSeq[1:320]!="." )) - } - return(sort(unique(Indices))) - }) - repertoireOut <- repertoireIn - repertoireOut <- lapply(names(repertoireOut), function(repertoireName){ - alleleName <- strsplit(repertoireName,"|",fixed=TRUE)[[1]][2] - geneSegmentName <- strsplit(alleleName,"*",fixed=TRUE)[[1]][1] - alleleSeq <- s2c(repertoireOut[[repertoireName]]) - alleleSeq[as.numeric(unlist(SNPs[geneSegmentName]))] <- "N" - alleleSeq <- c2s(alleleSeq) - repertoireOut[[repertoireName]] <- alleleSeq - }) - names(repertoireOut) <- names(repertoireIn) - write.fasta(repertoireOut,names(repertoireOut),file.out=repertoireOutFile) - -} - - - - - - -############ -groupBayes2 = function(indexes, param_resultMat){ - - BayesGDist_Focused_CDR = calculate_bayesG( x=param_resultMat[indexes,1], N=apply(param_resultMat[indexes,c(1,2,4)],1,sum,na.rm=T), p=apply(param_resultMat[indexes,5:8],1,function(x){x[1]/(x[1]+x[2]+x[4])})) - BayesGDist_Focused_FWR = calculate_bayesG( x=param_resultMat[indexes,3], N=apply(param_resultMat[indexes,c(3,2,4)],1,sum,na.rm=T), p=apply(param_resultMat[indexes,5:8],1,function(x){x[3]/(x[3]+x[2]+x[4])})) - #BayesGDist_Local_CDR = calculate_bayesG( x=param_resultMat[indexes,1], N=apply(param_resultMat[indexes,c(1,2)],1,sum,na.rm=T), p=apply(param_resultMat[indexes,5:8],1,function(x){x[1]/(x[1]+x[2])})) - #BayesGDist_Local_FWR = calculate_bayesG( x=param_resultMat[indexes,3], N=apply(param_resultMat[indexes,c(3,4)],1,sum,na.rm=T), p=apply(param_resultMat[indexes,5:8],1,function(x){x[3]/(x[3]+x[4])})) - #BayesGDist_Global_CDR = calculate_bayesG( x=param_resultMat[indexes,1], N=apply(param_resultMat[indexes,c(1,2,3,4)],1,sum,na.rm=T), p=apply(param_resultMat[indexes,5:8],1,function(x){x[1]/(x[1]+x[2]+x[3]+x[4])})) - #BayesGDist_Global_FWR = calculate_bayesG( x=param_resultMat[indexes,3], N=apply(param_resultMat[indexes,c(1,2,3,4)],1,sum,na.rm=T), p=apply(param_resultMat[indexes,5:8],1,function(x){x[3]/(x[1]+x[2]+x[3]+x[4])})) - return ( list("BayesGDist_Focused_CDR"=BayesGDist_Focused_CDR, - "BayesGDist_Focused_FWR"=BayesGDist_Focused_FWR) ) - #"BayesGDist_Local_CDR"=BayesGDist_Local_CDR, - #"BayesGDist_Local_FWR" = BayesGDist_Local_FWR)) -# "BayesGDist_Global_CDR" = BayesGDist_Global_CDR, -# "BayesGDist_Global_FWR" = BayesGDist_Global_FWR) ) - - -} - - -calculate_bayesG <- function( x=array(), N=array(), p=array(), max_sigma=20, length_sigma=4001){ - G <- max(length(x),length(N),length(p)) - x=array(x,dim=G) - N=array(N,dim=G) - p=array(p,dim=G) - - indexOfZero = N>0 & p>0 - N = N[indexOfZero] - x = x[indexOfZero] - p = p[indexOfZero] - G <- length(x) - - if(G){ - - cons<-array( dim=c(length_sigma,G) ) - if(G==1) { - return(calculate_bayes(x=x[G],N=N[G],p=p[G],max_sigma=max_sigma,length_sigma=length_sigma)) - } - else { - for(g in 1:G) cons[,g] <- calculate_bayes(x=x[g],N=N[g],p=p[g],max_sigma=max_sigma,length_sigma=length_sigma) - listMatG <- convolutionPowersOfTwoByTwos(cons,length_sigma=length_sigma) - y<-calculate_bayesGHelper(listMatG,length_sigma=length_sigma) - return( y/sum(y)/(2*max_sigma/(length_sigma-1)) ) - } - }else{ - return(NA) - } -} - - -calculate_bayesGHelper <- function( listMatG,length_sigma=4001 ){ - matG <- listMatG[[1]] - groups <- listMatG[[2]] - i = 1 - resConv <- matG[,i] - denom <- 2^groups[i] - if(length(groups)>1){ - while( i<length(groups) ){ - i = i + 1 - resConv <- weighted_conv(resConv, matG[,i], w= {{2^groups[i]}/denom} ,length_sigma=length_sigma) - #cat({{2^groups[i]}/denom},"\n") - denom <- denom + 2^groups[i] - } - } - return(resConv) -} - -weighted_conv<-function(x,y,w=1,m=100,length_sigma=4001){ -lx<-length(x) -ly<-length(y) -if({lx<m}| {{lx*w}<m}| {{ly}<m}| {{ly*w}<m}){ -if(w<1){ -y1<-approx(1:ly,y,seq(1,ly,length.out=m))$y -x1<-approx(1:lx,x,seq(1,lx,length.out=m/w))$y -lx<-length(x1) -ly<-length(y1) -} -else { -y1<-approx(1:ly,y,seq(1,ly,length.out=m*w))$y -x1<-approx(1:lx,x,seq(1,lx,length.out=m))$y -lx<-length(x1) -ly<-length(y1) -} -} -else{ -x1<-x -y1<-approx(1:ly,y,seq(1,ly,length.out=floor(lx*w)))$y -ly<-length(y1) -} -tmp<-approx(x=1:(lx+ly-1),y=convolve(x1,rev(y1),type="open"),xout=seq(1,lx+ly-1,length.out=length_sigma))$y -tmp[tmp<=0] = 0 -return(tmp/sum(tmp)) -} - -######################## - - - - -mutabilityMatrixONE<-rep(0,4) -names(mutabilityMatrixONE)<-NUCLEOTIDES - - # triMutability Background Count - buildMutabilityModelONE <- function( inputMatrixIndex, model=0 , multipleMutation=0, seqWithStops=0, stopMutations=0){ - - #rowOrigMatInput = matInput[inputMatrixIndex,] - seqGL = gsub("-", "", matInput[inputMatrixIndex,2]) - seqInput = gsub("-", "", matInput[inputMatrixIndex,1]) - matInput[inputMatrixIndex,] <<- c(seqInput,seqGL) - seqLength = nchar(seqGL) - mutationCount <- analyzeMutations(inputMatrixIndex, model, multipleMutation, seqWithStops) - BackgroundMatrix = mutabilityMatrixONE - MutationMatrix = mutabilityMatrixONE - MutationCountMatrix = mutabilityMatrixONE - if(!is.na(mutationCount)){ - if((stopMutations==0 & model==0) | (stopMutations==1 & (sum(mutationCount=="Stop")<length(mutationCount))) | (model==1 & (sum(mutationCount=="S")>0)) ){ - -# ONEmerStartPos = 1:(seqLength) -# ONEmerLength <- length(ONEmerStartPos) - ONEmerGL <- s2c(seqGL) - ONEmerSeq <- s2c(seqInput) - - #Background - for(ONEmerIndex in 1:seqLength){ - ONEmer = ONEmerGL[ONEmerIndex] - if(ONEmer!="N"){ - ONEmerCodonPos = getCodonPos(ONEmerIndex) - ONEmerReadingFrameCodon = c2s(ONEmerGL[ONEmerCodonPos]) - ONEmerReadingFrameCodonInputSeq = c2s(ONEmerSeq[ONEmerCodonPos] ) - - # All mutations model - #if(!any(grep("N",ONEmerReadingFrameCodon))){ - if(model==0){ - if(stopMutations==0){ - if(!any(grep("N",ONEmerReadingFrameCodonInputSeq))) - BackgroundMatrix[ONEmer] <- (BackgroundMatrix[ONEmer] + 1) - }else{ - if( !any(grep("N",ONEmerReadingFrameCodonInputSeq)) & translateCodonToAminoAcid(ONEmerReadingFrameCodonInputSeq)!="*"){ - positionWithinCodon = which(ONEmerCodonPos==ONEmerIndex)#positionsWithinCodon[(ONEmerCodonPos[1]%%3)+1] - BackgroundMatrix[ONEmer] <- (BackgroundMatrix[ONEmer] + probNonStopMutations[ONEmerReadingFrameCodon,positionWithinCodon]) - } - } - }else{ # Only silent mutations - if( !any(grep("N",ONEmerReadingFrameCodonInputSeq)) & translateCodonToAminoAcid(ONEmerReadingFrameCodonInputSeq)!="*" & translateCodonToAminoAcid(ONEmerReadingFrameCodonInputSeq)==translateCodonToAminoAcid(ONEmerReadingFrameCodon) ){ - positionWithinCodon = which(ONEmerCodonPos==ONEmerIndex) - BackgroundMatrix[ONEmer] <- (BackgroundMatrix[ONEmer] + probSMutations[ONEmerReadingFrameCodon,positionWithinCodon]) - } - } - } - } - } - - #Mutations - if(stopMutations==1) mutationCount = mutationCount[mutationCount!="Stop"] - if(model==1) mutationCount = mutationCount[mutationCount=="S"] - mutationPositions = as.numeric(names(mutationCount)) - mutationCount = mutationCount[mutationPositions>2 & mutationPositions<(seqLength-1)] - mutationPositions = mutationPositions[mutationPositions>2 & mutationPositions<(seqLength-1)] - countMutations = 0 - for(mutationPosition in mutationPositions){ - ONEmerIndex = mutationPosition - ONEmer = ONEmerSeq[ONEmerIndex] - GLONEmer = ONEmerGL[ONEmerIndex] - ONEmerCodonPos = getCodonPos(ONEmerIndex) - ONEmerReadingFrameCodon = c2s(ONEmerSeq[ONEmerCodonPos]) - ONEmerReadingFrameCodonGL =c2s(ONEmerGL[ONEmerCodonPos]) - if(!any(grep("N",ONEmer)) & !any(grep("N",GLONEmer))){ - if(model==0){ - countMutations = countMutations + 1 - MutationMatrix[GLONEmer] <- (MutationMatrix[GLONEmer] + 1) - MutationCountMatrix[GLONEmer] <- (MutationCountMatrix[GLONEmer] + 1) - }else{ - if( translateCodonToAminoAcid(ONEmerReadingFrameCodonGL)!="*" ){ - countMutations = countMutations + 1 - positionWithinCodon = which(ONEmerCodonPos==ONEmerIndex) - glNuc = substr(ONEmerReadingFrameCodonGL,positionWithinCodon,positionWithinCodon) - inputNuc = substr(ONEmerReadingFrameCodon,positionWithinCodon,positionWithinCodon) - MutationMatrix[GLONEmer] <- (MutationMatrix[GLONEmer] + substitution[glNuc,inputNuc]) - MutationCountMatrix[GLONEmer] <- (MutationCountMatrix[GLONEmer] + 1) - } - } - } - } - - seqMutability = MutationMatrix/BackgroundMatrix - seqMutability = seqMutability/sum(seqMutability,na.rm=TRUE) - #cat(inputMatrixIndex,"\t",countMutations,"\n") - return(list("seqMutability" = seqMutability,"numbMutations" = countMutations,"seqMutabilityCount" = MutationCountMatrix, "BackgroundMatrix"=BackgroundMatrix)) -# tmp<-list("seqMutability" = seqMutability,"numbMutations" = countMutations,"seqMutabilityCount" = MutationCountMatrix) - } - } - -################ -# $Id: trim.R 989 2006-10-29 15:28:26Z ggorjan $ - -trim <- function(s, recode.factor=TRUE, ...) - UseMethod("trim", s) - -trim.default <- function(s, recode.factor=TRUE, ...) - s - -trim.character <- function(s, recode.factor=TRUE, ...) -{ - s <- sub(pattern="^ +", replacement="", x=s) - s <- sub(pattern=" +$", replacement="", x=s) - s -} - -trim.factor <- function(s, recode.factor=TRUE, ...) -{ - levels(s) <- trim(levels(s)) - if(recode.factor) { - dots <- list(x=s, ...) - if(is.null(dots$sort)) dots$sort <- sort - s <- do.call(what=reorder.factor, args=dots) - } - s -} - -trim.list <- function(s, recode.factor=TRUE, ...) - lapply(s, trim, recode.factor=recode.factor, ...) - -trim.data.frame <- function(s, recode.factor=TRUE, ...) -{ - s[] <- trim.list(s, recode.factor=recode.factor, ...) - s -} -####################################### -# Compute the expected for each sequence-germline pair by codon -getExpectedIndividualByCodon <- function(matInput){ -if( any(grep("multicore",search())) ){ - facGL <- factor(matInput[,2]) - facLevels = levels(facGL) - LisGLs_MutabilityU = mclapply(1:length(facLevels), function(x){ - computeMutabilities(facLevels[x]) - }) - facIndex = match(facGL,facLevels) - - LisGLs_Mutability = mclapply(1:nrow(matInput), function(x){ - cInput = rep(NA,nchar(matInput[x,1])) - cInput[s2c(matInput[x,1])!="N"] = 1 - LisGLs_MutabilityU[[facIndex[x]]] * cInput - }) - - LisGLs_Targeting = mclapply(1:dim(matInput)[1], function(x){ - computeTargeting(matInput[x,2],LisGLs_Mutability[[x]]) - }) - - LisGLs_MutationTypes = mclapply(1:length(matInput[,2]),function(x){ - #print(x) - computeMutationTypes(matInput[x,2]) - }) - - LisGLs_R_Exp = mclapply(1:nrow(matInput), function(x){ - Exp_R <- rollapply(as.zoo(1:readEnd),width=3,by=3, - function(codonNucs){ - RPos = which(LisGLs_MutationTypes[[x]][,codonNucs]=="R") - sum( LisGLs_Targeting[[x]][,codonNucs][RPos], na.rm=T ) - } - ) - }) - - LisGLs_S_Exp = mclapply(1:nrow(matInput), function(x){ - Exp_S <- rollapply(as.zoo(1:readEnd),width=3,by=3, - function(codonNucs){ - SPos = which(LisGLs_MutationTypes[[x]][,codonNucs]=="S") - sum( LisGLs_Targeting[[x]][,codonNucs][SPos], na.rm=T ) - } - ) - }) - - Exp_R = matrix(unlist(LisGLs_R_Exp),nrow=nrow(matInput),ncol=readEnd/3,T) - Exp_S = matrix(unlist(LisGLs_S_Exp),nrow=nrow(matInput),ncol=readEnd/3,T) - return( list( "Expected_R"=Exp_R, "Expected_S"=Exp_S) ) - }else{ - facGL <- factor(matInput[,2]) - facLevels = levels(facGL) - LisGLs_MutabilityU = lapply(1:length(facLevels), function(x){ - computeMutabilities(facLevels[x]) - }) - facIndex = match(facGL,facLevels) - - LisGLs_Mutability = lapply(1:nrow(matInput), function(x){ - cInput = rep(NA,nchar(matInput[x,1])) - cInput[s2c(matInput[x,1])!="N"] = 1 - LisGLs_MutabilityU[[facIndex[x]]] * cInput - }) - - LisGLs_Targeting = lapply(1:dim(matInput)[1], function(x){ - computeTargeting(matInput[x,2],LisGLs_Mutability[[x]]) - }) - - LisGLs_MutationTypes = lapply(1:length(matInput[,2]),function(x){ - #print(x) - computeMutationTypes(matInput[x,2]) - }) - - LisGLs_R_Exp = lapply(1:nrow(matInput), function(x){ - Exp_R <- rollapply(as.zoo(1:readEnd),width=3,by=3, - function(codonNucs){ - RPos = which(LisGLs_MutationTypes[[x]][,codonNucs]=="R") - sum( LisGLs_Targeting[[x]][,codonNucs][RPos], na.rm=T ) - } - ) - }) - - LisGLs_S_Exp = lapply(1:nrow(matInput), function(x){ - Exp_S <- rollapply(as.zoo(1:readEnd),width=3,by=3, - function(codonNucs){ - SPos = which(LisGLs_MutationTypes[[x]][,codonNucs]=="S") - sum( LisGLs_Targeting[[x]][,codonNucs][SPos], na.rm=T ) - } - ) - }) - - Exp_R = matrix(unlist(LisGLs_R_Exp),nrow=nrow(matInput),ncol=readEnd/3,T) - Exp_S = matrix(unlist(LisGLs_S_Exp),nrow=nrow(matInput),ncol=readEnd/3,T) - return( list( "Expected_R"=Exp_R, "Expected_S"=Exp_S) ) - } -} - -# getObservedMutationsByCodon <- function(listMutations){ -# numbSeqs <- length(listMutations) -# obsMu_R <- matrix(0,nrow=numbSeqs,ncol=readEnd/3,dimnames=list(c(1:numbSeqs),c(1:(readEnd/3)))) -# obsMu_S <- obsMu_R -# temp <- mclapply(1:length(listMutations), function(i){ -# arrMutations = listMutations[[i]] -# RPos = as.numeric(names(arrMutations)[arrMutations=="R"]) -# RPos <- sapply(RPos,getCodonNumb) -# if(any(RPos)){ -# tabR <- table(RPos) -# obsMu_R[i,as.numeric(names(tabR))] <<- tabR -# } -# -# SPos = as.numeric(names(arrMutations)[arrMutations=="S"]) -# SPos <- sapply(SPos,getCodonNumb) -# if(any(SPos)){ -# tabS <- table(SPos) -# obsMu_S[i,names(tabS)] <<- tabS -# } -# } -# ) -# return( list( "Observed_R"=obsMu_R, "Observed_S"=obsMu_S) ) -# } - -getObservedMutationsByCodon <- function(listMutations){ - numbSeqs <- length(listMutations) - obsMu_R <- matrix(0,nrow=numbSeqs,ncol=readEnd/3,dimnames=list(c(1:numbSeqs),c(1:(readEnd/3)))) - obsMu_S <- obsMu_R - temp <- lapply(1:length(listMutations), function(i){ - arrMutations = listMutations[[i]] - RPos = as.numeric(names(arrMutations)[arrMutations=="R"]) - RPos <- sapply(RPos,getCodonNumb) - if(any(RPos)){ - tabR <- table(RPos) - obsMu_R[i,as.numeric(names(tabR))] <<- tabR - } - - SPos = as.numeric(names(arrMutations)[arrMutations=="S"]) - SPos <- sapply(SPos,getCodonNumb) - if(any(SPos)){ - tabS <- table(SPos) - obsMu_S[i,names(tabS)] <<- tabS - } - } - ) - return( list( "Observed_R"=obsMu_R, "Observed_S"=obsMu_S) ) -} -
--- a/baseline/Baseline_Main.r Tue Sep 01 16:03:44 2020 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,388 +0,0 @@ -######################################################################################### -# License Agreement -# -# THIS WORK IS PROVIDED UNDER THE TERMS OF THIS CREATIVE COMMONS PUBLIC LICENSE -# ("CCPL" OR "LICENSE"). THE WORK IS PROTECTED BY COPYRIGHT AND/OR OTHER -# APPLICABLE LAW. ANY USE OF THE WORK OTHER THAN AS AUTHORIZED UNDER THIS LICENSE -# OR COPYRIGHT LAW IS PROHIBITED. -# -# BY EXERCISING ANY RIGHTS TO THE WORK PROVIDED HERE, YOU ACCEPT AND AGREE TO BE -# BOUND BY THE TERMS OF THIS LICENSE. TO THE EXTENT THIS LICENSE MAY BE CONSIDERED -# TO BE A CONTRACT, THE LICENSOR GRANTS YOU THE RIGHTS CONTAINED HERE IN -# CONSIDERATION OF YOUR ACCEPTANCE OF SUCH TERMS AND CONDITIONS. -# -# BASELIne: Bayesian Estimation of Antigen-Driven Selection in Immunoglobulin Sequences -# Coded by: Mohamed Uduman & Gur Yaari -# Copyright 2012 Kleinstein Lab -# Version: 1.3 (01/23/2014) -######################################################################################### - -op <- options(); -options(showWarnCalls=FALSE, showErrorCalls=FALSE, warn=-1) -library('seqinr') -if( F & Sys.info()[1]=="Linux"){ - library("multicore") -} - -# Load functions and initialize global variables -source("Baseline_Functions.r") - -# Initialize parameters with user provided arguments - arg <- commandArgs(TRUE) - #arg = c(2,1,5,5,0,1,"1:26:38:55:65:104:116", "test.fasta","","sample") - #arg = c(1,1,5,5,0,1,"1:38:55:65:104:116:200", "test.fasta","","sample") - #arg = c(1,1,5,5,1,1,"1:26:38:55:65:104:116", "/home/mu37/Wu/Wu_Cloned_gapped_sequences_D-masked.fasta","/home/mu37/Wu/","Wu") - testID <- as.numeric(arg[1]) # 1 = Focused, 2 = Local - species <- as.numeric(arg[2]) # 1 = Human. 2 = Mouse - substitutionModel <- as.numeric(arg[3]) # 0 = Uniform substitution, 1 = Smith DS et al. 1996, 5 = FiveS - mutabilityModel <- as.numeric(arg[4]) # 0 = Uniform mutablity, 1 = Tri-nucleotide (Shapiro GS et al. 2002) , 5 = FiveS - clonal <- as.numeric(arg[5]) # 0 = Independent sequences, 1 = Clonally related, 2 = Clonally related & only non-terminal mutations - fixIndels <- as.numeric(arg[6]) # 0 = Do nothing, 1 = Try and fix Indels - region <- as.numeric(strsplit(arg[7],":")[[1]]) # StartPos:LastNucleotideF1:C1:F2:C2:F3:C3 - inputFilePath <- arg[8] # Full path to input file - outputPath <- arg[9] # Full path to location of output files - outputID <- arg[10] # ID for session output - - - if(testID==5){ - traitChangeModel <- 1 - if( !is.na(any(arg[11])) ) traitChangeModel <- as.numeric(arg[11]) # 1 <- Chothia 1998 - initializeTraitChange(traitChangeModel) - } - -# Initialize other parameters/variables - - # Initialzie the codon table ( definitions of R/S ) - computeCodonTable(testID) - - # Initialize - # Test Name - testName<-"Focused" - if(testID==2) testName<-"Local" - if(testID==3) testName<-"Imbalanced" - if(testID==4) testName<-"ImbalancedSilent" - - # Indel placeholders initialization - indelPos <- NULL - delPos <- NULL - insPos <- NULL - - # Initialize in Tranistion & Mutability matrixes - substitution <- initializeSubstitutionMatrix(substitutionModel,species) - mutability <- initializeMutabilityMatrix(mutabilityModel,species) - - # FWR/CDR boundaries - flagTrim <- F - if( is.na(region[7])){ - flagTrim <- T - region[7]<-region[6] - } - readStart = min(region,na.rm=T) - readEnd = max(region,na.rm=T) - if(readStart>1){ - region = region - (readStart - 1) - } - region_Nuc = c( (region[1]*3-2) , (region[2:7]*3) ) - region_Cod = region - - readStart = (readStart*3)-2 - readEnd = (readEnd*3) - - FWR_Nuc <- c( rep(TRUE,(region_Nuc[2])), - rep(FALSE,(region_Nuc[3]-region_Nuc[2])), - rep(TRUE,(region_Nuc[4]-region_Nuc[3])), - rep(FALSE,(region_Nuc[5]-region_Nuc[4])), - rep(TRUE,(region_Nuc[6]-region_Nuc[5])), - rep(FALSE,(region_Nuc[7]-region_Nuc[6])) - ) - CDR_Nuc <- (1-FWR_Nuc) - CDR_Nuc <- as.logical(CDR_Nuc) - FWR_Nuc_Mat <- matrix( rep(FWR_Nuc,4), ncol=length(FWR_Nuc), nrow=4, byrow=T) - CDR_Nuc_Mat <- matrix( rep(CDR_Nuc,4), ncol=length(CDR_Nuc), nrow=4, byrow=T) - - FWR_Codon <- c( rep(TRUE,(region[2])), - rep(FALSE,(region[3]-region[2])), - rep(TRUE,(region[4]-region[3])), - rep(FALSE,(region[5]-region[4])), - rep(TRUE,(region[6]-region[5])), - rep(FALSE,(region[7]-region[6])) - ) - CDR_Codon <- (1-FWR_Codon) - CDR_Codon <- as.logical(CDR_Codon) - - -# Read input FASTA file - tryCatch( - inputFASTA <- baseline.read.fasta(inputFilePath, seqtype="DNA",as.string=T,set.attributes=F,forceDNAtolower=F) - , error = function(ex){ - cat("Error|Error reading input. Please enter or upload a valid FASTA file.\n") - q() - } - ) - - if (length(inputFASTA)==1) { - cat("Error|Error reading input. Please enter or upload a valid FASTA file.\n") - q() - } - - # Process sequence IDs/names - names(inputFASTA) <- sapply(names(inputFASTA),function(x){trim(x)}) - - # Convert non nucleotide characters to N - inputFASTA[length(inputFASTA)] = gsub("\t","",inputFASTA[length(inputFASTA)]) - inputFASTA <- lapply(inputFASTA,replaceNonFASTAChars) - - # Process the FASTA file and conver to Matrix[inputSequence, germlineSequence] - processedInput <- processInputAdvanced(inputFASTA) - matInput <- processedInput[[1]] - germlines <- processedInput[[2]] - lenGermlines = length(unique(germlines)) - groups <- processedInput[[3]] - lenGroups = length(unique(groups)) - rm(processedInput) - rm(inputFASTA) - -# # remove clones with less than 2 seqeunces -# tableGL <- table(germlines) -# singletons <- which(tableGL<8) -# rowsToRemove <- match(singletons,germlines) -# if(any(rowsToRemove)){ -# matInput <- matInput[-rowsToRemove,] -# germlines <- germlines[-rowsToRemove] -# groups <- groups[-rowsToRemove] -# } -# -# # remove unproductive seqs -# nonFuctionalSeqs <- sapply(rownames(matInput),function(x){any(grep("unproductive",x))}) -# if(any(nonFuctionalSeqs)){ -# if(sum(nonFuctionalSeqs)==length(germlines)){ -# write.table("Unproductive",file=paste(outputPath,outputID,".txt",sep=""),quote=F,sep="\t",row.names=F,col.names=T) -# q() -# } -# matInput <- matInput[-which(nonFuctionalSeqs),] -# germlines <- germlines[-which(nonFuctionalSeqs)] -# germlines[1:length(germlines)] <- 1:length(germlines) -# groups <- groups[-which(nonFuctionalSeqs)] -# } -# -# if(class(matInput)=="character"){ -# write.table("All unproductive seqs",file=paste(outputPath,outputID,".txt",sep=""),quote=F,sep="\t",row.names=F,col.names=T) -# q() -# } -# -# if(nrow(matInput)<10 | is.null(nrow(matInput))){ -# write.table(paste(nrow(matInput), "seqs only",sep=""),file=paste(outputPath,outputID,".txt",sep=""),quote=F,sep="\t",row.names=F,col.names=T) -# q() -# } - -# replace leading & trailing "-" with "N: - matInput <- t(apply(matInput,1,replaceLeadingTrailingDashes,readEnd)) - - # Trim (nucleotide) input sequences to the last codon - #matInput[,1] <- apply(matrix(matInput[,1]),1,trimToLastCodon) - -# # Check for Indels -# if(fixIndels){ -# delPos <- fixDeletions(matInput) -# insPos <- fixInsertions(matInput) -# }else{ -# # Check for indels -# indelPos <- checkForInDels(matInput) -# indelPos <- apply(cbind(indelPos[[1]],indelPos[[2]]),1,function(x){(x[1]==T & x[2]==T)}) -# } - - # If indels are present, remove mutations in the seqeunce & throw warning at end - #matInput[indelPos,] <- apply(matrix(matInput[indelPos,],nrow=sum(indelPos),ncol=2),1,function(x){x[1]=x[2]; return(x) }) - - colnames(matInput)=c("Input","Germline") - - # If seqeunces are clonal, create effective sequence for each clone & modify germline/group definitions - germlinesOriginal = NULL - if(clonal){ - germlinesOriginal <- germlines - collapseCloneResults <- tapply(1:nrow(matInput),germlines,function(i){ - collapseClone(matInput[i,1],matInput[i[1],2],readEnd,nonTerminalOnly=(clonal-1)) - }) - matInput = t(sapply(collapseCloneResults,function(x){return(x[[1]])})) - names_groups = tapply(groups,germlines,function(x){names(x[1])}) - groups = tapply(groups,germlines,function(x){array(x[1],dimnames=names(x[1]))}) - names(groups) = names_groups - - names_germlines = tapply(germlines,germlines,function(x){names(x[1])}) - germlines = tapply( germlines,germlines,function(x){array(x[1],dimnames=names(x[1]))} ) - names(germlines) = names_germlines - matInputErrors = sapply(collapseCloneResults,function(x){return(x[[2]])}) - } - - -# Selection Analysis - - -# if (length(germlines)>sequenceLimit) { -# # Code to parallelize processing goes here -# stop( paste("Error: Cannot process more than ", Upper_limit," sequences",sep="") ) -# } - -# if (length(germlines)<sequenceLimit) {} - - # Compute expected mutation frequencies - matExpected <- getExpectedIndividual(matInput) - - # Count observed number of mutations in the different regions - mutations <- lapply( 1:nrow(matInput), function(i){ - #cat(i,"\n") - seqI = s2c(matInput[i,1]) - seqG = s2c(matInput[i,2]) - matIGL = matrix(c(seqI,seqG),ncol=length(seqI),nrow=2,byrow=T) - retVal <- NA - tryCatch( - retVal <- analyzeMutations2NucUri(matIGL) - , error = function(ex){ - retVal <- NA - } - ) - - - return( retVal ) - }) - - matObserved <- t(sapply( mutations, processNucMutations2 )) - numberOfSeqsWithMutations <- numberOfSeqsWithMutations(matObserved, testID) - - #if(sum(numberOfSeqsWithMutations)==0){ - # write.table("No mutated sequences",file=paste(outputPath,outputID,".txt",sep=""),quote=F,sep="\t",row.names=F,col.names=T) - # q() - #} - - matMutationInfo <- cbind(matObserved,matExpected) - rm(matObserved,matExpected) - - - #Bayesian PDFs - bayes_pdf = computeBayesianScore(matMutationInfo, test=testName, max_sigma=20,length_sigma=4001) - bayesPDF_cdr = bayes_pdf[[1]] - bayesPDF_fwr = bayes_pdf[[2]] - rm(bayes_pdf) - - bayesPDF_germlines_cdr = tapply(bayesPDF_cdr,germlines,function(x) groupPosteriors(x,length_sigma=4001)) - bayesPDF_germlines_fwr = tapply(bayesPDF_fwr,germlines,function(x) groupPosteriors(x,length_sigma=4001)) - - bayesPDF_groups_cdr = tapply(bayesPDF_cdr,groups,function(x) groupPosteriors(x,length_sigma=4001)) - bayesPDF_groups_fwr = tapply(bayesPDF_fwr,groups,function(x) groupPosteriors(x,length_sigma=4001)) - - if(lenGroups>1){ - groups <- c(groups,lenGroups+1) - names(groups)[length(groups)] = "All sequences combined" - bayesPDF_groups_cdr[[lenGroups+1]] = groupPosteriors(bayesPDF_groups_cdr,length_sigma=4001) - bayesPDF_groups_fwr[[lenGroups+1]] = groupPosteriors(bayesPDF_groups_fwr,length_sigma=4001) - } - - #Bayesian Outputs - bayes_cdr = t(sapply(bayesPDF_cdr,calcBayesOutputInfo)) - bayes_fwr = t(sapply(bayesPDF_fwr,calcBayesOutputInfo)) - bayes_germlines_cdr = t(sapply(bayesPDF_germlines_cdr,calcBayesOutputInfo)) - bayes_germlines_fwr = t(sapply(bayesPDF_germlines_fwr,calcBayesOutputInfo)) - bayes_groups_cdr = t(sapply(bayesPDF_groups_cdr,calcBayesOutputInfo)) - bayes_groups_fwr = t(sapply(bayesPDF_groups_fwr,calcBayesOutputInfo)) - - #P-values - simgaP_cdr = sapply(bayesPDF_cdr,computeSigmaP) - simgaP_fwr = sapply(bayesPDF_fwr,computeSigmaP) - - simgaP_germlines_cdr = sapply(bayesPDF_germlines_cdr,computeSigmaP) - simgaP_germlines_fwr = sapply(bayesPDF_germlines_fwr,computeSigmaP) - - simgaP_groups_cdr = sapply(bayesPDF_groups_cdr,computeSigmaP) - simgaP_groups_fwr = sapply(bayesPDF_groups_fwr,computeSigmaP) - - - #Format output - - # Round expected mutation frequencies to 3 decimal places - matMutationInfo[germlinesOriginal[indelPos],] = NA - if(nrow(matMutationInfo)==1){ - matMutationInfo[5:8] = round(matMutationInfo[,5:8]/sum(matMutationInfo[,5:8],na.rm=T),3) - }else{ - matMutationInfo[,5:8] = t(round(apply(matMutationInfo[,5:8],1,function(x){ return(x/sum(x,na.rm=T)) }),3)) - } - - listPDFs = list() - nRows = length(unique(groups)) + length(unique(germlines)) + length(groups) - - matOutput = matrix(NA,ncol=18,nrow=nRows) - rowNumb = 1 - for(G in unique(groups)){ - #print(G) - matOutput[rowNumb,c(1,2,11:18)] = c("Group",names(groups)[groups==G][1],bayes_groups_cdr[G,],bayes_groups_fwr[G,],simgaP_groups_cdr[G],simgaP_groups_fwr[G]) - listPDFs[[rowNumb]] = list("CDR"=bayesPDF_groups_cdr[[G]],"FWR"=bayesPDF_groups_fwr[[G]]) - names(listPDFs)[rowNumb] = names(groups[groups==paste(G)])[1] - #if(names(groups)[which(groups==G)[1]]!="All sequences combined"){ - gs = unique(germlines[groups==G]) - rowNumb = rowNumb+1 - if( !is.na(gs) ){ - for( g in gs ){ - matOutput[rowNumb,c(1,2,11:18)] = c("Germline",names(germlines)[germlines==g][1],bayes_germlines_cdr[g,],bayes_germlines_fwr[g,],simgaP_germlines_cdr[g],simgaP_germlines_fwr[g]) - listPDFs[[rowNumb]] = list("CDR"=bayesPDF_germlines_cdr[[g]],"FWR"=bayesPDF_germlines_fwr[[g]]) - names(listPDFs)[rowNumb] = names(germlines[germlines==paste(g)])[1] - rowNumb = rowNumb+1 - indexesOfInterest = which(germlines==g) - numbSeqsOfInterest = length(indexesOfInterest) - rowNumb = seq(rowNumb,rowNumb+(numbSeqsOfInterest-1)) - matOutput[rowNumb,] = matrix( c( rep("Sequence",numbSeqsOfInterest), - rownames(matInput)[indexesOfInterest], - c(matMutationInfo[indexesOfInterest,1:4]), - c(matMutationInfo[indexesOfInterest,5:8]), - c(bayes_cdr[indexesOfInterest,]), - c(bayes_fwr[indexesOfInterest,]), - c(simgaP_cdr[indexesOfInterest]), - c(simgaP_fwr[indexesOfInterest]) - ), ncol=18, nrow=numbSeqsOfInterest,byrow=F) - increment=0 - for( ioi in indexesOfInterest){ - listPDFs[[min(rowNumb)+increment]] = list("CDR"=bayesPDF_cdr[[ioi]] , "FWR"=bayesPDF_fwr[[ioi]]) - names(listPDFs)[min(rowNumb)+increment] = rownames(matInput)[ioi] - increment = increment + 1 - } - rowNumb=max(rowNumb)+1 - - } - } - } - colsToFormat = 11:18 - matOutput[,colsToFormat] = formatC( matrix(as.numeric(matOutput[,colsToFormat]), nrow=nrow(matOutput), ncol=length(colsToFormat)) , digits=3) - matOutput[matOutput== " NaN"] = NA - - - - colnames(matOutput) = c("Type", "ID", "Observed_CDR_R", "Observed_CDR_S", "Observed_FWR_R", "Observed_FWR_S", - "Expected_CDR_R", "Expected_CDR_S", "Expected_FWR_R", "Expected_FWR_S", - paste( rep(testName,6), rep(c("Sigma","CIlower","CIupper"),2),rep(c("CDR","FWR"),each=3), sep="_"), - paste( rep(testName,2), rep("P",2),c("CDR","FWR"), sep="_") - ) - fileName = paste(outputPath,outputID,".txt",sep="") - write.table(matOutput,file=fileName,quote=F,sep="\t",row.names=T,col.names=NA) - fileName = paste(outputPath,outputID,".RData",sep="") - save(listPDFs,file=fileName) - -indelWarning = FALSE -if(sum(indelPos)>0){ - indelWarning = "<P>Warning: The following sequences have either gaps and/or deletions, and have been ommited from the analysis."; - indelWarning = paste( indelWarning , "<UL>", sep="" ) - for(indels in names(indelPos)[indelPos]){ - indelWarning = paste( indelWarning , "<LI>", indels, "</LI>", sep="" ) - } - indelWarning = paste( indelWarning , "</UL></P>", sep="" ) -} - -cloneWarning = FALSE -if(clonal==1){ - if(sum(matInputErrors)>0){ - cloneWarning = "<P>Warning: The following clones have sequences of unequal length."; - cloneWarning = paste( cloneWarning , "<UL>", sep="" ) - for(clone in names(matInputErrors)[matInputErrors]){ - cloneWarning = paste( cloneWarning , "<LI>", names(germlines)[as.numeric(clone)], "</LI>", sep="" ) - } - cloneWarning = paste( cloneWarning , "</UL></P>", sep="" ) - } -} -cat(paste("Success",outputID,indelWarning,cloneWarning,sep="|"))
--- a/baseline/IMGT-reference-seqs-IGHV-2015-11-05.fa Tue Sep 01 16:03:44 2020 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,703 +0,0 @@ ->IGHV1-18*01 -caggttcagctggtgcagtctggagct...gaggtgaagaagcctggggcctcagtgaaggtctcctgcaaggcttctggttacaccttt............accagctatggtatcagctgggtgcgacaggcccctggacaagggcttgagtggatgggatggatcagcgcttac......aatggtaacacaaactatgcacagaagctccag...ggcagagtcaccatgaccacagacacatccacgagcacagcctacatggagctgaggagcctgagatctgacgacacggccgtgtattactgtgcgagaga ->IGHV1-18*02 -caggttcagctggtgcagtctggagct...gaggtgaagaagcctggggcctcagtgaaggtctcctgcaaggcttctggttacaccttt............accagctatggtatcagctgggtgcgacaggcccctggacaagggcttgagtggatgggatggatcagcgcttac......aatggtaacacaaactatgcacagaagctccag...ggcagagtcaccatgaccacagacacatccacgagcacagcctacatggagctgaggagcctaagatctgacgacacggcc ->IGHV1-18*03 -caggttcagctggtgcagtctggagct...gaggtgaagaagcctggggcctcagtgaaggtctcctgcaaggcttctggttacaccttt............accagctatggtatcagctgggtgcgacaggcccctggacaagggcttgagtggatgggatggatcagcgcttac......aatggtaacacaaactatgcacagaagctccag...ggcagagtcaccatgaccacagacacatccacgagcacagcctacatggagctgaggagcctgagatctgacgacatggccgtgtattactgtgcgagaga ->IGHV1-18*04 -caggttcagctggtgcagtctggagct...gaggtgaagaagcctggggcctcagtgaaggtctcctgcaaggcttctggttacaccttt............accagctacggtatcagctgggtgcgacaggcccctggacaagggcttgagtggatgggatggatcagcgcttac......aatggtaacacaaactatgcacagaagctccag...ggcagagtcaccatgaccacagacacatccacgagcacagcctacatggagctgaggagcctgagatctgacgacacggccgtgtattactgtgcgagaga ->IGHV1-2*01 -caggtgcagctggtgcagtctggggct...gaggtgaagaagcctggggcctcagtgaaggtctcctgcaaggcttctggatacaccttc............accggctactatatgcactgggtgcgacaggcccctggacaagggcttgagtggatgggacggatcaaccctaac......agtggtggcacaaactatgcacagaagtttcag...ggcagggtcaccagtaccagggacacgtccatcagcacagcctacatggagctgagcaggctgagatctgacgacacggtcgtgtattactgtgcgagaga ->IGHV1-2*02 -caggtgcagctggtgcagtctggggct...gaggtgaagaagcctggggcctcagtgaaggtctcctgcaaggcttctggatacaccttc............accggctactatatgcactgggtgcgacaggcccctggacaagggcttgagtggatgggatggatcaaccctaac......agtggtggcacaaactatgcacagaagtttcag...ggcagggtcaccatgaccagggacacgtccatcagcacagcctacatggagctgagcaggctgagatctgacgacacggccgtgtattactgtgcgagaga ->IGHV1-2*03 -caggtgcagctggtgcagtctggggct...gaggtgaagaagcttggggcctcagtgaaggtctcctgcaaggcttctggatacaccttc............accggctactatatgcactgggtgcnacaggcccctggacaagggcttgagtggatgggatggatcaaccctaac......agtggtggcacaaactatgcacagaagtttcag...ggcagggtcaccatgaccagggacacgtccatcagcacagcctacatggagctgagcaggctgagatctgacgacacggccgtgtattactgtgcgagaga ->IGHV1-2*04 -caggtgcagctggtgcagtctggggct...gaggtgaagaagcctggggcctcagtgaaggtctcctgcaaggcttctggatacaccttc............accggctactatatgcactgggtgcgacaggcccctggacaagggcttgagtggatgggatggatcaaccctaac......agtggtggcacaaactatgcacagaagtttcag...ggctgggtcaccatgaccagggacacgtccatcagcacagcctacatggagctgagcaggctgagatctgacgacacggccgtgtattactgtgcgagaga ->IGHV1-2*05 -caggtgcagctggtgcagtctggggct...gaggtgaagaagcctggggcctcagtgaaggtctcctgcaaggcttctggatacaccttc............accggctactatatgcactgggtgcgacaggcccctggacaagggcttgagtggatgggacggatcaaccctaac......agtggtggcacaaactatgcacagaagtttcag...ggcagggtcaccatgaccagggacacgtccatcagcacagcctacatggagctgagcaggctgagatctgacgacacggtcgtgtattactgtgcgagaga ->IGHV1-24*01 -caggtccagctggtacagtctggggct...gaggtgaagaagcctggggcctcagtgaaggtctcctgcaaggtttccggatacaccctc............actgaattatccatgcactgggtgcgacaggctcctggaaaagggcttgagtggatgggaggttttgatcctgaa......gatggtgaaacaatctacgcacagaagttccag...ggcagagtcaccatgaccgaggacacatctacagacacagcctacatggagctgagcagcctgagatctgaggacacggccgtgtattactgtgcaacaga ->IGHV1-3*01 -caggtccagcttgtgcagtctggggct...gaggtgaagaagcctggggcctcagtgaaggtttcctgcaaggcttctggatacaccttc............actagctatgctatgcattgggtgcgccaggcccccggacaaaggcttgagtggatgggatggatcaacgctggc......aatggtaacacaaaatattcacagaagttccag...ggcagagtcaccattaccagggacacatccgcgagcacagcctacatggagctgagcagcctgagatctgaagacacggctgtgtattactgtgcgagaga ->IGHV1-3*02 -caggttcagctggtgcagtctggggct...gaggtgaagaagcctggggcctcagtgaaggtttcctgcaaggcttctggatacaccttc............actagctatgctatgcattgggtgcgccaggcccccggacaaaggcttgagtggatgggatggagcaacgctggc......aatggtaacacaaaatattcacaggagttccag...ggcagagtcaccattaccagggacacatccgcgagcacagcctacatggagctgagcagcctgagatctgaggacatggctgtgtattactgtgcgagaga ->IGHV1-38-4*01 -caggtccagctggtgcagtcttgggct...gaggtgaggaagtctggggcctcagtgaaagtctcctgtagtttttctgggtttaccatc............accagctacggtatacattgggtgcaacagtcccctggacaagggcttgagtggatgggatggatcaaccctggc......aatggtagcccaagctatgccaagaagtttcag...ggcagattcaccatgaccagggacatgtccacaaccacagcctacacagacctgagcagcctgacatctgaggacatggctgtgtattactatgcaagaca ->IGHV1-45*01 -cagatgcagctggtgcagtctggggct...gaggtgaagaagactgggtcctcagtgaaggtttcctgcaaggcttccggatacaccttc............acctaccgctacctgcactgggtgcgacaggcccccggacaagcgcttgagtggatgggatggatcacacctttc......aatggtaacaccaactacgcacagaaattccag...gacagagtcaccattactagggacaggtctatgagcacagcctacatggagctgagcagcctgagatctgaggacacagccatgtattactgtgcaagana ->IGHV1-45*02 -cagatgcagctggtgcagtctggggct...gaggtgaagaagactgggtcctcagtgaaggtttcctgcaaggcttccggatacaccttc............acctaccgctacctgcactgggtgcgacaggcccccggacaagcgcttgagtggatgggatggatcacacctttc......aatggtaacaccaactacgcacagaaattccag...gacagagtcaccattaccagggacaggtctatgagcacagcctacatggagctgagcagcctgagatctgaggacacagccatgtattactgtgcaagata ->IGHV1-45*03 -.....................................agaagactgggtcctcagtgaaggtttcctgcaaggcttccggatacaccttc............acctaccgctacctgcactgggtgcgacaggcccccagacaagcgcttgagtggatgggatggatcacacctttc......aatggtaacaccaactacgcacagaaattccag...gacagagtcaccattaccagggacaggtctatgagcacagcctacatggagctgagcagcctgagatctgaggacacagccatgtattactgtgcaaga ->IGHV1-46*01 -caggtgcagctggtgcagtctggggct...gaggtgaagaagcctggggcctcagtgaaggtttcctgcaaggcatctggatacaccttc............accagctactatatgcactgggtgcgacaggcccctggacaagggcttgagtggatgggaataatcaaccctagt......ggtggtagcacaagctacgcacagaagttccag...ggcagagtcaccatgaccagggacacgtccacgagcacagtctacatggagctgagcagcctgagatctgaggacacggccgtgtattactgtgcgagaga ->IGHV1-46*02 -caggtgcagctggtgcagtctggggct...gaggtgaagaagcctggggcctcagtgaaggtttcctgcaaggcatctggatacaccttc............aacagctactatatgcactgggtgcgacaggcccctggacaagggcttgagtggatgggaataatcaaccctagt......ggtggtagcacaagctacgcacagaagttccag...ggcagagtcaccatgaccagggacacgtccacgagcacagtctacatggagctgagcagcctgagatctgaggacacggccgtgtattactgtgcgagaga ->IGHV1-46*03 -caggtgcagctggtgcagtctggggct...gaggtgaagaagcctggggcctcagtgaaggtttcctgcaaggcatctggatacaccttc............accagctactatatgcactgggtgcgacaggcccctggacaagggcttgagtggatgggaataatcaaccctagt......ggtggtagcacaagctacgcacagaagttccag...ggcagagtcaccatgaccagggacacgtccacgagcacagtctacatggagctgagcagcctgagatctgaggacacggccgtgtattactgtgctagaga ->IGHV1-58*01 -caaatgcagctggtgcagtctgggcct...gaggtgaagaagcctgggacctcagtgaaggtctcctgcaaggcttctggattcaccttt............actagctctgctgtgcagtgggtgcgacaggctcgtggacaacgccttgagtggataggatggatcgtcgttggc......agtggtaacacaaactacgcacagaagttccag...gaaagagtcaccattaccagggacatgtccacaagcacagcctacatggagctgagcagcctgagatccgaggacacggccgtgtattactgtgcggcaga ->IGHV1-58*02 -caaatgcagctggtgcagtctgggcct...gaggtgaagaagcctgggacctcagtgaaggtctcctgcaaggcttctggattcaccttt............actagctctgctatgcagtgggtgcgacaggctcgtggacaacgccttgagtggataggatggatcgtcgttggc......agtggtaacacaaactacgcacagaagttccag...gaaagagtcaccattaccagggacatgtccacaagcacagcctacatggagctgagcagcctgagatccgaggacacggccgtgtattactgtgcggcaga ->IGHV1-68*01 -caggtgcagctggggcagtctgaggct...gaggtaaagaagcctggggcctcagtgaaggtctcctgcaaggcttccggatacaccttc............acttgctgctccttgcactggttgcaacaggcccctggacaagggcttgaaaggatgagatggatcacactttac......aatggtaacaccaactatgcaaagaagttccag...ggcagagtcaccattaccagggacatgtccctgaggacagcctacatagagctgagcagcctgagatctgaggactcggctgtgtattactgggcaagata ->IGHV1-69*01 -caggtgcagctggtgcagtctggggct...gaggtgaagaagcctgggtcctcggtgaaggtctcctgcaaggcttctggaggcaccttc............agcagctatgctatcagctgggtgcgacaggcccctggacaagggcttgagtggatgggagggatcatccctatc......tttggtacagcaaactacgcacagaagttccag...ggcagagtcacgattaccgcggacgaatccacgagcacagcctacatggagctgagcagcctgagatctgaggacacggccgtgtattactgtgcgagaga ->IGHV1-69*02 -caggtccagctggtgcaatctggggct...gaggtgaagaagcctgggtcctcggtgaaggtctcctgcaaggcttctggaggcaccttc............agcagctatactatcagctgggtgcgacaggcccctggacaagggcttgagtggatgggaaggatcatccctatc......cttggtatagcaaactacgcacagaagttccag...ggcagagtcacgattaccgcggacaaatccacgagcacagcctacatggagctgagcagcctgagatctgaggacacggccgtgtattactgtgcgaga ->IGHV1-69*03 -caggtgcagctggtgcagtctggggct...gaggtgaagaagcctgggtcctcggtgaaggtctcctgcaaggcttctggaggcaccttc............agcagctatgctatcagctgggtgcgacaggcccctggacaagggcttgagtggatgggagggatcatccctatc......tttggtacagcaaactacgcacagaagttccag...ggcagagtcacgattaccgcggacgaatccacgagcacagcctacatggagctgagcagcctgagatctgatgacacggc ->IGHV1-69*04 -caggtccagctggtgcagtctggggct...gaggtgaagaagcctgggtcctcggtgaaggtctcctgcaaggcttctggaggcaccttc............agcagctatgctatcagctgggtgcgacaggcccctggacaagggcttgagtggatgggaaggatcatccctatc......cttggtatagcaaactacgcacagaagttccag...ggcagagtcacgattaccgcggacaaatccacgagcacagcctacatggagctgagcagcctgagatctgaggacacggccgtgtattactgtgcgagaga ->IGHV1-69*05 -caggtccagctggtgcagtctggggct...gaggtgaagaagcctgggtcctcggtgaaggtctcctgcaaggcttctggaggcaccttc............agcagctatgctatcagctgggtgcgacaggcccctggacaagggcttgagtggatgggagggatcatccctatc......tttggtacagcaaactacgcacagaagttccag...ggcagagtcacgattaccacggacgaatccacgagcacagcctacatggagctgagcagcctgagatctgaggacacggccgtgtattactgtgcgaga ->IGHV1-69*06 -caggtgcagctggtgcagtctggggct...gaggtgaagaagcctgggtcctcggtgaaggtctcctgcaaggcttctggaggcaccttc............agcagctatgctatcagctgggtgcgacaggcccctggacaagggcttgagtggatgggagggatcatccctatc......tttggtacagcaaactacgcacagaagttccag...ggcagagtcacgattaccgcggacaaatccacgagcacagcctacatggagctgagcagcctgagatctgaggacacggccgtgtattactgtgcgagaga ->IGHV1-69*07 -.....................................agaagcctgggtcctcggtgaaggtctcctgcaaggcttctggaggcaccttc............agcagctatgctatcagctgggtgcgacaggcccctggacaagggcttgagtggatgggaaggatcatccctatc......tttggtacagcaaactacgcacagaagttccag...ggcagagtcacgattaccgcggacgaatccacgagcacagcctacatggagctgagcagcctgagatctgag ->IGHV1-69*08 -caggtccagctggtgcaatctggggct...gaggtgaagaagcctgggtcctcggtgaaggtctcctgcaaggcttctggaggcaccttc............agcagctatactatcagctgggtgcgacaggcccctggacaagggcttgagtggatgggaaggatcatccctatc......cttggtacagcaaactacgcacagaagttccag...ggcagagtcacgattaccgcggacaaatccacgagcacagcctacatggagctgagcagcctgagatctgaggacacggccgtgtattactgtgcgagaga ->IGHV1-69*09 -caggtgcagctggtgcagtctggggct...gaggtgaagaagcctgggtcctcggtgaaggtctcctgcaaggcttctggaggcaccttc............agcagctatgctatcagctgggtgcgacaggcccctggacaagggcttgagtggatgggaaggatcatccctatc......cttggtatagcaaactacgcacagaagttccag...ggcagagtcacgattaccgcggacaaatccacgagcacagcctacatggagctgagcagcctgagatctgaggacacggccgtgtattactgtgcgagaga ->IGHV1-69*10 -caggtccagctggtgcagtctggggct...gaggtgaagaagcctgggtcctcagtgaaggtctcctgcaaggcttctggaggcaccttc............agcagctatgctatcagctgggtgcgacaggcccctggacaagggcttgagtggatgggagggatcatccctatc......cttggtatagcaaactacgcacagaagttccag...ggcagagtcacgattaccgcggacaaatccacgagcacagcctacatggagctgagcagcctgagatctgaggacacggccgtgtattactgtgcgagaga ->IGHV1-69*11 -caggtccagctggtgcagtctggggct...gaggtgaagaagcctgggtcctcggtgaaggtctcctgcaaggcttctggaggcaccttc............agcagctatgctatcagctgggtgcgacaggcccctggacaagggcttgagtggatgggaaggatcatccctatc......cttggtacagcaaactacgcacagaagttccag...ggcagagtcacgattaccgcggacgaatccacgagcacagcctacatggagctgagcagcctgagatctgaggacacggccgtgtattactgtgcgagaga ->IGHV1-69*12 -caggtccagctggtgcagtctggggct...gaggtgaagaagcctgggtcctcggtgaaggtctcctgcaaggcttctggaggcaccttc............agcagctatgctatcagctgggtgcgacaggcccctggacaagggcttgagtggatgggagggatcatccctatc......tttggtacagcaaactacgcacagaagttccag...ggcagagtcacgattaccgcggacgaatccacgagcacagcctacatggagctgagcagcctgagatctgaggacacggccgtgtattactgtgcgagaga ->IGHV1-69*13 -caggtccagctggtgcagtctggggct...gaggtgaagaagcctgggtcctcagtgaaggtctcctgcaaggcttctggaggcaccttc............agcagctatgctatcagctgggtgcgacaggcccctggacaagggcttgagtggatgggagggatcatccctatc......tttggtacagcaaactacgcacagaagttccag...ggcagagtcacgattaccgcggacgaatccacgagcacagcctacatggagctgagcagcctgagatctgaggacacggccgtgtattactgtgcgagaga ->IGHV1-69*14 -caggtccagctggtgcagtctggggct...gaggtgaagaagcctgggtcctcggtgaaggtctcctgcaaggcttctggaggcaccttc............agcagctatgctatcagctgggtgcgacaggcccctggacaagggcttgagtggatgggagggatcatccctatc......tttggtacagcaaactacgcacagaagttccag...ggcagagtcacgattaccgcggacaaatccacgagcacagcctacatggagctgagcagcctgagatctgaggacacggccgtgtattactgtgcgagaga ->IGHV1-69-2*01 -gaggtccagctggtacagtctggggct...gaggtgaagaagcctggggctacagtgaaaatctcctgcaaggtttctggatacaccttc............accgactactacatgcactgggtgcaacaggcccctggaaaagggcttgagtggatgggacttgttgatcctgaa......gatggtgaaacaatatacgcagagaagttccag...ggcagagtcaccataaccgcggacacgtctacagacacagcctacatggagctgagcagcctgagatctgaggacacggccgtgtattactgtgcaacaga ->IGHV1-69-2*02 -.....................................agaagcctggggctacagtgaaaatctcctgcaaggtttctggatacaccttc............accgactactacatgcactgggtgcaacaggcccctggaaaagggcttgagtggatgggacttgttgatcctgaa......gatggtgaaacaatatatgcagagaagttccag...ggcagagtcaccataaccgcggacacgtctacagacacagcctacatggagctgagcagcctgagatctgag ->IGHV1-69D*01 -caggtgcagctggtgcagtctggggct...gaggtgaagaagcctgggtcctcggtgaaggtctcctgcaaggcttctggaggcaccttc............agcagctatgctatcagctgggtgcgacaggcccctggacaagggcttgagtggatgggagggatcatccctatc......tttggtacagcaaactacgcacagaagttccag...ggcagagtcacgattaccgcggacgaatccacgagcacagcctacatggagctgagcagcctgagatctgaggacacggccgtgtattactgtgcgagaga ->IGHV1-8*01 -caggtgcagctggtgcagtctggggct...gaggtgaagaagcctggggcctcagtgaaggtctcctgcaaggcttctggatacaccttc............accagttatgatatcaactgggtgcgacaggccactggacaagggcttgagtggatgggatggatgaaccctaac......agtggtaacacaggctatgcacagaagttccag...ggcagagtcaccatgaccaggaacacctccataagcacagcctacatggagctgagcagcctgagatctgaggacacggccgtgtattactgtgcgagagg ->IGHV1-8*02 -caggtgcagctggtgcagtctggggct...gaggtgaagaagcctggggcctcagtgaaggtctcctgcaaggcttctggatacaccttc............accagctatgatatcaactgggtgcgacaggccactggacaagggcttgagtggatgggatggatgaaccctaac......agtggtaacacaggctatgcacagaagttccag...ggcagagtcaccatgaccaggaacacctccataagcacagcctacatggagctgagcagcctgagatctgaggacacggccgtgtattactgtgcgagagg ->IGHV1-NL1*01 -caggttcagctgttgcagcctggggtc...caggtgaagaagcctgggtcctcagtgaaggtctcctgctaggcttccagatacaccttc............accaaatactttacacggtgggtgtgacaaagccctggacaagggcatnagtggatgggatgaatcaacccttac......aacgataacacacactacgcacagacgttctgg...ggcagagtcaccattaccagtgacaggtccatgagcacagcctacatggagctgagcngcctgagatccgaagacatggtcgtgtattactgtgtgagaga ->IGHV1/OR15-1*01 -caggtgcagctggtgcagtctggggct...gaggtgaagaagcctggggcctcagtgaaggtctcctgcaaggcttctggatacatcttc............accgactactatatgcactgggtgcgacaggcccctggacaagagcttgggtggatgggacggatcaaccctaac......agtggtggcacaaactatgcacagaagtttcag...ggcagagtcaccatgaccagggacacgtccatcagcacagcctacacggagctgagcagcctgagatctgaggacacggccacgtattactgtgcgaga ->IGHV1/OR15-1*02 -caggtgcagctggtgcagtctggggct...gaggtgaagaagcctggggcctcagtgaaggtctcctgcaaggcttctggatacatcttc............accgactactatatgcactgggtgcgacaggcccctggacaagagcttgggtggatgggacggatcaaccctaac......agtggtggcacaaactatgcacagaagtttcag...ggcagagtcaccatgaccagggacacgtccatcagcacagcctgcacggagctgagcagcctgagatctgaggacacggccacgtattactgtgcgagaga ->IGHV1/OR15-1*03 -caggtgcagctggtgcagtctggggct...gaggtgaagaagcctggggcctcagtgaaggtctcctgcaaggcttctggatacatcttc............accgactactatatgcactgggtgcgacaggcccctggacaagagcttgggtggatgggacggatcaaccctaac......agtggtggcacaaactatgcacagaagtttcag...ggcagagtcaccatgaccagggacacgtccatcagcacagcctacacggagctgagcagcctgagatctgaggacacagccacgtattactgtgcgagaga ->IGHV1/OR15-1*04 -caggtgcagctggtgcagtctggggct...gaggtgaagaagcctggggcctcagtgaaggtctcctgcaaggcttctggatacatcttc............accgactactatatgcactgggtgcgacaggcccctggacaagagcttgggtggatgggacggatcaaccctaac......agtggtggcacaaactatgcacagaagtttcag...ggcagagtcaccatgaccagggacacgtccatcagcacagcctacatggagctgagcagcctgagatctgaggacacggccacgtattactgtgcgagaga ->IGHV1/OR15-2*01 -caggtgcagctggtgcagtctggagct...gaggtgaagaagcctagagcctcagtgaaggtctcctgcaaggcttctggttacaccttt............accagctactatatgcactgggtgtgacaggcccctgaacaagggcttgagtggatgggatggatcaacacttac......aatggtaacacaaactacccacagaagctccag...ggcagagtcaccatgaccagagacacatccacgagcacagcctacatggagctgagcaggctgagatctgacgacatggccgtgtattactgtgcgagaga ->IGHV1/OR15-2*02 -caggtgcagctggtgcagtctggagct...gaggtgaagaagcctggagcctcagtgaaggtctcctgcaaggcttctggttacaccttt............accagctactatatgcactgggtgtgacaggcccctgaacaagggcttgagtggatgggatggatcaacacttac......aatggtaacacaaactacccacagaagctccag...ggcagagtcaccatgaccagagacacatccacgagcacagcctacatggagctgagcagcctgagatctgacgacatggccgtgtattactgtgcgagaga ->IGHV1/OR15-2*03 -caggtgcagctggtgcagtctggagct...gaggtgaagaagcctagagcctcagtgaaggtctcctgcaaggcttctggttacaccttt............accagctactatatgcactgggtgtgacaggcccctgaacaagggcttgagtggatgggatggatcaacacttac......aatggtaacacaaactacccacagaagctccag...ggcagagtcaccatgaccagagacacatccacgagcacagcctacatggagctgagcagcctgagatctgacgacatggccgtgtattactgtgcgagaga ->IGHV1/OR15-3*01 -caggtccaactggtgtagtctggagct...gaggtgaagaagcctggggcctcagtgaaggtctcctgcaaggcttctggatacaccttc............accgactactttatgaactggatgcgccaggcccctggacaaaggcttgagtggatgggatggatcaacgctggc......aatggtaacacaaaatattcacagaagctccag...ggcagagtcaccattaccagggacacatcttcgagcacagcctacatgcagctgagcagcctgagatctgaggacacggccgtgtattactgtgcgaga ->IGHV1/OR15-3*02 -caggtccaactggtgtagtctggagct...gaggtgaagaagcctggggcctcagtgaaggtctcctgcaaggcttctggatacaccttc............accgactactttatgaactggatgcgccaggcccctggacaaaggcttgagtggatgggatggatcaacgctggc......aatggtaacacaaaatattcacagaagctccag...ggcagagtcaccattaccagggacacatctgcgagcacagcctacatgcagctgagcagcctgagatctgaggacacggccgtgtattactgtgcgagaga ->IGHV1/OR15-3*03 -caggtccaactggtgtagtctggagct...gaggtgaagaagcctggggcctcagtgaaggtctcctgcaaggcttctggatacaccttc............accagctactatatgaactggatgcgccaggcccctggacaaggcttcgagtggatgggatggatcaacgctggc......aatggtaacacaaagtattcacagaagctccag...ggcagagtcaccattaccagggacacatctgcgagcacagcctacatgcagctgagcagcctgagatctgaggacacggccgtgtattactgtgcgaga ->IGHV1/OR15-4*01 -caggaccagttggtgcagtctggggct...gaggtgaagaagcctctgtcctcagtgaaggtctccttcaaggcttctggatacaccttc............accaacaactttatgcactgggtgtgacaggcccctggacaaggacttgagtggatgggatggatcaatgctggc......aatggtaacacaacatatgcacagaagttccag...ggcagagtcaccataaccagggacacgtccatgagcacagcctacacggagctgagcagcctgagatctgaggacacggccgtgtattactgtgcgaga ->IGHV1/OR15-5*01 -.....................................agaagcctggggcctcagtgaaggtctcctgcaaggcttctggatacaccttc............accagctactgtatgcactgggtgcaccaggtccatgcacaagggcttgagtggatgggattggtgtgccctagt......gatggcagcacaagctatgcacagaagttccag...gccagagtcaccataaccagggacacatccatgagcacagcctacatggagctaagcagtctgagatctgaggacacggccatgtattactgtgtgaga ->IGHV1/OR15-5*02 -caggtacagctggtgcagtctggggct...gaggtgaagaagcctggggcctcagtgaaggtctcctgcaaggcttctggatacaccttc............accaactactgtatgcactgggtgcgccaggtccatgcacaagggcttgagtggatgggattggtgtgccctagt......gatggcagcacaagctatgcacaaaagttccag...gccagagtcaccataaccagggacacatccatgagcacagcctacatggagctaagcagtctgagatctgaggacacggccatgtattactgtgtgaga ->IGHV1/OR15-9*01 -caggtacagctgatgcagtctggggct...gaggtgaagaagcctggggcctcagtgaggatctcctgcaaggcttctggatacaccttc............accagctactgtatgcactgggtgtgccaggcccatgcacaagggcttgagtggatgggattggtgtgccctagt......gatggcagcacaagctatgcacagaagttccag...ggcagagtcaccataaccagggacacatccatgggcacagcctacatggagctaagcagcctgagatctgaggacacggccatgtattactgtgtgagaga ->IGHV1/OR21-1*01 -caggtacagctggtgcagtctggggct...gaggtgaagaagcctggggcctcagtgaaggtctcctgcaaggcttctggatacaccatc............accagctactgtatgcactgggtgcaccaggtccatgcacaagggcttgagtggatgggattggtgtgccctagt......gatggcagcacaagctatgcacagaagttccag...gccagagtcaccataaccagggacacatccatgagcacagcctacatggagctaagcagtctgagatctgaggacacggccatgtattactgtgtgagaga ->IGHV2-10*01 -caggtcaccttgaaggagtctggtcct...gcactggtgaaacccacacagaccctcatgctgacctgcaccttctctgggttctcactcagc......acttctggaatgggtgtgggttagatctgtcagccctcagcaaaggccctggagtggcttgcacacatttattagaat.........gataataaatactacagcccatctctgaag...agtaggctcattatctccaaggacacctccaagaatgaagtggttctaacagtgatcaacatggacattgtggacacagccacacattactgtgcaaggagac ->IGHV2-26*01 -caggtcaccttgaaggagtctggtcct...gtgctggtgaaacccacagagaccctcacgctgacctgcaccgtctctgggttctcactcagc......aatgctagaatgggtgtgagctggatccgtcagcccccagggaaggccctggagtggcttgcacacattttttcgaat.........gacgaaaaatcctacagcacatctctgaag...agcaggctcaccatctccaaggacacctccaaaagccaggtggtccttaccatgaccaacatggaccctgtggacacagccacatattactgtgcacggatac ->IGHV2-5*01 -cagatcaccttgaaggagtctggtcct...acgctggtgaaacccacacagaccctcacgctgacctgcaccttctctgggttctcactcagc......actagtggagtgggtgtgggctggatccgtcagcccccaggaaaggccctggagtggcttgcactcatttattggaat.........gatgataagcgctacagcccatctctgaag...agcaggctcaccatcaccaaggacacctccaaaaaccaggtggtccttacaatgaccaacatggaccctgtggacacagccacatattactgtgcacacagac ->IGHV2-5*02 -cagatcaccttgaaggagtctggtcct...acgctggtgaaacccacacagaccctcacgctgacctgcaccttctctgggttctcactcagc......actagtggagtgggtgtgggctggatccgtcagcccccaggaaaggccctggagtggcttgcactcatttattgggat.........gatgataagcgctacagcccatctctgaag...agcaggctcaccatcaccaaggacacctccaaaaaccaggtggtccttacaatgaccaacatggaccctgtggacacagccacatattactgtgcacacagac ->IGHV2-5*03 -................................gctggtgaaacccacacagaccctcacgctgacctgcaccttctctgggttctcactcagc......actagtggagtgggtgtgggctggatccgtcagcccccaggaaaggccctggagtggcttgcactcatttattgggat.........gatgataagcgctacagcccatctctgaag...agcaggctcaccattaccaaggacacctccaaaaaccaggt ->IGHV2-5*04| -cagatcaccttgaaggagtctggtcct...acgctggtgaaacccacacagaccctcacgctgacctgcaccttctctgggttctcactcagc......actagtggagtgggtgtgggctggatccgtcagcccccaggaaaggccctggagtggcttgcactcatttattggaat.........gatgataagcgctacagcccatctctgaag...agcaggctcaccatcaccaaggacacctccaaaaaccaggtggtccttacaatgaccaacatggaccctgtggacacaggcacatattactgtgtac ->IGHV2-5*05 -cagatcaccttgaaggagtctggtcct...acgctggtgaaacccacacagaccctcacgctgacctgcaccttctctgggttctcactcagc......actagtggagtgggtgtgggctggatccgtcagcccccaggaaaggccctggagtggcttgcactcatttattgggat.........gatgataagcgctacggcccatctctgaag...agcaggctcaccatcaccaaggacacctccaaaaaccaggtggtccttacaatgaccaacatggaccctgtggacacagccacatattactgtgcacacagac ->IGHV2-5*06 -cagatcaccttgaaggagtctggtcct...acgctggtaaaacccacacagaccctcacgctgacctgcaccttctctgggttctcactcagc......actagtggagtgggtgtgggctggatccgtcagcccccaggaaaggccctggagtggcttgcactcatttattgggat.........gatgataagcgctacggcccatctctgaag...agcaggctcaccatcaccaaggacacctccaaaaaccaggtggtccttacaatgaccaacatggaccctgtggacacagccacatattactgtgcacacaga ->IGHV2-5*08 -caggtcaccttgaaggagtctggtcct...gcgctggtgaaacccacacagaccctcacactgacctgcaccttctctgggttctcactcagc......actagtggaatgcgtgtgagctggatccgtcagcccccaggaaaggccctggagtggcttgcactcatttattgggat.........gatgataagcgctacagcccatctctgaag...agcaggctcaccatcaccaaggacacctccaaaaaccaggtggtccttacaatgaccaacatggaccctgtggacacagccacatattactgtgcacacagac ->IGHV2-5*09 -caggtcaccttgaaggagtctggtcct...acgctggtgaaacccacacagaccctcacgctgacctgcaccttctctgggttctcactcagc......actagtggagtgggtgtgggctggatccgtcagcccccaggaaaggccctggagtggcttgcactcatttattgggat.........gatgataagcgctacggcccatctctgaag...agcaggctcaccatcaccaaggacacctccaaaaaccaggtggtccttacaatgaccaacatggaccctgtggacacagccacatattactgtgcacacagac ->IGHV2-70*01 -caggtcaccttgagggagtctggtcct...gcgctggtgaaacccacacagaccctcacactgacctgcaccttctctgggttctcactcagc......actagtggaatgtgtgtgagctggatccgtcagcccccagggaaggccctggagtggcttgcactcattgattgggat.........gatgataaatactacagcacatctctgaag...accaggctcaccatctccaaggacacctccaaaaaccaggtggtccttacaatgaccaacatggaccctgtggacacagccacgtattactgtgcacggatac ->IGHV2-70*02 -caggtcaccttgagggagtctggtcct...gcgctggtgaaacccacacagaccctcacactgacctgcaccttctctgggttctcactcagc......actagtggaatgtgtgtgagctggatccgtcagcccccagggaaggccctggagtggcttgcactcattgattgggat.........gatgataaatactacagcacatctctgaag...accaggctcaccatctccaaggacacctccaaaaaccaggtggtccttacaatgaccaacatggaccctgtggacacggccgtgtattactg ->IGHV2-70*03 -caggtcaccttgaaggagtctggtcct...gcgctggtgaaacccacacagaccctcacactgacctgcaccttctctgggttctcactcagc......actagtggaatgcgtgtgagctggatccgtcagcccccagggaaggccctggagtggcttgcacgcattgattgggat.........gatgataaattctacagcacatctctgaag...accaggctcaccatctccaaggacacctccaaaaaccaggtggtccttacaatgaccaacatggaccctgtggacacggccgtgtattactg ->IGHV2-70*04 -caggtcaccttgaaggagtctggtcct...gcgctggtgaaacccacacagaccctcacactgacctgcaccttctctgggttctcactcagc......actagtggaatgcgtgtgagctggatccgtcagcccccagggaaggccctggagtggcttgcacgcattgattgggat.........gatgataaattctacagcacatctctgaag...accaggctcaccatctccaaggacacctccaaaaaccaggtggtccttacaatgaccaacatggaccctgtggacacagccacgtattac ->IGHV2-70*05 -..........................t...gcgctggtgaaacccacacagaccctcacactgacctgcaccttctctgggttctcactcagc......actagtggaatgcgtgcgagctggatccgtcagcccccagggaaggccctggagtggcttgcacgcattgattgggat.........gatgataaattctacagcacatctctgaag...accaggctcaccatctccaaggacacctccaaaaaccaggtggtccttacaatgaccaacatgga ->IGHV2-70*06 -caggtcaccttgaaggagtctggtcct...gcgctggtgaaacccacacagaccctcacactgacctgcaccttctctgggttctcactcagc......actagtggaatgcgtgtgagctggatccgtcagcccccagggaaggccctggagtggcttgcacgcattgattgggat.........gatgataaattctacagcacatccctgaag...accaggctcaccatctccaaggacacctccaaaaaccaggtggtccttacaatgaccaacatggaccctgtggacacggccgtgtattactg ->IGHV2-70*07 -caggtcaccttgagggagtctggtcct...gcgctggtgaaacccacacagaccctcacactgacctgcaccttctctgggttctcactcagc......actagtggaatgtgtgtgagctggatccgtcagcccccggggaaggccctggagtggcttgcactcattgattgggat.........gatgataaatactacagcacatctctgaag...accaggctcaccatctccaaggacacctccaaaaaccaggtggtccttacaatgaccaacatggaccctgtggacacggccgtgtattactg ->IGHV2-70*08 -caggtcaccttgagggagtctggtcct...gcgctggtgaaacccacacagaccctcacactgacctgcgccttctctgggttctcactcagc......actagtggaatgtgtgtgagctggatccgtcagcccccagggaaggccctggagtggcttgcacgcattgattgggat.........gatgataaatactacagcacatctctgaag...accaggctcaccatctccaaggacacctccaaaaaccaggtggtccttacaatgaccaacatggaccctgtggacacggccgtgtattactg ->IGHV2-70*09 -cagatcaccttgaaggagtctggtcct...acgctggtgaaacccacacagaccctcacgctgacccgcaccttctctgggttctcactcagc......actagtggaatgtgtgtgagctggatccgtcagcccccagggaaggccctggagtggcttgcactcattgattgggat.........gatgataaatactacagcacatctctgaac...accaggctcaccatctccaaggacacctccaaaaaccaggtggtccttacaatgaccaacatggaccctgtggacacaggcacatattactgtgtacgg ->IGHV2-70*10 -caggtcaccttgaaggagtctggtcct...gcgctggtgaaacccacacagaccctcacactgacctgcaccttctctgggttctcactcagc......actagtggaatgcgtgtgagctggatccgtcagcccccagggaaggccctggagtggattgcacgcattgattgggat.........gatgataaatactacagcacatctctgaag...accaggctcaccatctccaaggacacctccaaaaaccaggtggtccttacaatgaccaacatggaccctgtggacacagccacgtattactgtgcacggatac ->IGHV2-70*11 -cgggtcaccttgagggagtctggtcct...gcgctggtgaaacccacacagaccctcacactgacctgcaccttctctgggttctcactcagc......actagtggaatgtgtgtgagctggatccgtcagcccccagggaaggccctggagtggcttgcacgcattgattgggat.........gatgataaatactacagcacatctctgaag...accaggctcaccatctccaaggacacctccaaaaaccaggtggtccttacaatgaccaacatggaccctgtggacacagccacgtattactgtgcacggatac ->IGHV2-70*12 -cagatcaccttgaaggagtctggtcct...acgctggtgaaacccacacagaccctcacgctgacctgcaccttctctgggttctcactcagc......actagtggaatgtgtgtgagctggatccgtcagcccccagggaaggccctggagtggcttgcactcattgattgggat.........gatgataaatactacagcacatctctgaag...accaggctcaccatctccaaggacacctccaaaaaccaggtggtccttacaatgaccaacatggaccctgtggacacagccacatattactgtgcacacagac ->IGHV2-70*13 -caggtcaccttgagggagtctggtcct...gcgctggtgaaacccacacagaccctcacactgacctgcaccttctctgggttctcactcagc......actagtggaatgtgtgtgagctggatccgtcagcccccagggaaggccctggagtggcttgcactcattgattgggat.........gatgataaatactacagcacatctctgaag...accaggctcaccatctccaaggacacctccaaaaaccaggtggtccttacaatgaccaacatggaccctgtggacacagccacgtattattgtgcacggatac ->IGHV2-70D*04 -caggtcaccttgaaggagtctggtcct...gcgctggtgaaacccacacagaccctcacactgacctgcaccttctctgggttctcactcagc......actagtggaatgcgtgtgagctggatccgtcagcccccagggaaggccctggagtggcttgcacgcattgattgggat.........gatgataaattctacagcacatctctgaag...accaggctcaccatctccaaggacacctccaaaaaccaggtggtccttacaatgaccaacatggaccctgtggacacagccacgtattactgtgcacggatac ->IGHV2-70D*14 -caggtcaccttgaaggagtctggtcct...gcgctggtgaaacccacacagaccctcacactgacctgcaccttctctgggttctcactcagc......actagtggaatgcgtgtgagctggatccgtcagcccccaggtaaggccctggagtggcttgcacgcattgattgggat.........gatgataaattctacagcacatctctgaag...accaggctcaccatctccaaggacacctccaaaaaccaggtggtccttacaatgaccaacatggaccctgtggacacagccacgtattactgtgcacggatac ->IGHV2/OR16-5*01 -caggtcaccttgaaggagtctggtcct...gcgctggtgaaacccacagagaccctcacgctgacctgcactctctctgggttctcactcagc......acttctggaatgggtatgagctggatccgtcagcccccagggaaggccctggagtggcttgctcacatttttttgaat.........gacaaaaaatcctacagcacgtctctgaag...aacaggctcatcatctccaaggacacctccaaaagccaggtggtccttaccatgaccaacatggaccctgtggacacagccacgtattactgtgcatggagag ->IGHV3-11*01 -caggtgcagctggtggagtctggggga...ggcttggtcaagcctggagggtccctgagactctcctgtgcagcctctggattcaccttc............agtgactactacatgagctggatccgccaggctccagggaaggggctggagtgggtttcatacattagtagtagt......ggtagtaccatatactacgcagactctgtgaag...ggccgattcaccatctccagggacaacgccaagaactcactgtatctgcaaatgaacagcctgagagccgaggacacggccgtgtattactgtgcgagaga ->IGHV3-11*03 -caggtgcagctgttggagtctggggga...ggcttggtcaagcctggagggtccctgagactctcctgtgcagcctctggattcaccttc............agtgactactacatgagctggatccgccaggctccagggaaggggctggagtgggtttcatacattagtagtagt......agtagttacacaaactacgcagactctgtgaag...ggccgattcaccatctccagagacaacgccaagaactcactgtatctgcaaatgaacagcctgagagccgaggacacggccgtgtattactgtgcgaga ->IGHV3-11*04 -caggtgcagctggtggagtctggggga...ggcttggtcaagcctggagggtccctgagactctcctgtgcagcctctggattcaccttc............agtgactactacatgagctggatccgccaggctccagggaaggggctggagtgggtttcatacattagtagtagt......ggtagtaccatatactacgcagactctgtgaag...ggccgattcaccatctccagggacaacgccaagaactcactgtatctgcaaatgaacagcctgagagccgaggacacggctgtgtattactgtgcgagaga ->IGHV3-11*05 -caggtgcagctggtggagtctggggga...ggcttggtcaagcctggagggtccctgagactctcctgtgcagcctctggattcaccttc............agtgactactacatgagctggatccgccaggctccagggaaggggctggagtgggtttcatacattagtagtagt......agtagttacacaaactacgcagactctgtgaag...ggccgattcaccatctccagagacaacgccaagaactcactgtatctgcaaatgaacagcctgagagccgaggacacggccgtgtattactgtgcgagaga ->IGHV3-11*06 -caggtgcagctggtggagtctggggga...ggcttggtcaagcctggagggtccctgagactctcctgtgcagcctctggattcaccttc............agtgactactacatgagctggatccgccaggctccagggaaggggctggagtgggtttcatacattagtagtagt......agtagttacacaaactacgcagactctgtgaag...ggccgattcaccatctccagagacaacgccaagaactcactgtatctgcaaatgaacagcctgagagccgaggacacggctgtgtattactgtgcgagaga ->IGHV3-13*01 -gaggtgcagctggtggagtctggggga...ggcttggtacagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctacgacatgcactgggtccgccaagctacaggaaaaggtctggagtgggtctcagctattggtactgct.........ggtgacacatactatccaggctccgtgaag...ggccgattcaccatctccagagaaaatgccaagaactccttgtatcttcaaatgaacagcctgagagccggggacacggctgtgtattactgtgcaagaga ->IGHV3-13*02 -gaggtgcatctggtggagtctggggga...ggcttggtacagcctgggggggccctgagactctcctgtgcagcctctggattcaccttc............agtaactacgacatgcactgggtccgccaagctacaggaaaaggtctggagtgggtctcagccaatggtactgct.........ggtgacacatactatccaggctccgtgaag...gggcgattcaccatctccagagaaaatgccaagaactccttgtatcttcaaatgaacagcctgagagccggggacacggctgtgtattactgtgcaagaga ->IGHV3-13*03 -gaggtgcagctggtggagtctggggga...ggcttggtacagcctggggggtccctgagactctcctgtgcagcctgtggattcaccttc............agtagctacgacatgcactgggtccgccaagctacaggaaaaggtctggagtgggtctcagctattggtactgct.........ggtgacacatactatccaggctccgtgaag...ggccaattcaccatctccagagaaaatgccaagaactccttgtatcttcaaatgaacagcctgagagccggggacacggctgtgtattactgtgcaaga ->IGHV3-13*04 -gaggtgcagctggtggagtctggggga...ggcttggtacagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctacgacatgcactgggtccgccaagctacaggaaaaggtctggaatgggtctcagctattggtactgct.........ggtgacacatactatccaggctccgtgaag...ggccgattcaccatctccagagaaaatgccaagaactccttgtatcttcaaatgaacagcctgagagccggggacacggctgtgtattactgtgcaagaga ->IGHV3-13*05 -gaggtgcagctggtggagtctggggga...ggcttggtacagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctacgacatgcactgggtccgccaagctacaggaaaaggtctggagtgggtctcagctattggtactgct.........ggtgacccatactatccaggctccgtgaag...ggccgattcaccatctccagagaaaatgccaagaactccttgtatcttcaaatgaacagcctgagagccggggacacggctgtgtattactgtgcaagaga ->IGHV3-15*01 -gaggtgcagctggtggagtctggggga...ggcttggtaaagcctggggggtcccttagactctcctgtgcagcctctggattcactttc............agtaacgcctggatgagctgggtccgccaggctccagggaaggggctggagtgggttggccgtattaaaagcaaaactgatggtgggacaacagactacgctgcacccgtgaaa...ggcagattcaccatctcaagagatgattcaaaaaacacgctgtatctgcaaatgaacagcctgaaaaccgaggacacagccgtgtattactgtaccacaga ->IGHV3-15*02 -gaggtgcagctggtggagtctggggga...gccttggtaaagcctggggggtcccttagactctcctgtgcagcctctggattcactttc............agtaacgcctggatgagctgggtccgccaggctccagggaaggggctggagtgggttggccgtattaaaagcaaaactgatggtgggacaacagactacgctgcacccgtgaaa...ggcagattcaccatctcaagagatgattcaaaaaacacgctgtatctgcaaatgaacagcctgaaaaccgaggacacagccgtgtattactgtaccacaga ->IGHV3-15*03 -gaggtgcagctggtggagtctgccgga...gccttggtacagcctggggggtcccttagactctcctgtgcagcctctggattcacttgc............agtaacgcctggatgagctgggtccgccaggctccagggaaggggctggagtgggttggccgtattaaaagcaaagctaatggtgggacaacagactacgctgcacctgtgaaa...ggcagattcaccatctcaagagttgattcaaaaaacacgctgtatctgcaaatgaacagcctgaaaaccgaggacacagccgtgtattactgtaccacaga ->IGHV3-15*04 -gaggtgcagctggtggagtctggggga...ggcttggtaaagcctggggggtcccttagactctcctgtgcagcctctggattcactttc............agtaacgcctggatgagctgggtccgccaggctccagggaaggggctggagtgggttggccgtattgaaagcaaaactgatggtgggacaacagactacgctgcacccgtgaaa...ggcagattcaccatctcaagagatgattcaaaaaacacgctgtatctgcaaatgaacagcctgaaaaccgaggacacagccgtgtattactgtaccacaga ->IGHV3-15*05 -gaggtgcagctggtggagtctggggga...ggcttggtaaagcctggggggtcccttagactctcctgtgcagcctctggattcactttc............agtaacgcctggatgagctgggtccgccaggctccagggaaggggctggagtgggttggccgtattaaaagcaaaactgatggtgggacaacagactacgctgcacccgtgaaa...ggcagattcaccatctcaagagatgattcaaaaaacacgctgtatctgcaaatgaacagtctgaaaaccgaggacacagccgtgtattactgtaccacaga ->IGHV3-15*06 -gaggtgcagctggtggagtctggggga...ggcttggtaaagcctggggggtcccttagactctcctgtgcagcctctggattcactttc............agtaacgcctggatgagctgggtccgccaggctccagggaaggggctggagtgggtcggccgtattaaaagcaaaactgatggtgggacaacaaactacgctgcacccgtgaaa...ggcagattcaccatctcaagagatgattcaaaaaacacgctgtatctgcaaatgaacagcctgaaaaccgaggacacagccgtgtattactgtaccacaga ->IGHV3-15*07 -gaggtgcagctggtggagtctggggga...ggcttggtaaagcctggggggtcccttagactctcctgtgcagcctctggtttcactttc............agtaacgcctggatgaactgggtccgccaggctccagggaaggggctggagtgggtcggccgtattaaaagcaaaactgatggtgggacaacagactacgctgcacccgtgaaa...ggcagattcaccatctcaagagatgattcaaaaaacacgctgtatctgcaaatgaacagcctgaaaaccgaggacacagccgtgtattactgtaccacaga ->IGHV3-15*08 -gaggtgcagctggtggagtctgcggga...ggcttggtacagcctggggggtcccttagactctcctgtgcagcctctggattcacttgc............agtaacgcctggatgagctgggtccgccaggctccagggaaggggctggagtgggttggctgtattaaaagcaaagctaatggtgggacaacagactacgctgcacctgtgaaa...ggcagattcaccatctcaagagatgattcaaaaaacacgctgtatctgcaaatgatcagcctgaaaaccgaggacacggccgtgtattactgtaccacagg ->IGHV3-16*01 -gaggtacaactggtggagtctggggga...ggcttggtacagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agtaacagtgacatgaactgggcccgcaaggctccaggaaaggggctggagtgggtatcgggtgttagttggaat......ggcagtaggacgcactatgtggactccgtgaag...cgccgattcatcatctccagagacaattccaggaactccctgtatctgcaaaagaacagacggagagccgaggacatggctgtgtattactgtgtgagaaa ->IGHV3-16*02 -gaggtgcagctggtggagtctggggga...ggcttggtacagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agtaacagtgacatgaactgggcccgcaaggctccaggaaaggggctggagtgggtatcgggtgttagttggaat......ggcagtaggacgcactatgtggactccgtgaag...cgccgattcatcatctccagagacaattccaggaactccctgtatctgcaaaagaacagacggagagccgaggacatggctgtgtattactgtgtgagaaa ->IGHV3-19*01 -acagtgcagctggtggagtctggggga...ggcttggtagagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agtaacagtgacatgaactgggtccgccaggctccaggaaaggggctggagtgggtatcgggtgttagttggaat......ggcagtaggacgcactatgcagactctgtgaag...ggccgattcatcatctccagagacaattccaggaacttcctgtatcagcaaatgaacagcctgaggcccgaggacatggctgtgtattactgtgtgagaaa ->IGHV3-20*01 -gaggtgcagctggtggagtctggggga...ggtgtggtacggcctggggggtccctgagactctcctgtgcagcctctggattcaccttt............gatgattatggcatgagctgggtccgccaagctccagggaaggggctggagtgggtctctggtattaattggaat......ggtggtagcacaggttatgcagactctgtgaag...ggccgattcaccatctccagagacaacgccaagaactccctgtatctgcaaatgaacagtctgagagccgaggacacggccttgtatcactgtgcgagaga ->IGHV3-20*02 -gaggtgcagctggtggagtctggggga...ggtgtggtacggcctggggggtccctgagactctcctttgcagcctctggattcaccttt............gatgattatggcatgagctgggtccgccaagctccagggaaggggctggagtgggtctctggtattaattggaat......ggtggtagcacaggttatgcagactctgtgaag...ggccgattcaccatctccagagacaacgccaagaactccctgtatctgcaaatgaacagtctgagagccgaggacacggccttgtatcactgtgcgagaga ->IGHV3-21*01 -gaggtgcagctggtggagtctggggga...ggcctggtcaagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatagcatgaactgggtccgccaggctccagggaaggggctggagtgggtctcatccattagtagtagt......agtagttacatatactacgcagactcagtgaag...ggccgattcaccatctccagagacaacgccaagaactcactgtatctgcaaatgaacagcctgagagccgaggacacggctgtgtattactgtgcgagaga ->IGHV3-21*02 -gaggtgcaactggtggagtctggggga...ggcctggtcaagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatagcatgaactgggtccgccaggctccagggaaggggctggagtgggtctcatccattagtagtagt......agtagttacatatactacgcagactcagtgaag...ggccgattcaccatctccagagacaacgccaagaactcactgtatctgcaaatgaacagcctgagagccgaggacacggctgtgtattactgtgcgagaga ->IGHV3-21*03 -gaggtgcagctggtggagtctggggga...ggcctggtcaagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatagcatgaactgggtccgccaggctccagggaaggggctggagtgggtctcatccattagtagtagt......agtagttacatatactacgcagactcagtgaag...ggccgattcaccatctccagagacaacgccaagaactcactgtatctgcaaatgaacagcctgagagccgaggacacagctgtgtattactgtgcgagaga ->IGHV3-21*04 -gaggtgcagctggtggagtctggggga...ggcctggtcaagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatagcatgaactgggtccgccaggctccagggaaggggctggagtgggtctcatccattagtagtagt......agtagttacatatactacgcagactcagtgaag...ggccgattcaccatctccagagacaacgccaagaactcactgtatctgcaaatgaacagcctgagagccgaggacacggccgtgtattactgtgcgagaga ->IGHV3-22*01 -gaggtgcatctggtggagtctggggga...gccttggtacagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agttactactacatgagcggggtccgccaggctcccgggaaggggctggaatgggtaggtttcattagaaacaaagctaatggtgggacaacagaatagaccacgtctgtgaaa...ggcagattcacaatctcaagagatgattccaaaagcatcacctatctgcaaatgaagagcctgaaaaccgaggacacggccgtgtattactgttccagaga ->IGHV3-22*02 -gaggtgcagctggtggagtctggggga...ggcttggtacagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agttactactacatgagcggggtccgccaggctcccgggaaggggctggaatgggtaggtttcattagaaacaaagctaatggtgggacaacagaatagaccacgtctgtgaaa...ggcagattcacaatctcaagagatgattccaaaagcatcacctatctgcaaatgaagagcctgaaaaccgaggacacggccgtgtattactgttccagaga ->IGHV3-23*01 -gaggtgcagctgttggagtctggggga...ggcttggtacagcctggggggtccctgagactctcctgtgcagcctctggattcaccttt............agcagctatgccatgagctgggtccgccaggctccagggaaggggctggagtgggtctcagctattagtggtagt......ggtggtagcacatactacgcagactccgtgaag...ggccggttcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagccgaggacacggccgtatattactgtgcgaaaga ->IGHV3-23*02 -gaggtgcagctgttggagtctggggga...ggcttggtacagcctggggggtccctgagactctcctgtgcagcctctggattcaccttt............agcagctatgccatgagctgggtccgccaggctccagggaaggggctggagtgggtctcagctattagtggtagt......ggtggtagcacatactacggagactccgtgaag...ggccggttcaccatctcaagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagccgaggacacggccgtatattactgtgcgaaaga ->IGHV3-23*03 -gaggtgcagctgttggagtctggggga...ggcttggtacagcctggggggtccctgagactctcctgtgcagcctctggattcaccttt............agcagctatgccatgagctgggtccgccaggctccagggaaggggctggagtgggtctcagttatttatagcggt......ggtagtagcacatactatgcagactccgtgaag...ggccggttcaccatctccagagataattccaagaacacgctgtatctgcaaatgaacagcctgagagccgaggacacggccgtatattactgtgcgaaaga ->IGHV3-23*04 -gaggtgcagctggtggagtctggggga...ggcttggtacagcctggggggtccctgagactctcctgtgcagcctctggattcaccttt............agcagctatgccatgagctgggtccgccaggctccagggaaggggctggagtgggtctcagctattagtggtagt......ggtggtagcacatactacgcagactccgtgaag...ggccggttcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagccgaggacacggccgtatattactgtgcgaaaga ->IGHV3-23*05 -gaggtgcagctgttggagtctggggga...ggcttggtacagcctggggggtccctgagactctcctgtgcagcctctggattcaccttt............agcagctatgccatgagctgggtccgccaggctccagggaaggggctggagtgggtctcagctatttatagcagt......ggtagtagcacatactatgcagactccgtgaag...ggccggttcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagccgaggacacggccgtatattactgtgcgaaa ->IGHV3-23D*01 -gaggtgcagctgttggagtctggggga...ggcttggtacagcctggggggtccctgagactctcctgtgcagcctctggattcaccttt............agcagctatgccatgagctgggtccgccaggctccagggaaggggctggagtgggtctcagctattagtggtagt......ggtggtagcacatactacgcagactccgtgaag...ggccggttcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagccgaggacacggccgtatattactgtgcgaaaga ->IGHV3-23D*02 -gaggtgcagctggtggagtctggggga...ggcttggtacagcctggggggtccctgagactctcctgtgcagcctctggattcaccttt............agcagctatgccatgagctgggtccgccaggctccagggaaggggctggagtgggtctcagctattagtggtagt......ggtggtagcacatactacgcagactccgtgaag...ggccggttcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagccgaggacacggccgtatattactgtgcgaaaga ->IGHV3-25*01 -gagatgcagctggtggagtctggggga...ggcttgcaaaagcctgcgtggtccccgagactctcctgtgcagcctctcaattcaccttc............agtagctactacatgaactgtgtccgccaggctccagggaatgggctggagttggtttgacaagttaatcctaat......gggggtagcacatacctcatagactccggtaag...gaccgattcaatacctccagagataacgccaagaacacacttcatctgcaaatgaacagcctgaaaaccgaggacacggccctctattagtgtaccagaga ->IGHV3-25*02 -gagatgcagctggtggagtctggggga...ggcttggcaaagcctgcgtggtccccgagactctcctgtgcagcctctcaattcaccttc............agtagctactacatgaactgtgtccgccaggctccagggaatgggctggagttggtttgacaagttaatcctaat......gggggtagcacatacctcatagactccggtaag...gaccgattcaatacctccagagataacgccaagaacacacttcatctgcaaatgaacagcctgaaaaccgaggacacggccctctattagtgtaccagaga ->IGHV3-25*03 -gagatgcagctggtggagtctggggga...ggcttggcaaagcctgcgtggtccccgagactctcctgtgcagcctctcaattcaccttc............agtagctactacatgaactgtgtccgccaggctccagggaatgggctggagttggttggacaagttaatcctaat......gggggtagcacatacctcatagactccggtaag...gaccgattcaatacctccagagataacgccaagaacacacttcatctgcaaatgaacagcctgaaaaccgaggacacggccctgtattagtgtaccaga ->IGHV3-25*04 -gagacgcagctggtggagtctggggga...ggcttggcaaagcctgggcggtccccgagactctcctgtgcagcctctcaattcaccttc............agtagctactacatgaactgtgtccgccaggctccagggaatgggctggagttggttggacaagttaatcctaat......gggggtagcacatacctcatagactccggtaag...gaccgattcaatacctccagagataacgccaagaacacacttcatctgcaaatgaacagcctgaaaaccgaggacacggccctgtattactgtaccagaga ->IGHV3-25*05 -gagatgcagctggtggagtctggggga...ggcttggcaaagcctgcgtggtccccgagactctcctgtgcagcctctcaattcaccttc............agtagctactacatgaactgtgtccgccaggctccagggaatgggctggagttggttggacaagttaatcctaat......gggggtagcacatacctcatagactccggtaag...gaccgattcaatacctccagagataacgccaagaacacacttcatctgcaaatgaacagcctgaaaaccgaggacacggccctctattagtgtaccagaga ->IGHV3-29*01 -gaggtggagctgatagagcccacagag...gacctgagacaacctgggaagttcctgagactctcctgtgtagcctctagattcgccttc............agtagcttctgaatgagcccagttcaccagtctgcaggcaaggggctggagtgagtaatagatataaaagatgat......ggaagtcagatacaccatgcagactctgtgaag...ggcagattctccatctccaaagacaatgctaagaactctctgtatctgcaaatgaacagtcagagaactgaggacatggctgtgtatggctgtacataaggtt ->IGHV3-30*01 -caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatgctatgcactgggtccgccaggctccaggcaaggggctagagtgggtggcagttatatcatatgat......ggaagtaataaatactacgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgcgagaga ->IGHV3-30*02 -caggtgcagctggtggagtctggggga...ggcgtggtccagcctggggggtccctgagactctcctgtgcagcgtctggattcaccttc............agtagctatggcatgcactgggtccgccaggctccaggcaaggggctggagtgggtggcatttatacggtatgat......ggaagtaataaatactatgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgcgaaaga ->IGHV3-30*03 -caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatggcatgcactgggtccgccaggctccaggcaaggggctggagtgggtggcagttatatcatatgat......ggaagtaataaatactatgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgcgagaga ->IGHV3-30*04 -caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatgctatgcactgggtccgccaggctccaggcaaggggctggagtgggtggcagttatatcatatgat......ggaagtaataaatactacgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgcgagaga ->IGHV3-30*05 -caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatggcatgcactgggtccgccaggctccaggcaaggggctagagtgggtggcagttatatcatatgat......ggaagtaataaatactacgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagctgagggcacggctgtgtattactgtgcgagaga ->IGHV3-30*06 -caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcgtctggattcaccttc............agtagctatggcatgcactgggtccgccaggctccaggcaaggggctagagtgggtggcagttatatcatatgat......ggaagtaataaatactacgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgcgagaga ->IGHV3-30*07 -caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatgctatgcactgggtccgccaggctccaggcaaggggctagagtgggtggcagttatatcatatgat......ggaagtaataaatactacgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagccgaggacacggctgtgtattactgtgcgagaga ->IGHV3-30*08 -caggtgcagctggtggactctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcctctgcattcaccttc............agtagctatgctatgcactgggtccgccaggctccaggcaaggggctagagtgggtggcagttatatcatatgat......ggaagtaataaatactacgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgcgaga ->IGHV3-30*09 -caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatgctatgcactgggtccgccaggctccaggcaaggggctggagtgggtggcagttatatcatatgat......ggaagtaataaatactacgcagactccgtgaag...ggccgattcgccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgcgagaga ->IGHV3-30*10 -caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatgctatgcactgggtccgccaggctccaggcaaggggctagagtgggtggcagttatatcatatgat......ggaagtaataaatactacacagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgcgagaga ->IGHV3-30*11 -caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcgtctggattcaccttc............agtagctatgctatgcactgggtccgccaggctccaggcaaggggctagagtgggtggcagttatatcatatgat......ggaagtaataaatactacgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgcgagaga ->IGHV3-30*12 -caggtgcagctggtggagtctgggggg...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcgtctggattcaccttc............agtagctatggcatgcactgggtccgccaggctccaggcaaggggctagagtgggtggcagttatatcatatgat......ggaagtaataaatactacgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagccgaggacacggctgtgtattactgtgcgagaga ->IGHV3-30*13 -caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatggcatgcactgggtccgccaggctccaggcaaggggctagagtgggtggcagttatatcatatgat......ggaagtaataaatactacgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacaggctgtatctgcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgcgagaga ->IGHV3-30*14 -caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatgctatgcactgggtccgccaggctccaggcaaggggctggagtgggtggcagttatatcatatgat......ggaagtaataaatactacgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatcttcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgcgagaga ->IGHV3-30*15 -caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatgctatgcactgggtccgccaggctccaggcaaggggctagagtgggtggcagttatatcatatgat......ggaagtaataaatactacgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgagcagcctgagagctgaggacacggctgtgtattactgtgcgagaga ->IGHV3-30*16 -caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatgctatgcactgggtccgccaggccccaggcaaggggctagagtgggtggcagttatatcatatgat......ggaagtaataaatactacgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgcgagaga ->IGHV3-30*17 -caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatgctatgcactgggtccgccaggctccgggcaaggggctagagtgggtggcagttatatcatatgat......ggaagtaataaatactacgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgcgagaga ->IGHV3-30*18 -caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatggcatgcactgggtccgccaggctccaggcaaggggctggagtgggtggcagttatatcatatgat......ggaagtaataaatactatgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgcgaaaga ->IGHV3-30*19 -caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcgtctggattcaccttc............agtagctatggcatgcactgggtccgccaggctccaggcaaggggctggagtgggtggcagttatatcatatgat......ggaagtaataaatactacgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgcgagaga ->IGHV3-30-2*01 -gaggtacagctcgtggagtccggagag...gacccaagacaacctgggggatccctgagactctcctgtgcagactctggattaaccttc............agtagctactgaaggaactcggtttcccaggctccagggaaggggctggagtgagtagtagatatacagtgtgat......ggaagtcagatatgttatgcataatctttgaag...agcaaattcaccatctccaaagaaaatgccaagaactcactgtatttgctaatgaacagtctgagagcagcgggcacagctgtgtgttactgtatgtgaggca ->IGHV3-30-22*01 -gaggtggagctgatagagtccatagag...gacctgagacaacctgggaagttcctgagactctcctgtgtagcctctagattcgccttc............agtagcttctgaatgagccgagttcaccagtctccaggcaaggggctggagtgagtaatagatataaaagatgat......ggaagtcagatacaccatgcagactctgtgaag...ggcagattctccatctccaaagacaatgctaagaactctctgtatctgcaaatgaacagtcagagagctgaggacatggacgtgtatggctgtacataaggtc ->IGHV3-30-3*01 -caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatgctatgcactgggtccgccaggctccaggcaaggggctggagtgggtggcagttatatcatatgat......ggaagcaataaatactacgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgcgagaga ->IGHV3-30-3*02 -caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcgtctggattcaccttc............agtagctatgctatgcactgggtccgccaggctccaggcaaggggctggagtgggtggcagttatatcatatgat......ggaagcaataaatactacgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgcgaaaga ->IGHV3-30-3*03 -caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatgctatgcactgggtccgccaggctccaggcaaggggctggagtgggtggcagttatatcatatgat......ggaagtaataaatactacgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgcgagaga ->IGHV3-30-33*01 -gaggtacagctcgtggagtccggagag...gacccaagacaacctgggggatccctgagactctcctgtgcagactctggattaaccttc............agtagctactgaaggagctcggtttcccaggctccagggaaggggctggagtgagtagtagatatacagtgtgat......ggaagtcagatatgttatgcataatctttgaag...agcaaattcaccatctccaaagaaaatgccaagaactcactgtatttgctaatgaacagtctgagagcagagggcacagctgtgtgttactgtatgtgagg ->IGHV3-30-42*01 -gaggtggagctgatagagcccacagag...gacctgagacaacctgggaagttcctgagactctcctgtgtagcctctagattcgccttc............agtagcttctgaatgagcccagttcaccagtctgcaggcaaggggctggagtgagtaatagatataaaagatgat......ggaagtcagatacaccatgcagactctgtgaag...ggcagattctccatctccaaagacaatgctaagaactctctgtatctgcaaatgaacagtcagagaactgaggacatggctgtgtatggctgtacataaggtt ->IGHV3-30-5*01 -caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatggcatgcactgggtccgccaggctccaggcaaggggctggagtgggtggcagttatatcatatgat......ggaagtaataaatactatgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgcgaaaga ->IGHV3-30-5*02 -caggtgcagctggtggagtctggggga...ggcgtggtccagcctggggggtccctgagactctcctgtgcagcgtctggattcaccttc............agtagctatggcatgcactgggtccgccaggctccaggcaaggggctggagtgggtggcatttatacggtatgat......ggaagtaataaatactatgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgcgaaaga ->IGHV3-30-52*01 -gaggtacagctcgtggagtccggagag...gacccaagacaacctgggggatccctgagactctcctgtgcagactctggattaaccttc............agtagctactgaaggaactcggtttcccaggctccagggaaggggctggagtgagtagtagatatacagtgtgat......ggaagtcagatatgttatgcataatctttgaag...agcaaattcaccatctccaaagaaaatgccaagaactcactgtatttgctaatgaacagtctgagagcagcgggcacagctgtgtgttactgtatgtgagg ->IGHV3-32*01 -gaggtggagctgatagagtccatagag...gacctgagacaacctgggaagttcctgagactctcctgtgtagcctctagattcgccttc............agtagcttctgaatgagccgagttcaccagtctccaggcaaggggctggagtgagtaatagatataaaagatgat......ggaagtcagatacaccatgcagactctgtgaag...ggcagattctccatctccaaagacaatgctaagaactctctgtatctgcaaatgaacactcagagagctgaggacgtggccgtgtatggctatacataaggtc ->AIGHV3-33*01 -caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcgtctggattcaccttc............agtagctatggcatgcactgggtccgccaggctccaggcaaggggctggagtgggtggcagttatatggtatgat......ggaagtaataaatactatgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagccgaggacacggctgtgtattactgtgcgagaga ->IGHV3-33*02 -caggtacagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcgtctggattcaccttc............agtagctatggcatgcactgggtccgccaggctccaggcaaggggctggagtgggtggcagttatatggtatgat......ggaagtaataaatactatgcagactccgcgaag...ggccgattcaccatctccagagacaattccacgaacacgctgtttctgcaaatgaacagcctgagagccgaggacacggctgtgtattactgtgcgagaga ->IGHV3-33*03 -caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcgtctggattcaccttc............agtagctatggcatgcactgggtccgccaggctccaggcaaggggctggagtgggtggcagttatatggtatgat......ggaagtaataaatactatgcagactccgtgaag...ggccgattcaccatctccagagacaactccaagaacacgctgtatctgcaaatgaacagcctgagagccgaggacacggctgtgtattactgtgcgaaaga ->IGHV3-33*04 -caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcgtctggattcaccttc............agtagctatggcatgcactgggtccgccaggctccaggcaaggggctagagtgggtggcagttatatggtatgac......ggaagtaataaatactatgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagccgaggacacggctgtgtattactgtgcgagaga ->IGHV3-33*05 -caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcgtctggattcaccttc............agtagctatggcatgcactgggtccgccaggctccaggcaaggggctggagtgggtggcagttatatcatatgat......ggaagtaataaatactatgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagccgaggacacggctgtgtattactgtgcgagaga ->IGHV3-33*06 -caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcgtctggattcaccttc............agtagctatggcatgcactgggtccgccaggctccaggcaaggggctggagtgggtggcagttatatggtatgat......ggaagtaataaatactatgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagccgaggacacggctgtgtattactgtgcgaaaga ->IGHV3-33-2*01 -gaggtacagctcgtggagtccggagag...gacccaagacaacctgggggatccttgagactctcctgtgcagactctggattaaccttc............agtagctactgaatgagctcggtttcccaggctccagggaaggggctggagtgagtagtagatatacagtgtgat......ggaagtcagatatgttatgcccaatctgtgaag...agcaaattcaccatctccaaagaaaatgccaagaactcactgtatttgcaaatgaacagtctgagagcagagggcacagctgtgtgttactgtatgtgaggca ->IGHV3-35*01 -gaggtgcagctggtggagtctggggga...ggcttggtacagcctgggggatccctgagactctcctgtgcagcctctggattcaccttc............agtaacagtgacatgaactgggtccatcaggctccaggaaaggggctggagtgggtatcgggtgttagttggaat......ggcagtaggacgcactatgcagactctgtgaag...ggccgattcatcatctccagagacaattccaggaacaccctgtatctgcaaacgaatagcctgagggccgaggacacggctgtgtattactgtgtgagaaa ->IGHV3-38*01| -gaggtgcagctggtggagtctggggga...ggcttggtacagcctagggggtccctgagactctcctgtgcagcctctggattcaccgtc............agtagcaatgagatgagctggatccgccaggctccagggaaggggctggagtgggtctcatccattagtggt............ggtagcacatactacgcagactccaggaag...ggcagattcaccatctccagagacaattccaagaacacgctgtatcttcaaatgaacaacctgagagctgagggcacggccgcgtattactgtgccagatata ->IGHV3-38*02 -gaggtgcagctggtggagtctggggga...ggcttggtacagcctagggggtccctgagactctcctgtgcagcctctggattcaccgtc............agtagcaatgagatgagctggatccgccaggctccagggaaggggctggagtgggtctcatccattagtggt............ggtagcacatactacgcagactccaggaag...ggcagattcaccatctccagagacaattccaagaacacgctgtatcttcaaatgaacaacctgagagctgagggcacggccgtgtattactgtgccagatata ->IGHV3-38*03 -gaggtgcagctggtggagtctggggga...ggcttggtacagcctagggggtccctgagactctcctgtgcagcctctggattcaccgtc............agtagcaatgagatgagctggatccgccaggctccagggaagggtctggagtgggtctcatccattagtggt............ggtagcacatactacgcagactccaggaag...ggcagattcaccatctccagagacaattccaagaacacgctgtatcttcaaatgaacaacctgagagctgagggcacggccgtgtattactgtgccagatata ->IGHV3-38-3*01 -gaggtgcagctggtggagtctcgggga...gtcttggtacagcctggggggtccctgagactctcctgtgcagcctctggattcaccgtc............agtagcaatgagatgagctgggtccgccaggctccagggaagggtctggagtgggtctcatccattagtggt............ggtagcacatactacgcagactccaggaag...ggcagattcaccatctccagagacaattccaagaacacgctgcatcttcaaatgaacagcctgagagctgaggacacggctgtgtattactgtaagaaaga ->IGHV3-43*01 -gaagtgcagctggtggagtctggggga...gtcgtggtacagcctggggggtccctgagactctcctgtgcagcctctggattcaccttt............gatgattataccatgcactgggtccgtcaagctccggggaagggtctggagtgggtctctcttattagttgggat......ggtggtagcacatactatgcagactctgtgaag...ggccgattcaccatctccagagacaacagcaaaaactccctgtatctgcaaatgaacagtctgagaactgaggacaccgccttgtattactgtgcaaaagata ->IGHV3-43*02 -gaagtgcagctggtggagtctggggga...ggcgtggtacagcctggggggtccctgagactctcctgtgcagcctctggattcaccttt............gatgattatgccatgcactgggtccgtcaagctccagggaagggtctggagtgggtctctcttattagtggggat......ggtggtagcacatactatgcagactctgtgaag...ggccgattcaccatctccagagacaacagcaaaaactccctgtatctgcaaatgaacagtctgagaactgaggacaccgccttgtattactgtgcaaaagata ->IGHV3-43D*01 -gaagtgcagctggtggagtctggggga...gtcgtggtacagcctggggggtccctgagactctcctgtgcagcctctggattcaccttt............gatgattatgccatgcactgggtccgtcaagctccggggaagggtctggagtgggtctctcttattagttgggat......ggtggtagcacctactatgcagactctgtgaag...ggtcgattcaccatctccagagacaacagcaaaaactccctgtatctgcaaatgaacagtctgagagctgaggacaccgccttgtattactgtgcaaaagata ->IGHV3-47*01 -gaggatcagctggtggagtctggggga...ggcttggtacagcctggggggtccctgcgaccctcctgtgcagcctctggattcgccttc............agtagctatgctctgcactgggttcgccgggctccagggaagggtctggagtgggtatcagctattggtactggt.........ggtgatacatactatgcagactccgtgatg...ggccgattcaccatctccagagacaacgccaagaagtccttgtatcttcatatgaacagcctgatagctgaggacatggctgtgtattattgtgcaaga ->IGHV3-47*02 -gaggatcagctggtggagtctggggga...ggcttggtacagcctggggggtccctgagaccctcctgtgcagcctctggattcgccttc............agtagctatgttctgcactgggttcgccgggctccagggaagggtccggagtgggtatcagctattggtactggt.........ggtgatacatactatgcagactccgtgatg...ggccgattcaccatctccagagacaacgccaagaagtccttgtatcttcaaatgaacagcctgatagctgaggacatggctgtgtattattgtgcaagaga ->IGHV3-48*01 -gaggtgcagctggtggagtctggggga...ggcttggtacagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatagcatgaactgggtccgccaggctccagggaaggggctggagtgggtttcatacattagtagtagt......agtagtaccatatactacgcagactctgtgaag...ggccgattcaccatctccagagacaatgccaagaactcactgtatctgcaaatgaacagcctgagagccgaggacacggctgtgtattactgtgcgagaga ->IGHV3-48*02 -gaggtgcagctggtggagtctggggga...ggcttggtacagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatagcatgaactgggtccgccaggctccagggaaggggctggagtgggtttcatacattagtagtagt......agtagtaccatatactacgcagactctgtgaag...ggccgattcaccatctccagagacaatgccaagaactcactgtatctgcaaatgaacagcctgagagacgaggacacggctgtgtattactgtgcgagaga ->IGHV3-48*03 -gaggtgcagctggtggagtctggggga...ggcttggtacagcctggagggtccctgagactctcctgtgcagcctctggattcaccttc............agtagttatgaaatgaactgggtccgccaggctccagggaaggggctggagtgggtttcatacattagtagtagt......ggtagtaccatatactacgcagactctgtgaag...ggccgattcaccatctccagagacaacgccaagaactcactgtatctgcaaatgaacagcctgagagccgaggacacggctgtttattactgtgcgagaga ->IGHV3-48*04 -gaggtgcagctggtggagtctggggga...ggcttggtacagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatagcatgaactgggtccgccaggctccagggaaggggctggagtgggtttcatacattagtagtagt......agtagtaccatatactacgcagactctgtgaag...ggccgattcaccatctccagagacaacgccaagaactcactgtatctgcaaatgaacagcctgagagccgaggacacggctgtgtattactgtgcgagaga ->IGHV3-49*01 -gaggtgcagctggtggagtctggggga...ggcttggtacagccagggcggtccctgagactctcctgtacagcttctggattcaccttt............ggtgattatgctatgagctggttccgccaggctccagggaaggggctggagtgggtaggtttcattagaagcaaagcttatggtgggacaacagaatacaccgcgtctgtgaaa...ggcagattcaccatctcaagagatggttccaaaagcatcgcctatctgcaaatgaacagcctgaaaaccgaggacacagccgtgtattactgtactagaga ->IGHV3-49*02 -gaggtgcagctggtggagtctggggga...ggcttggtacagccagggccgtccctgagactctcctgtacagcttctggattcaccttt............gggtattatcctatgagctgggtccgccaggctccagggaaggggctggagtgggtaggtttcattagaagcaaagcttatggtgggacaacagaatacgccgcgtctgtgaaa...ggcagattcaccatctcaagagatgattccaaaagcatcgcctatctgcaaatgaacagcctgaaaaccgaggacacagccgtgtattactgtactagaga ->IGHV3-49*03 -gaggtgcagctggtggagtctggggga...ggcttggtacagccagggcggtccctgagactctcctgtacagcttctggattcaccttt............ggtgattatgctatgagctggttccgccaggctccagggaaggggctggagtgggtaggtttcattagaagcaaagcttatggtgggacaacagaatacgccgcgtctgtgaaa...ggcagattcaccatctcaagagatgattccaaaagcatcgcctatctgcaaatgaacagcctgaaaaccgaggacacagccgtgtattactgtactagaga ->IGHV3-49*04 -gaggtgcagctggtggagtctggggga...ggcttggtacagccagggcggtccctgagactctcctgtacagcttctggattcaccttt............ggtgattatgctatgagctgggtccgccaggctccagggaaggggctggagtgggtaggtttcattagaagcaaagcttatggtgggacaacagaatacgccgcgtctgtgaaa...ggcagattcaccatctcaagagatgattccaaaagcatcgcctatctgcaaatgaacagcctgaaaaccgaggacacagccgtgtattactgtactagaga ->IGHV3-49*05 -gaggtgcagctggtggagtctggggga...ggcttggtaaagccagggcggtccctgagactctcctgtacagcttctggattcaccttt............ggtgattatgctatgagctggttccgccaggctccagggaaggggctggagtgggtaggtttcattagaagcaaagcttatggtgggacaacagaatacgccgcgtctgtgaaa...ggcagattcaccatctcaagagatgattccaaaagcatcgcctatctgcaaatgaacagcctgaaaaccgaggacacagccgtgtattactgtactagaga ->IGHV3-52*01 -gaggtgcagctggtggagtctgggtga...ggcttggtacagcctggagggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctcctggatgcactgggtctgccaggctccggagaaggggctggagtgggtggccgacataaagtgtgac......ggaagtgagaaatactatgtagactctgtgaag...ggccgattgaccatctccagagacaatgccaagaactccctctatctgcaagtgaacagcctgagagctgaggacatgaccgtgtattactgtgtgagagg ->IGHV3-52*02 -gaggtgcagctggtggagtctgggtga...ggcttggtacagcctggagggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctcctggatgcactgggtctgccaggctccggagaaggggcaggagtgggtggccgacataaagtgtgac......ggaagtgagaaatactatgtagactctgtgaag...ggccgattgaccatctccagagacaatgccaagaactccctctatctgcaagtgaacagcctgagagctgaggacatgaccgtgtattactgtgtgaga ->IGHV3-52*03 -gaggtgcagctggtcgagtctgggtga...ggcttggtacagcctggagggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctcctggatgcactgggtctgccaggctccggagaaggggctggagtgggtggccgacataaagtgtgac......ggaagtgagaaatactatgtagactctgtgaag...ggccgattgaccatctccagagacaatgccaagaactccctctatctgcaagtgaacagcctgagagctgaggacatgaccgtgtattactgtgtgaga ->IGHV3-53*01 -gaggtgcagctggtggagtctggagga...ggcttgatccagcctggggggtccctgagactctcctgtgcagcctctgggttcaccgtc............agtagcaactacatgagctgggtccgccaggctccagggaaggggctggagtgggtctcagttatttatagcggt.........ggtagcacatactacgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatcttcaaatgaacagcctgagagccgaggacacggccgtgtattactgtgcgagaga ->IGHV3-53*02 -gaggtgcagctggtggagactggagga...ggcttgatccagcctggggggtccctgagactctcctgtgcagcctctgggttcaccgtc............agtagcaactacatgagctgggtccgccaggctccagggaaggggctggagtgggtctcagttatttatagcggt.........ggtagcacatactacgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatcttcaaatgaacagcctgagagccgaggacacggccgtgtattactgtgcgagaga ->IGHV3-53*03 -gaggtgcagctggtggagtctggagga...ggcttgatccagcctggggggtccctgagactctcctgtgcagcctctgggttcaccgtc............agtagcaactacatgagctgggtccgccagcctccagggaaggggctggagtgggtctcagttatttatagcggt.........ggtagcacatactacgcagactctgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatcttcaaatgaacagcctgagagccgaggacacggccgtgtattactgtgctaggga ->IGHV3-53*04 -gaggtgcagctggtggagtctggagga...ggcttggtccagcctggggggtccctgagactctcctgtgcagcctctgggttcaccgtc............agtagcaactacatgagctgggtccgccaggctccagggaaggggctggagtgggtctcagttatttatagcggt.........ggtagcacatactacgcagactccgtgaag...ggccgattcaccatctccagacacaattccaagaacacgctgtatcttcaaatgaacagcctgagagctgaggacacggccgtgtattactgtgcgagaga ->IGHV3-54*01 -gaggtacagctggtggagtctgaagaa...aaccaaagacaacttgggggatccctgagactctcctgtgcagactctggattaaccttc............agtagctactgaatgagctcagattcccaagctccagggaaggggctggagtgagtagtagatatatagtaggat......agaagtcagctatgttatgcacaatctgtgaag...agcagattcaccatctccaaagaaaatgccaagaactcactctgtttgcaaatgaacagtctgagagcagagggcacggccgtgtattactgtatgtgagt ->IGHV3-54*02 -gaggtacagctggtggagtctgaagaa...aaccaaagacaacttgggggatccctgagactctcctgtgcagactctggattaaccttc............agtagctactgaatgagctcagattcccaggctccagggaaggggctggagtgagtagtagatatatagtacgat......agaagtcagatatgttatgcacaatctgtgaag...agcagattcaccatctccaaagaaaatgccaagaactcactccgtttgcaaatgaacagtctgagagcagagggcacggccgtgtattactgtatgtgagg ->IGHV3-54*04 -gaggtacagctggtggagtctgaagaa...aaccaaagacaacttgggggatccctgagactctcctgtgcagactctggattaaccttc............agtagctactgaatgagctcagattcccaggctccagggaaggggctggagtgagtagtagatatatagtaggat......agaagtcagctatgttatgcacaatctgtgaag...agcagattcaccatctccaaagaaaatgccaagaactcactctgtttgcaaatgaacagtctgagagcagagggcacggccgtgtattactgtatgtgagt ->IGHV3-62*01 -gaggtgcagctggtggagtctggggaa...ggcttggtccagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctctgctatgcactgggtccgccaggctccaagaaagggtttgtagtgggtctcagttattagtacaagt......ggtgataccgtactctacacagactctgtgaag...ggccgattcaccatctccagagacaatgcccagaattcactgtctctgcaaatgaacagcctgagagccgagggcacagttgtgtactactgtgtgaaaga ->IGHV3-63*01 -gaggtggagctgatagagtccatagag...ggcctgagacaacttgggaagttcctgagactctcctgtgtagcctctggattcaccttc............agtagctactgaatgagctgggtcaatgagactctagggaaggggctggagggagtaatagatgtaaaatatgat......ggaagtcagatataccatgcagactctgtgaag...ggcagattcaccatctccaaagacaatgctaagaactcaccgtatctccaaacgaacagtctgagagctgaggacatgaccatgcatggctgtacataaggtt ->IGHV3-63*02 -gaggtggagctgatagagtccatagag...ggcctgagacaacttgggaagttcctgagactctcctgtgtagcctctggattcaccttc............agtagctactgaatgagctgggtcaatgagactctagggaaggggctggagggagtaatagatgtaaaatatgat......ggaagtcagatataccatgcagactctgtgaag...ggcagattcaccatctccaaagacaatgctaagaactcaccgtatctgcaaacgaacagtctgagagctgaggacatgaccatgcatggctgtacataa ->IGHV3-64*01 -gaggtgcagctggtggagtctggggga...ggcttggtccagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatgctatgcactgggtccgccaggctccagggaagggactggaatatgtttcagctattagtagtaat......gggggtagcacatattatgcaaactctgtgaag...ggcagattcaccatctccagagacaattccaagaacacgctgtatcttcaaatgggcagcctgagagctgaggacatggctgtgtattactgtgcgagaga ->IGHV3-64*02 -gaggtgcagctggtggagtctggggaa...ggcttggtccagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatgctatgcactgggtccgccaggctccagggaagggactggaatatgtttcagctattagtagtaat......gggggtagcacatattatgcagactctgtgaag...ggcagattcaccatctccagagacaattccaagaacacgctgtatcttcaaatgggcagcctgagagctgaggacatggctgtgtattactgtgcgagaga ->IGHV3-64*03 -gaggtgcagctggtggagtctggggga...ggcttggtccagcctggggggtccctgagactctcctgttcagcctctggattcaccttc............agtagctatgctatgcactgggtccgccaggctccagggaagggactggaatatgtttcagctattagtagtaat......gggggtagcacatactacgcagactcagtgaag...ggcagattcaccatctccagagacaattccaagaacacgctgtatgtccaaatgagcagtctgagagctgaggacacggctgtgtattactgtgtgaaaga ->IGHV3-64*04 -caggtgcagctggtggagtctggggga...ggcttggtccagcctggggggtccctgagactctcctgttcagcctctggattcaccttc............agtagctatgctatgcactgggtccgccaggctccagggaagggactggaatatgtttcagctattagtagtaat......gggggtagcacatactacgcagactcagtgaag...ggcagattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgcgagaga ->IGHV3-64*05 -gaggtgcagctggtggagtctggggga...ggcttggtccagcctggggggtccctgagactctcctgttcagcctctggattcaccttc............agtagctatgctatgcactgggtccgccaggctccagggaagggactggaatatgtttcagctattagtagtaat......gggggtagcacatactacgcagactcagtgaag...ggcagattcaccatctccagagacaattccaagaacacgctgtatgttcaaatgagcagtctgagagctgaggacacggctgtgtattactgtgtgaaaga ->IGHV3-64D*06 -gaggtgcagctggtggagtctggggga...ggcttggtccagcctggggggtccctgagactctcctgttcagcctctggattcaccttc............agtagctatgctatgcactgggtccgccaggctccagggaagggactggaatatgtttcagctattagtagtaat......gggggtagcacatactacgcagactccgtgaag...ggcagattcaccatctccagagacaattccaagaacacgctgtatcttcaaatgagcagtctgagagctgaggacacggctgtgtattactgtgtgaaaga ->IGHV3-66*01 -gaggtgcagctggtggagtctggggga...ggcttggtccagcctggggggtccctgagactctcctgtgcagcctctggattcaccgtc............agtagcaactacatgagctgggtccgccaggctccagggaaggggctggagtgggtctcagttatttatagcggt.........ggtagcacatactacgcagactccgtgaag...ggcagattcaccatctccagagacaattccaagaacacgctgtatcttcaaatgaacagcctgagagccgaggacacggctgtgtattactgtgcgagaga ->IGHV3-66*02 -gaggtgcagctggtggagtctggggga...ggcttggtccagcctggggggtccctgagactctcctgtgcagcctctggattcaccgtc............agtagcaactacatgagctgggtccgccaggctccagggaaggggctggagtgggtctcagttatttatagcggt.........ggtagcacatactacgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatcttcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgcgaga ->IGHV3-66*03 -gaggtgcagctggtggagtctggagga...ggcttgatccagcctggggggtccctgagactctcctgtgcagcctctgggttcaccgtc............agtagcaactacatgagctgggtccgccaggctccagggaaggggctggagtgggtctcagttatttatagctgt.........ggtagcacatactacgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatcttcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgcgagaga ->IGHV3-66*04 -gaggtgcagctggtggagtctggggga...ggcttggtccagcctggggggtccctgagactctcctgtgcagcctctggattcaccgtc............agtagcaactacatgagctgggtccgccaggctccagggaaggggctggagtgggtctcagttatttatagcggt.........ggtagcacatactacgcagactccgtgaag...ggcagattcaccatctccagagacaattccaagaacacgctgtatcttcaaatgaacagcctgagagccgaggacacggctgtgtattactgtgcgagaca ->IGHV3-69-1*01 -gaggtgcagctggtggagtctggggga...ggcttggtaaagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agtgactactacatgaactgggtccgccaggctccagggaaggggctggagtgggtctcatccattagtagtagt.........agtaccatatactacgcagactctgtgaag...ggccgattcaccatctccagagacaacgccaagaactcactgtatctgcaaatgaacagcctgagagccgaggacacggctgtgtattactgtgcgagaga ->IGHV3-69-1*02 -gaggtgcagctggtggagtctggggga...ggcttggtaaagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agtgactactacatgaactgggtccgccaggctccagggaaggggctggagtgggtctcatccattagtagtagt.........agtaccatatactacgcagactctgtgaag...ggccgattcaccatctccagagacaacgccaagaactcactgtatctgcaaatgaacagcctgagagccgaggacacggctgtttattactgtgcgagaga ->IGHV3-7*01 -gaggtgcagctggtggagtctggggga...ggcttggtccagcctggggggtccctgagactctcctgtgcagcctctggattcaccttt............agtagctattggatgagctgggtccgccaggctccagggaaggggctggagtgggtggccaacataaagcaagat......ggaagtgagaaatactatgtggactctgtgaag...ggccgattcaccatctccagagacaacgccaagaactcactgtatctgcaaatgaacagcctgagagccgaggacacggctgtgtattactgtgcgagaga ->IGHV3-7*02 -gaggtgcagctggtggagtctggggga...ggcttggtccagcctggggggtccctgagactctcctgtgcagcctctggattcaccttt............agtagctattggatgagctgggtccgccaggctccagggaaagggctggagtgggtggccaacataaagcaagat......ggaagtgagaaatactatgtggactctgtgaag...ggccgattcaccatctccagagacaacgccaagaactcactgtatctgcaaatgaacagcctgagagccgaggacacggctgtgtattactgtgcgaga ->IGHV3-7*03 -gaggtgcagctggtggagtctggggga...ggcttggtccagcctggggggtccctgagactctcctgtgcagcctctggattcaccttt............agtagctattggatgagctgggtccgccaggctccagggaaggggctggagtgggtggccaacataaagcaagat......ggaagtgagaaatactatgtggactctgtgaag...ggccgattcaccatctccagagacaacgccaagaactcactgtatctgcaaatgaacagcctgagagccgaggacacggccgtgtattactgtgcgagaga ->IGHV3-71*01 -gaggtgcagctggtggagtccggggga...ggcttggtccagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agtgactactacatgagctgggtccgccaggctcccgggaaggggctggagtgggtaggtttcattagaaacaaagctaatggtgggacaacagaatagaccacgtctgtgaaa...ggcagattcacaatctcaagagatgattccaaaagcatcacctatctgcaaatgaacagcctgagagccgaggacacggccgtgtattactgtgcgagaga ->IGHV3-71*02 -gaggtgcagctggtggagtctggggga...ggcttggtccagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agtgactactacatgagctgggtccgccaggctcccgggaaggggctggagtgggtaggtttcattagaaacaaagctaatggtgggacaacagaatagaccacgtctgtgaaa...ggcagattcacaatctcaagagatgattccaaaagcatcacctatctgcaaatgaacagcctgagagccgaggacatggctgtgtattactgtgcgagaga ->IGHV3-71*03 -gaggtgcagctggtggagtctggggga...ggcttggtccagcctggggggtccctgagactctcctgtgcagcctctggtttcaccttc............agtgactactacatgagctgggtccgccaggctcccgggaaggggctggagtgggtaggtttcattagaaacaaagctaatggtgggacaacagaatagaccacgtctgtgaaa...ggcagattcacaatctcaagagatgattccaaaagcatcacctatctgcaaatgaacagcctgagagccgaggacacggctgtgtattactgtgcgagaga ->IGHV3-72*01 -gaggtgcagctggtggagtctggggga...ggcttggtccagcctggagggtccctgagactctcctgtgcagcctctggattcaccttc............agtgaccactacatggactgggtccgccaggctccagggaaggggctggagtgggttggccgtactagaaacaaagctaacagttacaccacagaatacgccgcgtctgtgaaa...ggcagattcaccatctcaagagatgattcaaagaactcactgtatctgcaaatgaacagcctgaaaaccgaggacacggccgtgtattactgtgctagaga ->IGHV3-72*02 -....................................................................................accttc............agtgaccactacatggactgggtccgccaggctccagggaaggggctggagtgggttggccgtactagaaacaaagctaacagctacaccacagaatacgccgcgtctgtgaaa...ggcagattcaccatctcaagagatgattcaaagaactcactgtat ->IGHV3-73*01 -gaggtgcagctggtggagtctggggga...ggcttggtccagcctggggggtccctgaaactctcctgtgcagcctctgggttcaccttc............agtggctctgctatgcactgggtccgccaggcttccgggaaagggctggagtgggttggccgtattagaagcaaagctaacagttacgcgacagcatatgctgcgtcggtgaaa...ggcaggttcaccatctccagagatgattcaaagaacacggcgtatctgcaaatgaacagcctgaaaaccgaggacacggccgtgtattactgtactagaca ->IGHV3-73*02 -gaggtgcagctggtggagtccggggga...ggcttggtccagcctggggggtccctgaaactctcctgtgcagcctctgggttcaccttc............agtggctctgctatgcactgggtccgccaggcttccgggaaagggctggagtgggttggccgtattagaagcaaagctaacagttacgcgacagcatatgctgcgtcggtgaaa...ggcaggttcaccatctccagagatgattcaaagaacacggcgtatctgcaaatgaacagcctgaaaaccgaggacacggccgtgtattactgtactagaca ->IGHV3-74*01 -gaggtgcagctggtggagtccggggga...ggcttagttcagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctactggatgcactgggtccgccaagctccagggaaggggctggtgtgggtctcacgtattaatagtgat......gggagtagcacaagctacgcggactccgtgaag...ggccgattcaccatctccagagacaacgccaagaacacgctgtatctgcaaatgaacagtctgagagccgaggacacggctgtgtattactgtgcaagaga ->IGHV3-74*02 -gaggtgcagctggtggagtctggggga...ggcttagttcagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctactggatgcactgggtccgccaagctccagggaaggggctggtgtgggtctcacgtattaatagtgat......gggagtagcacaagctacgcggactccgtgaag...ggccgattcaccatctccagagacaacgccaagaacacgctgtatctgcaaatgaacagtctgagagccgaggacacggctgtgtattactgtgcaaga ->IGHV3-74*03 -gaggtgcagctggtggagtccggggga...ggcttagttcagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctactggatgcactgggtccgccaagctccagggaaggggctggtgtgggtctcacgtattaatagtgat......gggagtagcacaacgtacgcggactccgtgaag...ggccgattcaccatctccagagacaacgccaagaacacgctgtatctgcaaatgaacagtctgagagccgaggacacggctgtgtattactgtgcaagaga ->IGHV3-9*01 -gaagtgcagctggtggagtctggggga...ggcttggtacagcctggcaggtccctgagactctcctgtgcagcctctggattcaccttt............gatgattatgccatgcactgggtccggcaagctccagggaagggcctggagtgggtctcaggtattagttggaat......agtggtagcataggctatgcggactctgtgaag...ggccgattcaccatctccagagacaacgccaagaactccctgtatctgcaaatgaacagtctgagagctgaggacacggccttgtattactgtgcaaaagata ->IGHV3-9*02 -gaagtgcagctggtggagtctggggga...ggcttggtacagcctggcaggtccctgagactctcctgtgcagcctctggattcacctct............gatgattatgccatgcactgggtccggcaagctccagggaagggcctggagtgggtctcaggtattagttggaat......agtggtagcataggctatgcggactctgtgaag...ggccgattcaccatctccagagacaacgccaagaactccctgtatctgcaaatgaacagtctgagagctgaggacacggccttgtattactgtgcaaaagata ->IGHV3-9*03 -gaagtgcagctggtggagtctggggga...ggcttggtacagcctggcaggtccctgagactctcctgtgcagcctctggattcaccttt............gatgattatgccatgcactgggtccggcaagctccagggaagggcctggagtgggtctcaggtattagttggaat......agtggtagcataggctatgcggactctgtgaag...ggccgattcaccatctccagagacaacgccaagaactccctgtatctgcaaatgaacagtctgagagctgaggacatggccttgtattactgtgcaaaagata ->IGHV3-NL1*01 -caggtgcagctggtggagtctggggga...ggcgtggtccagcctggggggtccctgagactctcctgtgcagcgtctggattcaccttc............agtagctatggcatgcactgggtccgccaggctccaggcaaggggctggagtgggtctcagttatttatagcggt......ggtagtagcacatactatgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgcgaaaga ->IGHV3/OR15-7*01 -gaggtgcagctggtggagtctggggga...ggcttggtccagcctgggggttctctgagactctcatgtgcagcctctggattcaccttc............agtgaccactacatgagctgggtccgccaggctcaagggaaagggctagagttggtaggtttaataagaaacaaagctaacagttacacgacagaatatgctgcgtctgtgaaa...ggcagacttaccatctcaagagaggattcaaagaacacgatgtatctgcaaatgagcaacctgaaaaccgaggacttggccgtgtattactgtgctaga ->IGHV3/OR15-7*02 -gaggtgcagctgttggagtctggggga...ggcttggtccagcctgggggttctctgagactctcatgtgctgcctctggattcaccttc............agtgaccactacatgagctgggtccgccaggctcaagggaaagggctagagttggtaggtttaataagaaacaaagctaacagttacacgacagaatatgctgcgtctgtgaaa...ggcagacttaccatctcaagagaggattcaaagaacacgctgtatctgcaaatgagcagcctgaaaaccgaggacttggccgtgtattactgtgctaga ->IGHV3/OR15-7*03 -gaggtgcagctggtggagtctggggga...ggcttggtccagcctgggggttctctgagactctcatgtgcagcctctggattcaccttc............agtgaccactacatgagctgggtccgccaggctcaagggaaagggctagagttggtaggtttaataagaaacaaagctaacagttacacgacagaatatgctgcgtctgtgaaa...ggcagacttaccatctcaagagaggattcaaagaacacgctgtatctgcaaatgagcagcctgaaaaccgaggacttggccgtgtattactgtgctaga ->IGHV3/OR15-7*05 -gaggtgcagctggtggagtctggggga...ggcttggtccagcctgggggttctctgagactctcatgtgcagcctctggattcaccttc............agtgaccactacatgagctgggtccgccaggctcaagggaaagggctagagttggtaggtttaataagaaacaaagctaacagttacacgacagaatatgctgcgtctgtgaaa...ggcagacttaccatctcaagagaggattcaaagaacacgctgtatctgcaaatgagcaacctgaaaaccgaggacttggccgtgtattactgtgctagaga ->IGHV3/OR16-10*01 -gaggttcagctggtgcagtctggggga...ggcttggtacatcctggggggtccctgagactctcctgtgcaggctctggattcaccttc............agtagctatgctatgcactgggttcgccaggctccaggaaaaggtctggagtgggtatcagctattggtactggt.........ggtggcacatactatgcagactccgtgaag...ggccgattcaccatctccagagacaatgccaagaactccttgtatcttcaaatgaacagcctgagagccgaggacatggctgtgtattactgtgcaaga ->IGHV3/OR16-10*02 -gaggttcagctggtgcagtctggggga...ggcttggtacagcctggggggtccctgagactctcctgtgcaggctctggattcaccttc............agtagctatgctatgcactgggttcgccaggctccaggaaaaggtctggagtgggtatcagctattggtactggt.........ggtggcacatactatgcagactccgtgaag...ggccgattcaccatctccagagacaatgccaagaactccttgtatcttcaaatgaacagcctgagagccgaggacatggctgtgtattactgtgcaaga ->IGHV3/OR16-10*03 -gaggtgcagctggtggagtctggggga...ggcttggtacagcctggggggtccctgagactctcctgtgcaggctctggattcaccttc............agtagctatgctatgcactgggttcgccaggctccaggaaaaggtctggagtgggtatcagctattggtactggt.........ggtggcacatactatgcagactccgtgaag...ggccgattcaccatctccagagacaatgccaagaactccttgtatcttcaaatgaacagcctgagagccgaggacatggctgtgtattactgtgcaagaga ->IGHV3/OR16-12*01 -gaggtgcagctggtagagtctgggaga...ggcttggcccagcctggggggtacctaaaactctccggtgcagcctctggattcaccgtc............ggtagctggtacatgagctggatccaccaggctccagggaagggtctggagtgggtctcatacattagtagtagt......ggttgtagcacaaactacgcagactctgtgaag...ggcagattcaccatctccacagacaactcaaagaacacgctctacctgcaaatgaacagcctgagagtggaggacacggccgtgtattactgtgcaaga ->IGHV3/OR16-13*01 -gaggtgcagctggtggagtctggggga...ggcttagtacagcctggagggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctactggatgcactgggtccgccaagctccagggaaggggctggtgtgggtctcacgtattaatagtgat......gggagtagcacaagctacgcagactccatgaag...ggccaattcaccatctccagagacaatgctaagaacacgctgtatctgcaaatgaacagtctgagagctgaggacatggctgtgtattactgtactaga ->IGHV3/OR16-14*01 -gaggtgcagctggaggagtctggggga...ggcttagtacagcctggagggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctactggatgcactgggtccgccaatctccagggaaggggctggtgtgagtctcacgtattaatagtgat......gggagtagcacaagctacgcagactccttgaag...ggccaattcaccatctccagagacaatgctaagaacacgctgtatctgcaaatgaacagtctgagagctgaggacatggctgtgtattactgtactaga ->IGHV3/OR16-15*01 -gaagtgcagctggtggagtctggggga...ggcttggtccagcctggggggtccctgagactctcctgtgcagcctctgtattcaccttc............agtaacagtgacataaactgggtcctctaggctccaggaaaggggctggagtgggtctcgggtattagttggaat......ggcggtaagacgcactatgtggactccgtgaag...ggccaattttccatctccagagacaattccagcaagtccctgtatctgcaaaagaacagacagagagccaaggacatggccgtgtattactgtgtgagaaa ->IGHV3/OR16-15*02 -gaggtgcagctggtggagtctggggga...ggcttggtccagcctggggggtccctgagacactcctgtgcagcctctggattcaccttc............agtaacagtgacatgaactgggtcctctaggctccaggaaaggggctggagtgggtctcgggtattagttggaat......ggcggtaagacgcactatgtggactccgtgaag...ggccaatttaccatctccagagacaattccagcaagtccctgtatctgcaaaagaacagacagagagccaaagacatggccgtgtattactgtgtgaga ->IGHV3/OR16-16*01 -gaggtgcagctggtggagtctggggga...ggcttggtccagcctggggggtccctgagacactcctgtgcagcctctggattcaccttc............agtaacagtgacatgaactgggtcctctaggctccaggaaaggggctggagtgggtctcggatattagttggaat......ggcggtaagacgcactatgtggactccgtgaag...ggccaatttaccatctccagagacaattccagcaagtccctgtatctgcaaaagaacagacagagagccaaggacatggccgtgtattactgtgtgaga ->IGHV3/OR16-6*02 -gaggtgcagctggtggagtctgcggga...ggccttggtacagcctgggggtcccttagactctcctgtgcagcctctggattcacttgc............agtaacgcctggatgagctgggtccgccaggctccagggaaggggctggagtgggttggctgtattaaaagcaaagctaatggtgggacaacagactacgctgcacctgtgaaa...ggcagattcaccatctcaagagatgattcaaaaaacacgctgtatctgcaaatgatcagcctgaaaaccgaggacacggccgtgtattactgtaccacagg ->IGHV3/OR16-8*01 -gaggtgcagctggtggagtctggggga...ggcttggtacagcctggggggtccctgagactgtcctgtccagcctctggattcaccttc............agtaaccactacatgagctgggtccgccaggctccagggaagggactggagtgggtttcatacattagtggtgat......agtggttacacaaactacgcagactctgtgaag...ggccgattcaccatctccagggacaacgccaataactcaccgtatctgcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgtgaaa ->IGHV3/OR16-8*02 -gaggtgcagctggtggagtctggggga...ggcttggtacagcctggggggtccctgagactgtcctgtccagactctggattcaccttc............agtaaccactacatgagctgggtccgccaggctccagggaagggactggagtggatttcatacattagtggtgat......agtggttacacaaactacgcagactctgtgaag...ggccgattcaccatctccagggacaacgccaataactcaccgtatctgcaaatgaacagcttgagagctgaggacacggctgtgtattactgtgtgaaaca ->IGHV3/OR16-9*01 -gaggtgcagctggtggagtctggagga...ggcttggtacagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agtaaccactacacgagctgggtccgccaggctccagggaagggactggagtgggtttcatacagtagtggtaat......agtggttacacaaactacgcagactctgtgaaa...ggccgattcaccatctccagggacaacgccaagaactcactgtatctgcaaatgaacagcctgagagccgaggacacggctgtgtattactgtgtgaaa ->IGHV4-28*01 -caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggacaccctgtccctcacctgcgctgtctctggttactccatcagc.........agtagtaactggtggggctggatccggcagcccccagggaagggactggagtggattgggtacatctattatagt.........gggagcacctactacaacccgtccctcaag...agtcgagtcaccatgtcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgtggacacggccgtgtattactgtgcgagaaa ->IGHV4-28*02 -caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcacagaccctgtccctcacctgcgctgtctctggttactccatcagc.........agtagtaactggtggggctggatccggcagcccccagggaagggactggagtggattgggtacatctattatagt.........gggagcatctactacaacccgtccctcaag...agtcgagtcaccatgtcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgtggacacggccgtgtattactgtgcgagaaa ->IGHV4-28*03 -caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggacaccctgtccctcacctgcgctgtctctggttactccatcagc.........agtagtaactggtggggctggatccggcagcccccagggaagggactggagtggattgggtacatctattatagt.........gggagcacctactacaacccgtccctcaag...agtcgagtcaccatgtcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgtggacacggccgtgtattactgtgcgagaga ->IGHV4-28*04 -caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggacaccctgtccctcacctgcgctgtctctggttactccatcagc.........agtagtaactggtggggctggatccggcagcccccagggaagggactggagtggattgggtacatctattatagt.........gggagcacctactacaacccgtccctcaag...agtcgagtcaccatgtcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgtggacaccggcgtgtattactgtgcgaga ->IGHV4-28*05 -caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggacaccctgtccctcacctgcgctgtctctggttactccatcagc.........agtagtaactggtggggctggatccggcagcccccagggaagggactggagtggattgggtacatctattatagt.........gggagcatctactacaacccgtccctcaag...agtcgagtcaccatgtcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgtggacacggccgtgtattactgtgcgagaaa ->IGHV4-28*06 -caggtgcagctacaggagtcgggccca...ggactggtgaagccttcggacaccctgtccctcacctgcgctgtctctggttactccatcagc.........agtagtaactggtggggctggatccggcagcccccagggaagggactggagtggattgggtacatctattatagt.........gggagcaccaactacaacccgtccctcaag...agtcgagtcaccatgtcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccttggacacggccgtgtattactgtgcgagaaa ->IGHV4-28*07 -caggtacagctgcaggagtcgggccca...ggactggtgaagccttcggacaccctgtccctcacctgcgctgtctctggttactccatcagc.........agtagtaactggtggggctggatccggcagcccccagggaagggactggagtggattgggtacatctattatagt.........gggagcacctactacaacccgtccctcaag...agtcgagtcaccatgtcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgtggacacggccgtgtattactgtgcgagaaa ->IGHV4-30-2*01 -cagctgcagctgcaggagtccggctca...ggactggtgaagccttcacagaccctgtccctcacctgcgctgtctctggtggctccatcagc......agtggtggttactcctggagctggatccggcagccaccagggaagggcctggagtggattgggtacatctatcatagt.........gggagcacctactacaacccgtccctcaag...agtcgagtcaccatatcagtagacaggtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggccgtgtattactgtgccagaga ->IGHV4-30-2*02 -cagctgcagctgcaggagtccggctca...ggactggtgaagccttcacagaccctgtccctcacctgcgctgtctctggtggctccatcagc......agtggtggttactcctggagctggatccggcagccaccagggaagggcctggagtggattgggtacatctatcatagt.........gggagcacctactacaacccgtccctcaag...agtcgagtcaccatatcagtagacaggtccaagaaccagttctccctgaagctgagctctgtgaccgctgcggacacggccgtgtattactgtgcg ->IGHV4-30-2*03 -cagctgcagctgcaggagtccggctca...ggactggtgaagccttcacagaccctgtccctcacctgcgctgtctctggtggctccatcagc......agtggtggttactcctggagctggatccggcagccaccagggaagggcctggagtggattgggagtatctattatagt.........gggagcacctactacaacccgtccctcaag...agtcgagtcaccatatccgtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgctgcagacacggctgtgtattactgtgcgagaca ->IGHV4-30-2*04 -...........................................................................tctggtggctccatcagc......agtggtggttactcctggagctggatccggcagccaccagggaagggcctggagtggattgggtacatctatcatagt.........gggagcacctactacaacccgtccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcagacacggccgtgtattactgtgcgagaga ->IGHV4-30-2*05 -cagctgcagctgcaggagtccggctca...ggactggtgaagccttcacagaccctgtccctcacctgcgctgtctctggtggctccatcagc......agtggtggttactcctggagctggatccggcagccaccagggaagggcctggagtggattgggtacatctatcatagt.........gggagcacctactacaacccgtccctcaag...agtcgagttaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgactgccgcagacacggccgtgtattactgtgccagaga ->IGHV4-30-2*06 -cagctgcagctgcaggagtccggctca...ggactggtgaagccttcacagaccctgtccctcacctgcgctgtctctggtggctccatcagc......agtggtggttactcctggagctggatccggcagtcaccagggaagggcctggagtggattgggtacatctatcatagt.........gggagcacctactacaacccgtccctcaag...agtcgagtcaccatatcagtagacaggtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggccgtgtattactgtgccagaga ->IGHV4-30-4*01 -caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcacagaccctgtccctcacctgcactgtctctggtggctccatcagc......agtggtgattactactggagttggatccgccagcccccagggaagggcctggagtggattgggtacatctattacagt.........gggagcacctactacaacccgtccctcaag...agtcgagttaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgactgccgcagacacggccgtgtattactgtgccagaga ->IGHV4-30-4*02 -caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggacaccctgtccctcacctgcactgtctctggtggctccatcagc......agtggtgattactactggagttggatccgccagcccccagggaagggcctggagtggattgggtacatctattacagt.........gggagcacctactacaacccgtccctcaag...agtcgagttaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgactgcagcagacacggccgtgtattactgtgccagaga ->IGHV4-30-4*03 -caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcacagaccctgtccctcacctgcactgtctctggtggctccatcagc......agtggtgattactactggagttggatccgccagcccccagggaagggcctggagtggattgggtacatctattacagt.........gggagcacctactacaacccgtccctcaag...agtcgagttaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgactgccgcggacacggccgtgtattactg ->XIGHV4-30-4*04 -caggtgcagctgcaggactcgggccca...ggactggtgaagccttcacagaccctgtccctcacctgcactgtctctggtggctccatcagc......agtggtgattactactggagttggatccgccagcccccagggaagggcctggagtggattgggtacttctattacagt.........gggagcacctactacaacccgtccctcaag...agtcgagttaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgactgccgcagacacggccgtgtattactg ->IGHV4-30-4*05 -..........................................................................ctctggtggctccatcagc......agtggtgattactactggagttggatccgccagcncccagggaagggcctggagtggattgggtacatctattacagt.........gggagcacctactacaacccgtccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgactgccgcagacacggccgtgtattactgtgccagaga ->IGHV4-30-4*06 -...........................................................................tctggtggctccatcagc......agtggtgattactactggagttggatccgccagcacccagggaagggcctggagtggattgggtacatctattacagt.........gggagcacctactacaacccgtccctcaag...agtcgagttaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgactgccgcagacacggccgtgtattactgtgccagaga ->IGHV4-30-4*07 -caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcacagaccctgtccctcacctgcgctgtctctggtggctccatcagc......agtggtggttactcctggagctggatccggcagccaccagggaagggactggagtggattgggtatatctattacagt.........gggagcacctactacaacccgtccctcaag...agtcgagttaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggccgtgtattactgtgccagaga ->IGHV4-31*01 -caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcacagaccctgtccctcacctgcactgtctctggtggctccatcagc......agtggtggttactactggagctggatccgccagcacccagggaagggcctggagtggattgggtacatctattacagt.........gggagcacctactacaacccgtccctcaag...agtctagttaccatatcagtagacacgtctaagaaccagttctccctgaagctgagctctgtgactgccgcggacacggccgtgtattactgtgcgagaga ->IGHV4-31*02 -caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcacagaccctgtccctcacctgtactgtctctggtggctccatcagc......agtggtggttactactggagctggatccgccagcacccagggaagggcctggagtggattgggtacatctattacagt.........gggagcacctactacaacccgtccctcaag...agtcgagttaccatatcagtagacacgtctaagaaccagttctccctgaagctgagctctgtgactgccgcggacacggccgtgtattactgtgcgagaga ->IGHV4-31*03 -caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcacagaccctgtccctcacctgcactgtctctggtggctccatcagc......agtggtggttactactggagctggatccgccagcacccagggaagggcctggagtggattgggtacatctattacagt.........gggagcacctactacaacccgtccctcaag...agtcgagttaccatatcagtagacacgtctaagaaccagttctccctgaagctgagctctgtgactgccgcggacacggccgtgtattactgtgcgagaga ->IGHV4-31*04 -caggtgcggctgcaggagtcgggccca...ggactggtgaagccttcacagaccctgtccctcacctgcactgtctctggtggctccatcagc......agtggtggttactactggagctggatccgccagcacccagggaagggcctggagtggattgggtacatctattacagt.........gggagcacctactacaacccgtccctcaag...agtcgagttaccatatcagtagacacgtctaagaaccagttctccctgaagctgagctctgtgactgccgcggacacggccgtgtattactgtgcg ->IGHV4-31*05 -caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcacagaccctgtccctcacctgcactgtctctggtggctccatcagc......agtggtggttactactggagctggatccgccagcacccagggaagggcctggagtggattgggtacatctattacagt.........gggagcacctactacaacccgtccctcaag...agtcgagttaccatatcagtagacacgtctaagaaccagttctccctgaagctgagctctgtgacc...gcggacgcggccgtgtattactgtgcg ->IGHV4-31*06 -caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcacagaccctgtccctcacctgcactgtctctggtggctccatcagc......agtggtagttactactggagctggatccgccagcacccagggaagggcctggagtggattgggtacatctattacagt.........gggagcacctactacaacccgtccctcaag...agtcgagttaccatatcagtagacacgtctaagaaccagttctccctgaagctgagctctgtgactgccgcggacacggccgtgtattactg ->IGHV4-31*07 -caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcacagaccctgtccctcacctgcactgtctctggtggatccatcagc......agtggtggttactactggagctggatccgccagcacccagggaagggcctggagtggattgggtacatctattacagt.........gggagcacctactacaacccgtccctcaag...agtcgagttaccatatcagtagacacgtctaagaaccagttctccctgaagctgagctctgtgactgccgcggacacggccgtgtattactg ->IGHV4-31*08 -caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcacagaccctgtccctcacctgcactgtctctggtggctccatcagc......agtggtggttactactggagctggatccgccagcacccagggaagggcctggagtggattgggtacatctattacagt.........gggagcacctactacaacccgtccctcaag...agtcgagttaccatatccgtagacacgtccaagaaccagttctccctgaagctgagctctgtgactgccgcggacacggccgtgtattactg ->IGHV4-31*09 -caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcacagaccctgtccctcacctgcactgtctctggtggctccatcagc......agtggtggttactactggagctggatccgccagcacccagggaagggcctggagtggattgggtacatctattacagt.........gggagcacctactacaacccgtccctcaag...agtcgagttaccatatcagtagacaagtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggccgtgtattactg ->IGHV4-31*10 -caggtgcagctgcaggagtcgggccca...ggactgttgaagccttcacagaccctgtccctcacctgcactgtctctggtggctccatcagc......agtggtggttactactggagctggatccgccagcacccagggaagggcctggagtggattgggtgcatctattacagt.........gggagcacctactacaacccgtccctcaag...agtcgagttaccatatcagtagacccgtccaagaaccagttctccctgaagccgagctctgtgactgccgcggacacggccgtggattactgtgcgagaga ->IGHV4-34*01 -caggtgcagctacagcagtggggcgca...ggactgttgaagccttcggagaccctgtccctcacctgcgctgtctatggtgggtccttc............agtggttactactggagctggatccgccagcccccagggaaggggctggagtggattggggaaatcaatcatagt.........ggaagcaccaactacaacccgtccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggctgtgtattactgtgcgagagg ->IGHV4-34*02 -caggtgcagctacaacagtggggcgca...ggactgttgaagccttcggagaccctgtccctcacctgcgctgtctatggtgggtccttc............agtggttactactggagctggatccgccagcccccagggaaggggctggagtggattggggaaatcaatcatagt.........ggaagcaccaactacaacccgtccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggctgtgtattactgtgcgagagg ->IGHV4-34*03 -caggtgcagctacagcagtggggcgca...ggactgttgaagccttcggagaccctgtccctcacctgcgctgtctatggtgggtccttc............agtggttactactggagctggatccgccagcccccagggaaggggctggagtggattggggaaatcaatcatagt.........ggaagcaccaactacaacccgtccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggccgtgtattactg ->IGHV4-34*04 -caggtgcagctacagcagtggggcgca...ggactgttgaagccttcggagaccctgtccctcacctgcgctgtctatggtgggtccttc............agtggttactactggagctggatccgccagcccccagggaaggggctggagtggattggggaaatcaatcatagt.........ggaagcaccaacaacaacccgtccctcaag...agtcgagccaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggctgtgtattactgtgcgagagg ->IGHV4-34*05 -caggtgcagctacagcagtggggcgca...ggactgttgaagccttcggagaccctgtccctcacctgcgctgtctatggtgggtccttc............agtggttactactggtgctggatccgccagcccctagggaaggggctggagtggattggggaaatcaatcatagt.........ggaagcaccaacaacaacccgtccctcaag...agtcgagccaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggctgtgtattactgtgcgagagg ->IGHV4-34*06 -caggtgcagctacagcagtggggcgca...ggactgttgaagccttcggagaccctgtccctcacctgcgctgtctatggtgggtccttc............agtggttactactggagctggatccgccagcccccagggaaggggctggagtggattggggaaatcaatcatagt.........ggaagcaccaactacaacccgtccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttctccctgaagctgggctctgtgaccgccgcggacacggccgtgtattactg ->IGHV4-34*07 -caggtgcagctacagcagtggggcgca...ggactgttgaagccttcggagaccctgtccctcacctgcgctgtctatggtgggtccttc............agtggttactactggagctggatccgccagcccccagggaaggggctggagtggattggggaaatcaaccatagt.........ggaagcaccaactacaacccgtccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggccgtgtattactg ->IGHV4-34*08 -caggtgcagctacagcagtggggcgca...ggactgttgaagccttcggagaccctgtccctcacctgcgctgtctatggtgggaccttc............agtggttactactggagctggatccgccagcccccagggaaggggctggagtggattggggaaatcaatcatagt.........ggaagcaccaactacaacccgtccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggctgtgtattactgtgcg ->IGHV4-34*09 -caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcacagaccctgtccctcacctgcgctgtctatggtgggtccttc............agtggttactactggagctggatccgccagcccccagggaagggactggagtggattggggaaatcaatcatagt.........ggaagcaccaactacaacccgtccctcaag...agtcgagttaccatatcagtagacacgtctaagaaccagttctccctgaagctgagctctgtgactgccgcggacacggccgtgtattactgtgcgagaga ->IGHV4-34*10 -caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcgctgtctatggtgggtccttc............agtggttactactggagctggatccgccagcccccagggaagggactggagtggattggggaaatcaatcatagt.........ggaagcaccaactacaacccgtccctcaag...agtcgaatcaccatgtcagtagacacgtccaagaaccagttctacctgaagctgagctctgtgaccgccgcggacacggccgtgtattactgtgcgagata ->IGHV4-34*11 -caggtgcagctacagcagtggggcgca...ggactgttgaagccttcggagaccctgtccctcacctgcgctgtctatggtgggtccgtc............agtggttactactggagctggatccggcagcccccagggaaggggctggagtggattgggtatatctattatagt.........gggagcaccaacaacaacccctccctcaag...agtcgagccaccatatcagtagacacgtccaagaaccagttctccctgaacctgagctctgtgaccgccgcggacacggccgtgtattgctgtgcgagaga ->IGHV4-34*12 -caggtgcagctacagcagtggggcgca...ggactgttgaagccttcggagaccctgtccctcacctgcgctgtctatggtgggtccttc............agtggttactactggagctggatccgccagcccccagggaaggggctggagtggattggggaaatcattcatagt.........ggaagcaccaactacaacccgtccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggctgtgtattactgtgcgaga ->IGHV4-34*13 -...........................................................................tatggtgggtccttc............agtggttactactggagctggatccgccagcccccagggaaggggctggagtggattggggaaatcaatcatagt.........ggaagcaccaactacaacccctccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggctgtgtattactgtgcgagagg ->IGHV4-38-2*01 -caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcgctgtctctggttactccatcagc.........agtggttactactggggctggatccggcagcccccagggaaggggctggagtggattgggagtatctatcatagt.........gggagcacctactacaacccgtccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcagacacggccgtgtattactgtgcgaga ->IGHV4-38-2*02 -caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcactgtctctggttactccatcagc.........agtggttactactggggctggatccggcagcccccagggaaggggctggagtggattgggagtatctatcatagt.........gggagcacctactacaacccgtccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcagacacggccgtgtattactgtgcgagaga ->IGHV4-39*01 -cagctgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcactgtctctggtggctccatcagc......agtagtagttactactggggctggatccgccagcccccagggaaggggctggagtggattgggagtatctattatagt.........gggagcacctactacaacccgtccctcaag...agtcgagtcaccatatccgtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcagacacggctgtgtattactgtgcgagaca ->IGHV4-39*02 -cagctgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcactgtctctggtggctccatcagc......agtagtagttactactggggctggatccgccagcccccagggaaggggctggagtggattgggagtatctattatagt.........gggagcacctactacaacccgtccctcaag...agtcgagtcaccatatccgtagacacgtccaagaaccacttctccctgaagctgagctctgtgaccgccgcagacacggctgtgtattactgtgcgagaga ->IGHV4-39*03 -cagctgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcactgtctctggtggctccatcagc......agtagtagttactactggggctggatccgccagcccccagggaaggggctggagtggattgggagtatctattatagt.........gggagcacctactacaacccgtccctcaag...agtcgagtcaccatatccgtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcagacacggccgtgtattactg ->IGHV4-39*04 -..................................................................................gctccatcagc......agtagtagttactactggggctggatccgccagcccccagggaaggggctggagtggattgggagtatctattatagt.........gggagcacctactacaacccgtccctcaag...agtcgagtcaccatatccgtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacac ->IGHV4-39*05 -cagctgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccccgtccctcacctgcactgtctctggtggctccatcagc......agtagtagttactactggggctggatccgccagcccccagggaaggggctggagtggattgggagtatctattatagt.........gggagcacctactacaacccgtccctcaag...agtcgagtcaccatatccgtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcagacacggctgtgtattactgtgcg ->IGHV4-39*06 -cggctgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcactgtctctggtggctccatcagc......agtagtagttactactggggctggatccgccagcccccagggaaggggctggagtggattgggagtatctattatagt.........gggagcacctactacaacccgtccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttccccctgaagctgagctctgtgaccgccgcggacacggccgtgtattactgtgcgagaga ->IGHV4-39*07 -cagctgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcactgtctctggtggctccatcagc......agtagtagttactactggggctggatccgccagcccccagggaaggggctggagtggattgggagtatctattatagt.........gggagcacctactacaacccgtccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggccgtgtattactgtgcgagaga ->IGHV4-4*01 -caggtgcagctgcaggagtcgggccca...ggactggtgaagcctccggggaccctgtccctcacctgcgctgtctctggtggctccatcagc.........agtagtaactggtggagttgggtccgccagcccccagggaaggggctggagtggattggggaaatctatcatagt.........gggagcaccaactacaacccgtccctcaag...agtcgagtcaccatatcagtagacaagtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggccgtgtattgctgtgcgagaga ->IGHV4-4*02 -caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggggaccctgtccctcacctgcgctgtctctggtggctccatcagc.........agtagtaactggtggagttgggtccgccagcccccagggaaggggctggagtggattggggaaatctatcatagt.........gggagcaccaactacaacccgtccctcaag...agtcgagtcaccatatcagtagacaagtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggccgtgtattactgtgcgagaga ->IGHV4-4*03 -caggtgcagctgcaggagtcgggccca...ggactggtgaagcctccggggaccctgtccctcacctgcgctgtctctggtggctccatcagc.........agtagtaactggtggagttgggtccgccagcccccagggaaggggctggagtggattggggaaatctatcatagt.........gggagcaccaactacaacccgtccctcaag...agtcgagtcaccatatcagtagacaagtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggccgtgtattactg ->IGHV4-4*04 -caggtgcagctgcaggagtcgggccca...ggactggtgaagcctccggggaccctgtccctcacctgcgctatctctggtggctccatcagc.........agtagtaactggtggagttgggtccgccagcccccagggaaggggctggagtggattggggaaatctatcatagt.........gggagcaccaactacaacccgtccctcaag...agtcgagtcaccatatcagtagacaagtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggccgtgtattactg ->IGHV4-4*05 -caggtgcagctgcaggagttgggccca...ggactggtgaagcctccggggaccctgtccctcacctgcgctgtctctggtggctccatcagc.........agtagtaactggtggagttgggtccgccagcccccagggaaggggctggagtggattggggaaatctatcatagt.........gggagcaccaactacaacccgtccctcaag...agtcgagtcaccatatcagtagacaagtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggccgtgtattactg ->IGHV4-4*06 -............................................................ -...............tctggtggctccatcagc.........agtagtaactggtggagttgggtccgccagcccccagggannnggctggagtggattggggaaatctatcatagt.........gggagcaccaactacaacccgtccctcaag...agtcgagtcaccatgtcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggccgtgtattactgtgcgagaga ->IGHV4-4*07 -caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcactgtctctggtggctccatc............agtagttactactggagctggatccggcagcccgccgggaagggactggagtggattgggcgtatctataccagt.........gggagcaccaactacaacccctccctcaag...agtcgagtcaccatgtcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggccgtgtattactgtgcgagaga ->IGHV4-4*08 -caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcactgtctctggtggctccatc............agtagttactactggagctggatccggcagcccccagggaagggactggagtggattgggtatatctataccagt.........gggagcaccaactacaacccctccctcaag...agtcgagtcaccatatccgtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcagacacggccgtgtattactgtgcgagaga ->IGHV4-55*01 -caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcatctgcgctgtctctggtgactccatcagc.........agtggtaactggtgaatctgggtccgccagcccccagggaaggggctggagtggattggggaaatccatcatagt.........gggagcacctactacaacccgtccctcaag...agtcgaatcaccatgtccgtagacacgtccaagaaccagttctacctgaagctgagctctgtgaccgccgcggacacggccgtgtattactgtgcgagata ->IGHV4-55*02 -caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcatctgcgctgtctctggtgactccatcagc.........agtggtaactggtgaatctgggtccgccagcccccagggaaggggctggagtggattggggaaatccatcatagt.........gggagcacctactacaacccgtccctcaag...agtcgaatcaccatgtcagtagacacgtccaagaaccagttctacctgaagctgagctctgtgaccgccgcggacacggccgtgtattactgtgcgagata ->IGHV4-55*03 -caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcatctgcgctgtctctggtgactccatcagc.........agtggtaactggtgaatctgggtccgccagcccccagggaaggggctggagtggattggggaaatccatcatagt.........gggagcacctactacaacccgtccctcaag...agtcgaatcaccatgtcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggccgtgtattactg ->IGHV4-55*04 -caggtgcagctgcaggagtcgggccca...ggactggtgaagctttcggagaccctgtccctcatctgcgctgtctctggtgactccatcagc.........agtggtaactggtgaatctgggtccgccagcccccagggaaggggctggagtggattggggaaatccatcatagt.........gggagcacctactacaacccgtccctcaag...agtcgaatcaccatgtcagtagacacgtccaagaaccagttctacctgaagctgagctctgtgaccgccgcggacacggccgtgtattactg ->IGHV4-55*05 -caggtgcagctgcaggagtcgggccca...ggactggtgaagctttcggagaccctgtccctcatctgcgctgtctctggtgactccatcagc.........agtggtaactggtgaatctgggtccgccagcccccagggaaggggctggagtggattggggaaatccatcatagt.........gggagcacctactacaacccgtccctcaag...agtcgaatcaccatgtccgtagacacgtccaagaaccagttctacctgaagctgagctctgtgaccgccgcggacacggccgtgtattactg ->IGHV4-55*06 -caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcatctgcgctgtctctggtgactccatcagc.........agtggtaactggtgaatctgggtccgccagcccccagggaaggggctggagtggattggggaaatccatcatagt.........gggagcacctactacaacccgtccctcaag...agtcgaatcaccatgtccgtagacacgtccaagaagcagttctacctgaagctgagctctgtgaccgctgcggacacggccgtgtattactg ->IGHV4-55*07 -caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcatctgcgctgtctctggtgactccatcagc.........agtggtaactggtgaatctgggtccgccagcccccagggaaggggctggagtggattggggaaatccatcatagt.........gggagcacctactacaacccgtccctcaag...agtcgaatcaccatgtccgtagacacgtccaggaaccagttctccctgaagctgagctctgtgaccgccgcagacacggccgtgtattactg ->IGHV4-55*08 -caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcatctgcgctgtctctggtgactccatcagc.........agtggtaactggtgaatctgggtccgccagcccccagggaaggggctggagtggattggggaaatccatcatagt.........gggagcacctactacaacccgtccctcaag...agtcgaatcaccatgtcagtagacacgtccaagaaccagttctacctgaagctgagctctgtgaccgccgcggacacggccgtgtattactgtgcgagaga ->IGHV4-55*09 -caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcatctgcgctgtctctggtgactccatcagc.........agtggtaactggtgaatctgggtccgccagcccccagggaaggggctggagtggattggggaaatccatcatagt.........gggagcacctactacaacccgtccctcaag...agtcgaatcaccatgtccgtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgtggacacggccgtgtattactgtgcgagaaa ->IGHV4-59*01 -caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcactgtctctggtggctccatc............agtagttactactggagctggatccggcagcccccagggaagggactggagtggattgggtatatctattacagt.........gggagcaccaactacaacccctccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgctgcggacacggccgtgtattactgtgcgagaga ->IGHV4-59*02 -caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcactgtctctggtggctccgtc............agtagttactactggagctggatccggcagcccccagggaagggactggagtggattgggtatatctattacagt.........gggagcaccaactacaacccctccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgctgcggacacggccgtgtattactgtgcgagaga ->IGHV4-59*03 -caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcactgtctctggtggctccatc............agtagttactactggagctggatccggcagcccccagggaagggactggagtggattgggtatatctattacagt.........gggagcaccaactacaacccctccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccaattctccctgaagctgagctctgtgaccgctgcggacacggccgtgtattactgtgcg ->IGHV4-59*04 -caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcactgtctctggtggctccatc............agtagttactactggagctggatccggcagcccccagggaagggactggagtggattgggtatatctattatagt.........gggagcacctactacaacccgtccctcaag...agtcgagtcaccatgtcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcagacacggctgtgtattactgtgcg ->IGHV4-59*05 -caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcactgtctctggtggctccatc............agtagttactactggagctggatccggcagccgccggggaagggactggagtggattgggcgtatctattatagt.........gggagcacctactacaacccgtccctcaag...agtcgagtcaccatatccgtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcagacacggctgtgtattactgtgcg ->IGHV4-59*06 -caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcactgtcactggtggctccatc............agtagttactactggagctggatccggcagcccgctgggaagggcctggagtggattgggtacatctattacagt.........gggagcacctactacaacccgtccctcaag...agtcgagttaccatatcagtagacacgtctaagaaccagttctccctgaagctgagctctgtgactgccgcggacacggccgtgtattactgtgcg ->IGHV4-59*07 -caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggacaccctgtccctcacctgcactgtctctggtggctccatc............agtagttactactggagctggatccggcagcccccagggaagggactggagtggattgggtatatctattacagt.........gggagcaccaactacaacccctccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgctgcggacacggccgtgtattactgtgcgaga ->IGHV4-59*08 -caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcactgtctctggtggctccatc............agtagttactactggagctggatccggcagcccccagggaagggactggagtggattgggtatatctattacagt.........gggagcaccaactacaacccctccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcagacacggccgtgtattactgtgcgagaca ->IGHV4-59*09 -...........................................................................tctggtggctccatc............agtagttactactggagctggatccggcagcccccaggnannngactggagtggattgggtatatctattacagt.........gggagcaccaactacaacccctccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgctgcggacacggccgtgtattactgtgcgagagg ->IGHV4-59*10 -caggtgcagctacagcagtggggcgca...ggactgttgaagccttcggagaccctgtccctcacctgcgctgtctatggtggctccatc............agtagttactactggagctggatccggcagcccgccgggaaggggctggagtggattgggcgtatctataccagt.........gggagcaccaactacaacccctccctcaag...agtcgagtcaccatgtcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggccgtgtattactgtgcgagata ->IGHV4-61*01 -caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcactgtctctggtggctccgtcagc......agtggtagttactactggagctggatccggcagcccccagggaagggactggagtggattgggtatatctattacagt.........gggagcaccaactacaacccctccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgctgcggacacggccgtgtattactgtgcgagaga ->IGHV4-61*02 -caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcacagaccctgtccctcacctgcactgtctctggtggctccatcagc......agtggtagttactactggagctggatccggcagcccgccgggaagggactggagtggattgggcgtatctataccagt.........gggagcaccaactacaacccctccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcagacacggccgtgtattactgtgcgagaga ->IGHV4-61*03 -caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcactgtctctggtggctccgtcagc......agtggtagttactactggagctggatccggcagcccccagggaagggactggagtggattgggtatatctattacagt.........gggagcaccaactacaacccctccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccacttctccctgaagctgagctctgtgaccgctgcggacacggccgtgtattactgtgcgagaga ->IGHV4-61*04 -caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcactgtctctggtggctccgtcagc......agtggtagttactactggagctggatccggcagcccccagggaagggactggagtggattggatatatctattacagt.........gggagcaccaactacaacccctccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgct...gacacggccgtgtattactg ->IGHV4-61*05 -cagctgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcactgtctctggtggctccatcagc......agtagtagttactactggggctggatccggcagcccccagggaagggactggagtggattgggtatatctattacagt.........gggagcaccaactacaacccctccctcaag...agtcgagtcaccatatcagtagacaagtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggccgtgtattactgtgcgaga ->IGHV4-61*06 -...........................................................................tctggtggctccgtcagc......agtggtagttactactggagctggatccggcagcccccagggaagggactggagtggattgggtatatctattacagt.........gggagcaccaactacaacccctccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggccgtgtattactgtgccagaga ->IGHV4-61*07 -...........................................................................tctggtggctccgtcagc......agtggtagttactactggagctggatccggcagcccccagggaagggactggagtggattgggtatatctattacagt.........gggagcaccaactacaacccctccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgctgcggacacggccgtgtattactgtgcgagaca ->IGHV4-61*08 -caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcactgtctctggtggctccgtcagc......agtggtggttactactggagctggatccggcagcccccagggaagggactggagtggattgggtatatctattacagt.........gggagcaccaactacaacccctccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgctgcggacacggccgtgtattactgtgcgagaga ->IGHV4/OR15-8*01 -caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcgttgtctctggtggctccatcagc.........agtagtaactggtggagctgggtccgccagcccccagggaaggggctggagtggattggggaaatctatcatagt.........gggagccccaactacaacccgtccctcaag...agtcgagtcaccatatcagtagacaagtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggccgtgtattactgtgcgagaga ->IGHV4/OR15-8*02 -caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcgttgtctctggtggctccatcagc.........agtagtaactggtggagctgggtccgccagcccccagggaaggggctggagtggattggggaaatctatcatagt.........gggaaccccaactacaacccgtccctcaag...agtcgagtcaccatatcaatagacaagtccaagaaccaattctccctgaagctgagctctgtgaccgccgcggacacggccgtgtattactgtgcgagaga ->IGHV4/OR15-8*03 -caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcgttgtctctggtggctccatcagc.........agtagtaactggtggagctgggtccgccagcccccagggaaggggctggagtggattggggaaatctatcatagt.........gggagccccaactacaacccatccctcaag...agtcgagtcaccatatcagtagacaagtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggccgtgtattactgtgcgagaga ->IGHV5-10-1*01 -gaagtgcagctggtgcagtctggagca...gaggtgaaaaagcccggggagtctctgaggatctcctgtaagggttctggatacagcttt............accagctactggatcagctgggtgcgccagatgcccgggaaaggcctggagtggatggggaggattgatcctagt......gactcttataccaactacagcccgtccttccaa...ggccacgtcaccatctcagctgacaagtccatcagcactgcctacctgcagtggagcagcctgaaggcctcggacaccgccatgtattactgtgcgaga ->IGHV5-10-1*02 -gaagtgcagctggtgcagtctggagca...gaggtgaaaaagcccggggagtctctgaggatctcctgtaagggttctggatacagcttt............accagctactggatcagctgggtgcgccagatgcccgggaaaggcttggagtggatggggaggattgatcctagt......gactcttataccaactacagcccgtccttccaa...ggccacgtcaccatctcagctgacaagtccatcagcactgcctacctgcagtggagcagcctgaaggc.tcggacaccgccatgtattactgtgcgagaca ->IGHV5-10-1*03 -gaagtgcagctggtgcagtccggagca...gaggtgaaaaagcccggggagtctctgaggatctcctgtaagggttctggatacagcttt............accagctactggatcagctgggtgcgccagatgcccgggaaaggcctggagtggatggggaggattgatcctagt......gactcttataccaactacagcccgtccttccaa...ggccacgtcaccatctcagctgacaagtccatcagcactgcctacctgcagtggagcagcctgaaggcctcggacaccgccatgtattactgtgcgaga ->IGHV5-10-1*04 -gaagtgcagctggtgcagtctggagca...gaggtgaaaaagcccggggagtctctgaggatctcctgtaagggttctggatacagcttt............accagctactggatcagctgggtgcgccagatgcccgggaaaggcctggagtggatggggaggattgatcctagt......gactcttataccaactacagcccgtccttccaa...ggccaggtcaccatctcagctgacaagtccatcagcactgcctacctgcagtggagcagcctgaaggcctcggacaccgccatgtattactgtgcgaga ->IGHV5-51*01 -gaggtgcagctggtgcagtctggagca...gaggtgaaaaagcccggggagtctctgaagatctcctgtaagggttctggatacagcttt............accagctactggatcggctgggtgcgccagatgcccgggaaaggcctggagtggatggggatcatctatcctggt......gactctgataccagatacagcccgtccttccaa...ggccaggtcaccatctcagccgacaagtccatcagcaccgcctacctgcagtggagcagcctgaaggcctcggacaccgccatgtattactgtgcgagaca ->IGHV5-51*02 -gaggtgcagctggtgcagtctggagca...gaggtgaaaaagcccggggagtctctgaagatctcctgtaagggttctggatacagcttt............accagctactggaccggctgggtgcgccagatgcccgggaaaggcttggagtggatggggatcatctatcctggt......gactctgataccagatacagcccgtccttccaa...ggccaggtcaccatctcagccgacaagtccatcagcaccgcctacctgcagtggagcagcctgaaggcctcggacaccgccatgtattactgtgcgagaca ->IGHV5-51*03 -gaggtgcagctggtgcagtctggagca...gaggtgaaaaagccgggggagtctctgaagatctcctgtaagggttctggatacagcttt............accagctactggatcggctgggtgcgccagatgcccgggaaaggcctggagtggatggggatcatctatcctggt......gactctgataccagatacagcccgtccttccaa...ggccaggtcaccatctcagccgacaagtccatcagcaccgcctacctgcagtggagcagcctgaaggcctcggacaccgccatgtattactgtgcgaga ->IGHV5-51*04 -gaggtgcagctggtgcagtctggagca...gaggtgaaaaagccgggggagtctctgaagatctcctgtaagggttctggatacagcttt............accagctactggatcggctgggtgcgccagatgcccgggaaaggcctggagtggatggggatcatctatcctggt......gactctgataccagatacagcccgtccttccaa...ggccaggtcaccatctcagccgacaagcccatcagcaccgcctacctgcagtggagcagcctgaaggcctcggacaccgccatgtattactgtgcgaga ->IGHV5-51*05 -.....................................aaaagcccggggagtctctgaagatctcctgtaagggttctggatacagcttt............accagctactggatcggctgggtgcgccagatgcccaggaaaggcctggagtggatggggatcatctatcctggt......gactctgataccagatacagcccgtccttccaa...ggccaggtcaccatctcagccgacaagtccatcagcaccgcctacctgcagtggagcagcctgaaggcctcggacaccgccatg ->IGHV5-78*01 -gaggtgcagctgttgcagtctgcagca...gaggtgaaaagacccggggagtctctgaggatctcctgtaagacttctggatacagcttt............accagctactggatccactgggtgcgccagatgcccgggaaagaactggagtggatggggagcatctatcctggg......aactctgataccagatacagcccatccttccaa...ggccacgtcaccatctcagccgacagctccagcagcaccgcctacctgcagtggagcagcctgaaggcctcggacgccgccatgtattattgtgtgaga ->IGHV6-1*01 -caggtacagctgcagcagtcaggtcca...ggactggtgaagccctcgcagaccctctcactcacctgtgccatctccggggacagtgtctct......agcaacagtgctgcttggaactggatcaggcagtccccatcgagaggccttgagtggctgggaaggacatactacaggtcc...aagtggtataatgattatgcagtatctgtgaaa...agtcgaataaccatcaacccagacacatccaagaaccagttctccctgcagctgaactctgtgactcccgaggacacggctgtgtattactgtgcaagaga ->IGHV6-1*02 -caggtacagctgcagcagtcaggtccg...ggactggtgaagccctcgcagaccctctcactcacctgtgccatctccggggacagtgtctct......agcaacagtgctgcttggaactggatcaggcagtccccatcgagaggccttgagtggctgggaaggacatactacaggtcc...aagtggtataatgattatgcagtatctgtgaaa...agtcgaataaccatcaacccagacacatccaagaaccagttctccctgcagctgaactctgtgactcccgaggacacggctgtgtattactgtgcaagaga ->IGHV7-34-1*01 -...ctgcagctggtgcagtctgggcct...gaggtgaagaagcctggggcctcagtgaaggtctcctataagtcttctggttacaccttc............accatctatggtatgaattgggtatgatagacccctggacagggctttgagtggatgtgatggatcatcacctac......actgggaacccaacgtatacccacggcttcaca...ggatggtttgtcttctccatggacacgtctgtcagcacggcgtgtcttcagatcagcagcctaaaggctgaggacacggccgagtattactgtgcgaagta ->IGHV7-34-1*02 -...ctgcagctggtgcagtctgggcct...gaggtgaagaagcctggggcctcagtgaaggtctcctataagtcttctggttacaccttc............accatctatggtatgaattgggtatgatagacccctggacagggctttgagtggatgtgatggatcatcacctac......aatgggaacccaacgtatacccacggcttcaca...ggatggtttgtcttctccatggacacgtctgtcagcacggcgtgtcttcagatcagcagcctaaaggctgaggacacggccgagtattactgtgcgaagta ->IGHV7-4-1*01 -caggtgcagctggtgcaatctgggtct...gagttgaagaagcctggggcctcagtgaaggtttcctgcaaggcttctggatacaccttc............actagctatgctatgaattgggtgcgacaggcccctggacaagggcttgagtggatgggatggatcaacaccaac......actgggaacccaacgtatgcccagggcttcaca...ggacggtttgtcttctccttggacacctctgtcagcacggcatatctgcagatctgcagcctaaaggctgaggacactgccgtgtattactgtgcgaga ->IGHV7-4-1*02 -caggtgcagctggtgcaatctgggtct...gagttgaagaagcctggggcctcagtgaaggtttcctgcaaggcttctggatacaccttc............actagctatgctatgaattgggtgcgacaggcccctggacaagggcttgagtggatgggatggatcaacaccaac......actgggaacccaacgtatgcccagggcttcaca...ggacggtttgtcttctccttggacacctctgtcagcacggcatatctgcagatcagcagcctaaaggctgaggacactgccgtgtattactgtgcgagaga ->IGHV7-4-1*03 -caggtgcagctggtgcaatctgggtct...gagttgaagaagcctggggcctcagtgaaggtttcctgcaaggcttctggatacaccttc............actagctatgctatgaattgggtgcgacaggcccctggacaagggcttgagtggatgggatggatcaacaccaac......actgggaacccaacgtatgcccagggcttcaca...ggacggtttgtcttctccttggacacctctgtcagcacggcatatctgcagatcagcacgctaaaggctgaggacactg ->IGHV7-4-1*04 -caggtgcagctggtgcaatctgggtct...gagttgaagaagcctggggcctcagtgaaggtttcctgcaaggcttctggatacaccttc............actagctatgctatgaattgggtgcgacaggcccctggacaagggcttgagtggatgggatggatcaacaccaac......actgggaacccaacgtatgcccagggcttcaca...ggacggtttgtcttctccttggacacctctgtcagcatggcatatctgcagatcagcagcctaaaggctgaggacactgccgtgtattactgtgcgagaga ->IGHV7-4-1*05 -caggtgcagctggtgcaatctgggtct...gagttgaagaagcctggggcctcagtgaaggtttcctgcaaggcttctggatacaccttc............actagctatgctatgaattgggtgcgacaggcccctggacaagggcttgagtggatgggatggatcaacaccaac......actgggaacccaacgtatgcccagggcttcaca...ggacggtttgtcttctccttggacacctctgtcagcatggcatatctgcagatcagcagcctaaaggctgaggacactgccgtgtgttactgtgcgagaga ->AIGHV7-40*03| -ttttcaatagaaaagtcaaataatcta...agtgtcaatcagtggatgattagataaaatatgatatatgtaaatcatggaatactatgc............agccagtatggtatgaattcagtgtgaccagcccctggacaagggcttgagtggatgggatggatcatcacctac......actgggaacccaacatataccaacggcttcaca...ggacggtttctattctccatggacacctctgtcagcatggcgtatctgcagatcagcagcctaaaggctgaggacacggccgtgtatgactgtatgagaga ->IGHV7-81*01 -caggtgcagctggtgcagtctggccat...gaggtgaagcagcctggggcctcagtgaaggtctcctgcaaggcttctggttacagtttc............accacctatggtatgaattgggtgccacaggcccctggacaagggcttgagtggatgggatggttcaacacctac......actgggaacccaacatatgcccagggcttcaca...ggacggtttgtcttctccatggacacctctgccagcacagcatacctgcagatcagcagcctaaaggctgaggacatggccatgtattactgtgcgagata
--- a/baseline/IMGTVHreferencedataset20161215.fa Tue Sep 01 16:03:44 2020 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,1 +0,0 @@ ->IGHV1-18*01 caggttcagctggtgcagtctggagct...gaggtgaagaagcctggggcctcagtgaaggtctcctgcaaggcttctggttacaccttt............accagctatggtatcagctgggtgcgacaggcccctggacaagggcttgagtggatgggatggatcagcgcttac......aatggtaacacaaactatgcacagaagctccag...ggcagagtcaccatgaccacagacacatccacgagcacagcctacatggagctgaggagcctgagatctgacgacacggccgtgtattactgtgcgagaga >IGHV1-18*02 caggttcagctggtgcagtctggagct...gaggtgaagaagcctggggcctcagtgaaggtctcctgcaaggcttctggttacaccttt............accagctatggtatcagctgggtgcgacaggcccctggacaagggcttgagtggatgggatggatcagcgcttac......aatggtaacacaaactatgcacagaagctccag...ggcagagtcaccatgaccacagacacatccacgagcacagcctacatggagctgaggagcctaagatctgacgacacggcc >IGHV1-18*03 caggttcagctggtgcagtctggagct...gaggtgaagaagcctggggcctcagtgaaggtctcctgcaaggcttctggttacaccttt............accagctatggtatcagctgggtgcgacaggcccctggacaagggcttgagtggatgggatggatcagcgcttac......aatggtaacacaaactatgcacagaagctccag...ggcagagtcaccatgaccacagacacatccacgagcacagcctacatggagctgaggagcctgagatctgacgacatggccgtgtattactgtgcgagaga >IGHV1-18*04 caggttcagctggtgcagtctggagct...gaggtgaagaagcctggggcctcagtgaaggtctcctgcaaggcttctggttacaccttt............accagctacggtatcagctgggtgcgacaggcccctggacaagggcttgagtggatgggatggatcagcgcttac......aatggtaacacaaactatgcacagaagctccag...ggcagagtcaccatgaccacagacacatccacgagcacagcctacatggagctgaggagcctgagatctgacgacacggccgtgtattactgtgcgagaga >IGHV1-2*01 caggtgcagctggtgcagtctggggct...gaggtgaagaagcctggggcctcagtgaaggtctcctgcaaggcttctggatacaccttc............accggctactatatgcactgggtgcgacaggcccctggacaagggcttgagtggatgggacggatcaaccctaac......agtggtggcacaaactatgcacagaagtttcag...ggcagggtcaccagtaccagggacacgtccatcagcacagcctacatggagctgagcaggctgagatctgacgacacggtcgtgtattactgtgcgagaga >IGHV1-2*02 caggtgcagctggtgcagtctggggct...gaggtgaagaagcctggggcctcagtgaaggtctcctgcaaggcttctggatacaccttc............accggctactatatgcactgggtgcgacaggcccctggacaagggcttgagtggatgggatggatcaaccctaac......agtggtggcacaaactatgcacagaagtttcag...ggcagggtcaccatgaccagggacacgtccatcagcacagcctacatggagctgagcaggctgagatctgacgacacggccgtgtattactgtgcgagaga >IGHV1-2*03 caggtgcagctggtgcagtctggggct...gaggtgaagaagcttggggcctcagtgaaggtctcctgcaaggcttctggatacaccttc............accggctactatatgcactgggtgcnacaggcccctggacaagggcttgagtggatgggatggatcaaccctaac......agtggtggcacaaactatgcacagaagtttcag...ggcagggtcaccatgaccagggacacgtccatcagcacagcctacatggagctgagcaggctgagatctgacgacacggccgtgtattactgtgcgagaga >IGHV1-2*04 caggtgcagctggtgcagtctggggct...gaggtgaagaagcctggggcctcagtgaaggtctcctgcaaggcttctggatacaccttc............accggctactatatgcactgggtgcgacaggcccctggacaagggcttgagtggatgggatggatcaaccctaac......agtggtggcacaaactatgcacagaagtttcag...ggctgggtcaccatgaccagggacacgtccatcagcacagcctacatggagctgagcaggctgagatctgacgacacggccgtgtattactgtgcgagaga >IGHV1-2*05 caggtgcagctggtgcagtctggggct...gaggtgaagaagcctggggcctcagtgaaggtctcctgcaaggcttctggatacaccttc............accggctactatatgcactgggtgcgacaggcccctggacaagggcttgagtggatgggacggatcaaccctaac......agtggtggcacaaactatgcacagaagtttcag...ggcagggtcaccatgaccagggacacgtccatcagcacagcctacatggagctgagcaggctgagatctgacgacacggtcgtgtattactgtgcgagaga >IGHV1-24*01 caggtccagctggtacagtctggggct...gaggtgaagaagcctggggcctcagtgaaggtctcctgcaaggtttccggatacaccctc............actgaattatccatgcactgggtgcgacaggctcctggaaaagggcttgagtggatgggaggttttgatcctgaa......gatggtgaaacaatctacgcacagaagttccag...ggcagagtcaccatgaccgaggacacatctacagacacagcctacatggagctgagcagcctgagatctgaggacacggccgtgtattactgtgcaacaga >IGHV1-3*01 caggtccagcttgtgcagtctggggct...gaggtgaagaagcctggggcctcagtgaaggtttcctgcaaggcttctggatacaccttc............actagctatgctatgcattgggtgcgccaggcccccggacaaaggcttgagtggatgggatggatcaacgctggc......aatggtaacacaaaatattcacagaagttccag...ggcagagtcaccattaccagggacacatccgcgagcacagcctacatggagctgagcagcctgagatctgaagacacggctgtgtattactgtgcgagaga >IGHV1-3*02 caggttcagctggtgcagtctggggct...gaggtgaagaagcctggggcctcagtgaaggtttcctgcaaggcttctggatacaccttc............actagctatgctatgcattgggtgcgccaggcccccggacaaaggcttgagtggatgggatggagcaacgctggc......aatggtaacacaaaatattcacaggagttccag...ggcagagtcaccattaccagggacacatccgcgagcacagcctacatggagctgagcagcctgagatctgaggacatggctgtgtattactgtgcgagaga >IGHV1-38-4*01 caggtccagctggtgcagtcttgggct...gaggtgaggaagtctggggcctcagtgaaagtctcctgtagtttttctgggtttaccatc............accagctacggtatacattgggtgcaacagtcccctggacaagggcttgagtggatgggatggatcaaccctggc......aatggtagcccaagctatgccaagaagtttcag...ggcagattcaccatgaccagggacatgtccacaaccacagcctacacagacctgagcagcctgacatctgaggacatggctgtgtattactatgcaagaca >IGHV1-45*01 cagatgcagctggtgcagtctggggct...gaggtgaagaagactgggtcctcagtgaaggtttcctgcaaggcttccggatacaccttc............acctaccgctacctgcactgggtgcgacaggcccccggacaagcgcttgagtggatgggatggatcacacctttc......aatggtaacaccaactacgcacagaaattccag...gacagagtcaccattactagggacaggtctatgagcacagcctacatggagctgagcagcctgagatctgaggacacagccatgtattactgtgcaagana >IGHV1-45*02 cagatgcagctggtgcagtctggggct...gaggtgaagaagactgggtcctcagtgaaggtttcctgcaaggcttccggatacaccttc............acctaccgctacctgcactgggtgcgacaggcccccggacaagcgcttgagtggatgggatggatcacacctttc......aatggtaacaccaactacgcacagaaattccag...gacagagtcaccattaccagggacaggtctatgagcacagcctacatggagctgagcagcctgagatctgaggacacagccatgtattactgtgcaagata >IGHV1-45*03 .....................................agaagactgggtcctcagtgaaggtttcctgcaaggcttccggatacaccttc............acctaccgctacctgcactgggtgcgacaggcccccagacaagcgcttgagtggatgggatggatcacacctttc......aatggtaacaccaactacgcacagaaattccag...gacagagtcaccattaccagggacaggtctatgagcacagcctacatggagctgagcagcctgagatctgaggacacagccatgtattactgtgcaaga >IGHV1-46*01 caggtgcagctggtgcagtctggggct...gaggtgaagaagcctggggcctcagtgaaggtttcctgcaaggcatctggatacaccttc............accagctactatatgcactgggtgcgacaggcccctggacaagggcttgagtggatgggaataatcaaccctagt......ggtggtagcacaagctacgcacagaagttccag...ggcagagtcaccatgaccagggacacgtccacgagcacagtctacatggagctgagcagcctgagatctgaggacacggccgtgtattactgtgcgagaga >IGHV1-46*02 caggtgcagctggtgcagtctggggct...gaggtgaagaagcctggggcctcagtgaaggtttcctgcaaggcatctggatacaccttc............aacagctactatatgcactgggtgcgacaggcccctggacaagggcttgagtggatgggaataatcaaccctagt......ggtggtagcacaagctacgcacagaagttccag...ggcagagtcaccatgaccagggacacgtccacgagcacagtctacatggagctgagcagcctgagatctgaggacacggccgtgtattactgtgcgagaga >IGHV1-46*03 caggtgcagctggtgcagtctggggct...gaggtgaagaagcctggggcctcagtgaaggtttcctgcaaggcatctggatacaccttc............accagctactatatgcactgggtgcgacaggcccctggacaagggcttgagtggatgggaataatcaaccctagt......ggtggtagcacaagctacgcacagaagttccag...ggcagagtcaccatgaccagggacacgtccacgagcacagtctacatggagctgagcagcctgagatctgaggacacggccgtgtattactgtgctagaga >IGHV1-58*01 caaatgcagctggtgcagtctgggcct...gaggtgaagaagcctgggacctcagtgaaggtctcctgcaaggcttctggattcaccttt............actagctctgctgtgcagtgggtgcgacaggctcgtggacaacgccttgagtggataggatggatcgtcgttggc......agtggtaacacaaactacgcacagaagttccag...gaaagagtcaccattaccagggacatgtccacaagcacagcctacatggagctgagcagcctgagatccgaggacacggccgtgtattactgtgcggcaga >IGHV1-58*02 caaatgcagctggtgcagtctgggcct...gaggtgaagaagcctgggacctcagtgaaggtctcctgcaaggcttctggattcaccttt............actagctctgctatgcagtgggtgcgacaggctcgtggacaacgccttgagtggataggatggatcgtcgttggc......agtggtaacacaaactacgcacagaagttccag...gaaagagtcaccattaccagggacatgtccacaagcacagcctacatggagctgagcagcctgagatccgaggacacggccgtgtattactgtgcggcaga >IGHV1-68*01 caggtgcagctggggcagtctgaggct...gaggtaaagaagcctggggcctcagtgaaggtctcctgcaaggcttccggatacaccttc............acttgctgctccttgcactggttgcaacaggcccctggacaagggcttgaaaggatgagatggatcacactttac......aatggtaacaccaactatgcaaagaagttccag...ggcagagtcaccattaccagggacatgtccctgaggacagcctacatagagctgagcagcctgagatctgaggactcggctgtgtattactgggcaagata >IGHV1-69*01 caggtgcagctggtgcagtctggggct...gaggtgaagaagcctgggtcctcggtgaaggtctcctgcaaggcttctggaggcaccttc............agcagctatgctatcagctgggtgcgacaggcccctggacaagggcttgagtggatgggagggatcatccctatc......tttggtacagcaaactacgcacagaagttccag...ggcagagtcacgattaccgcggacgaatccacgagcacagcctacatggagctgagcagcctgagatctgaggacacggccgtgtattactgtgcgagaga >IGHV1-69*02 caggtccagctggtgcaatctggggct...gaggtgaagaagcctgggtcctcggtgaaggtctcctgcaaggcttctggaggcaccttc............agcagctatactatcagctgggtgcgacaggcccctggacaagggcttgagtggatgggaaggatcatccctatc......cttggtatagcaaactacgcacagaagttccag...ggcagagtcacgattaccgcggacaaatccacgagcacagcctacatggagctgagcagcctgagatctgaggacacggccgtgtattactgtgcgaga >IGHV1-69*03 caggtgcagctggtgcagtctggggct...gaggtgaagaagcctgggtcctcggtgaaggtctcctgcaaggcttctggaggcaccttc............agcagctatgctatcagctgggtgcgacaggcccctggacaagggcttgagtggatgggagggatcatccctatc......tttggtacagcaaactacgcacagaagttccag...ggcagagtcacgattaccgcggacgaatccacgagcacagcctacatggagctgagcagcctgagatctgatgacacggc >IGHV1-69*04 caggtccagctggtgcagtctggggct...gaggtgaagaagcctgggtcctcggtgaaggtctcctgcaaggcttctggaggcaccttc............agcagctatgctatcagctgggtgcgacaggcccctggacaagggcttgagtggatgggaaggatcatccctatc......cttggtatagcaaactacgcacagaagttccag...ggcagagtcacgattaccgcggacaaatccacgagcacagcctacatggagctgagcagcctgagatctgaggacacggccgtgtattactgtgcgagaga >IGHV1-69*05 caggtccagctggtgcagtctggggct...gaggtgaagaagcctgggtcctcggtgaaggtctcctgcaaggcttctggaggcaccttc............agcagctatgctatcagctgggtgcgacaggcccctggacaagggcttgagtggatgggagggatcatccctatc......tttggtacagcaaactacgcacagaagttccag...ggcagagtcacgattaccacggacgaatccacgagcacagcctacatggagctgagcagcctgagatctgaggacacggccgtgtattactgtgcgaga >IGHV1-69*06 caggtgcagctggtgcagtctggggct...gaggtgaagaagcctgggtcctcggtgaaggtctcctgcaaggcttctggaggcaccttc............agcagctatgctatcagctgggtgcgacaggcccctggacaagggcttgagtggatgggagggatcatccctatc......tttggtacagcaaactacgcacagaagttccag...ggcagagtcacgattaccgcggacaaatccacgagcacagcctacatggagctgagcagcctgagatctgaggacacggccgtgtattactgtgcgagaga >IGHV1-69*07 .....................................agaagcctgggtcctcggtgaaggtctcctgcaaggcttctggaggcaccttc............agcagctatgctatcagctgggtgcgacaggcccctggacaagggcttgagtggatgggaaggatcatccctatc......tttggtacagcaaactacgcacagaagttccag...ggcagagtcacgattaccgcggacgaatccacgagcacagcctacatggagctgagcagcctgagatctgag >IGHV1-69*08 caggtccagctggtgcaatctggggct...gaggtgaagaagcctgggtcctcggtgaaggtctcctgcaaggcttctggaggcaccttc............agcagctatactatcagctgggtgcgacaggcccctggacaagggcttgagtggatgggaaggatcatccctatc......cttggtacagcaaactacgcacagaagttccag...ggcagagtcacgattaccgcggacaaatccacgagcacagcctacatggagctgagcagcctgagatctgaggacacggccgtgtattactgtgcgagaga >IGHV1-69*09 caggtgcagctggtgcagtctggggct...gaggtgaagaagcctgggtcctcggtgaaggtctcctgcaaggcttctggaggcaccttc............agcagctatgctatcagctgggtgcgacaggcccctggacaagggcttgagtggatgggaaggatcatccctatc......cttggtatagcaaactacgcacagaagttccag...ggcagagtcacgattaccgcggacaaatccacgagcacagcctacatggagctgagcagcctgagatctgaggacacggccgtgtattactgtgcgagaga >IGHV1-69*10 caggtccagctggtgcagtctggggct...gaggtgaagaagcctgggtcctcagtgaaggtctcctgcaaggcttctggaggcaccttc............agcagctatgctatcagctgggtgcgacaggcccctggacaagggcttgagtggatgggagggatcatccctatc......cttggtatagcaaactacgcacagaagttccag...ggcagagtcacgattaccgcggacaaatccacgagcacagcctacatggagctgagcagcctgagatctgaggacacggccgtgtattactgtgcgagaga >IGHV1-69*11 caggtccagctggtgcagtctggggct...gaggtgaagaagcctgggtcctcggtgaaggtctcctgcaaggcttctggaggcaccttc............agcagctatgctatcagctgggtgcgacaggcccctggacaagggcttgagtggatgggaaggatcatccctatc......cttggtacagcaaactacgcacagaagttccag...ggcagagtcacgattaccgcggacgaatccacgagcacagcctacatggagctgagcagcctgagatctgaggacacggccgtgtattactgtgcgagaga >IGHV1-69*12 caggtccagctggtgcagtctggggct...gaggtgaagaagcctgggtcctcggtgaaggtctcctgcaaggcttctggaggcaccttc............agcagctatgctatcagctgggtgcgacaggcccctggacaagggcttgagtggatgggagggatcatccctatc......tttggtacagcaaactacgcacagaagttccag...ggcagagtcacgattaccgcggacgaatccacgagcacagcctacatggagctgagcagcctgagatctgaggacacggccgtgtattactgtgcgagaga >IGHV1-69*13 caggtccagctggtgcagtctggggct...gaggtgaagaagcctgggtcctcagtgaaggtctcctgcaaggcttctggaggcaccttc............agcagctatgctatcagctgggtgcgacaggcccctggacaagggcttgagtggatgggagggatcatccctatc......tttggtacagcaaactacgcacagaagttccag...ggcagagtcacgattaccgcggacgaatccacgagcacagcctacatggagctgagcagcctgagatctgaggacacggccgtgtattactgtgcgagaga >IGHV1-69*14 caggtccagctggtgcagtctggggct...gaggtgaagaagcctgggtcctcggtgaaggtctcctgcaaggcttctggaggcaccttc............agcagctatgctatcagctgggtgcgacaggcccctggacaagggcttgagtggatgggagggatcatccctatc......tttggtacagcaaactacgcacagaagttccag...ggcagagtcacgattaccgcggacaaatccacgagcacagcctacatggagctgagcagcctgagatctgaggacacggccgtgtattactgtgcgagaga >IGHV1-69-2*01 gaggtccagctggtacagtctggggct...gaggtgaagaagcctggggctacagtgaaaatctcctgcaaggtttctggatacaccttc............accgactactacatgcactgggtgcaacaggcccctggaaaagggcttgagtggatgggacttgttgatcctgaa......gatggtgaaacaatatacgcagagaagttccag...ggcagagtcaccataaccgcggacacgtctacagacacagcctacatggagctgagcagcctgagatctgaggacacggccgtgtattactgtgcaacaga >IGHV1-69-2*02 .....................................agaagcctggggctacagtgaaaatctcctgcaaggtttctggatacaccttc............accgactactacatgcactgggtgcaacaggcccctggaaaagggcttgagtggatgggacttgttgatcctgaa......gatggtgaaacaatatatgcagagaagttccag...ggcagagtcaccataaccgcggacacgtctacagacacagcctacatggagctgagcagcctgagatctgag >IGHV1-69D*01 caggtgcagctggtgcagtctggggct...gaggtgaagaagcctgggtcctcggtgaaggtctcctgcaaggcttctggaggcaccttc............agcagctatgctatcagctgggtgcgacaggcccctggacaagggcttgagtggatgggagggatcatccctatc......tttggtacagcaaactacgcacagaagttccag...ggcagagtcacgattaccgcggacgaatccacgagcacagcctacatggagctgagcagcctgagatctgaggacacggccgtgtattactgtgcgagaga >IGHV1-8*01 caggtgcagctggtgcagtctggggct...gaggtgaagaagcctggggcctcagtgaaggtctcctgcaaggcttctggatacaccttc............accagttatgatatcaactgggtgcgacaggccactggacaagggcttgagtggatgggatggatgaaccctaac......agtggtaacacaggctatgcacagaagttccag...ggcagagtcaccatgaccaggaacacctccataagcacagcctacatggagctgagcagcctgagatctgaggacacggccgtgtattactgtgcgagagg >IGHV1-8*02 caggtgcagctggtgcagtctggggct...gaggtgaagaagcctggggcctcagtgaaggtctcctgcaaggcttctggatacaccttc............accagctatgatatcaactgggtgcgacaggccactggacaagggcttgagtggatgggatggatgaaccctaac......agtggtaacacaggctatgcacagaagttccag...ggcagagtcaccatgaccaggaacacctccataagcacagcctacatggagctgagcagcctgagatctgaggacacggccgtgtattactgtgcgagagg >IGHV1-NL1*01 caggttcagctgttgcagcctggggtc...caggtgaagaagcctgggtcctcagtgaaggtctcctgctaggcttccagatacaccttc............accaaatactttacacggtgggtgtgacaaagccctggacaagggcatnagtggatgggatgaatcaacccttac......aacgataacacacactacgcacagacgttctgg...ggcagagtcaccattaccagtgacaggtccatgagcacagcctacatggagctgagcngcctgagatccgaagacatggtcgtgtattactgtgtgagaga >IGHV1/OR15-1*01 caggtgcagctggtgcagtctggggct...gaggtgaagaagcctggggcctcagtgaaggtctcctgcaaggcttctggatacatcttc............accgactactatatgcactgggtgcgacaggcccctggacaagagcttgggtggatgggacggatcaaccctaac......agtggtggcacaaactatgcacagaagtttcag...ggcagagtcaccatgaccagggacacgtccatcagcacagcctacacggagctgagcagcctgagatctgaggacacggccacgtattactgtgcgaga >IGHV1/OR15-1*02 caggtgcagctggtgcagtctggggct...gaggtgaagaagcctggggcctcagtgaaggtctcctgcaaggcttctggatacatcttc............accgactactatatgcactgggtgcgacaggcccctggacaagagcttgggtggatgggacggatcaaccctaac......agtggtggcacaaactatgcacagaagtttcag...ggcagagtcaccatgaccagggacacgtccatcagcacagcctgcacggagctgagcagcctgagatctgaggacacggccacgtattactgtgcgagaga >IGHV1/OR15-1*03 caggtgcagctggtgcagtctggggct...gaggtgaagaagcctggggcctcagtgaaggtctcctgcaaggcttctggatacatcttc............accgactactatatgcactgggtgcgacaggcccctggacaagagcttgggtggatgggacggatcaaccctaac......agtggtggcacaaactatgcacagaagtttcag...ggcagagtcaccatgaccagggacacgtccatcagcacagcctacacggagctgagcagcctgagatctgaggacacagccacgtattactgtgcgagaga >IGHV1/OR15-1*04 caggtgcagctggtgcagtctggggct...gaggtgaagaagcctggggcctcagtgaaggtctcctgcaaggcttctggatacatcttc............accgactactatatgcactgggtgcgacaggcccctggacaagagcttgggtggatgggacggatcaaccctaac......agtggtggcacaaactatgcacagaagtttcag...ggcagagtcaccatgaccagggacacgtccatcagcacagcctacatggagctgagcagcctgagatctgaggacacggccacgtattactgtgcgagaga >IGHV1/OR15-2*01 caggtgcagctggtgcagtctggagct...gaggtgaagaagcctagagcctcagtgaaggtctcctgcaaggcttctggttacaccttt............accagctactatatgcactgggtgtgacaggcccctgaacaagggcttgagtggatgggatggatcaacacttac......aatggtaacacaaactacccacagaagctccag...ggcagagtcaccatgaccagagacacatccacgagcacagcctacatggagctgagcaggctgagatctgacgacatggccgtgtattactgtgcgagaga >IGHV1/OR15-2*02 caggtgcagctggtgcagtctggagct...gaggtgaagaagcctggagcctcagtgaaggtctcctgcaaggcttctggttacaccttt............accagctactatatgcactgggtgtgacaggcccctgaacaagggcttgagtggatgggatggatcaacacttac......aatggtaacacaaactacccacagaagctccag...ggcagagtcaccatgaccagagacacatccacgagcacagcctacatggagctgagcagcctgagatctgacgacatggccgtgtattactgtgcgagaga >IGHV1/OR15-2*03 caggtgcagctggtgcagtctggagct...gaggtgaagaagcctagagcctcagtgaaggtctcctgcaaggcttctggttacaccttt............accagctactatatgcactgggtgtgacaggcccctgaacaagggcttgagtggatgggatggatcaacacttac......aatggtaacacaaactacccacagaagctccag...ggcagagtcaccatgaccagagacacatccacgagcacagcctacatggagctgagcagcctgagatctgacgacatggccgtgtattactgtgcgagaga >IGHV1/OR15-3*01 caggtccaactggtgtagtctggagct...gaggtgaagaagcctggggcctcagtgaaggtctcctgcaaggcttctggatacaccttc............accgactactttatgaactggatgcgccaggcccctggacaaaggcttgagtggatgggatggatcaacgctggc......aatggtaacacaaaatattcacagaagctccag...ggcagagtcaccattaccagggacacatcttcgagcacagcctacatgcagctgagcagcctgagatctgaggacacggccgtgtattactgtgcgaga >IGHV1/OR15-3*02 caggtccaactggtgtagtctggagct...gaggtgaagaagcctggggcctcagtgaaggtctcctgcaaggcttctggatacaccttc............accgactactttatgaactggatgcgccaggcccctggacaaaggcttgagtggatgggatggatcaacgctggc......aatggtaacacaaaatattcacagaagctccag...ggcagagtcaccattaccagggacacatctgcgagcacagcctacatgcagctgagcagcctgagatctgaggacacggccgtgtattactgtgcgagaga >IGHV1/OR15-3*03 caggtccaactggtgtagtctggagct...gaggtgaagaagcctggggcctcagtgaaggtctcctgcaaggcttctggatacaccttc............accagctactatatgaactggatgcgccaggcccctggacaaggcttcgagtggatgggatggatcaacgctggc......aatggtaacacaaagtattcacagaagctccag...ggcagagtcaccattaccagggacacatctgcgagcacagcctacatgcagctgagcagcctgagatctgaggacacggccgtgtattactgtgcgaga >IGHV1/OR15-4*01 caggaccagttggtgcagtctggggct...gaggtgaagaagcctctgtcctcagtgaaggtctccttcaaggcttctggatacaccttc............accaacaactttatgcactgggtgtgacaggcccctggacaaggacttgagtggatgggatggatcaatgctggc......aatggtaacacaacatatgcacagaagttccag...ggcagagtcaccataaccagggacacgtccatgagcacagcctacacggagctgagcagcctgagatctgaggacacggccgtgtattactgtgcgaga >IGHV1/OR15-5*01 .....................................agaagcctggggcctcagtgaaggtctcctgcaaggcttctggatacaccttc............accagctactgtatgcactgggtgcaccaggtccatgcacaagggcttgagtggatgggattggtgtgccctagt......gatggcagcacaagctatgcacagaagttccag...gccagagtcaccataaccagggacacatccatgagcacagcctacatggagctaagcagtctgagatctgaggacacggccatgtattactgtgtgaga >IGHV1/OR15-5*02 caggtacagctggtgcagtctggggct...gaggtgaagaagcctggggcctcagtgaaggtctcctgcaaggcttctggatacaccttc............accaactactgtatgcactgggtgcgccaggtccatgcacaagggcttgagtggatgggattggtgtgccctagt......gatggcagcacaagctatgcacaaaagttccag...gccagagtcaccataaccagggacacatccatgagcacagcctacatggagctaagcagtctgagatctgaggacacggccatgtattactgtgtgaga >IGHV1/OR15-9*01 caggtacagctgatgcagtctggggct...gaggtgaagaagcctggggcctcagtgaggatctcctgcaaggcttctggatacaccttc............accagctactgtatgcactgggtgtgccaggcccatgcacaagggcttgagtggatgggattggtgtgccctagt......gatggcagcacaagctatgcacagaagttccag...ggcagagtcaccataaccagggacacatccatgggcacagcctacatggagctaagcagcctgagatctgaggacacggccatgtattactgtgtgagaga >IGHV1/OR21-1*01 caggtacagctggtgcagtctggggct...gaggtgaagaagcctggggcctcagtgaaggtctcctgcaaggcttctggatacaccatc............accagctactgtatgcactgggtgcaccaggtccatgcacaagggcttgagtggatgggattggtgtgccctagt......gatggcagcacaagctatgcacagaagttccag...gccagagtcaccataaccagggacacatccatgagcacagcctacatggagctaagcagtctgagatctgaggacacggccatgtattactgtgtgagaga >IGHV2-10*01 caggtcaccttgaaggagtctggtcct...gcactggtgaaacccacacagaccctcatgctgacctgcaccttctctgggttctcactcagc......acttctggaatgggtgtgggttagatctgtcagccctcagcaaaggccctggagtggcttgcacacatttattagaat.........gataataaatactacagcccatctctgaag...agtaggctcattatctccaaggacacctccaagaatgaagtggttctaacagtgatcaacatggacattgtggacacagccacacattactgtgcaaggagac >IGHV2-26*01 caggtcaccttgaaggagtctggtcct...gtgctggtgaaacccacagagaccctcacgctgacctgcaccgtctctgggttctcactcagc......aatgctagaatgggtgtgagctggatccgtcagcccccagggaaggccctggagtggcttgcacacattttttcgaat.........gacgaaaaatcctacagcacatctctgaag...agcaggctcaccatctccaaggacacctccaaaagccaggtggtccttaccatgaccaacatggaccctgtggacacagccacatattactgtgcacggatac >IGHV2-5*01 cagatcaccttgaaggagtctggtcct...acgctggtgaaacccacacagaccctcacgctgacctgcaccttctctgggttctcactcagc......actagtggagtgggtgtgggctggatccgtcagcccccaggaaaggccctggagtggcttgcactcatttattggaat.........gatgataagcgctacagcccatctctgaag...agcaggctcaccatcaccaaggacacctccaaaaaccaggtggtccttacaatgaccaacatggaccctgtggacacagccacatattactgtgcacacagac >IGHV2-5*02 cagatcaccttgaaggagtctggtcct...acgctggtgaaacccacacagaccctcacgctgacctgcaccttctctgggttctcactcagc......actagtggagtgggtgtgggctggatccgtcagcccccaggaaaggccctggagtggcttgcactcatttattgggat.........gatgataagcgctacagcccatctctgaag...agcaggctcaccatcaccaaggacacctccaaaaaccaggtggtccttacaatgaccaacatggaccctgtggacacagccacatattactgtgcacacagac >IGHV2-5*03 ................................gctggtgaaacccacacagaccctcacgctgacctgcaccttctctgggttctcactcagc......actagtggagtgggtgtgggctggatccgtcagcccccaggaaaggccctggagtggcttgcactcatttattgggat.........gatgataagcgctacagcccatctctgaag...agcaggctcaccattaccaaggacacctccaaaaaccaggt >IGHV2-5*04 cagatcaccttgaaggagtctggtcct...acgctggtgaaacccacacagaccctcacgctgacctgcaccttctctgggttctcactcagc......actagtggagtgggtgtgggctggatccgtcagcccccaggaaaggccctggagtggcttgcactcatttattggaat.........gatgataagcgctacagcccatctctgaag...agcaggctcaccatcaccaaggacacctccaaaaaccaggtggtccttacaatgaccaacatggaccctgtggacacaggcacatattactgtgtac >IGHV2-5*05 cagatcaccttgaaggagtctggtcct...acgctggtgaaacccacacagaccctcacgctgacctgcaccttctctgggttctcactcagc......actagtggagtgggtgtgggctggatccgtcagcccccaggaaaggccctggagtggcttgcactcatttattgggat.........gatgataagcgctacggcccatctctgaag...agcaggctcaccatcaccaaggacacctccaaaaaccaggtggtccttacaatgaccaacatggaccctgtggacacagccacatattactgtgcacacagac >IGHV2-5*06 cagatcaccttgaaggagtctggtcct...acgctggtaaaacccacacagaccctcacgctgacctgcaccttctctgggttctcactcagc......actagtggagtgggtgtgggctggatccgtcagcccccaggaaaggccctggagtggcttgcactcatttattgggat.........gatgataagcgctacggcccatctctgaag...agcaggctcaccatcaccaaggacacctccaaaaaccaggtggtccttacaatgaccaacatggaccctgtggacacagccacatattactgtgcacacaga >IGHV2-5*08 caggtcaccttgaaggagtctggtcct...gcgctggtgaaacccacacagaccctcacactgacctgcaccttctctgggttctcactcagc......actagtggaatgcgtgtgagctggatccgtcagcccccaggaaaggccctggagtggcttgcactcatttattgggat.........gatgataagcgctacagcccatctctgaag...agcaggctcaccatcaccaaggacacctccaaaaaccaggtggtccttacaatgaccaacatggaccctgtggacacagccacatattactgtgcacacagac >IGHV2-5*09 caggtcaccttgaaggagtctggtcct...acgctggtgaaacccacacagaccctcacgctgacctgcaccttctctgggttctcactcagc......actagtggagtgggtgtgggctggatccgtcagcccccaggaaaggccctggagtggcttgcactcatttattgggat.........gatgataagcgctacggcccatctctgaag...agcaggctcaccatcaccaaggacacctccaaaaaccaggtggtccttacaatgaccaacatggaccctgtggacacagccacatattactgtgcacacagac >IGHV2-70*01 caggtcaccttgagggagtctggtcct...gcgctggtgaaacccacacagaccctcacactgacctgcaccttctctgggttctcactcagc......actagtggaatgtgtgtgagctggatccgtcagcccccagggaaggccctggagtggcttgcactcattgattgggat.........gatgataaatactacagcacatctctgaag...accaggctcaccatctccaaggacacctccaaaaaccaggtggtccttacaatgaccaacatggaccctgtggacacagccacgtattactgtgcacggatac >IGHV2-70*02 caggtcaccttgagggagtctggtcct...gcgctggtgaaacccacacagaccctcacactgacctgcaccttctctgggttctcactcagc......actagtggaatgtgtgtgagctggatccgtcagcccccagggaaggccctggagtggcttgcactcattgattgggat.........gatgataaatactacagcacatctctgaag...accaggctcaccatctccaaggacacctccaaaaaccaggtggtccttacaatgaccaacatggaccctgtggacacggccgtgtattactg >IGHV2-70*03 caggtcaccttgaaggagtctggtcct...gcgctggtgaaacccacacagaccctcacactgacctgcaccttctctgggttctcactcagc......actagtggaatgcgtgtgagctggatccgtcagcccccagggaaggccctggagtggcttgcacgcattgattgggat.........gatgataaattctacagcacatctctgaag...accaggctcaccatctccaaggacacctccaaaaaccaggtggtccttacaatgaccaacatggaccctgtggacacggccgtgtattactg >IGHV2-70*04 caggtcaccttgaaggagtctggtcct...gcgctggtgaaacccacacagaccctcacactgacctgcaccttctctgggttctcactcagc......actagtggaatgcgtgtgagctggatccgtcagcccccagggaaggccctggagtggcttgcacgcattgattgggat.........gatgataaattctacagcacatctctgaag...accaggctcaccatctccaaggacacctccaaaaaccaggtggtccttacaatgaccaacatggaccctgtggacacagccacgtattac >IGHV2-70*05 ..........................t...gcgctggtgaaacccacacagaccctcacactgacctgcaccttctctgggttctcactcagc......actagtggaatgcgtgcgagctggatccgtcagcccccagggaaggccctggagtggcttgcacgcattgattgggat.........gatgataaattctacagcacatctctgaag...accaggctcaccatctccaaggacacctccaaaaaccaggtggtccttacaatgaccaacatgga >IGHV2-70*06 caggtcaccttgaaggagtctggtcct...gcgctggtgaaacccacacagaccctcacactgacctgcaccttctctgggttctcactcagc......actagtggaatgcgtgtgagctggatccgtcagcccccagggaaggccctggagtggcttgcacgcattgattgggat.........gatgataaattctacagcacatccctgaag...accaggctcaccatctccaaggacacctccaaaaaccaggtggtccttacaatgaccaacatggaccctgtggacacggccgtgtattactg >IGHV2-70*07 caggtcaccttgagggagtctggtcct...gcgctggtgaaacccacacagaccctcacactgacctgcaccttctctgggttctcactcagc......actagtggaatgtgtgtgagctggatccgtcagcccccggggaaggccctggagtggcttgcactcattgattgggat.........gatgataaatactacagcacatctctgaag...accaggctcaccatctccaaggacacctccaaaaaccaggtggtccttacaatgaccaacatggaccctgtggacacggccgtgtattactg >IGHV2-70*08 caggtcaccttgagggagtctggtcct...gcgctggtgaaacccacacagaccctcacactgacctgcgccttctctgggttctcactcagc......actagtggaatgtgtgtgagctggatccgtcagcccccagggaaggccctggagtggcttgcacgcattgattgggat.........gatgataaatactacagcacatctctgaag...accaggctcaccatctccaaggacacctccaaaaaccaggtggtccttacaatgaccaacatggaccctgtggacacggccgtgtattactg >IGHV2-70*09 cagatcaccttgaaggagtctggtcct...acgctggtgaaacccacacagaccctcacgctgacccgcaccttctctgggttctcactcagc......actagtggaatgtgtgtgagctggatccgtcagcccccagggaaggccctggagtggcttgcactcattgattgggat.........gatgataaatactacagcacatctctgaac...accaggctcaccatctccaaggacacctccaaaaaccaggtggtccttacaatgaccaacatggaccctgtggacacaggcacatattactgtgtacgg >IGHV2-70*10 caggtcaccttgaaggagtctggtcct...gcgctggtgaaacccacacagaccctcacactgacctgcaccttctctgggttctcactcagc......actagtggaatgcgtgtgagctggatccgtcagcccccagggaaggccctggagtggattgcacgcattgattgggat.........gatgataaatactacagcacatctctgaag...accaggctcaccatctccaaggacacctccaaaaaccaggtggtccttacaatgaccaacatggaccctgtggacacagccacgtattactgtgcacggatac >IGHV2-70*11 cgggtcaccttgagggagtctggtcct...gcgctggtgaaacccacacagaccctcacactgacctgcaccttctctgggttctcactcagc......actagtggaatgtgtgtgagctggatccgtcagcccccagggaaggccctggagtggcttgcacgcattgattgggat.........gatgataaatactacagcacatctctgaag...accaggctcaccatctccaaggacacctccaaaaaccaggtggtccttacaatgaccaacatggaccctgtggacacagccacgtattactgtgcacggatac >IGHV2-70*12 cagatcaccttgaaggagtctggtcct...acgctggtgaaacccacacagaccctcacgctgacctgcaccttctctgggttctcactcagc......actagtggaatgtgtgtgagctggatccgtcagcccccagggaaggccctggagtggcttgcactcattgattgggat.........gatgataaatactacagcacatctctgaag...accaggctcaccatctccaaggacacctccaaaaaccaggtggtccttacaatgaccaacatggaccctgtggacacagccacatattactgtgcacacagac >IGHV2-70*13 caggtcaccttgagggagtctggtcct...gcgctggtgaaacccacacagaccctcacactgacctgcaccttctctgggttctcactcagc......actagtggaatgtgtgtgagctggatccgtcagcccccagggaaggccctggagtggcttgcactcattgattgggat.........gatgataaatactacagcacatctctgaag...accaggctcaccatctccaaggacacctccaaaaaccaggtggtccttacaatgaccaacatggaccctgtggacacagccacgtattattgtgcacggatac >IGHV2-70D*04 caggtcaccttgaaggagtctggtcct...gcgctggtgaaacccacacagaccctcacactgacctgcaccttctctgggttctcactcagc......actagtggaatgcgtgtgagctggatccgtcagcccccagggaaggccctggagtggcttgcacgcattgattgggat.........gatgataaattctacagcacatctctgaag...accaggctcaccatctccaaggacacctccaaaaaccaggtggtccttacaatgaccaacatggaccctgtggacacagccacgtattactgtgcacggatac >IGHV2-70D*14 caggtcaccttgaaggagtctggtcct...gcgctggtgaaacccacacagaccctcacactgacctgcaccttctctgggttctcactcagc......actagtggaatgcgtgtgagctggatccgtcagcccccaggtaaggccctggagtggcttgcacgcattgattgggat.........gatgataaattctacagcacatctctgaag...accaggctcaccatctccaaggacacctccaaaaaccaggtggtccttacaatgaccaacatggaccctgtggacacagccacgtattactgtgcacggatac >IGHV2/OR16-5*01 caggtcaccttgaaggagtctggtcct...gcgctggtgaaacccacagagaccctcacgctgacctgcactctctctgggttctcactcagc......acttctggaatgggtatgagctggatccgtcagcccccagggaaggccctggagtggcttgctcacatttttttgaat.........gacaaaaaatcctacagcacgtctctgaag...aacaggctcatcatctccaaggacacctccaaaagccaggtggtccttaccatgaccaacatggaccctgtggacacagccacgtattactgtgcatggagag >IGHV3-11*01 caggtgcagctggtggagtctggggga...ggcttggtcaagcctggagggtccctgagactctcctgtgcagcctctggattcaccttc............agtgactactacatgagctggatccgccaggctccagggaaggggctggagtgggtttcatacattagtagtagt......ggtagtaccatatactacgcagactctgtgaag...ggccgattcaccatctccagggacaacgccaagaactcactgtatctgcaaatgaacagcctgagagccgaggacacggccgtgtattactgtgcgagaga >IGHV3-11*03 caggtgcagctgttggagtctggggga...ggcttggtcaagcctggagggtccctgagactctcctgtgcagcctctggattcaccttc............agtgactactacatgagctggatccgccaggctccagggaaggggctggagtgggtttcatacattagtagtagt......agtagttacacaaactacgcagactctgtgaag...ggccgattcaccatctccagagacaacgccaagaactcactgtatctgcaaatgaacagcctgagagccgaggacacggccgtgtattactgtgcgaga >IGHV3-11*04 caggtgcagctggtggagtctggggga...ggcttggtcaagcctggagggtccctgagactctcctgtgcagcctctggattcaccttc............agtgactactacatgagctggatccgccaggctccagggaaggggctggagtgggtttcatacattagtagtagt......ggtagtaccatatactacgcagactctgtgaag...ggccgattcaccatctccagggacaacgccaagaactcactgtatctgcaaatgaacagcctgagagccgaggacacggctgtgtattactgtgcgagaga >IGHV3-11*05 caggtgcagctggtggagtctggggga...ggcttggtcaagcctggagggtccctgagactctcctgtgcagcctctggattcaccttc............agtgactactacatgagctggatccgccaggctccagggaaggggctggagtgggtttcatacattagtagtagt......agtagttacacaaactacgcagactctgtgaag...ggccgattcaccatctccagagacaacgccaagaactcactgtatctgcaaatgaacagcctgagagccgaggacacggccgtgtattactgtgcgagaga >IGHV3-11*06 caggtgcagctggtggagtctggggga...ggcttggtcaagcctggagggtccctgagactctcctgtgcagcctctggattcaccttc............agtgactactacatgagctggatccgccaggctccagggaaggggctggagtgggtttcatacattagtagtagt......agtagttacacaaactacgcagactctgtgaag...ggccgattcaccatctccagagacaacgccaagaactcactgtatctgcaaatgaacagcctgagagccgaggacacggctgtgtattactgtgcgagaga >IGHV3-13*01 gaggtgcagctggtggagtctggggga...ggcttggtacagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctacgacatgcactgggtccgccaagctacaggaaaaggtctggagtgggtctcagctattggtactgct.........ggtgacacatactatccaggctccgtgaag...ggccgattcaccatctccagagaaaatgccaagaactccttgtatcttcaaatgaacagcctgagagccggggacacggctgtgtattactgtgcaagaga >IGHV3-13*02 gaggtgcatctggtggagtctggggga...ggcttggtacagcctgggggggccctgagactctcctgtgcagcctctggattcaccttc............agtaactacgacatgcactgggtccgccaagctacaggaaaaggtctggagtgggtctcagccaatggtactgct.........ggtgacacatactatccaggctccgtgaag...gggcgattcaccatctccagagaaaatgccaagaactccttgtatcttcaaatgaacagcctgagagccggggacacggctgtgtattactgtgcaagaga >IGHV3-13*03 gaggtgcagctggtggagtctggggga...ggcttggtacagcctggggggtccctgagactctcctgtgcagcctgtggattcaccttc............agtagctacgacatgcactgggtccgccaagctacaggaaaaggtctggagtgggtctcagctattggtactgct.........ggtgacacatactatccaggctccgtgaag...ggccaattcaccatctccagagaaaatgccaagaactccttgtatcttcaaatgaacagcctgagagccggggacacggctgtgtattactgtgcaaga >IGHV3-13*04 gaggtgcagctggtggagtctggggga...ggcttggtacagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctacgacatgcactgggtccgccaagctacaggaaaaggtctggaatgggtctcagctattggtactgct.........ggtgacacatactatccaggctccgtgaag...ggccgattcaccatctccagagaaaatgccaagaactccttgtatcttcaaatgaacagcctgagagccggggacacggctgtgtattactgtgcaagaga >IGHV3-13*05 gaggtgcagctggtggagtctggggga...ggcttggtacagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctacgacatgcactgggtccgccaagctacaggaaaaggtctggagtgggtctcagctattggtactgct.........ggtgacccatactatccaggctccgtgaag...ggccgattcaccatctccagagaaaatgccaagaactccttgtatcttcaaatgaacagcctgagagccggggacacggctgtgtattactgtgcaagaga >IGHV3-15*01 gaggtgcagctggtggagtctggggga...ggcttggtaaagcctggggggtcccttagactctcctgtgcagcctctggattcactttc............agtaacgcctggatgagctgggtccgccaggctccagggaaggggctggagtgggttggccgtattaaaagcaaaactgatggtgggacaacagactacgctgcacccgtgaaa...ggcagattcaccatctcaagagatgattcaaaaaacacgctgtatctgcaaatgaacagcctgaaaaccgaggacacagccgtgtattactgtaccacaga >IGHV3-15*02 gaggtgcagctggtggagtctggggga...gccttggtaaagcctggggggtcccttagactctcctgtgcagcctctggattcactttc............agtaacgcctggatgagctgggtccgccaggctccagggaaggggctggagtgggttggccgtattaaaagcaaaactgatggtgggacaacagactacgctgcacccgtgaaa...ggcagattcaccatctcaagagatgattcaaaaaacacgctgtatctgcaaatgaacagcctgaaaaccgaggacacagccgtgtattactgtaccacaga >IGHV3-15*03 gaggtgcagctggtggagtctgccgga...gccttggtacagcctggggggtcccttagactctcctgtgcagcctctggattcacttgc............agtaacgcctggatgagctgggtccgccaggctccagggaaggggctggagtgggttggccgtattaaaagcaaagctaatggtgggacaacagactacgctgcacctgtgaaa...ggcagattcaccatctcaagagttgattcaaaaaacacgctgtatctgcaaatgaacagcctgaaaaccgaggacacagccgtgtattactgtaccacaga >IGHV3-15*04 gaggtgcagctggtggagtctggggga...ggcttggtaaagcctggggggtcccttagactctcctgtgcagcctctggattcactttc............agtaacgcctggatgagctgggtccgccaggctccagggaaggggctggagtgggttggccgtattgaaagcaaaactgatggtgggacaacagactacgctgcacccgtgaaa...ggcagattcaccatctcaagagatgattcaaaaaacacgctgtatctgcaaatgaacagcctgaaaaccgaggacacagccgtgtattactgtaccacaga >IGHV3-15*05 gaggtgcagctggtggagtctggggga...ggcttggtaaagcctggggggtcccttagactctcctgtgcagcctctggattcactttc............agtaacgcctggatgagctgggtccgccaggctccagggaaggggctggagtgggttggccgtattaaaagcaaaactgatggtgggacaacagactacgctgcacccgtgaaa...ggcagattcaccatctcaagagatgattcaaaaaacacgctgtatctgcaaatgaacagtctgaaaaccgaggacacagccgtgtattactgtaccacaga >IGHV3-15*06 gaggtgcagctggtggagtctggggga...ggcttggtaaagcctggggggtcccttagactctcctgtgcagcctctggattcactttc............agtaacgcctggatgagctgggtccgccaggctccagggaaggggctggagtgggtcggccgtattaaaagcaaaactgatggtgggacaacaaactacgctgcacccgtgaaa...ggcagattcaccatctcaagagatgattcaaaaaacacgctgtatctgcaaatgaacagcctgaaaaccgaggacacagccgtgtattactgtaccacaga >IGHV3-15*07 gaggtgcagctggtggagtctggggga...ggcttggtaaagcctggggggtcccttagactctcctgtgcagcctctggtttcactttc............agtaacgcctggatgaactgggtccgccaggctccagggaaggggctggagtgggtcggccgtattaaaagcaaaactgatggtgggacaacagactacgctgcacccgtgaaa...ggcagattcaccatctcaagagatgattcaaaaaacacgctgtatctgcaaatgaacagcctgaaaaccgaggacacagccgtgtattactgtaccacaga >IGHV3-15*08 gaggtgcagctggtggagtctgcggga...ggcttggtacagcctggggggtcccttagactctcctgtgcagcctctggattcacttgc............agtaacgcctggatgagctgggtccgccaggctccagggaaggggctggagtgggttggctgtattaaaagcaaagctaatggtgggacaacagactacgctgcacctgtgaaa...ggcagattcaccatctcaagagatgattcaaaaaacacgctgtatctgcaaatgatcagcctgaaaaccgaggacacggccgtgtattactgtaccacagg >IGHV3-16*01 gaggtacaactggtggagtctggggga...ggcttggtacagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agtaacagtgacatgaactgggcccgcaaggctccaggaaaggggctggagtgggtatcgggtgttagttggaat......ggcagtaggacgcactatgtggactccgtgaag...cgccgattcatcatctccagagacaattccaggaactccctgtatctgcaaaagaacagacggagagccgaggacatggctgtgtattactgtgtgagaaa >IGHV3-16*02 gaggtgcagctggtggagtctggggga...ggcttggtacagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agtaacagtgacatgaactgggcccgcaaggctccaggaaaggggctggagtgggtatcgggtgttagttggaat......ggcagtaggacgcactatgtggactccgtgaag...cgccgattcatcatctccagagacaattccaggaactccctgtatctgcaaaagaacagacggagagccgaggacatggctgtgtattactgtgtgagaaa >IGHV3-19*01 acagtgcagctggtggagtctggggga...ggcttggtagagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agtaacagtgacatgaactgggtccgccaggctccaggaaaggggctggagtgggtatcgggtgttagttggaat......ggcagtaggacgcactatgcagactctgtgaag...ggccgattcatcatctccagagacaattccaggaacttcctgtatcagcaaatgaacagcctgaggcccgaggacatggctgtgtattactgtgtgagaaa >IGHV3-20*01 gaggtgcagctggtggagtctggggga...ggtgtggtacggcctggggggtccctgagactctcctgtgcagcctctggattcaccttt............gatgattatggcatgagctgggtccgccaagctccagggaaggggctggagtgggtctctggtattaattggaat......ggtggtagcacaggttatgcagactctgtgaag...ggccgattcaccatctccagagacaacgccaagaactccctgtatctgcaaatgaacagtctgagagccgaggacacggccttgtatcactgtgcgagaga >IGHV3-20*02 gaggtgcagctggtggagtctggggga...ggtgtggtacggcctggggggtccctgagactctcctttgcagcctctggattcaccttt............gatgattatggcatgagctgggtccgccaagctccagggaaggggctggagtgggtctctggtattaattggaat......ggtggtagcacaggttatgcagactctgtgaag...ggccgattcaccatctccagagacaacgccaagaactccctgtatctgcaaatgaacagtctgagagccgaggacacggccttgtatcactgtgcgagaga >IGHV3-21*01 gaggtgcagctggtggagtctggggga...ggcctggtcaagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatagcatgaactgggtccgccaggctccagggaaggggctggagtgggtctcatccattagtagtagt......agtagttacatatactacgcagactcagtgaag...ggccgattcaccatctccagagacaacgccaagaactcactgtatctgcaaatgaacagcctgagagccgaggacacggctgtgtattactgtgcgagaga >IGHV3-21*02 gaggtgcaactggtggagtctggggga...ggcctggtcaagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatagcatgaactgggtccgccaggctccagggaaggggctggagtgggtctcatccattagtagtagt......agtagttacatatactacgcagactcagtgaag...ggccgattcaccatctccagagacaacgccaagaactcactgtatctgcaaatgaacagcctgagagccgaggacacggctgtgtattactgtgcgagaga >IGHV3-21*03 gaggtgcagctggtggagtctggggga...ggcctggtcaagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatagcatgaactgggtccgccaggctccagggaaggggctggagtgggtctcatccattagtagtagt......agtagttacatatactacgcagactcagtgaag...ggccgattcaccatctccagagacaacgccaagaactcactgtatctgcaaatgaacagcctgagagccgaggacacagctgtgtattactgtgcgagaga >IGHV3-21*04 gaggtgcagctggtggagtctggggga...ggcctggtcaagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatagcatgaactgggtccgccaggctccagggaaggggctggagtgggtctcatccattagtagtagt......agtagttacatatactacgcagactcagtgaag...ggccgattcaccatctccagagacaacgccaagaactcactgtatctgcaaatgaacagcctgagagccgaggacacggccgtgtattactgtgcgagaga >IGHV3-22*01 gaggtgcatctggtggagtctggggga...gccttggtacagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agttactactacatgagcggggtccgccaggctcccgggaaggggctggaatgggtaggtttcattagaaacaaagctaatggtgggacaacagaatagaccacgtctgtgaaa...ggcagattcacaatctcaagagatgattccaaaagcatcacctatctgcaaatgaagagcctgaaaaccgaggacacggccgtgtattactgttccagaga >IGHV3-22*02 gaggtgcagctggtggagtctggggga...ggcttggtacagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agttactactacatgagcggggtccgccaggctcccgggaaggggctggaatgggtaggtttcattagaaacaaagctaatggtgggacaacagaatagaccacgtctgtgaaa...ggcagattcacaatctcaagagatgattccaaaagcatcacctatctgcaaatgaagagcctgaaaaccgaggacacggccgtgtattactgttccagaga >IGHV3-23*01 gaggtgcagctgttggagtctggggga...ggcttggtacagcctggggggtccctgagactctcctgtgcagcctctggattcaccttt............agcagctatgccatgagctgggtccgccaggctccagggaaggggctggagtgggtctcagctattagtggtagt......ggtggtagcacatactacgcagactccgtgaag...ggccggttcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagccgaggacacggccgtatattactgtgcgaaaga >IGHV3-23*02 gaggtgcagctgttggagtctggggga...ggcttggtacagcctggggggtccctgagactctcctgtgcagcctctggattcaccttt............agcagctatgccatgagctgggtccgccaggctccagggaaggggctggagtgggtctcagctattagtggtagt......ggtggtagcacatactacggagactccgtgaag...ggccggttcaccatctcaagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagccgaggacacggccgtatattactgtgcgaaaga >IGHV3-23*03 gaggtgcagctgttggagtctggggga...ggcttggtacagcctggggggtccctgagactctcctgtgcagcctctggattcaccttt............agcagctatgccatgagctgggtccgccaggctccagggaaggggctggagtgggtctcagttatttatagcggt......ggtagtagcacatactatgcagactccgtgaag...ggccggttcaccatctccagagataattccaagaacacgctgtatctgcaaatgaacagcctgagagccgaggacacggccgtatattactgtgcgaaaga >IGHV3-23*04 gaggtgcagctggtggagtctggggga...ggcttggtacagcctggggggtccctgagactctcctgtgcagcctctggattcaccttt............agcagctatgccatgagctgggtccgccaggctccagggaaggggctggagtgggtctcagctattagtggtagt......ggtggtagcacatactacgcagactccgtgaag...ggccggttcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagccgaggacacggccgtatattactgtgcgaaaga >IGHV3-23*05 gaggtgcagctgttggagtctggggga...ggcttggtacagcctggggggtccctgagactctcctgtgcagcctctggattcaccttt............agcagctatgccatgagctgggtccgccaggctccagggaaggggctggagtgggtctcagctatttatagcagt......ggtagtagcacatactatgcagactccgtgaag...ggccggttcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagccgaggacacggccgtatattactgtgcgaaa >IGHV3-23D*01 gaggtgcagctgttggagtctggggga...ggcttggtacagcctggggggtccctgagactctcctgtgcagcctctggattcaccttt............agcagctatgccatgagctgggtccgccaggctccagggaaggggctggagtgggtctcagctattagtggtagt......ggtggtagcacatactacgcagactccgtgaag...ggccggttcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagccgaggacacggccgtatattactgtgcgaaaga >IGHV3-25*01 gagatgcagctggtggagtctggggga...ggcttgcaaaagcctgcgtggtccccgagactctcctgtgcagcctctcaattcaccttc............agtagctactacatgaactgtgtccgccaggctccagggaatgggctggagttggtttgacaagttaatcctaat......gggggtagcacatacctcatagactccggtaag...gaccgattcaatacctccagagataacgccaagaacacacttcatctgcaaatgaacagcctgaaaaccgaggacacggccctctattagtgtaccagaga >IGHV3-25*02 gagatgcagctggtggagtctggggga...ggcttggcaaagcctgcgtggtccccgagactctcctgtgcagcctctcaattcaccttc............agtagctactacatgaactgtgtccgccaggctccagggaatgggctggagttggtttgacaagttaatcctaat......gggggtagcacatacctcatagactccggtaag...gaccgattcaatacctccagagataacgccaagaacacacttcatctgcaaatgaacagcctgaaaaccgaggacacggccctctattagtgtaccagaga >IGHV3-25*03 gagatgcagctggtggagtctggggga...ggcttggcaaagcctgcgtggtccccgagactctcctgtgcagcctctcaattcaccttc............agtagctactacatgaactgtgtccgccaggctccagggaatgggctggagttggttggacaagttaatcctaat......gggggtagcacatacctcatagactccggtaag...gaccgattcaatacctccagagataacgccaagaacacacttcatctgcaaatgaacagcctgaaaaccgaggacacggccctgtattagtgtaccaga >IGHV3-25*04 gagacgcagctggtggagtctggggga...ggcttggcaaagcctgggcggtccccgagactctcctgtgcagcctctcaattcaccttc............agtagctactacatgaactgtgtccgccaggctccagggaatgggctggagttggttggacaagttaatcctaat......gggggtagcacatacctcatagactccggtaag...gaccgattcaatacctccagagataacgccaagaacacacttcatctgcaaatgaacagcctgaaaaccgaggacacggccctgtattactgtaccagaga >IGHV3-25*05 gagatgcagctggtggagtctggggga...ggcttggcaaagcctgcgtggtccccgagactctcctgtgcagcctctcaattcaccttc............agtagctactacatgaactgtgtccgccaggctccagggaatgggctggagttggttggacaagttaatcctaat......gggggtagcacatacctcatagactccggtaag...gaccgattcaatacctccagagataacgccaagaacacacttcatctgcaaatgaacagcctgaaaaccgaggacacggccctctattagtgtaccagaga >IGHV3-29*01 gaggtggagctgatagagcccacagag...gacctgagacaacctgggaagttcctgagactctcctgtgtagcctctagattcgccttc............agtagcttctgaatgagcccagttcaccagtctgcaggcaaggggctggagtgagtaatagatataaaagatgat......ggaagtcagatacaccatgcagactctgtgaag...ggcagattctccatctccaaagacaatgctaagaactctctgtatctgcaaatgaacagtcagagaactgaggacatggctgtgtatggctgtacataaggtt >IGHV3-30*01 caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatgctatgcactgggtccgccaggctccaggcaaggggctagagtgggtggcagttatatcatatgat......ggaagtaataaatactacgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgcgagaga >IGHV3-30*02 caggtgcagctggtggagtctggggga...ggcgtggtccagcctggggggtccctgagactctcctgtgcagcgtctggattcaccttc............agtagctatggcatgcactgggtccgccaggctccaggcaaggggctggagtgggtggcatttatacggtatgat......ggaagtaataaatactatgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgcgaaaga >IGHV3-30*03 caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatggcatgcactgggtccgccaggctccaggcaaggggctggagtgggtggcagttatatcatatgat......ggaagtaataaatactatgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgcgagaga >IGHV3-30*04 caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatgctatgcactgggtccgccaggctccaggcaaggggctggagtgggtggcagttatatcatatgat......ggaagtaataaatactacgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgcgagaga >IGHV3-30*05 caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatggcatgcactgggtccgccaggctccaggcaaggggctagagtgggtggcagttatatcatatgat......ggaagtaataaatactacgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagctgagggcacggctgtgtattactgtgcgagaga >IGHV3-30*06 caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcgtctggattcaccttc............agtagctatggcatgcactgggtccgccaggctccaggcaaggggctagagtgggtggcagttatatcatatgat......ggaagtaataaatactacgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgcgagaga >IGHV3-30*07 caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatgctatgcactgggtccgccaggctccaggcaaggggctagagtgggtggcagttatatcatatgat......ggaagtaataaatactacgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagccgaggacacggctgtgtattactgtgcgagaga >IGHV3-30*08 caggtgcagctggtggactctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcctctgcattcaccttc............agtagctatgctatgcactgggtccgccaggctccaggcaaggggctagagtgggtggcagttatatcatatgat......ggaagtaataaatactacgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgcgaga >IGHV3-30*09 caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatgctatgcactgggtccgccaggctccaggcaaggggctggagtgggtggcagttatatcatatgat......ggaagtaataaatactacgcagactccgtgaag...ggccgattcgccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgcgagaga >IGHV3-30*10 caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatgctatgcactgggtccgccaggctccaggcaaggggctagagtgggtggcagttatatcatatgat......ggaagtaataaatactacacagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgcgagaga >IGHV3-30*11 caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcgtctggattcaccttc............agtagctatgctatgcactgggtccgccaggctccaggcaaggggctagagtgggtggcagttatatcatatgat......ggaagtaataaatactacgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgcgagaga >IGHV3-30*12 caggtgcagctggtggagtctgggggg...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcgtctggattcaccttc............agtagctatggcatgcactgggtccgccaggctccaggcaaggggctagagtgggtggcagttatatcatatgat......ggaagtaataaatactacgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagccgaggacacggctgtgtattactgtgcgagaga >IGHV3-30*13 caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatggcatgcactgggtccgccaggctccaggcaaggggctagagtgggtggcagttatatcatatgat......ggaagtaataaatactacgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacaggctgtatctgcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgcgagaga >IGHV3-30*14 caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatgctatgcactgggtccgccaggctccaggcaaggggctggagtgggtggcagttatatcatatgat......ggaagtaataaatactacgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatcttcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgcgagaga >IGHV3-30*15 caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatgctatgcactgggtccgccaggctccaggcaaggggctagagtgggtggcagttatatcatatgat......ggaagtaataaatactacgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgagcagcctgagagctgaggacacggctgtgtattactgtgcgagaga >IGHV3-30*16 caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatgctatgcactgggtccgccaggccccaggcaaggggctagagtgggtggcagttatatcatatgat......ggaagtaataaatactacgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgcgagaga >IGHV3-30*17 caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatgctatgcactgggtccgccaggctccgggcaaggggctagagtgggtggcagttatatcatatgat......ggaagtaataaatactacgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgcgagaga >IGHV3-30*18 caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatggcatgcactgggtccgccaggctccaggcaaggggctggagtgggtggcagttatatcatatgat......ggaagtaataaatactatgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgcgaaaga >IGHV3-30*19 caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcgtctggattcaccttc............agtagctatggcatgcactgggtccgccaggctccaggcaaggggctggagtgggtggcagttatatcatatgat......ggaagtaataaatactacgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgcgagaga >IGHV3-30-2*01 gaggtacagctcgtggagtccggagag...gacccaagacaacctgggggatccctgagactctcctgtgcagactctggattaaccttc............agtagctactgaaggaactcggtttcccaggctccagggaaggggctggagtgagtagtagatatacagtgtgat......ggaagtcagatatgttatgcataatctttgaag...agcaaattcaccatctccaaagaaaatgccaagaactcactgtatttgctaatgaacagtctgagagcagcgggcacagctgtgtgttactgtatgtgaggca >IGHV3-30-22*01 gaggtggagctgatagagtccatagag...gacctgagacaacctgggaagttcctgagactctcctgtgtagcctctagattcgccttc............agtagcttctgaatgagccgagttcaccagtctccaggcaaggggctggagtgagtaatagatataaaagatgat......ggaagtcagatacaccatgcagactctgtgaag...ggcagattctccatctccaaagacaatgctaagaactctctgtatctgcaaatgaacagtcagagagctgaggacatggacgtgtatggctgtacataaggtc >IGHV3-30-3*01 caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatgctatgcactgggtccgccaggctccaggcaaggggctggagtgggtggcagttatatcatatgat......ggaagcaataaatactacgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgcgagaga >IGHV3-30-3*02 caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcgtctggattcaccttc............agtagctatgctatgcactgggtccgccaggctccaggcaaggggctggagtgggtggcagttatatcatatgat......ggaagcaataaatactacgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgcgaaaga >IGHV3-30-3*03 caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatgctatgcactgggtccgccaggctccaggcaaggggctggagtgggtggcagttatatcatatgat......ggaagtaataaatactacgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgcgagaga >IGHV3-30-33*01 gaggtacagctcgtggagtccggagag...gacccaagacaacctgggggatccctgagactctcctgtgcagactctggattaaccttc............agtagctactgaaggagctcggtttcccaggctccagggaaggggctggagtgagtagtagatatacagtgtgat......ggaagtcagatatgttatgcataatctttgaag...agcaaattcaccatctccaaagaaaatgccaagaactcactgtatttgctaatgaacagtctgagagcagagggcacagctgtgtgttactgtatgtgagg >IGHV3-30-42*01 gaggtggagctgatagagcccacagag...gacctgagacaacctgggaagttcctgagactctcctgtgtagcctctagattcgccttc............agtagcttctgaatgagcccagttcaccagtctgcaggcaaggggctggagtgagtaatagatataaaagatgat......ggaagtcagatacaccatgcagactctgtgaag...ggcagattctccatctccaaagacaatgctaagaactctctgtatctgcaaatgaacagtcagagaactgaggacatggctgtgtatggctgtacataaggtt >IGHV3-30-5*01 caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatggcatgcactgggtccgccaggctccaggcaaggggctggagtgggtggcagttatatcatatgat......ggaagtaataaatactatgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgcgaaaga >IGHV3-30-5*02 caggtgcagctggtggagtctggggga...ggcgtggtccagcctggggggtccctgagactctcctgtgcagcgtctggattcaccttc............agtagctatggcatgcactgggtccgccaggctccaggcaaggggctggagtgggtggcatttatacggtatgat......ggaagtaataaatactatgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgcgaaaga >IGHV3-30-52*01 gaggtacagctcgtggagtccggagag...gacccaagacaacctgggggatccctgagactctcctgtgcagactctggattaaccttc............agtagctactgaaggaactcggtttcccaggctccagggaaggggctggagtgagtagtagatatacagtgtgat......ggaagtcagatatgttatgcataatctttgaag...agcaaattcaccatctccaaagaaaatgccaagaactcactgtatttgctaatgaacagtctgagagcagcgggcacagctgtgtgttactgtatgtgagg >IGHV3-32*01 gaggtggagctgatagagtccatagag...gacctgagacaacctgggaagttcctgagactctcctgtgtagcctctagattcgccttc............agtagcttctgaatgagccgagttcaccagtctccaggcaaggggctggagtgagtaatagatataaaagatgat......ggaagtcagatacaccatgcagactctgtgaag...ggcagattctccatctccaaagacaatgctaagaactctctgtatctgcaaatgaacactcagagagctgaggacgtggccgtgtatggctatacataaggtc >IGHV3-33*01 caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcgtctggattcaccttc............agtagctatggcatgcactgggtccgccaggctccaggcaaggggctggagtgggtggcagttatatggtatgat......ggaagtaataaatactatgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagccgaggacacggctgtgtattactgtgcgagaga >IGHV3-33*02 caggtacagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcgtctggattcaccttc............agtagctatggcatgcactgggtccgccaggctccaggcaaggggctggagtgggtggcagttatatggtatgat......ggaagtaataaatactatgcagactccgcgaag...ggccgattcaccatctccagagacaattccacgaacacgctgtttctgcaaatgaacagcctgagagccgaggacacggctgtgtattactgtgcgagaga >IGHV3-33*03 caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcgtctggattcaccttc............agtagctatggcatgcactgggtccgccaggctccaggcaaggggctggagtgggtggcagttatatggtatgat......ggaagtaataaatactatgcagactccgtgaag...ggccgattcaccatctccagagacaactccaagaacacgctgtatctgcaaatgaacagcctgagagccgaggacacggctgtgtattactgtgcgaaaga >IGHV3-33*04 caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcgtctggattcaccttc............agtagctatggcatgcactgggtccgccaggctccaggcaaggggctagagtgggtggcagttatatggtatgac......ggaagtaataaatactatgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagccgaggacacggctgtgtattactgtgcgagaga >IGHV3-33*05 caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcgtctggattcaccttc............agtagctatggcatgcactgggtccgccaggctccaggcaaggggctggagtgggtggcagttatatcatatgat......ggaagtaataaatactatgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagccgaggacacggctgtgtattactgtgcgagaga >IGHV3-33*06 caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcgtctggattcaccttc............agtagctatggcatgcactgggtccgccaggctccaggcaaggggctggagtgggtggcagttatatggtatgat......ggaagtaataaatactatgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagccgaggacacggctgtgtattactgtgcgaaaga >IGHV3-33-2*01 gaggtacagctcgtggagtccggagag...gacccaagacaacctgggggatccttgagactctcctgtgcagactctggattaaccttc............agtagctactgaatgagctcggtttcccaggctccagggaaggggctggagtgagtagtagatatacagtgtgat......ggaagtcagatatgttatgcccaatctgtgaag...agcaaattcaccatctccaaagaaaatgccaagaactcactgtatttgcaaatgaacagtctgagagcagagggcacagctgtgtgttactgtatgtgaggca >IGHV3-35*01 gaggtgcagctggtggagtctggggga...ggcttggtacagcctgggggatccctgagactctcctgtgcagcctctggattcaccttc............agtaacagtgacatgaactgggtccatcaggctccaggaaaggggctggagtgggtatcgggtgttagttggaat......ggcagtaggacgcactatgcagactctgtgaag...ggccgattcatcatctccagagacaattccaggaacaccctgtatctgcaaacgaatagcctgagggccgaggacacggctgtgtattactgtgtgagaaa >IGHV3-38*01 gaggtgcagctggtggagtctggggga...ggcttggtacagcctagggggtccctgagactctcctgtgcagcctctggattcaccgtc............agtagcaatgagatgagctggatccgccaggctccagggaaggggctggagtgggtctcatccattagtggt............ggtagcacatactacgcagactccaggaag...ggcagattcaccatctccagagacaattccaagaacacgctgtatcttcaaatgaacaacctgagagctgagggcacggccgcgtattactgtgccagatata >IGHV3-38*02 gaggtgcagctggtggagtctggggga...ggcttggtacagcctagggggtccctgagactctcctgtgcagcctctggattcaccgtc............agtagcaatgagatgagctggatccgccaggctccagggaaggggctggagtgggtctcatccattagtggt............ggtagcacatactacgcagactccaggaag...ggcagattcaccatctccagagacaattccaagaacacgctgtatcttcaaatgaacaacctgagagctgagggcacggccgtgtattactgtgccagatata >IGHV3-38*03 gaggtgcagctggtggagtctggggga...ggcttggtacagcctagggggtccctgagactctcctgtgcagcctctggattcaccgtc............agtagcaatgagatgagctggatccgccaggctccagggaagggtctggagtgggtctcatccattagtggt............ggtagcacatactacgcagactccaggaag...ggcagattcaccatctccagagacaattccaagaacacgctgtatcttcaaatgaacaacctgagagctgagggcacggccgtgtattactgtgccagatata >IGHV3-38-3*01 gaggtgcagctggtggagtctcgggga...gtcttggtacagcctggggggtccctgagactctcctgtgcagcctctggattcaccgtc............agtagcaatgagatgagctgggtccgccaggctccagggaagggtctggagtgggtctcatccattagtggt............ggtagcacatactacgcagactccaggaag...ggcagattcaccatctccagagacaattccaagaacacgctgcatcttcaaatgaacagcctgagagctgaggacacggctgtgtattactgtaagaaaga >IGHV3-43*01 gaagtgcagctggtggagtctggggga...gtcgtggtacagcctggggggtccctgagactctcctgtgcagcctctggattcaccttt............gatgattataccatgcactgggtccgtcaagctccggggaagggtctggagtgggtctctcttattagttgggat......ggtggtagcacatactatgcagactctgtgaag...ggccgattcaccatctccagagacaacagcaaaaactccctgtatctgcaaatgaacagtctgagaactgaggacaccgccttgtattactgtgcaaaagata >IGHV3-43*02 gaagtgcagctggtggagtctggggga...ggcgtggtacagcctggggggtccctgagactctcctgtgcagcctctggattcaccttt............gatgattatgccatgcactgggtccgtcaagctccagggaagggtctggagtgggtctctcttattagtggggat......ggtggtagcacatactatgcagactctgtgaag...ggccgattcaccatctccagagacaacagcaaaaactccctgtatctgcaaatgaacagtctgagaactgaggacaccgccttgtattactgtgcaaaagata >IGHV3-43D*01 gaagtgcagctggtggagtctggggga...gtcgtggtacagcctggggggtccctgagactctcctgtgcagcctctggattcaccttt............gatgattatgccatgcactgggtccgtcaagctccggggaagggtctggagtgggtctctcttattagttgggat......ggtggtagcacctactatgcagactctgtgaag...ggtcgattcaccatctccagagacaacagcaaaaactccctgtatctgcaaatgaacagtctgagagctgaggacaccgccttgtattactgtgcaaaagata >IGHV3-47*01 gaggatcagctggtggagtctggggga...ggcttggtacagcctggggggtccctgcgaccctcctgtgcagcctctggattcgccttc............agtagctatgctctgcactgggttcgccgggctccagggaagggtctggagtgggtatcagctattggtactggt.........ggtgatacatactatgcagactccgtgatg...ggccgattcaccatctccagagacaacgccaagaagtccttgtatcttcatatgaacagcctgatagctgaggacatggctgtgtattattgtgcaaga >IGHV3-47*02 gaggatcagctggtggagtctggggga...ggcttggtacagcctggggggtccctgagaccctcctgtgcagcctctggattcgccttc............agtagctatgttctgcactgggttcgccgggctccagggaagggtccggagtgggtatcagctattggtactggt.........ggtgatacatactatgcagactccgtgatg...ggccgattcaccatctccagagacaacgccaagaagtccttgtatcttcaaatgaacagcctgatagctgaggacatggctgtgtattattgtgcaagaga >IGHV3-48*01 gaggtgcagctggtggagtctggggga...ggcttggtacagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatagcatgaactgggtccgccaggctccagggaaggggctggagtgggtttcatacattagtagtagt......agtagtaccatatactacgcagactctgtgaag...ggccgattcaccatctccagagacaatgccaagaactcactgtatctgcaaatgaacagcctgagagccgaggacacggctgtgtattactgtgcgagaga >IGHV3-48*02 gaggtgcagctggtggagtctggggga...ggcttggtacagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatagcatgaactgggtccgccaggctccagggaaggggctggagtgggtttcatacattagtagtagt......agtagtaccatatactacgcagactctgtgaag...ggccgattcaccatctccagagacaatgccaagaactcactgtatctgcaaatgaacagcctgagagacgaggacacggctgtgtattactgtgcgagaga >IGHV3-48*03 gaggtgcagctggtggagtctggggga...ggcttggtacagcctggagggtccctgagactctcctgtgcagcctctggattcaccttc............agtagttatgaaatgaactgggtccgccaggctccagggaaggggctggagtgggtttcatacattagtagtagt......ggtagtaccatatactacgcagactctgtgaag...ggccgattcaccatctccagagacaacgccaagaactcactgtatctgcaaatgaacagcctgagagccgaggacacggctgtttattactgtgcgagaga >IGHV3-48*04 gaggtgcagctggtggagtctggggga...ggcttggtacagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatagcatgaactgggtccgccaggctccagggaaggggctggagtgggtttcatacattagtagtagt......agtagtaccatatactacgcagactctgtgaag...ggccgattcaccatctccagagacaacgccaagaactcactgtatctgcaaatgaacagcctgagagccgaggacacggctgtgtattactgtgcgagaga >IGHV3-49*01 gaggtgcagctggtggagtctggggga...ggcttggtacagccagggcggtccctgagactctcctgtacagcttctggattcaccttt............ggtgattatgctatgagctggttccgccaggctccagggaaggggctggagtgggtaggtttcattagaagcaaagcttatggtgggacaacagaatacaccgcgtctgtgaaa...ggcagattcaccatctcaagagatggttccaaaagcatcgcctatctgcaaatgaacagcctgaaaaccgaggacacagccgtgtattactgtactagaga >IGHV3-49*02 gaggtgcagctggtggagtctggggga...ggcttggtacagccagggccgtccctgagactctcctgtacagcttctggattcaccttt............gggtattatcctatgagctgggtccgccaggctccagggaaggggctggagtgggtaggtttcattagaagcaaagcttatggtgggacaacagaatacgccgcgtctgtgaaa...ggcagattcaccatctcaagagatgattccaaaagcatcgcctatctgcaaatgaacagcctgaaaaccgaggacacagccgtgtattactgtactagaga >IGHV3-49*03 gaggtgcagctggtggagtctggggga...ggcttggtacagccagggcggtccctgagactctcctgtacagcttctggattcaccttt............ggtgattatgctatgagctggttccgccaggctccagggaaggggctggagtgggtaggtttcattagaagcaaagcttatggtgggacaacagaatacgccgcgtctgtgaaa...ggcagattcaccatctcaagagatgattccaaaagcatcgcctatctgcaaatgaacagcctgaaaaccgaggacacagccgtgtattactgtactagaga >IGHV3-49*04 gaggtgcagctggtggagtctggggga...ggcttggtacagccagggcggtccctgagactctcctgtacagcttctggattcaccttt............ggtgattatgctatgagctgggtccgccaggctccagggaaggggctggagtgggtaggtttcattagaagcaaagcttatggtgggacaacagaatacgccgcgtctgtgaaa...ggcagattcaccatctcaagagatgattccaaaagcatcgcctatctgcaaatgaacagcctgaaaaccgaggacacagccgtgtattactgtactagaga >IGHV3-49*05 gaggtgcagctggtggagtctggggga...ggcttggtaaagccagggcggtccctgagactctcctgtacagcttctggattcaccttt............ggtgattatgctatgagctggttccgccaggctccagggaaggggctggagtgggtaggtttcattagaagcaaagcttatggtgggacaacagaatacgccgcgtctgtgaaa...ggcagattcaccatctcaagagatgattccaaaagcatcgcctatctgcaaatgaacagcctgaaaaccgaggacacagccgtgtattactgtactagaga >IGHV3-52*01 gaggtgcagctggtggagtctgggtga...ggcttggtacagcctggagggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctcctggatgcactgggtctgccaggctccggagaaggggctggagtgggtggccgacataaagtgtgac......ggaagtgagaaatactatgtagactctgtgaag...ggccgattgaccatctccagagacaatgccaagaactccctctatctgcaagtgaacagcctgagagctgaggacatgaccgtgtattactgtgtgagagg >IGHV3-52*02 gaggtgcagctggtggagtctgggtga...ggcttggtacagcctggagggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctcctggatgcactgggtctgccaggctccggagaaggggcaggagtgggtggccgacataaagtgtgac......ggaagtgagaaatactatgtagactctgtgaag...ggccgattgaccatctccagagacaatgccaagaactccctctatctgcaagtgaacagcctgagagctgaggacatgaccgtgtattactgtgtgaga >IGHV3-52*03 gaggtgcagctggtcgagtctgggtga...ggcttggtacagcctggagggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctcctggatgcactgggtctgccaggctccggagaaggggctggagtgggtggccgacataaagtgtgac......ggaagtgagaaatactatgtagactctgtgaag...ggccgattgaccatctccagagacaatgccaagaactccctctatctgcaagtgaacagcctgagagctgaggacatgaccgtgtattactgtgtgaga >IGHV3-53*01 gaggtgcagctggtggagtctggagga...ggcttgatccagcctggggggtccctgagactctcctgtgcagcctctgggttcaccgtc............agtagcaactacatgagctgggtccgccaggctccagggaaggggctggagtgggtctcagttatttatagcggt.........ggtagcacatactacgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatcttcaaatgaacagcctgagagccgaggacacggccgtgtattactgtgcgagaga >IGHV3-53*02 gaggtgcagctggtggagactggagga...ggcttgatccagcctggggggtccctgagactctcctgtgcagcctctgggttcaccgtc............agtagcaactacatgagctgggtccgccaggctccagggaaggggctggagtgggtctcagttatttatagcggt.........ggtagcacatactacgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatcttcaaatgaacagcctgagagccgaggacacggccgtgtattactgtgcgagaga >IGHV3-53*03 gaggtgcagctggtggagtctggagga...ggcttgatccagcctggggggtccctgagactctcctgtgcagcctctgggttcaccgtc............agtagcaactacatgagctgggtccgccagcctccagggaaggggctggagtgggtctcagttatttatagcggt.........ggtagcacatactacgcagactctgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatcttcaaatgaacagcctgagagccgaggacacggccgtgtattactgtgctaggga >IGHV3-53*04 gaggtgcagctggtggagtctggagga...ggcttggtccagcctggggggtccctgagactctcctgtgcagcctctgggttcaccgtc............agtagcaactacatgagctgggtccgccaggctccagggaaggggctggagtgggtctcagttatttatagcggt.........ggtagcacatactacgcagactccgtgaag...ggccgattcaccatctccagacacaattccaagaacacgctgtatcttcaaatgaacagcctgagagctgaggacacggccgtgtattactgtgcgagaga >IGHV3-54*01 gaggtacagctggtggagtctgaagaa...aaccaaagacaacttgggggatccctgagactctcctgtgcagactctggattaaccttc............agtagctactgaatgagctcagattcccaagctccagggaaggggctggagtgagtagtagatatatagtaggat......agaagtcagctatgttatgcacaatctgtgaag...agcagattcaccatctccaaagaaaatgccaagaactcactctgtttgcaaatgaacagtctgagagcagagggcacggccgtgtattactgtatgtgagt >IGHV3-54*02 gaggtacagctggtggagtctgaagaa...aaccaaagacaacttgggggatccctgagactctcctgtgcagactctggattaaccttc............agtagctactgaatgagctcagattcccaggctccagggaaggggctggagtgagtagtagatatatagtacgat......agaagtcagatatgttatgcacaatctgtgaag...agcagattcaccatctccaaagaaaatgccaagaactcactccgtttgcaaatgaacagtctgagagcagagggcacggccgtgtattactgtatgtgagg >IGHV3-54*04 gaggtacagctggtggagtctgaagaa...aaccaaagacaacttgggggatccctgagactctcctgtgcagactctggattaaccttc............agtagctactgaatgagctcagattcccaggctccagggaaggggctggagtgagtagtagatatatagtaggat......agaagtcagctatgttatgcacaatctgtgaag...agcagattcaccatctccaaagaaaatgccaagaactcactctgtttgcaaatgaacagtctgagagcagagggcacggccgtgtattactgtatgtgagt >IGHV3-62*01 gaggtgcagctggtggagtctggggaa...ggcttggtccagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctctgctatgcactgggtccgccaggctccaagaaagggtttgtagtgggtctcagttattagtacaagt......ggtgataccgtactctacacagactctgtgaag...ggccgattcaccatctccagagacaatgcccagaattcactgtctctgcaaatgaacagcctgagagccgagggcacagttgtgtactactgtgtgaaaga >IGHV3-63*01 gaggtggagctgatagagtccatagag...ggcctgagacaacttgggaagttcctgagactctcctgtgtagcctctggattcaccttc............agtagctactgaatgagctgggtcaatgagactctagggaaggggctggagggagtaatagatgtaaaatatgat......ggaagtcagatataccatgcagactctgtgaag...ggcagattcaccatctccaaagacaatgctaagaactcaccgtatctccaaacgaacagtctgagagctgaggacatgaccatgcatggctgtacataaggtt >IGHV3-63*02 gaggtggagctgatagagtccatagag...ggcctgagacaacttgggaagttcctgagactctcctgtgtagcctctggattcaccttc............agtagctactgaatgagctgggtcaatgagactctagggaaggggctggagggagtaatagatgtaaaatatgat......ggaagtcagatataccatgcagactctgtgaag...ggcagattcaccatctccaaagacaatgctaagaactcaccgtatctgcaaacgaacagtctgagagctgaggacatgaccatgcatggctgtacataa >IGHV3-64*01 gaggtgcagctggtggagtctggggga...ggcttggtccagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatgctatgcactgggtccgccaggctccagggaagggactggaatatgtttcagctattagtagtaat......gggggtagcacatattatgcaaactctgtgaag...ggcagattcaccatctccagagacaattccaagaacacgctgtatcttcaaatgggcagcctgagagctgaggacatggctgtgtattactgtgcgagaga >IGHV3-64*02 gaggtgcagctggtggagtctggggaa...ggcttggtccagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatgctatgcactgggtccgccaggctccagggaagggactggaatatgtttcagctattagtagtaat......gggggtagcacatattatgcagactctgtgaag...ggcagattcaccatctccagagacaattccaagaacacgctgtatcttcaaatgggcagcctgagagctgaggacatggctgtgtattactgtgcgagaga >IGHV3-64*03 gaggtgcagctggtggagtctggggga...ggcttggtccagcctggggggtccctgagactctcctgttcagcctctggattcaccttc............agtagctatgctatgcactgggtccgccaggctccagggaagggactggaatatgtttcagctattagtagtaat......gggggtagcacatactacgcagactcagtgaag...ggcagattcaccatctccagagacaattccaagaacacgctgtatgtccaaatgagcagtctgagagctgaggacacggctgtgtattactgtgtgaaaga >IGHV3-64*04 caggtgcagctggtggagtctggggga...ggcttggtccagcctggggggtccctgagactctcctgttcagcctctggattcaccttc............agtagctatgctatgcactgggtccgccaggctccagggaagggactggaatatgtttcagctattagtagtaat......gggggtagcacatactacgcagactcagtgaag...ggcagattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgcgagaga >IGHV3-64*05 gaggtgcagctggtggagtctggggga...ggcttggtccagcctggggggtccctgagactctcctgttcagcctctggattcaccttc............agtagctatgctatgcactgggtccgccaggctccagggaagggactggaatatgtttcagctattagtagtaat......gggggtagcacatactacgcagactcagtgaag...ggcagattcaccatctccagagacaattccaagaacacgctgtatgttcaaatgagcagtctgagagctgaggacacggctgtgtattactgtgtgaaaga >IGHV3-64D*06 gaggtgcagctggtggagtctggggga...ggcttggtccagcctggggggtccctgagactctcctgttcagcctctggattcaccttc............agtagctatgctatgcactgggtccgccaggctccagggaagggactggaatatgtttcagctattagtagtaat......gggggtagcacatactacgcagactccgtgaag...ggcagattcaccatctccagagacaattccaagaacacgctgtatcttcaaatgagcagtctgagagctgaggacacggctgtgtattactgtgtgaaaga >IGHV3-66*01 gaggtgcagctggtggagtctggggga...ggcttggtccagcctggggggtccctgagactctcctgtgcagcctctggattcaccgtc............agtagcaactacatgagctgggtccgccaggctccagggaaggggctggagtgggtctcagttatttatagcggt.........ggtagcacatactacgcagactccgtgaag...ggcagattcaccatctccagagacaattccaagaacacgctgtatcttcaaatgaacagcctgagagccgaggacacggctgtgtattactgtgcgagaga >IGHV3-66*02 gaggtgcagctggtggagtctggggga...ggcttggtccagcctggggggtccctgagactctcctgtgcagcctctggattcaccgtc............agtagcaactacatgagctgggtccgccaggctccagggaaggggctggagtgggtctcagttatttatagcggt.........ggtagcacatactacgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatcttcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgcgaga >IGHV3-66*03 gaggtgcagctggtggagtctggagga...ggcttgatccagcctggggggtccctgagactctcctgtgcagcctctgggttcaccgtc............agtagcaactacatgagctgggtccgccaggctccagggaaggggctggagtgggtctcagttatttatagctgt.........ggtagcacatactacgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatcttcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgcgagaga >IGHV3-66*04 gaggtgcagctggtggagtctggggga...ggcttggtccagcctggggggtccctgagactctcctgtgcagcctctggattcaccgtc............agtagcaactacatgagctgggtccgccaggctccagggaaggggctggagtgggtctcagttatttatagcggt.........ggtagcacatactacgcagactccgtgaag...ggcagattcaccatctccagagacaattccaagaacacgctgtatcttcaaatgaacagcctgagagccgaggacacggctgtgtattactgtgcgagaca >IGHV3-69-1*01 gaggtgcagctggtggagtctggggga...ggcttggtaaagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agtgactactacatgaactgggtccgccaggctccagggaaggggctggagtgggtctcatccattagtagtagt.........agtaccatatactacgcagactctgtgaag...ggccgattcaccatctccagagacaacgccaagaactcactgtatctgcaaatgaacagcctgagagccgaggacacggctgtgtattactgtgcgagaga >IGHV3-69-1*02 gaggtgcagctggtggagtctggggga...ggcttggtaaagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agtgactactacatgaactgggtccgccaggctccagggaaggggctggagtgggtctcatccattagtagtagt.........agtaccatatactacgcagactctgtgaag...ggccgattcaccatctccagagacaacgccaagaactcactgtatctgcaaatgaacagcctgagagccgaggacacggctgtttattactgtgcgagaga >IGHV3-7*01 gaggtgcagctggtggagtctggggga...ggcttggtccagcctggggggtccctgagactctcctgtgcagcctctggattcaccttt............agtagctattggatgagctgggtccgccaggctccagggaaggggctggagtgggtggccaacataaagcaagat......ggaagtgagaaatactatgtggactctgtgaag...ggccgattcaccatctccagagacaacgccaagaactcactgtatctgcaaatgaacagcctgagagccgaggacacggctgtgtattactgtgcgagaga >IGHV3-7*02 gaggtgcagctggtggagtctggggga...ggcttggtccagcctggggggtccctgagactctcctgtgcagcctctggattcaccttt............agtagctattggatgagctgggtccgccaggctccagggaaagggctggagtgggtggccaacataaagcaagat......ggaagtgagaaatactatgtggactctgtgaag...ggccgattcaccatctccagagacaacgccaagaactcactgtatctgcaaatgaacagcctgagagccgaggacacggctgtgtattactgtgcgaga >IGHV3-7*03 gaggtgcagctggtggagtctggggga...ggcttggtccagcctggggggtccctgagactctcctgtgcagcctctggattcaccttt............agtagctattggatgagctgggtccgccaggctccagggaaggggctggagtgggtggccaacataaagcaagat......ggaagtgagaaatactatgtggactctgtgaag...ggccgattcaccatctccagagacaacgccaagaactcactgtatctgcaaatgaacagcctgagagccgaggacacggccgtgtattactgtgcgagaga >IGHV3-71*01 gaggtgcagctggtggagtccggggga...ggcttggtccagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agtgactactacatgagctgggtccgccaggctcccgggaaggggctggagtgggtaggtttcattagaaacaaagctaatggtgggacaacagaatagaccacgtctgtgaaa...ggcagattcacaatctcaagagatgattccaaaagcatcacctatctgcaaatgaacagcctgagagccgaggacacggccgtgtattactgtgcgagaga >IGHV3-71*02 gaggtgcagctggtggagtctggggga...ggcttggtccagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agtgactactacatgagctgggtccgccaggctcccgggaaggggctggagtgggtaggtttcattagaaacaaagctaatggtgggacaacagaatagaccacgtctgtgaaa...ggcagattcacaatctcaagagatgattccaaaagcatcacctatctgcaaatgaacagcctgagagccgaggacatggctgtgtattactgtgcgagaga >IGHV3-71*03 gaggtgcagctggtggagtctggggga...ggcttggtccagcctggggggtccctgagactctcctgtgcagcctctggtttcaccttc............agtgactactacatgagctgggtccgccaggctcccgggaaggggctggagtgggtaggtttcattagaaacaaagctaatggtgggacaacagaatagaccacgtctgtgaaa...ggcagattcacaatctcaagagatgattccaaaagcatcacctatctgcaaatgaacagcctgagagccgaggacacggctgtgtattactgtgcgagaga >IGHV3-72*01 gaggtgcagctggtggagtctggggga...ggcttggtccagcctggagggtccctgagactctcctgtgcagcctctggattcaccttc............agtgaccactacatggactgggtccgccaggctccagggaaggggctggagtgggttggccgtactagaaacaaagctaacagttacaccacagaatacgccgcgtctgtgaaa...ggcagattcaccatctcaagagatgattcaaagaactcactgtatctgcaaatgaacagcctgaaaaccgaggacacggccgtgtattactgtgctagaga >IGHV3-72*02 ....................................................................................accttc............agtgaccactacatggactgggtccgccaggctccagggaaggggctggagtgggttggccgtactagaaacaaagctaacagctacaccacagaatacgccgcgtctgtgaaa...ggcagattcaccatctcaagagatgattcaaagaactcactgtat >IGHV3-73*01 gaggtgcagctggtggagtctggggga...ggcttggtccagcctggggggtccctgaaactctcctgtgcagcctctgggttcaccttc............agtggctctgctatgcactgggtccgccaggcttccgggaaagggctggagtgggttggccgtattagaagcaaagctaacagttacgcgacagcatatgctgcgtcggtgaaa...ggcaggttcaccatctccagagatgattcaaagaacacggcgtatctgcaaatgaacagcctgaaaaccgaggacacggccgtgtattactgtactagaca >IGHV3-73*02 gaggtgcagctggtggagtccggggga...ggcttggtccagcctggggggtccctgaaactctcctgtgcagcctctgggttcaccttc............agtggctctgctatgcactgggtccgccaggcttccgggaaagggctggagtgggttggccgtattagaagcaaagctaacagttacgcgacagcatatgctgcgtcggtgaaa...ggcaggttcaccatctccagagatgattcaaagaacacggcgtatctgcaaatgaacagcctgaaaaccgaggacacggccgtgtattactgtactagaca >IGHV3-74*01 gaggtgcagctggtggagtccggggga...ggcttagttcagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctactggatgcactgggtccgccaagctccagggaaggggctggtgtgggtctcacgtattaatagtgat......gggagtagcacaagctacgcggactccgtgaag...ggccgattcaccatctccagagacaacgccaagaacacgctgtatctgcaaatgaacagtctgagagccgaggacacggctgtgtattactgtgcaagaga >IGHV3-74*02 gaggtgcagctggtggagtctggggga...ggcttagttcagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctactggatgcactgggtccgccaagctccagggaaggggctggtgtgggtctcacgtattaatagtgat......gggagtagcacaagctacgcggactccgtgaag...ggccgattcaccatctccagagacaacgccaagaacacgctgtatctgcaaatgaacagtctgagagccgaggacacggctgtgtattactgtgcaaga >IGHV3-74*03 gaggtgcagctggtggagtccggggga...ggcttagttcagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctactggatgcactgggtccgccaagctccagggaaggggctggtgtgggtctcacgtattaatagtgat......gggagtagcacaacgtacgcggactccgtgaag...ggccgattcaccatctccagagacaacgccaagaacacgctgtatctgcaaatgaacagtctgagagccgaggacacggctgtgtattactgtgcaagaga >IGHV3-9*01 gaagtgcagctggtggagtctggggga...ggcttggtacagcctggcaggtccctgagactctcctgtgcagcctctggattcaccttt............gatgattatgccatgcactgggtccggcaagctccagggaagggcctggagtgggtctcaggtattagttggaat......agtggtagcataggctatgcggactctgtgaag...ggccgattcaccatctccagagacaacgccaagaactccctgtatctgcaaatgaacagtctgagagctgaggacacggccttgtattactgtgcaaaagata >IGHV3-9*02 gaagtgcagctggtggagtctggggga...ggcttggtacagcctggcaggtccctgagactctcctgtgcagcctctggattcacctct............gatgattatgccatgcactgggtccggcaagctccagggaagggcctggagtgggtctcaggtattagttggaat......agtggtagcataggctatgcggactctgtgaag...ggccgattcaccatctccagagacaacgccaagaactccctgtatctgcaaatgaacagtctgagagctgaggacacggccttgtattactgtgcaaaagata >IGHV3-9*03 gaagtgcagctggtggagtctggggga...ggcttggtacagcctggcaggtccctgagactctcctgtgcagcctctggattcaccttt............gatgattatgccatgcactgggtccggcaagctccagggaagggcctggagtgggtctcaggtattagttggaat......agtggtagcataggctatgcggactctgtgaag...ggccgattcaccatctccagagacaacgccaagaactccctgtatctgcaaatgaacagtctgagagctgaggacatggccttgtattactgtgcaaaagata >IGHV3-NL1*01 caggtgcagctggtggagtctggggga...ggcgtggtccagcctggggggtccctgagactctcctgtgcagcgtctggattcaccttc............agtagctatggcatgcactgggtccgccaggctccaggcaaggggctggagtgggtctcagttatttatagcggt......ggtagtagcacatactatgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgcgaaaga >IGHV3/OR15-7*01 gaggtgcagctggtggagtctggggga...ggcttggtccagcctgggggttctctgagactctcatgtgcagcctctggattcaccttc............agtgaccactacatgagctgggtccgccaggctcaagggaaagggctagagttggtaggtttaataagaaacaaagctaacagttacacgacagaatatgctgcgtctgtgaaa...ggcagacttaccatctcaagagaggattcaaagaacacgatgtatctgcaaatgagcaacctgaaaaccgaggacttggccgtgtattactgtgctaga >IGHV3/OR15-7*02 gaggtgcagctgttggagtctggggga...ggcttggtccagcctgggggttctctgagactctcatgtgctgcctctggattcaccttc............agtgaccactacatgagctgggtccgccaggctcaagggaaagggctagagttggtaggtttaataagaaacaaagctaacagttacacgacagaatatgctgcgtctgtgaaa...ggcagacttaccatctcaagagaggattcaaagaacacgctgtatctgcaaatgagcagcctgaaaaccgaggacttggccgtgtattactgtgctaga >IGHV3/OR15-7*03 gaggtgcagctggtggagtctggggga...ggcttggtccagcctgggggttctctgagactctcatgtgcagcctctggattcaccttc............agtgaccactacatgagctgggtccgccaggctcaagggaaagggctagagttggtaggtttaataagaaacaaagctaacagttacacgacagaatatgctgcgtctgtgaaa...ggcagacttaccatctcaagagaggattcaaagaacacgctgtatctgcaaatgagcagcctgaaaaccgaggacttggccgtgtattactgtgctaga >IGHV3/OR15-7*05 gaggtgcagctggtggagtctggggga...ggcttggtccagcctgggggttctctgagactctcatgtgcagcctctggattcaccttc............agtgaccactacatgagctgggtccgccaggctcaagggaaagggctagagttggtaggtttaataagaaacaaagctaacagttacacgacagaatatgctgcgtctgtgaaa...ggcagacttaccatctcaagagaggattcaaagaacacgctgtatctgcaaatgagcaacctgaaaaccgaggacttggccgtgtattactgtgctagaga >IGHV3/OR16-10*01 gaggttcagctggtgcagtctggggga...ggcttggtacatcctggggggtccctgagactctcctgtgcaggctctggattcaccttc............agtagctatgctatgcactgggttcgccaggctccaggaaaaggtctggagtgggtatcagctattggtactggt.........ggtggcacatactatgcagactccgtgaag...ggccgattcaccatctccagagacaatgccaagaactccttgtatcttcaaatgaacagcctgagagccgaggacatggctgtgtattactgtgcaaga >IGHV3/OR16-10*02 gaggttcagctggtgcagtctggggga...ggcttggtacagcctggggggtccctgagactctcctgtgcaggctctggattcaccttc............agtagctatgctatgcactgggttcgccaggctccaggaaaaggtctggagtgggtatcagctattggtactggt.........ggtggcacatactatgcagactccgtgaag...ggccgattcaccatctccagagacaatgccaagaactccttgtatcttcaaatgaacagcctgagagccgaggacatggctgtgtattactgtgcaaga >IGHV3/OR16-10*03 gaggtgcagctggtggagtctggggga...ggcttggtacagcctggggggtccctgagactctcctgtgcaggctctggattcaccttc............agtagctatgctatgcactgggttcgccaggctccaggaaaaggtctggagtgggtatcagctattggtactggt.........ggtggcacatactatgcagactccgtgaag...ggccgattcaccatctccagagacaatgccaagaactccttgtatcttcaaatgaacagcctgagagccgaggacatggctgtgtattactgtgcaagaga >IGHV3/OR16-12*01 gaggtgcagctggtagagtctgggaga...ggcttggcccagcctggggggtacctaaaactctccggtgcagcctctggattcaccgtc............ggtagctggtacatgagctggatccaccaggctccagggaagggtctggagtgggtctcatacattagtagtagt......ggttgtagcacaaactacgcagactctgtgaag...ggcagattcaccatctccacagacaactcaaagaacacgctctacctgcaaatgaacagcctgagagtggaggacacggccgtgtattactgtgcaaga >IGHV3/OR16-13*01 gaggtgcagctggtggagtctggggga...ggcttagtacagcctggagggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctactggatgcactgggtccgccaagctccagggaaggggctggtgtgggtctcacgtattaatagtgat......gggagtagcacaagctacgcagactccatgaag...ggccaattcaccatctccagagacaatgctaagaacacgctgtatctgcaaatgaacagtctgagagctgaggacatggctgtgtattactgtactaga >IGHV3/OR16-14*01 gaggtgcagctggaggagtctggggga...ggcttagtacagcctggagggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctactggatgcactgggtccgccaatctccagggaaggggctggtgtgagtctcacgtattaatagtgat......gggagtagcacaagctacgcagactccttgaag...ggccaattcaccatctccagagacaatgctaagaacacgctgtatctgcaaatgaacagtctgagagctgaggacatggctgtgtattactgtactaga >IGHV3/OR16-15*01 gaagtgcagctggtggagtctggggga...ggcttggtccagcctggggggtccctgagactctcctgtgcagcctctgtattcaccttc............agtaacagtgacataaactgggtcctctaggctccaggaaaggggctggagtgggtctcgggtattagttggaat......ggcggtaagacgcactatgtggactccgtgaag...ggccaattttccatctccagagacaattccagcaagtccctgtatctgcaaaagaacagacagagagccaaggacatggccgtgtattactgtgtgagaaa >IGHV3/OR16-15*02 gaggtgcagctggtggagtctggggga...ggcttggtccagcctggggggtccctgagacactcctgtgcagcctctggattcaccttc............agtaacagtgacatgaactgggtcctctaggctccaggaaaggggctggagtgggtctcgggtattagttggaat......ggcggtaagacgcactatgtggactccgtgaag...ggccaatttaccatctccagagacaattccagcaagtccctgtatctgcaaaagaacagacagagagccaaagacatggccgtgtattactgtgtgaga >IGHV3/OR16-16*01 gaggtgcagctggtggagtctggggga...ggcttggtccagcctggggggtccctgagacactcctgtgcagcctctggattcaccttc............agtaacagtgacatgaactgggtcctctaggctccaggaaaggggctggagtgggtctcggatattagttggaat......ggcggtaagacgcactatgtggactccgtgaag...ggccaatttaccatctccagagacaattccagcaagtccctgtatctgcaaaagaacagacagagagccaaggacatggccgtgtattactgtgtgaga >IGHV3/OR16-6*02 gaggtgcagctggtggagtctgcggga...ggccttggtacagcctgggggtcccttagactctcctgtgcagcctctggattcacttgc............agtaacgcctggatgagctgggtccgccaggctccagggaaggggctggagtgggttggctgtattaaaagcaaagctaatggtgggacaacagactacgctgcacctgtgaaa...ggcagattcaccatctcaagagatgattcaaaaaacacgctgtatctgcaaatgatcagcctgaaaaccgaggacacggccgtgtattactgtaccacagg >IGHV3/OR16-8*01 gaggtgcagctggtggagtctggggga...ggcttggtacagcctggggggtccctgagactgtcctgtccagcctctggattcaccttc............agtaaccactacatgagctgggtccgccaggctccagggaagggactggagtgggtttcatacattagtggtgat......agtggttacacaaactacgcagactctgtgaag...ggccgattcaccatctccagggacaacgccaataactcaccgtatctgcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgtgaaa >IGHV3/OR16-8*02 gaggtgcagctggtggagtctggggga...ggcttggtacagcctggggggtccctgagactgtcctgtccagactctggattcaccttc............agtaaccactacatgagctgggtccgccaggctccagggaagggactggagtggatttcatacattagtggtgat......agtggttacacaaactacgcagactctgtgaag...ggccgattcaccatctccagggacaacgccaataactcaccgtatctgcaaatgaacagcttgagagctgaggacacggctgtgtattactgtgtgaaaca >IGHV3/OR16-9*01 gaggtgcagctggtggagtctggagga...ggcttggtacagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agtaaccactacacgagctgggtccgccaggctccagggaagggactggagtgggtttcatacagtagtggtaat......agtggttacacaaactacgcagactctgtgaaa...ggccgattcaccatctccagggacaacgccaagaactcactgtatctgcaaatgaacagcctgagagccgaggacacggctgtgtattactgtgtgaaa >IGHV4-28*01 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggacaccctgtccctcacctgcgctgtctctggttactccatcagc.........agtagtaactggtggggctggatccggcagcccccagggaagggactggagtggattgggtacatctattatagt.........gggagcacctactacaacccgtccctcaag...agtcgagtcaccatgtcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgtggacacggccgtgtattactgtgcgagaaa >IGHV4-28*02 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcacagaccctgtccctcacctgcgctgtctctggttactccatcagc.........agtagtaactggtggggctggatccggcagcccccagggaagggactggagtggattgggtacatctattatagt.........gggagcatctactacaacccgtccctcaag...agtcgagtcaccatgtcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgtggacacggccgtgtattactgtgcgagaaa >IGHV4-28*03 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggacaccctgtccctcacctgcgctgtctctggttactccatcagc.........agtagtaactggtggggctggatccggcagcccccagggaagggactggagtggattgggtacatctattatagt.........gggagcacctactacaacccgtccctcaag...agtcgagtcaccatgtcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgtggacacggccgtgtattactgtgcgagaga >IGHV4-28*04 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggacaccctgtccctcacctgcgctgtctctggttactccatcagc.........agtagtaactggtggggctggatccggcagcccccagggaagggactggagtggattgggtacatctattatagt.........gggagcacctactacaacccgtccctcaag...agtcgagtcaccatgtcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgtggacaccggcgtgtattactgtgcgaga >IGHV4-28*05 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggacaccctgtccctcacctgcgctgtctctggttactccatcagc.........agtagtaactggtggggctggatccggcagcccccagggaagggactggagtggattgggtacatctattatagt.........gggagcatctactacaacccgtccctcaag...agtcgagtcaccatgtcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgtggacacggccgtgtattactgtgcgagaaa >IGHV4-28*06 caggtgcagctacaggagtcgggccca...ggactggtgaagccttcggacaccctgtccctcacctgcgctgtctctggttactccatcagc.........agtagtaactggtggggctggatccggcagcccccagggaagggactggagtggattgggtacatctattatagt.........gggagcaccaactacaacccgtccctcaag...agtcgagtcaccatgtcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccttggacacggccgtgtattactgtgcgagaaa >IGHV4-28*07 caggtacagctgcaggagtcgggccca...ggactggtgaagccttcggacaccctgtccctcacctgcgctgtctctggttactccatcagc.........agtagtaactggtggggctggatccggcagcccccagggaagggactggagtggattgggtacatctattatagt.........gggagcacctactacaacccgtccctcaag...agtcgagtcaccatgtcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgtggacacggccgtgtattactgtgcgagaaa >IGHV4-30-2*01 cagctgcagctgcaggagtccggctca...ggactggtgaagccttcacagaccctgtccctcacctgcgctgtctctggtggctccatcagc......agtggtggttactcctggagctggatccggcagccaccagggaagggcctggagtggattgggtacatctatcatagt.........gggagcacctactacaacccgtccctcaag...agtcgagtcaccatatcagtagacaggtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggccgtgtattactgtgccagaga >IGHV4-30-2*02 cagctgcagctgcaggagtccggctca...ggactggtgaagccttcacagaccctgtccctcacctgcgctgtctctggtggctccatcagc......agtggtggttactcctggagctggatccggcagccaccagggaagggcctggagtggattgggtacatctatcatagt.........gggagcacctactacaacccgtccctcaag...agtcgagtcaccatatcagtagacaggtccaagaaccagttctccctgaagctgagctctgtgaccgctgcggacacggccgtgtattactgtgcg >IGHV4-30-2*03 cagctgcagctgcaggagtccggctca...ggactggtgaagccttcacagaccctgtccctcacctgcgctgtctctggtggctccatcagc......agtggtggttactcctggagctggatccggcagccaccagggaagggcctggagtggattgggagtatctattatagt.........gggagcacctactacaacccgtccctcaag...agtcgagtcaccatatccgtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgctgcagacacggctgtgtattactgtgcgagaca >IGHV4-30-2*04 ...........................................................................tctggtggctccatcagc......agtggtggttactcctggagctggatccggcagccaccagggaagggcctggagtggattgggtacatctatcatagt.........gggagcacctactacaacccgtccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcagacacggccgtgtattactgtgcgagaga >IGHV4-30-2*05 cagctgcagctgcaggagtccggctca...ggactggtgaagccttcacagaccctgtccctcacctgcgctgtctctggtggctccatcagc......agtggtggttactcctggagctggatccggcagccaccagggaagggcctggagtggattgggtacatctatcatagt.........gggagcacctactacaacccgtccctcaag...agtcgagttaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgactgccgcagacacggccgtgtattactgtgccagaga >IGHV4-30-2*06 cagctgcagctgcaggagtccggctca...ggactggtgaagccttcacagaccctgtccctcacctgcgctgtctctggtggctccatcagc......agtggtggttactcctggagctggatccggcagtcaccagggaagggcctggagtggattgggtacatctatcatagt.........gggagcacctactacaacccgtccctcaag...agtcgagtcaccatatcagtagacaggtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggccgtgtattactgtgccagaga >IGHV4-30-4*01 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcacagaccctgtccctcacctgcactgtctctggtggctccatcagc......agtggtgattactactggagttggatccgccagcccccagggaagggcctggagtggattgggtacatctattacagt.........gggagcacctactacaacccgtccctcaag...agtcgagttaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgactgccgcagacacggccgtgtattactgtgccagaga >IGHV4-30-4*02 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggacaccctgtccctcacctgcactgtctctggtggctccatcagc......agtggtgattactactggagttggatccgccagcccccagggaagggcctggagtggattgggtacatctattacagt.........gggagcacctactacaacccgtccctcaag...agtcgagttaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgactgcagcagacacggccgtgtattactgtgccagaga >IGHV4-30-4*03 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcacagaccctgtccctcacctgcactgtctctggtggctccatcagc......agtggtgattactactggagttggatccgccagcccccagggaagggcctggagtggattgggtacatctattacagt.........gggagcacctactacaacccgtccctcaag...agtcgagttaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgactgccgcggacacggccgtgtattactg >IGHV4-30-4*04 caggtgcagctgcaggactcgggccca...ggactggtgaagccttcacagaccctgtccctcacctgcactgtctctggtggctccatcagc......agtggtgattactactggagttggatccgccagcccccagggaagggcctggagtggattgggtacttctattacagt.........gggagcacctactacaacccgtccctcaag...agtcgagttaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgactgccgcagacacggccgtgtattactg >IGHV4-30-4*05 ..........................................................................ctctggtggctccatcagc......agtggtgattactactggagttggatccgccagcncccagggaagggcctggagtggattgggtacatctattacagt.........gggagcacctactacaacccgtccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgactgccgcagacacggccgtgtattactgtgccagaga >IGHV4-30-4*06 ...........................................................................tctggtggctccatcagc......agtggtgattactactggagttggatccgccagcacccagggaagggcctggagtggattgggtacatctattacagt.........gggagcacctactacaacccgtccctcaag...agtcgagttaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgactgccgcagacacggccgtgtattactgtgccagaga >IGHV4-30-4*07 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcacagaccctgtccctcacctgcgctgtctctggtggctccatcagc......agtggtggttactcctggagctggatccggcagccaccagggaagggactggagtggattgggtatatctattacagt.........gggagcacctactacaacccgtccctcaag...agtcgagttaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggccgtgtattactgtgccagaga >IGHV4-31*01 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcacagaccctgtccctcacctgcactgtctctggtggctccatcagc......agtggtggttactactggagctggatccgccagcacccagggaagggcctggagtggattgggtacatctattacagt.........gggagcacctactacaacccgtccctcaag...agtctagttaccatatcagtagacacgtctaagaaccagttctccctgaagctgagctctgtgactgccgcggacacggccgtgtattactgtgcgagaga >IGHV4-31*02 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcacagaccctgtccctcacctgtactgtctctggtggctccatcagc......agtggtggttactactggagctggatccgccagcacccagggaagggcctggagtggattgggtacatctattacagt.........gggagcacctactacaacccgtccctcaag...agtcgagttaccatatcagtagacacgtctaagaaccagttctccctgaagctgagctctgtgactgccgcggacacggccgtgtattactgtgcgagaga >IGHV4-31*03 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcacagaccctgtccctcacctgcactgtctctggtggctccatcagc......agtggtggttactactggagctggatccgccagcacccagggaagggcctggagtggattgggtacatctattacagt.........gggagcacctactacaacccgtccctcaag...agtcgagttaccatatcagtagacacgtctaagaaccagttctccctgaagctgagctctgtgactgccgcggacacggccgtgtattactgtgcgagaga >IGHV4-31*04 caggtgcggctgcaggagtcgggccca...ggactggtgaagccttcacagaccctgtccctcacctgcactgtctctggtggctccatcagc......agtggtggttactactggagctggatccgccagcacccagggaagggcctggagtggattgggtacatctattacagt.........gggagcacctactacaacccgtccctcaag...agtcgagttaccatatcagtagacacgtctaagaaccagttctccctgaagctgagctctgtgactgccgcggacacggccgtgtattactgtgcg >IGHV4-31*05 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcacagaccctgtccctcacctgcactgtctctggtggctccatcagc......agtggtggttactactggagctggatccgccagcacccagggaagggcctggagtggattgggtacatctattacagt.........gggagcacctactacaacccgtccctcaag...agtcgagttaccatatcagtagacacgtctaagaaccagttctccctgaagctgagctctgtgacc...gcggacgcggccgtgtattactgtgcg >IGHV4-31*06 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcacagaccctgtccctcacctgcactgtctctggtggctccatcagc......agtggtagttactactggagctggatccgccagcacccagggaagggcctggagtggattgggtacatctattacagt.........gggagcacctactacaacccgtccctcaag...agtcgagttaccatatcagtagacacgtctaagaaccagttctccctgaagctgagctctgtgactgccgcggacacggccgtgtattactg >IGHV4-31*07 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcacagaccctgtccctcacctgcactgtctctggtggatccatcagc......agtggtggttactactggagctggatccgccagcacccagggaagggcctggagtggattgggtacatctattacagt.........gggagcacctactacaacccgtccctcaag...agtcgagttaccatatcagtagacacgtctaagaaccagttctccctgaagctgagctctgtgactgccgcggacacggccgtgtattactg >IGHV4-31*08 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcacagaccctgtccctcacctgcactgtctctggtggctccatcagc......agtggtggttactactggagctggatccgccagcacccagggaagggcctggagtggattgggtacatctattacagt.........gggagcacctactacaacccgtccctcaag...agtcgagttaccatatccgtagacacgtccaagaaccagttctccctgaagctgagctctgtgactgccgcggacacggccgtgtattactg >IGHV4-31*09 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcacagaccctgtccctcacctgcactgtctctggtggctccatcagc......agtggtggttactactggagctggatccgccagcacccagggaagggcctggagtggattgggtacatctattacagt.........gggagcacctactacaacccgtccctcaag...agtcgagttaccatatcagtagacaagtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggccgtgtattactg >IGHV4-31*10 caggtgcagctgcaggagtcgggccca...ggactgttgaagccttcacagaccctgtccctcacctgcactgtctctggtggctccatcagc......agtggtggttactactggagctggatccgccagcacccagggaagggcctggagtggattgggtgcatctattacagt.........gggagcacctactacaacccgtccctcaag...agtcgagttaccatatcagtagacccgtccaagaaccagttctccctgaagccgagctctgtgactgccgcggacacggccgtggattactgtgcgagaga >IGHV4-34*01 caggtgcagctacagcagtggggcgca...ggactgttgaagccttcggagaccctgtccctcacctgcgctgtctatggtgggtccttc............agtggttactactggagctggatccgccagcccccagggaaggggctggagtggattggggaaatcaatcatagt.........ggaagcaccaactacaacccgtccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggctgtgtattactgtgcgagagg >IGHV4-34*02 caggtgcagctacaacagtggggcgca...ggactgttgaagccttcggagaccctgtccctcacctgcgctgtctatggtgggtccttc............agtggttactactggagctggatccgccagcccccagggaaggggctggagtggattggggaaatcaatcatagt.........ggaagcaccaactacaacccgtccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggctgtgtattactgtgcgagagg >IGHV4-34*03 caggtgcagctacagcagtggggcgca...ggactgttgaagccttcggagaccctgtccctcacctgcgctgtctatggtgggtccttc............agtggttactactggagctggatccgccagcccccagggaaggggctggagtggattggggaaatcaatcatagt.........ggaagcaccaactacaacccgtccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggccgtgtattactg >IGHV4-34*04 caggtgcagctacagcagtggggcgca...ggactgttgaagccttcggagaccctgtccctcacctgcgctgtctatggtgggtccttc............agtggttactactggagctggatccgccagcccccagggaaggggctggagtggattggggaaatcaatcatagt.........ggaagcaccaacaacaacccgtccctcaag...agtcgagccaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggctgtgtattactgtgcgagagg >IGHV4-34*05 caggtgcagctacagcagtggggcgca...ggactgttgaagccttcggagaccctgtccctcacctgcgctgtctatggtgggtccttc............agtggttactactggtgctggatccgccagcccctagggaaggggctggagtggattggggaaatcaatcatagt.........ggaagcaccaacaacaacccgtccctcaag...agtcgagccaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggctgtgtattactgtgcgagagg >IGHV4-34*06 caggtgcagctacagcagtggggcgca...ggactgttgaagccttcggagaccctgtccctcacctgcgctgtctatggtgggtccttc............agtggttactactggagctggatccgccagcccccagggaaggggctggagtggattggggaaatcaatcatagt.........ggaagcaccaactacaacccgtccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttctccctgaagctgggctctgtgaccgccgcggacacggccgtgtattactg >IGHV4-34*07 caggtgcagctacagcagtggggcgca...ggactgttgaagccttcggagaccctgtccctcacctgcgctgtctatggtgggtccttc............agtggttactactggagctggatccgccagcccccagggaaggggctggagtggattggggaaatcaaccatagt.........ggaagcaccaactacaacccgtccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggccgtgtattactg >IGHV4-34*08 caggtgcagctacagcagtggggcgca...ggactgttgaagccttcggagaccctgtccctcacctgcgctgtctatggtgggaccttc............agtggttactactggagctggatccgccagcccccagggaaggggctggagtggattggggaaatcaatcatagt.........ggaagcaccaactacaacccgtccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggctgtgtattactgtgcg >IGHV4-34*09 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcacagaccctgtccctcacctgcgctgtctatggtgggtccttc............agtggttactactggagctggatccgccagcccccagggaagggactggagtggattggggaaatcaatcatagt.........ggaagcaccaactacaacccgtccctcaag...agtcgagttaccatatcagtagacacgtctaagaaccagttctccctgaagctgagctctgtgactgccgcggacacggccgtgtattactgtgcgagaga >IGHV4-34*10 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcgctgtctatggtgggtccttc............agtggttactactggagctggatccgccagcccccagggaagggactggagtggattggggaaatcaatcatagt.........ggaagcaccaactacaacccgtccctcaag...agtcgaatcaccatgtcagtagacacgtccaagaaccagttctacctgaagctgagctctgtgaccgccgcggacacggccgtgtattactgtgcgagata >IGHV4-34*11 caggtgcagctacagcagtggggcgca...ggactgttgaagccttcggagaccctgtccctcacctgcgctgtctatggtgggtccgtc............agtggttactactggagctggatccggcagcccccagggaaggggctggagtggattgggtatatctattatagt.........gggagcaccaacaacaacccctccctcaag...agtcgagccaccatatcagtagacacgtccaagaaccagttctccctgaacctgagctctgtgaccgccgcggacacggccgtgtattgctgtgcgagaga >IGHV4-34*12 caggtgcagctacagcagtggggcgca...ggactgttgaagccttcggagaccctgtccctcacctgcgctgtctatggtgggtccttc............agtggttactactggagctggatccgccagcccccagggaaggggctggagtggattggggaaatcattcatagt.........ggaagcaccaactacaacccgtccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggctgtgtattactgtgcgaga >IGHV4-34*13 ...........................................................................tatggtgggtccttc............agtggttactactggagctggatccgccagcccccagggaaggggctggagtggattggggaaatcaatcatagt.........ggaagcaccaactacaacccctccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggctgtgtattactgtgcgagagg >IGHV4-38-2*01 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcgctgtctctggttactccatcagc.........agtggttactactggggctggatccggcagcccccagggaaggggctggagtggattgggagtatctatcatagt.........gggagcacctactacaacccgtccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcagacacggccgtgtattactgtgcgaga >IGHV4-38-2*02 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcactgtctctggttactccatcagc.........agtggttactactggggctggatccggcagcccccagggaaggggctggagtggattgggagtatctatcatagt.........gggagcacctactacaacccgtccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcagacacggccgtgtattactgtgcgagaga >IGHV4-39*01 cagctgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcactgtctctggtggctccatcagc......agtagtagttactactggggctggatccgccagcccccagggaaggggctggagtggattgggagtatctattatagt.........gggagcacctactacaacccgtccctcaag...agtcgagtcaccatatccgtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcagacacggctgtgtattactgtgcgagaca >IGHV4-39*02 cagctgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcactgtctctggtggctccatcagc......agtagtagttactactggggctggatccgccagcccccagggaaggggctggagtggattgggagtatctattatagt.........gggagcacctactacaacccgtccctcaag...agtcgagtcaccatatccgtagacacgtccaagaaccacttctccctgaagctgagctctgtgaccgccgcagacacggctgtgtattactgtgcgagaga >IGHV4-39*03 cagctgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcactgtctctggtggctccatcagc......agtagtagttactactggggctggatccgccagcccccagggaaggggctggagtggattgggagtatctattatagt.........gggagcacctactacaacccgtccctcaag...agtcgagtcaccatatccgtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcagacacggccgtgtattactg >IGHV4-39*04 ..................................................................................gctccatcagc......agtagtagttactactggggctggatccgccagcccccagggaaggggctggagtggattgggagtatctattatagt.........gggagcacctactacaacccgtccctcaag...agtcgagtcaccatatccgtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacac >IGHV4-39*05 cagctgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccccgtccctcacctgcactgtctctggtggctccatcagc......agtagtagttactactggggctggatccgccagcccccagggaaggggctggagtggattgggagtatctattatagt.........gggagcacctactacaacccgtccctcaag...agtcgagtcaccatatccgtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcagacacggctgtgtattactgtgcg >IGHV4-39*06 cggctgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcactgtctctggtggctccatcagc......agtagtagttactactggggctggatccgccagcccccagggaaggggctggagtggattgggagtatctattatagt.........gggagcacctactacaacccgtccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttccccctgaagctgagctctgtgaccgccgcggacacggccgtgtattactgtgcgagaga >IGHV4-39*07 cagctgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcactgtctctggtggctccatcagc......agtagtagttactactggggctggatccgccagcccccagggaaggggctggagtggattgggagtatctattatagt.........gggagcacctactacaacccgtccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggccgtgtattactgtgcgagaga >IGHV4-4*01 caggtgcagctgcaggagtcgggccca...ggactggtgaagcctccggggaccctgtccctcacctgcgctgtctctggtggctccatcagc.........agtagtaactggtggagttgggtccgccagcccccagggaaggggctggagtggattggggaaatctatcatagt.........gggagcaccaactacaacccgtccctcaag...agtcgagtcaccatatcagtagacaagtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggccgtgtattgctgtgcgagaga >IGHV4-4*02 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggggaccctgtccctcacctgcgctgtctctggtggctccatcagc.........agtagtaactggtggagttgggtccgccagcccccagggaaggggctggagtggattggggaaatctatcatagt.........gggagcaccaactacaacccgtccctcaag...agtcgagtcaccatatcagtagacaagtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggccgtgtattactgtgcgagaga >IGHV4-4*03 caggtgcagctgcaggagtcgggccca...ggactggtgaagcctccggggaccctgtccctcacctgcgctgtctctggtggctccatcagc.........agtagtaactggtggagttgggtccgccagcccccagggaaggggctggagtggattggggaaatctatcatagt.........gggagcaccaactacaacccgtccctcaag...agtcgagtcaccatatcagtagacaagtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggccgtgtattactg >IGHV4-4*04 caggtgcagctgcaggagtcgggccca...ggactggtgaagcctccggggaccctgtccctcacctgcgctatctctggtggctccatcagc.........agtagtaactggtggagttgggtccgccagcccccagggaaggggctggagtggattggggaaatctatcatagt.........gggagcaccaactacaacccgtccctcaag...agtcgagtcaccatatcagtagacaagtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggccgtgtattactg >IGHV4-4*05 caggtgcagctgcaggagttgggccca...ggactggtgaagcctccggggaccctgtccctcacctgcgctgtctctggtggctccatcagc.........agtagtaactggtggagttgggtccgccagcccccagggaaggggctggagtggattggggaaatctatcatagt.........gggagcaccaactacaacccgtccctcaag...agtcgagtcaccatatcagtagacaagtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggccgtgtattactg >IGHV4-4*06 ...........................................................................tctggtggctccatcagc.........agtagtaactggtggagttgggtccgccagcccccagggannnggctggagtggattggggaaatctatcatagt.........gggagcaccaactacaacccgtccctcaag...agtcgagtcaccatgtcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggccgtgtattactgtgcgagaga >IGHV4-4*07 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcactgtctctggtggctccatc............agtagttactactggagctggatccggcagcccgccgggaagggactggagtggattgggcgtatctataccagt.........gggagcaccaactacaacccctccctcaag...agtcgagtcaccatgtcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggccgtgtattactgtgcgagaga >IGHV4-4*08 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcactgtctctggtggctccatc............agtagttactactggagctggatccggcagcccccagggaagggactggagtggattgggtatatctataccagt.........gggagcaccaactacaacccctccctcaag...agtcgagtcaccatatccgtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcagacacggccgtgtattactgtgcgagaga >IGHV4-55*01 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcatctgcgctgtctctggtgactccatcagc.........agtggtaactggtgaatctgggtccgccagcccccagggaaggggctggagtggattggggaaatccatcatagt.........gggagcacctactacaacccgtccctcaag...agtcgaatcaccatgtccgtagacacgtccaagaaccagttctacctgaagctgagctctgtgaccgccgcggacacggccgtgtattactgtgcgagata >IGHV4-55*02 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcatctgcgctgtctctggtgactccatcagc.........agtggtaactggtgaatctgggtccgccagcccccagggaaggggctggagtggattggggaaatccatcatagt.........gggagcacctactacaacccgtccctcaag...agtcgaatcaccatgtcagtagacacgtccaagaaccagttctacctgaagctgagctctgtgaccgccgcggacacggccgtgtattactgtgcgagata >IGHV4-55*03 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcatctgcgctgtctctggtgactccatcagc.........agtggtaactggtgaatctgggtccgccagcccccagggaaggggctggagtggattggggaaatccatcatagt.........gggagcacctactacaacccgtccctcaag...agtcgaatcaccatgtcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggccgtgtattactg >IGHV4-55*04 caggtgcagctgcaggagtcgggccca...ggactggtgaagctttcggagaccctgtccctcatctgcgctgtctctggtgactccatcagc.........agtggtaactggtgaatctgggtccgccagcccccagggaaggggctggagtggattggggaaatccatcatagt.........gggagcacctactacaacccgtccctcaag...agtcgaatcaccatgtcagtagacacgtccaagaaccagttctacctgaagctgagctctgtgaccgccgcggacacggccgtgtattactg >IGHV4-55*05 caggtgcagctgcaggagtcgggccca...ggactggtgaagctttcggagaccctgtccctcatctgcgctgtctctggtgactccatcagc.........agtggtaactggtgaatctgggtccgccagcccccagggaaggggctggagtggattggggaaatccatcatagt.........gggagcacctactacaacccgtccctcaag...agtcgaatcaccatgtccgtagacacgtccaagaaccagttctacctgaagctgagctctgtgaccgccgcggacacggccgtgtattactg >IGHV4-55*06 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcatctgcgctgtctctggtgactccatcagc.........agtggtaactggtgaatctgggtccgccagcccccagggaaggggctggagtggattggggaaatccatcatagt.........gggagcacctactacaacccgtccctcaag...agtcgaatcaccatgtccgtagacacgtccaagaagcagttctacctgaagctgagctctgtgaccgctgcggacacggccgtgtattactg >IGHV4-55*07 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcatctgcgctgtctctggtgactccatcagc.........agtggtaactggtgaatctgggtccgccagcccccagggaaggggctggagtggattggggaaatccatcatagt.........gggagcacctactacaacccgtccctcaag...agtcgaatcaccatgtccgtagacacgtccaggaaccagttctccctgaagctgagctctgtgaccgccgcagacacggccgtgtattactg >IGHV4-55*08 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcatctgcgctgtctctggtgactccatcagc.........agtggtaactggtgaatctgggtccgccagcccccagggaaggggctggagtggattggggaaatccatcatagt.........gggagcacctactacaacccgtccctcaag...agtcgaatcaccatgtcagtagacacgtccaagaaccagttctacctgaagctgagctctgtgaccgccgcggacacggccgtgtattactgtgcgagaga >IGHV4-55*09 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcatctgcgctgtctctggtgactccatcagc.........agtggtaactggtgaatctgggtccgccagcccccagggaaggggctggagtggattggggaaatccatcatagt.........gggagcacctactacaacccgtccctcaag...agtcgaatcaccatgtccgtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgtggacacggccgtgtattactgtgcgagaaa >IGHV4-59*01 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcactgtctctggtggctccatc............agtagttactactggagctggatccggcagcccccagggaagggactggagtggattgggtatatctattacagt.........gggagcaccaactacaacccctccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgctgcggacacggccgtgtattactgtgcgagaga >IGHV4-59*02 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcactgtctctggtggctccgtc............agtagttactactggagctggatccggcagcccccagggaagggactggagtggattgggtatatctattacagt.........gggagcaccaactacaacccctccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgctgcggacacggccgtgtattactgtgcgagaga >IGHV4-59*03 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcactgtctctggtggctccatc............agtagttactactggagctggatccggcagcccccagggaagggactggagtggattgggtatatctattacagt.........gggagcaccaactacaacccctccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccaattctccctgaagctgagctctgtgaccgctgcggacacggccgtgtattactgtgcg >IGHV4-59*04 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcactgtctctggtggctccatc............agtagttactactggagctggatccggcagcccccagggaagggactggagtggattgggtatatctattatagt.........gggagcacctactacaacccgtccctcaag...agtcgagtcaccatgtcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcagacacggctgtgtattactgtgcg >IGHV4-59*05 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcactgtctctggtggctccatc............agtagttactactggagctggatccggcagccgccggggaagggactggagtggattgggcgtatctattatagt.........gggagcacctactacaacccgtccctcaag...agtcgagtcaccatatccgtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcagacacggctgtgtattactgtgcg >IGHV4-59*06 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcactgtcactggtggctccatc............agtagttactactggagctggatccggcagcccgctgggaagggcctggagtggattgggtacatctattacagt.........gggagcacctactacaacccgtccctcaag...agtcgagttaccatatcagtagacacgtctaagaaccagttctccctgaagctgagctctgtgactgccgcggacacggccgtgtattactgtgcg >IGHV4-59*07 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggacaccctgtccctcacctgcactgtctctggtggctccatc............agtagttactactggagctggatccggcagcccccagggaagggactggagtggattgggtatatctattacagt.........gggagcaccaactacaacccctccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgctgcggacacggccgtgtattactgtgcgaga >IGHV4-59*08 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcactgtctctggtggctccatc............agtagttactactggagctggatccggcagcccccagggaagggactggagtggattgggtatatctattacagt.........gggagcaccaactacaacccctccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcagacacggccgtgtattactgtgcgagaca >IGHV4-59*09 ...........................................................................tctggtggctccatc............agtagttactactggagctggatccggcagcccccaggnannngactggagtggattgggtatatctattacagt.........gggagcaccaactacaacccctccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgctgcggacacggccgtgtattactgtgcgagagg >IGHV4-59*10 caggtgcagctacagcagtggggcgca...ggactgttgaagccttcggagaccctgtccctcacctgcgctgtctatggtggctccatc............agtagttactactggagctggatccggcagcccgccgggaaggggctggagtggattgggcgtatctataccagt.........gggagcaccaactacaacccctccctcaag...agtcgagtcaccatgtcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggccgtgtattactgtgcgagata >IGHV4-61*01 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcactgtctctggtggctccgtcagc......agtggtagttactactggagctggatccggcagcccccagggaagggactggagtggattgggtatatctattacagt.........gggagcaccaactacaacccctccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgctgcggacacggccgtgtattactgtgcgagaga >IGHV4-61*02 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcacagaccctgtccctcacctgcactgtctctggtggctccatcagc......agtggtagttactactggagctggatccggcagcccgccgggaagggactggagtggattgggcgtatctataccagt.........gggagcaccaactacaacccctccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcagacacggccgtgtattactgtgcgagaga >IGHV4-61*03 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcactgtctctggtggctccgtcagc......agtggtagttactactggagctggatccggcagcccccagggaagggactggagtggattgggtatatctattacagt.........gggagcaccaactacaacccctccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccacttctccctgaagctgagctctgtgaccgctgcggacacggccgtgtattactgtgcgagaga >IGHV4-61*04 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcactgtctctggtggctccgtcagc......agtggtagttactactggagctggatccggcagcccccagggaagggactggagtggattggatatatctattacagt.........gggagcaccaactacaacccctccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgct...gacacggccgtgtattactg >IGHV4-61*05 cagctgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcactgtctctggtggctccatcagc......agtagtagttactactggggctggatccggcagcccccagggaagggactggagtggattgggtatatctattacagt.........gggagcaccaactacaacccctccctcaag...agtcgagtcaccatatcagtagacaagtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggccgtgtattactgtgcgaga >IGHV4-61*06 ...........................................................................tctggtggctccgtcagc......agtggtagttactactggagctggatccggcagcccccagggaagggactggagtggattgggtatatctattacagt.........gggagcaccaactacaacccctccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggccgtgtattactgtgccagaga >IGHV4-61*07 ...........................................................................tctggtggctccgtcagc......agtggtagttactactggagctggatccggcagcccccagggaagggactggagtggattgggtatatctattacagt.........gggagcaccaactacaacccctccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgctgcggacacggccgtgtattactgtgcgagaca >IGHV4-61*08 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcactgtctctggtggctccgtcagc......agtggtggttactactggagctggatccggcagcccccagggaagggactggagtggattgggtatatctattacagt.........gggagcaccaactacaacccctccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgctgcggacacggccgtgtattactgtgcgagaga >IGHV4/OR15-8*01 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcgttgtctctggtggctccatcagc.........agtagtaactggtggagctgggtccgccagcccccagggaaggggctggagtggattggggaaatctatcatagt.........gggagccccaactacaacccgtccctcaag...agtcgagtcaccatatcagtagacaagtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggccgtgtattactgtgcgagaga >IGHV4/OR15-8*02 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcgttgtctctggtggctccatcagc.........agtagtaactggtggagctgggtccgccagcccccagggaaggggctggagtggattggggaaatctatcatagt.........gggaaccccaactacaacccgtccctcaag...agtcgagtcaccatatcaatagacaagtccaagaaccaattctccctgaagctgagctctgtgaccgccgcggacacggccgtgtattactgtgcgagaga >IGHV4/OR15-8*03 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcgttgtctctggtggctccatcagc.........agtagtaactggtggagctgggtccgccagcccccagggaaggggctggagtggattggggaaatctatcatagt.........gggagccccaactacaacccatccctcaag...agtcgagtcaccatatcagtagacaagtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggccgtgtattactgtgcgagaga >IGHV5-10-1*01 gaagtgcagctggtgcagtctggagca...gaggtgaaaaagcccggggagtctctgaggatctcctgtaagggttctggatacagcttt............accagctactggatcagctgggtgcgccagatgcccgggaaaggcctggagtggatggggaggattgatcctagt......gactcttataccaactacagcccgtccttccaa...ggccacgtcaccatctcagctgacaagtccatcagcactgcctacctgcagtggagcagcctgaaggcctcggacaccgccatgtattactgtgcgaga >IGHV5-10-1*02 gaagtgcagctggtgcagtctggagca...gaggtgaaaaagcccggggagtctctgaggatctcctgtaagggttctggatacagcttt............accagctactggatcagctgggtgcgccagatgcccgggaaaggcttggagtggatggggaggattgatcctagt......gactcttataccaactacagcccgtccttccaa...ggccacgtcaccatctcagctgacaagtccatcagcactgcctacctgcagtggagcagcctgaaggc.tcggacaccgccatgtattactgtgcgagaca >IGHV5-10-1*03 gaagtgcagctggtgcagtccggagca...gaggtgaaaaagcccggggagtctctgaggatctcctgtaagggttctggatacagcttt............accagctactggatcagctgggtgcgccagatgcccgggaaaggcctggagtggatggggaggattgatcctagt......gactcttataccaactacagcccgtccttccaa...ggccacgtcaccatctcagctgacaagtccatcagcactgcctacctgcagtggagcagcctgaaggcctcggacaccgccatgtattactgtgcgaga >IGHV5-10-1*04 gaagtgcagctggtgcagtctggagca...gaggtgaaaaagcccggggagtctctgaggatctcctgtaagggttctggatacagcttt............accagctactggatcagctgggtgcgccagatgcccgggaaaggcctggagtggatggggaggattgatcctagt......gactcttataccaactacagcccgtccttccaa...ggccaggtcaccatctcagctgacaagtccatcagcactgcctacctgcagtggagcagcctgaaggcctcggacaccgccatgtattactgtgcgaga >IGHV5-51*01 gaggtgcagctggtgcagtctggagca...gaggtgaaaaagcccggggagtctctgaagatctcctgtaagggttctggatacagcttt............accagctactggatcggctgggtgcgccagatgcccgggaaaggcctggagtggatggggatcatctatcctggt......gactctgataccagatacagcccgtccttccaa...ggccaggtcaccatctcagccgacaagtccatcagcaccgcctacctgcagtggagcagcctgaaggcctcggacaccgccatgtattactgtgcgagaca >IGHV5-51*02 gaggtgcagctggtgcagtctggagca...gaggtgaaaaagcccggggagtctctgaagatctcctgtaagggttctggatacagcttt............accagctactggaccggctgggtgcgccagatgcccgggaaaggcttggagtggatggggatcatctatcctggt......gactctgataccagatacagcccgtccttccaa...ggccaggtcaccatctcagccgacaagtccatcagcaccgcctacctgcagtggagcagcctgaaggcctcggacaccgccatgtattactgtgcgagaca >IGHV5-51*03 gaggtgcagctggtgcagtctggagca...gaggtgaaaaagccgggggagtctctgaagatctcctgtaagggttctggatacagcttt............accagctactggatcggctgggtgcgccagatgcccgggaaaggcctggagtggatggggatcatctatcctggt......gactctgataccagatacagcccgtccttccaa...ggccaggtcaccatctcagccgacaagtccatcagcaccgcctacctgcagtggagcagcctgaaggcctcggacaccgccatgtattactgtgcgaga >IGHV5-51*04 gaggtgcagctggtgcagtctggagca...gaggtgaaaaagccgggggagtctctgaagatctcctgtaagggttctggatacagcttt............accagctactggatcggctgggtgcgccagatgcccgggaaaggcctggagtggatggggatcatctatcctggt......gactctgataccagatacagcccgtccttccaa...ggccaggtcaccatctcagccgacaagcccatcagcaccgcctacctgcagtggagcagcctgaaggcctcggacaccgccatgtattactgtgcgaga >IGHV5-51*05 .....................................aaaagcccggggagtctctgaagatctcctgtaagggttctggatacagcttt............accagctactggatcggctgggtgcgccagatgcccaggaaaggcctggagtggatggggatcatctatcctggt......gactctgataccagatacagcccgtccttccaa...ggccaggtcaccatctcagccgacaagtccatcagcaccgcctacctgcagtggagcagcctgaaggcctcggacaccgccatg >IGHV5-78*01 gaggtgcagctgttgcagtctgcagca...gaggtgaaaagacccggggagtctctgaggatctcctgtaagacttctggatacagcttt............accagctactggatccactgggtgcgccagatgcccgggaaagaactggagtggatggggagcatctatcctggg......aactctgataccagatacagcccatccttccaa...ggccacgtcaccatctcagccgacagctccagcagcaccgcctacctgcagtggagcagcctgaaggcctcggacgccgccatgtattattgtgtgaga >IGHV6-1*01 caggtacagctgcagcagtcaggtcca...ggactggtgaagccctcgcagaccctctcactcacctgtgccatctccggggacagtgtctct......agcaacagtgctgcttggaactggatcaggcagtccccatcgagaggccttgagtggctgggaaggacatactacaggtcc...aagtggtataatgattatgcagtatctgtgaaa...agtcgaataaccatcaacccagacacatccaagaaccagttctccctgcagctgaactctgtgactcccgaggacacggctgtgtattactgtgcaagaga >IGHV6-1*02 caggtacagctgcagcagtcaggtccg...ggactggtgaagccctcgcagaccctctcactcacctgtgccatctccggggacagtgtctct......agcaacagtgctgcttggaactggatcaggcagtccccatcgagaggccttgagtggctgggaaggacatactacaggtcc...aagtggtataatgattatgcagtatctgtgaaa...agtcgaataaccatcaacccagacacatccaagaaccagttctccctgcagctgaactctgtgactcccgaggacacggctgtgtattactgtgcaagaga >IGHV7-34-1*01 ...ctgcagctggtgcagtctgggcct...gaggtgaagaagcctggggcctcagtgaaggtctcctataagtcttctggttacaccttc............accatctatggtatgaattgggtatgatagacccctggacagggctttgagtggatgtgatggatcatcacctac......actgggaacccaacgtatacccacggcttcaca...ggatggtttgtcttctccatggacacgtctgtcagcacggcgtgtcttcagatcagcagcctaaaggctgaggacacggccgagtattactgtgcgaagta >IGHV7-34-1*02 ...ctgcagctggtgcagtctgggcct...gaggtgaagaagcctggggcctcagtgaaggtctcctataagtcttctggttacaccttc............accatctatggtatgaattgggtatgatagacccctggacagggctttgagtggatgtgatggatcatcacctac......aatgggaacccaacgtatacccacggcttcaca...ggatggtttgtcttctccatggacacgtctgtcagcacggcgtgtcttcagatcagcagcctaaaggctgaggacacggccgagtattactgtgcgaagta >IGHV7-4-1*01 caggtgcagctggtgcaatctgggtct...gagttgaagaagcctggggcctcagtgaaggtttcctgcaaggcttctggatacaccttc............actagctatgctatgaattgggtgcgacaggcccctggacaagggcttgagtggatgggatggatcaacaccaac......actgggaacccaacgtatgcccagggcttcaca...ggacggtttgtcttctccttggacacctctgtcagcacggcatatctgcagatctgcagcctaaaggctgaggacactgccgtgtattactgtgcgaga >IGHV7-4-1*02 caggtgcagctggtgcaatctgggtct...gagttgaagaagcctggggcctcagtgaaggtttcctgcaaggcttctggatacaccttc............actagctatgctatgaattgggtgcgacaggcccctggacaagggcttgagtggatgggatggatcaacaccaac......actgggaacccaacgtatgcccagggcttcaca...ggacggtttgtcttctccttggacacctctgtcagcacggcatatctgcagatcagcagcctaaaggctgaggacactgccgtgtattactgtgcgagaga >IGHV7-4-1*03 caggtgcagctggtgcaatctgggtct...gagttgaagaagcctggggcctcagtgaaggtttcctgcaaggcttctggatacaccttc............actagctatgctatgaattgggtgcgacaggcccctggacaagggcttgagtggatgggatggatcaacaccaac......actgggaacccaacgtatgcccagggcttcaca...ggacggtttgtcttctccttggacacctctgtcagcacggcatatctgcagatcagcacgctaaaggctgaggacactg >IGHV7-4-1*04 caggtgcagctggtgcaatctgggtct...gagttgaagaagcctggggcctcagtgaaggtttcctgcaaggcttctggatacaccttc............actagctatgctatgaattgggtgcgacaggcccctggacaagggcttgagtggatgggatggatcaacaccaac......actgggaacccaacgtatgcccagggcttcaca...ggacggtttgtcttctccttggacacctctgtcagcatggcatatctgcagatcagcagcctaaaggctgaggacactgccgtgtattactgtgcgagaga >IGHV7-4-1*05 caggtgcagctggtgcaatctgggtct...gagttgaagaagcctggggcctcagtgaaggtttcctgcaaggcttctggatacaccttc............actagctatgctatgaattgggtgcgacaggcccctggacaagggcttgagtggatgggatggatcaacaccaac......actgggaacccaacgtatgcccagggcttcaca...ggacggtttgtcttctccttggacacctctgtcagcatggcatatctgcagatcagcagcctaaaggctgaggacactgccgtgtgttactgtgcgagaga >IGHV7-40*03 ttttcaatagaaaagtcaaataatcta...agtgtcaatcagtggatgattagataaaatatgatatatgtaaatcatggaatactatgc............agccagtatggtatgaattcagtgtgaccagcccctggacaagggcttgagtggatgggatggatcatcacctac......actgggaacccaacatataccaacggcttcaca...ggacggtttctattctccatggacacctctgtcagcatggcgtatctgcagatcagcagcctaaaggctgaggacacggccgtgtatgactgtatgagaga >IGHV7-81*01 caggtgcagctggtgcagtctggccat...gaggtgaagcagcctggggcctcagtgaaggtctcctgcaaggcttctggttacagtttc............accacctatggtatgaattgggtgccacaggcccctggacaagggcttgagtggatgggatggttcaacacctac......actgggaacccaacatatgcccagggcttcaca...ggacggtttgtcttctccatggacacctctgccagcacagcatacctgcagatcagcagcctaaaggctgaggacatggccatgtattactgtgcgagata
--- a/baseline/IMGTVHreferencedataset20161215.fasta Tue Sep 01 16:03:44 2020 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,1 +0,0 @@ ->IGHV1-18*01 caggttcagctggtgcagtctggagct...gaggtgaagaagcctggggcctcagtgaaggtctcctgcaaggcttctggttacaccttt............accagctatggtatcagctgggtgcgacaggcccctggacaagggcttgagtggatgggatggatcagcgcttac......aatggtaacacaaactatgcacagaagctccag...ggcagagtcaccatgaccacagacacatccacgagcacagcctacatggagctgaggagcctgagatctgacgacacggccgtgtattactgtgcgagaga >IGHV1-18*02 caggttcagctggtgcagtctggagct...gaggtgaagaagcctggggcctcagtgaaggtctcctgcaaggcttctggttacaccttt............accagctatggtatcagctgggtgcgacaggcccctggacaagggcttgagtggatgggatggatcagcgcttac......aatggtaacacaaactatgcacagaagctccag...ggcagagtcaccatgaccacagacacatccacgagcacagcctacatggagctgaggagcctaagatctgacgacacggcc >IGHV1-18*03 caggttcagctggtgcagtctggagct...gaggtgaagaagcctggggcctcagtgaaggtctcctgcaaggcttctggttacaccttt............accagctatggtatcagctgggtgcgacaggcccctggacaagggcttgagtggatgggatggatcagcgcttac......aatggtaacacaaactatgcacagaagctccag...ggcagagtcaccatgaccacagacacatccacgagcacagcctacatggagctgaggagcctgagatctgacgacatggccgtgtattactgtgcgagaga >IGHV1-18*04 caggttcagctggtgcagtctggagct...gaggtgaagaagcctggggcctcagtgaaggtctcctgcaaggcttctggttacaccttt............accagctacggtatcagctgggtgcgacaggcccctggacaagggcttgagtggatgggatggatcagcgcttac......aatggtaacacaaactatgcacagaagctccag...ggcagagtcaccatgaccacagacacatccacgagcacagcctacatggagctgaggagcctgagatctgacgacacggccgtgtattactgtgcgagaga >IGHV1-2*01 caggtgcagctggtgcagtctggggct...gaggtgaagaagcctggggcctcagtgaaggtctcctgcaaggcttctggatacaccttc............accggctactatatgcactgggtgcgacaggcccctggacaagggcttgagtggatgggacggatcaaccctaac......agtggtggcacaaactatgcacagaagtttcag...ggcagggtcaccagtaccagggacacgtccatcagcacagcctacatggagctgagcaggctgagatctgacgacacggtcgtgtattactgtgcgagaga >IGHV1-2*02 caggtgcagctggtgcagtctggggct...gaggtgaagaagcctggggcctcagtgaaggtctcctgcaaggcttctggatacaccttc............accggctactatatgcactgggtgcgacaggcccctggacaagggcttgagtggatgggatggatcaaccctaac......agtggtggcacaaactatgcacagaagtttcag...ggcagggtcaccatgaccagggacacgtccatcagcacagcctacatggagctgagcaggctgagatctgacgacacggccgtgtattactgtgcgagaga >IGHV1-2*03 caggtgcagctggtgcagtctggggct...gaggtgaagaagcttggggcctcagtgaaggtctcctgcaaggcttctggatacaccttc............accggctactatatgcactgggtgcnacaggcccctggacaagggcttgagtggatgggatggatcaaccctaac......agtggtggcacaaactatgcacagaagtttcag...ggcagggtcaccatgaccagggacacgtccatcagcacagcctacatggagctgagcaggctgagatctgacgacacggccgtgtattactgtgcgagaga >IGHV1-2*04 caggtgcagctggtgcagtctggggct...gaggtgaagaagcctggggcctcagtgaaggtctcctgcaaggcttctggatacaccttc............accggctactatatgcactgggtgcgacaggcccctggacaagggcttgagtggatgggatggatcaaccctaac......agtggtggcacaaactatgcacagaagtttcag...ggctgggtcaccatgaccagggacacgtccatcagcacagcctacatggagctgagcaggctgagatctgacgacacggccgtgtattactgtgcgagaga >IGHV1-2*05 caggtgcagctggtgcagtctggggct...gaggtgaagaagcctggggcctcagtgaaggtctcctgcaaggcttctggatacaccttc............accggctactatatgcactgggtgcgacaggcccctggacaagggcttgagtggatgggacggatcaaccctaac......agtggtggcacaaactatgcacagaagtttcag...ggcagggtcaccatgaccagggacacgtccatcagcacagcctacatggagctgagcaggctgagatctgacgacacggtcgtgtattactgtgcgagaga >IGHV1-24*01 caggtccagctggtacagtctggggct...gaggtgaagaagcctggggcctcagtgaaggtctcctgcaaggtttccggatacaccctc............actgaattatccatgcactgggtgcgacaggctcctggaaaagggcttgagtggatgggaggttttgatcctgaa......gatggtgaaacaatctacgcacagaagttccag...ggcagagtcaccatgaccgaggacacatctacagacacagcctacatggagctgagcagcctgagatctgaggacacggccgtgtattactgtgcaacaga >IGHV1-3*01 caggtccagcttgtgcagtctggggct...gaggtgaagaagcctggggcctcagtgaaggtttcctgcaaggcttctggatacaccttc............actagctatgctatgcattgggtgcgccaggcccccggacaaaggcttgagtggatgggatggatcaacgctggc......aatggtaacacaaaatattcacagaagttccag...ggcagagtcaccattaccagggacacatccgcgagcacagcctacatggagctgagcagcctgagatctgaagacacggctgtgtattactgtgcgagaga >IGHV1-3*02 caggttcagctggtgcagtctggggct...gaggtgaagaagcctggggcctcagtgaaggtttcctgcaaggcttctggatacaccttc............actagctatgctatgcattgggtgcgccaggcccccggacaaaggcttgagtggatgggatggagcaacgctggc......aatggtaacacaaaatattcacaggagttccag...ggcagagtcaccattaccagggacacatccgcgagcacagcctacatggagctgagcagcctgagatctgaggacatggctgtgtattactgtgcgagaga >IGHV1-38-4*01 caggtccagctggtgcagtcttgggct...gaggtgaggaagtctggggcctcagtgaaagtctcctgtagtttttctgggtttaccatc............accagctacggtatacattgggtgcaacagtcccctggacaagggcttgagtggatgggatggatcaaccctggc......aatggtagcccaagctatgccaagaagtttcag...ggcagattcaccatgaccagggacatgtccacaaccacagcctacacagacctgagcagcctgacatctgaggacatggctgtgtattactatgcaagaca >IGHV1-45*01 cagatgcagctggtgcagtctggggct...gaggtgaagaagactgggtcctcagtgaaggtttcctgcaaggcttccggatacaccttc............acctaccgctacctgcactgggtgcgacaggcccccggacaagcgcttgagtggatgggatggatcacacctttc......aatggtaacaccaactacgcacagaaattccag...gacagagtcaccattactagggacaggtctatgagcacagcctacatggagctgagcagcctgagatctgaggacacagccatgtattactgtgcaagana >IGHV1-45*02 cagatgcagctggtgcagtctggggct...gaggtgaagaagactgggtcctcagtgaaggtttcctgcaaggcttccggatacaccttc............acctaccgctacctgcactgggtgcgacaggcccccggacaagcgcttgagtggatgggatggatcacacctttc......aatggtaacaccaactacgcacagaaattccag...gacagagtcaccattaccagggacaggtctatgagcacagcctacatggagctgagcagcctgagatctgaggacacagccatgtattactgtgcaagata >IGHV1-45*03 .....................................agaagactgggtcctcagtgaaggtttcctgcaaggcttccggatacaccttc............acctaccgctacctgcactgggtgcgacaggcccccagacaagcgcttgagtggatgggatggatcacacctttc......aatggtaacaccaactacgcacagaaattccag...gacagagtcaccattaccagggacaggtctatgagcacagcctacatggagctgagcagcctgagatctgaggacacagccatgtattactgtgcaaga >IGHV1-46*01 caggtgcagctggtgcagtctggggct...gaggtgaagaagcctggggcctcagtgaaggtttcctgcaaggcatctggatacaccttc............accagctactatatgcactgggtgcgacaggcccctggacaagggcttgagtggatgggaataatcaaccctagt......ggtggtagcacaagctacgcacagaagttccag...ggcagagtcaccatgaccagggacacgtccacgagcacagtctacatggagctgagcagcctgagatctgaggacacggccgtgtattactgtgcgagaga >IGHV1-46*02 caggtgcagctggtgcagtctggggct...gaggtgaagaagcctggggcctcagtgaaggtttcctgcaaggcatctggatacaccttc............aacagctactatatgcactgggtgcgacaggcccctggacaagggcttgagtggatgggaataatcaaccctagt......ggtggtagcacaagctacgcacagaagttccag...ggcagagtcaccatgaccagggacacgtccacgagcacagtctacatggagctgagcagcctgagatctgaggacacggccgtgtattactgtgcgagaga >IGHV1-46*03 caggtgcagctggtgcagtctggggct...gaggtgaagaagcctggggcctcagtgaaggtttcctgcaaggcatctggatacaccttc............accagctactatatgcactgggtgcgacaggcccctggacaagggcttgagtggatgggaataatcaaccctagt......ggtggtagcacaagctacgcacagaagttccag...ggcagagtcaccatgaccagggacacgtccacgagcacagtctacatggagctgagcagcctgagatctgaggacacggccgtgtattactgtgctagaga >IGHV1-58*01 caaatgcagctggtgcagtctgggcct...gaggtgaagaagcctgggacctcagtgaaggtctcctgcaaggcttctggattcaccttt............actagctctgctgtgcagtgggtgcgacaggctcgtggacaacgccttgagtggataggatggatcgtcgttggc......agtggtaacacaaactacgcacagaagttccag...gaaagagtcaccattaccagggacatgtccacaagcacagcctacatggagctgagcagcctgagatccgaggacacggccgtgtattactgtgcggcaga >IGHV1-58*02 caaatgcagctggtgcagtctgggcct...gaggtgaagaagcctgggacctcagtgaaggtctcctgcaaggcttctggattcaccttt............actagctctgctatgcagtgggtgcgacaggctcgtggacaacgccttgagtggataggatggatcgtcgttggc......agtggtaacacaaactacgcacagaagttccag...gaaagagtcaccattaccagggacatgtccacaagcacagcctacatggagctgagcagcctgagatccgaggacacggccgtgtattactgtgcggcaga >IGHV1-68*01 caggtgcagctggggcagtctgaggct...gaggtaaagaagcctggggcctcagtgaaggtctcctgcaaggcttccggatacaccttc............acttgctgctccttgcactggttgcaacaggcccctggacaagggcttgaaaggatgagatggatcacactttac......aatggtaacaccaactatgcaaagaagttccag...ggcagagtcaccattaccagggacatgtccctgaggacagcctacatagagctgagcagcctgagatctgaggactcggctgtgtattactgggcaagata >IGHV1-69*01 caggtgcagctggtgcagtctggggct...gaggtgaagaagcctgggtcctcggtgaaggtctcctgcaaggcttctggaggcaccttc............agcagctatgctatcagctgggtgcgacaggcccctggacaagggcttgagtggatgggagggatcatccctatc......tttggtacagcaaactacgcacagaagttccag...ggcagagtcacgattaccgcggacgaatccacgagcacagcctacatggagctgagcagcctgagatctgaggacacggccgtgtattactgtgcgagaga >IGHV1-69*02 caggtccagctggtgcaatctggggct...gaggtgaagaagcctgggtcctcggtgaaggtctcctgcaaggcttctggaggcaccttc............agcagctatactatcagctgggtgcgacaggcccctggacaagggcttgagtggatgggaaggatcatccctatc......cttggtatagcaaactacgcacagaagttccag...ggcagagtcacgattaccgcggacaaatccacgagcacagcctacatggagctgagcagcctgagatctgaggacacggccgtgtattactgtgcgaga >IGHV1-69*03 caggtgcagctggtgcagtctggggct...gaggtgaagaagcctgggtcctcggtgaaggtctcctgcaaggcttctggaggcaccttc............agcagctatgctatcagctgggtgcgacaggcccctggacaagggcttgagtggatgggagggatcatccctatc......tttggtacagcaaactacgcacagaagttccag...ggcagagtcacgattaccgcggacgaatccacgagcacagcctacatggagctgagcagcctgagatctgatgacacggc >IGHV1-69*04 caggtccagctggtgcagtctggggct...gaggtgaagaagcctgggtcctcggtgaaggtctcctgcaaggcttctggaggcaccttc............agcagctatgctatcagctgggtgcgacaggcccctggacaagggcttgagtggatgggaaggatcatccctatc......cttggtatagcaaactacgcacagaagttccag...ggcagagtcacgattaccgcggacaaatccacgagcacagcctacatggagctgagcagcctgagatctgaggacacggccgtgtattactgtgcgagaga >IGHV1-69*05 caggtccagctggtgcagtctggggct...gaggtgaagaagcctgggtcctcggtgaaggtctcctgcaaggcttctggaggcaccttc............agcagctatgctatcagctgggtgcgacaggcccctggacaagggcttgagtggatgggagggatcatccctatc......tttggtacagcaaactacgcacagaagttccag...ggcagagtcacgattaccacggacgaatccacgagcacagcctacatggagctgagcagcctgagatctgaggacacggccgtgtattactgtgcgaga >IGHV1-69*06 caggtgcagctggtgcagtctggggct...gaggtgaagaagcctgggtcctcggtgaaggtctcctgcaaggcttctggaggcaccttc............agcagctatgctatcagctgggtgcgacaggcccctggacaagggcttgagtggatgggagggatcatccctatc......tttggtacagcaaactacgcacagaagttccag...ggcagagtcacgattaccgcggacaaatccacgagcacagcctacatggagctgagcagcctgagatctgaggacacggccgtgtattactgtgcgagaga >IGHV1-69*07 .....................................agaagcctgggtcctcggtgaaggtctcctgcaaggcttctggaggcaccttc............agcagctatgctatcagctgggtgcgacaggcccctggacaagggcttgagtggatgggaaggatcatccctatc......tttggtacagcaaactacgcacagaagttccag...ggcagagtcacgattaccgcggacgaatccacgagcacagcctacatggagctgagcagcctgagatctgag >IGHV1-69*08 caggtccagctggtgcaatctggggct...gaggtgaagaagcctgggtcctcggtgaaggtctcctgcaaggcttctggaggcaccttc............agcagctatactatcagctgggtgcgacaggcccctggacaagggcttgagtggatgggaaggatcatccctatc......cttggtacagcaaactacgcacagaagttccag...ggcagagtcacgattaccgcggacaaatccacgagcacagcctacatggagctgagcagcctgagatctgaggacacggccgtgtattactgtgcgagaga >IGHV1-69*09 caggtgcagctggtgcagtctggggct...gaggtgaagaagcctgggtcctcggtgaaggtctcctgcaaggcttctggaggcaccttc............agcagctatgctatcagctgggtgcgacaggcccctggacaagggcttgagtggatgggaaggatcatccctatc......cttggtatagcaaactacgcacagaagttccag...ggcagagtcacgattaccgcggacaaatccacgagcacagcctacatggagctgagcagcctgagatctgaggacacggccgtgtattactgtgcgagaga >IGHV1-69*10 caggtccagctggtgcagtctggggct...gaggtgaagaagcctgggtcctcagtgaaggtctcctgcaaggcttctggaggcaccttc............agcagctatgctatcagctgggtgcgacaggcccctggacaagggcttgagtggatgggagggatcatccctatc......cttggtatagcaaactacgcacagaagttccag...ggcagagtcacgattaccgcggacaaatccacgagcacagcctacatggagctgagcagcctgagatctgaggacacggccgtgtattactgtgcgagaga >IGHV1-69*11 caggtccagctggtgcagtctggggct...gaggtgaagaagcctgggtcctcggtgaaggtctcctgcaaggcttctggaggcaccttc............agcagctatgctatcagctgggtgcgacaggcccctggacaagggcttgagtggatgggaaggatcatccctatc......cttggtacagcaaactacgcacagaagttccag...ggcagagtcacgattaccgcggacgaatccacgagcacagcctacatggagctgagcagcctgagatctgaggacacggccgtgtattactgtgcgagaga >IGHV1-69*12 caggtccagctggtgcagtctggggct...gaggtgaagaagcctgggtcctcggtgaaggtctcctgcaaggcttctggaggcaccttc............agcagctatgctatcagctgggtgcgacaggcccctggacaagggcttgagtggatgggagggatcatccctatc......tttggtacagcaaactacgcacagaagttccag...ggcagagtcacgattaccgcggacgaatccacgagcacagcctacatggagctgagcagcctgagatctgaggacacggccgtgtattactgtgcgagaga >IGHV1-69*13 caggtccagctggtgcagtctggggct...gaggtgaagaagcctgggtcctcagtgaaggtctcctgcaaggcttctggaggcaccttc............agcagctatgctatcagctgggtgcgacaggcccctggacaagggcttgagtggatgggagggatcatccctatc......tttggtacagcaaactacgcacagaagttccag...ggcagagtcacgattaccgcggacgaatccacgagcacagcctacatggagctgagcagcctgagatctgaggacacggccgtgtattactgtgcgagaga >IGHV1-69*14 caggtccagctggtgcagtctggggct...gaggtgaagaagcctgggtcctcggtgaaggtctcctgcaaggcttctggaggcaccttc............agcagctatgctatcagctgggtgcgacaggcccctggacaagggcttgagtggatgggagggatcatccctatc......tttggtacagcaaactacgcacagaagttccag...ggcagagtcacgattaccgcggacaaatccacgagcacagcctacatggagctgagcagcctgagatctgaggacacggccgtgtattactgtgcgagaga >IGHV1-69-2*01 gaggtccagctggtacagtctggggct...gaggtgaagaagcctggggctacagtgaaaatctcctgcaaggtttctggatacaccttc............accgactactacatgcactgggtgcaacaggcccctggaaaagggcttgagtggatgggacttgttgatcctgaa......gatggtgaaacaatatacgcagagaagttccag...ggcagagtcaccataaccgcggacacgtctacagacacagcctacatggagctgagcagcctgagatctgaggacacggccgtgtattactgtgcaacaga >IGHV1-69-2*02 .....................................agaagcctggggctacagtgaaaatctcctgcaaggtttctggatacaccttc............accgactactacatgcactgggtgcaacaggcccctggaaaagggcttgagtggatgggacttgttgatcctgaa......gatggtgaaacaatatatgcagagaagttccag...ggcagagtcaccataaccgcggacacgtctacagacacagcctacatggagctgagcagcctgagatctgag >IGHV1-69D*01 caggtgcagctggtgcagtctggggct...gaggtgaagaagcctgggtcctcggtgaaggtctcctgcaaggcttctggaggcaccttc............agcagctatgctatcagctgggtgcgacaggcccctggacaagggcttgagtggatgggagggatcatccctatc......tttggtacagcaaactacgcacagaagttccag...ggcagagtcacgattaccgcggacgaatccacgagcacagcctacatggagctgagcagcctgagatctgaggacacggccgtgtattactgtgcgagaga >IGHV1-8*01 caggtgcagctggtgcagtctggggct...gaggtgaagaagcctggggcctcagtgaaggtctcctgcaaggcttctggatacaccttc............accagttatgatatcaactgggtgcgacaggccactggacaagggcttgagtggatgggatggatgaaccctaac......agtggtaacacaggctatgcacagaagttccag...ggcagagtcaccatgaccaggaacacctccataagcacagcctacatggagctgagcagcctgagatctgaggacacggccgtgtattactgtgcgagagg >IGHV1-8*02 caggtgcagctggtgcagtctggggct...gaggtgaagaagcctggggcctcagtgaaggtctcctgcaaggcttctggatacaccttc............accagctatgatatcaactgggtgcgacaggccactggacaagggcttgagtggatgggatggatgaaccctaac......agtggtaacacaggctatgcacagaagttccag...ggcagagtcaccatgaccaggaacacctccataagcacagcctacatggagctgagcagcctgagatctgaggacacggccgtgtattactgtgcgagagg >IGHV1-NL1*01 caggttcagctgttgcagcctggggtc...caggtgaagaagcctgggtcctcagtgaaggtctcctgctaggcttccagatacaccttc............accaaatactttacacggtgggtgtgacaaagccctggacaagggcatnagtggatgggatgaatcaacccttac......aacgataacacacactacgcacagacgttctgg...ggcagagtcaccattaccagtgacaggtccatgagcacagcctacatggagctgagcngcctgagatccgaagacatggtcgtgtattactgtgtgagaga >IGHV1/OR15-1*01 caggtgcagctggtgcagtctggggct...gaggtgaagaagcctggggcctcagtgaaggtctcctgcaaggcttctggatacatcttc............accgactactatatgcactgggtgcgacaggcccctggacaagagcttgggtggatgggacggatcaaccctaac......agtggtggcacaaactatgcacagaagtttcag...ggcagagtcaccatgaccagggacacgtccatcagcacagcctacacggagctgagcagcctgagatctgaggacacggccacgtattactgtgcgaga >IGHV1/OR15-1*02 caggtgcagctggtgcagtctggggct...gaggtgaagaagcctggggcctcagtgaaggtctcctgcaaggcttctggatacatcttc............accgactactatatgcactgggtgcgacaggcccctggacaagagcttgggtggatgggacggatcaaccctaac......agtggtggcacaaactatgcacagaagtttcag...ggcagagtcaccatgaccagggacacgtccatcagcacagcctgcacggagctgagcagcctgagatctgaggacacggccacgtattactgtgcgagaga >IGHV1/OR15-1*03 caggtgcagctggtgcagtctggggct...gaggtgaagaagcctggggcctcagtgaaggtctcctgcaaggcttctggatacatcttc............accgactactatatgcactgggtgcgacaggcccctggacaagagcttgggtggatgggacggatcaaccctaac......agtggtggcacaaactatgcacagaagtttcag...ggcagagtcaccatgaccagggacacgtccatcagcacagcctacacggagctgagcagcctgagatctgaggacacagccacgtattactgtgcgagaga >IGHV1/OR15-1*04 caggtgcagctggtgcagtctggggct...gaggtgaagaagcctggggcctcagtgaaggtctcctgcaaggcttctggatacatcttc............accgactactatatgcactgggtgcgacaggcccctggacaagagcttgggtggatgggacggatcaaccctaac......agtggtggcacaaactatgcacagaagtttcag...ggcagagtcaccatgaccagggacacgtccatcagcacagcctacatggagctgagcagcctgagatctgaggacacggccacgtattactgtgcgagaga >IGHV1/OR15-2*01 caggtgcagctggtgcagtctggagct...gaggtgaagaagcctagagcctcagtgaaggtctcctgcaaggcttctggttacaccttt............accagctactatatgcactgggtgtgacaggcccctgaacaagggcttgagtggatgggatggatcaacacttac......aatggtaacacaaactacccacagaagctccag...ggcagagtcaccatgaccagagacacatccacgagcacagcctacatggagctgagcaggctgagatctgacgacatggccgtgtattactgtgcgagaga >IGHV1/OR15-2*02 caggtgcagctggtgcagtctggagct...gaggtgaagaagcctggagcctcagtgaaggtctcctgcaaggcttctggttacaccttt............accagctactatatgcactgggtgtgacaggcccctgaacaagggcttgagtggatgggatggatcaacacttac......aatggtaacacaaactacccacagaagctccag...ggcagagtcaccatgaccagagacacatccacgagcacagcctacatggagctgagcagcctgagatctgacgacatggccgtgtattactgtgcgagaga >IGHV1/OR15-2*03 caggtgcagctggtgcagtctggagct...gaggtgaagaagcctagagcctcagtgaaggtctcctgcaaggcttctggttacaccttt............accagctactatatgcactgggtgtgacaggcccctgaacaagggcttgagtggatgggatggatcaacacttac......aatggtaacacaaactacccacagaagctccag...ggcagagtcaccatgaccagagacacatccacgagcacagcctacatggagctgagcagcctgagatctgacgacatggccgtgtattactgtgcgagaga >IGHV1/OR15-3*01 caggtccaactggtgtagtctggagct...gaggtgaagaagcctggggcctcagtgaaggtctcctgcaaggcttctggatacaccttc............accgactactttatgaactggatgcgccaggcccctggacaaaggcttgagtggatgggatggatcaacgctggc......aatggtaacacaaaatattcacagaagctccag...ggcagagtcaccattaccagggacacatcttcgagcacagcctacatgcagctgagcagcctgagatctgaggacacggccgtgtattactgtgcgaga >IGHV1/OR15-3*02 caggtccaactggtgtagtctggagct...gaggtgaagaagcctggggcctcagtgaaggtctcctgcaaggcttctggatacaccttc............accgactactttatgaactggatgcgccaggcccctggacaaaggcttgagtggatgggatggatcaacgctggc......aatggtaacacaaaatattcacagaagctccag...ggcagagtcaccattaccagggacacatctgcgagcacagcctacatgcagctgagcagcctgagatctgaggacacggccgtgtattactgtgcgagaga >IGHV1/OR15-3*03 caggtccaactggtgtagtctggagct...gaggtgaagaagcctggggcctcagtgaaggtctcctgcaaggcttctggatacaccttc............accagctactatatgaactggatgcgccaggcccctggacaaggcttcgagtggatgggatggatcaacgctggc......aatggtaacacaaagtattcacagaagctccag...ggcagagtcaccattaccagggacacatctgcgagcacagcctacatgcagctgagcagcctgagatctgaggacacggccgtgtattactgtgcgaga >IGHV1/OR15-4*01 caggaccagttggtgcagtctggggct...gaggtgaagaagcctctgtcctcagtgaaggtctccttcaaggcttctggatacaccttc............accaacaactttatgcactgggtgtgacaggcccctggacaaggacttgagtggatgggatggatcaatgctggc......aatggtaacacaacatatgcacagaagttccag...ggcagagtcaccataaccagggacacgtccatgagcacagcctacacggagctgagcagcctgagatctgaggacacggccgtgtattactgtgcgaga >IGHV1/OR15-5*01 .....................................agaagcctggggcctcagtgaaggtctcctgcaaggcttctggatacaccttc............accagctactgtatgcactgggtgcaccaggtccatgcacaagggcttgagtggatgggattggtgtgccctagt......gatggcagcacaagctatgcacagaagttccag...gccagagtcaccataaccagggacacatccatgagcacagcctacatggagctaagcagtctgagatctgaggacacggccatgtattactgtgtgaga >IGHV1/OR15-5*02 caggtacagctggtgcagtctggggct...gaggtgaagaagcctggggcctcagtgaaggtctcctgcaaggcttctggatacaccttc............accaactactgtatgcactgggtgcgccaggtccatgcacaagggcttgagtggatgggattggtgtgccctagt......gatggcagcacaagctatgcacaaaagttccag...gccagagtcaccataaccagggacacatccatgagcacagcctacatggagctaagcagtctgagatctgaggacacggccatgtattactgtgtgaga >IGHV1/OR15-9*01 caggtacagctgatgcagtctggggct...gaggtgaagaagcctggggcctcagtgaggatctcctgcaaggcttctggatacaccttc............accagctactgtatgcactgggtgtgccaggcccatgcacaagggcttgagtggatgggattggtgtgccctagt......gatggcagcacaagctatgcacagaagttccag...ggcagagtcaccataaccagggacacatccatgggcacagcctacatggagctaagcagcctgagatctgaggacacggccatgtattactgtgtgagaga >IGHV1/OR21-1*01 caggtacagctggtgcagtctggggct...gaggtgaagaagcctggggcctcagtgaaggtctcctgcaaggcttctggatacaccatc............accagctactgtatgcactgggtgcaccaggtccatgcacaagggcttgagtggatgggattggtgtgccctagt......gatggcagcacaagctatgcacagaagttccag...gccagagtcaccataaccagggacacatccatgagcacagcctacatggagctaagcagtctgagatctgaggacacggccatgtattactgtgtgagaga >IGHV2-10*01 caggtcaccttgaaggagtctggtcct...gcactggtgaaacccacacagaccctcatgctgacctgcaccttctctgggttctcactcagc......acttctggaatgggtgtgggttagatctgtcagccctcagcaaaggccctggagtggcttgcacacatttattagaat.........gataataaatactacagcccatctctgaag...agtaggctcattatctccaaggacacctccaagaatgaagtggttctaacagtgatcaacatggacattgtggacacagccacacattactgtgcaaggagac >IGHV2-26*01 caggtcaccttgaaggagtctggtcct...gtgctggtgaaacccacagagaccctcacgctgacctgcaccgtctctgggttctcactcagc......aatgctagaatgggtgtgagctggatccgtcagcccccagggaaggccctggagtggcttgcacacattttttcgaat.........gacgaaaaatcctacagcacatctctgaag...agcaggctcaccatctccaaggacacctccaaaagccaggtggtccttaccatgaccaacatggaccctgtggacacagccacatattactgtgcacggatac >IGHV2-5*01 cagatcaccttgaaggagtctggtcct...acgctggtgaaacccacacagaccctcacgctgacctgcaccttctctgggttctcactcagc......actagtggagtgggtgtgggctggatccgtcagcccccaggaaaggccctggagtggcttgcactcatttattggaat.........gatgataagcgctacagcccatctctgaag...agcaggctcaccatcaccaaggacacctccaaaaaccaggtggtccttacaatgaccaacatggaccctgtggacacagccacatattactgtgcacacagac >IGHV2-5*02 cagatcaccttgaaggagtctggtcct...acgctggtgaaacccacacagaccctcacgctgacctgcaccttctctgggttctcactcagc......actagtggagtgggtgtgggctggatccgtcagcccccaggaaaggccctggagtggcttgcactcatttattgggat.........gatgataagcgctacagcccatctctgaag...agcaggctcaccatcaccaaggacacctccaaaaaccaggtggtccttacaatgaccaacatggaccctgtggacacagccacatattactgtgcacacagac >IGHV2-5*03 ................................gctggtgaaacccacacagaccctcacgctgacctgcaccttctctgggttctcactcagc......actagtggagtgggtgtgggctggatccgtcagcccccaggaaaggccctggagtggcttgcactcatttattgggat.........gatgataagcgctacagcccatctctgaag...agcaggctcaccattaccaaggacacctccaaaaaccaggt >IGHV2-5*04 cagatcaccttgaaggagtctggtcct...acgctggtgaaacccacacagaccctcacgctgacctgcaccttctctgggttctcactcagc......actagtggagtgggtgtgggctggatccgtcagcccccaggaaaggccctggagtggcttgcactcatttattggaat.........gatgataagcgctacagcccatctctgaag...agcaggctcaccatcaccaaggacacctccaaaaaccaggtggtccttacaatgaccaacatggaccctgtggacacaggcacatattactgtgtac >IGHV2-5*05 cagatcaccttgaaggagtctggtcct...acgctggtgaaacccacacagaccctcacgctgacctgcaccttctctgggttctcactcagc......actagtggagtgggtgtgggctggatccgtcagcccccaggaaaggccctggagtggcttgcactcatttattgggat.........gatgataagcgctacggcccatctctgaag...agcaggctcaccatcaccaaggacacctccaaaaaccaggtggtccttacaatgaccaacatggaccctgtggacacagccacatattactgtgcacacagac >IGHV2-5*06 cagatcaccttgaaggagtctggtcct...acgctggtaaaacccacacagaccctcacgctgacctgcaccttctctgggttctcactcagc......actagtggagtgggtgtgggctggatccgtcagcccccaggaaaggccctggagtggcttgcactcatttattgggat.........gatgataagcgctacggcccatctctgaag...agcaggctcaccatcaccaaggacacctccaaaaaccaggtggtccttacaatgaccaacatggaccctgtggacacagccacatattactgtgcacacaga >IGHV2-5*08 caggtcaccttgaaggagtctggtcct...gcgctggtgaaacccacacagaccctcacactgacctgcaccttctctgggttctcactcagc......actagtggaatgcgtgtgagctggatccgtcagcccccaggaaaggccctggagtggcttgcactcatttattgggat.........gatgataagcgctacagcccatctctgaag...agcaggctcaccatcaccaaggacacctccaaaaaccaggtggtccttacaatgaccaacatggaccctgtggacacagccacatattactgtgcacacagac >IGHV2-5*09 caggtcaccttgaaggagtctggtcct...acgctggtgaaacccacacagaccctcacgctgacctgcaccttctctgggttctcactcagc......actagtggagtgggtgtgggctggatccgtcagcccccaggaaaggccctggagtggcttgcactcatttattgggat.........gatgataagcgctacggcccatctctgaag...agcaggctcaccatcaccaaggacacctccaaaaaccaggtggtccttacaatgaccaacatggaccctgtggacacagccacatattactgtgcacacagac >IGHV2-70*01 caggtcaccttgagggagtctggtcct...gcgctggtgaaacccacacagaccctcacactgacctgcaccttctctgggttctcactcagc......actagtggaatgtgtgtgagctggatccgtcagcccccagggaaggccctggagtggcttgcactcattgattgggat.........gatgataaatactacagcacatctctgaag...accaggctcaccatctccaaggacacctccaaaaaccaggtggtccttacaatgaccaacatggaccctgtggacacagccacgtattactgtgcacggatac >IGHV2-70*02 caggtcaccttgagggagtctggtcct...gcgctggtgaaacccacacagaccctcacactgacctgcaccttctctgggttctcactcagc......actagtggaatgtgtgtgagctggatccgtcagcccccagggaaggccctggagtggcttgcactcattgattgggat.........gatgataaatactacagcacatctctgaag...accaggctcaccatctccaaggacacctccaaaaaccaggtggtccttacaatgaccaacatggaccctgtggacacggccgtgtattactg >IGHV2-70*03 caggtcaccttgaaggagtctggtcct...gcgctggtgaaacccacacagaccctcacactgacctgcaccttctctgggttctcactcagc......actagtggaatgcgtgtgagctggatccgtcagcccccagggaaggccctggagtggcttgcacgcattgattgggat.........gatgataaattctacagcacatctctgaag...accaggctcaccatctccaaggacacctccaaaaaccaggtggtccttacaatgaccaacatggaccctgtggacacggccgtgtattactg >IGHV2-70*04 caggtcaccttgaaggagtctggtcct...gcgctggtgaaacccacacagaccctcacactgacctgcaccttctctgggttctcactcagc......actagtggaatgcgtgtgagctggatccgtcagcccccagggaaggccctggagtggcttgcacgcattgattgggat.........gatgataaattctacagcacatctctgaag...accaggctcaccatctccaaggacacctccaaaaaccaggtggtccttacaatgaccaacatggaccctgtggacacagccacgtattac >IGHV2-70*05 ..........................t...gcgctggtgaaacccacacagaccctcacactgacctgcaccttctctgggttctcactcagc......actagtggaatgcgtgcgagctggatccgtcagcccccagggaaggccctggagtggcttgcacgcattgattgggat.........gatgataaattctacagcacatctctgaag...accaggctcaccatctccaaggacacctccaaaaaccaggtggtccttacaatgaccaacatgga >IGHV2-70*06 caggtcaccttgaaggagtctggtcct...gcgctggtgaaacccacacagaccctcacactgacctgcaccttctctgggttctcactcagc......actagtggaatgcgtgtgagctggatccgtcagcccccagggaaggccctggagtggcttgcacgcattgattgggat.........gatgataaattctacagcacatccctgaag...accaggctcaccatctccaaggacacctccaaaaaccaggtggtccttacaatgaccaacatggaccctgtggacacggccgtgtattactg >IGHV2-70*07 caggtcaccttgagggagtctggtcct...gcgctggtgaaacccacacagaccctcacactgacctgcaccttctctgggttctcactcagc......actagtggaatgtgtgtgagctggatccgtcagcccccggggaaggccctggagtggcttgcactcattgattgggat.........gatgataaatactacagcacatctctgaag...accaggctcaccatctccaaggacacctccaaaaaccaggtggtccttacaatgaccaacatggaccctgtggacacggccgtgtattactg >IGHV2-70*08 caggtcaccttgagggagtctggtcct...gcgctggtgaaacccacacagaccctcacactgacctgcgccttctctgggttctcactcagc......actagtggaatgtgtgtgagctggatccgtcagcccccagggaaggccctggagtggcttgcacgcattgattgggat.........gatgataaatactacagcacatctctgaag...accaggctcaccatctccaaggacacctccaaaaaccaggtggtccttacaatgaccaacatggaccctgtggacacggccgtgtattactg >IGHV2-70*09 cagatcaccttgaaggagtctggtcct...acgctggtgaaacccacacagaccctcacgctgacccgcaccttctctgggttctcactcagc......actagtggaatgtgtgtgagctggatccgtcagcccccagggaaggccctggagtggcttgcactcattgattgggat.........gatgataaatactacagcacatctctgaac...accaggctcaccatctccaaggacacctccaaaaaccaggtggtccttacaatgaccaacatggaccctgtggacacaggcacatattactgtgtacgg >IGHV2-70*10 caggtcaccttgaaggagtctggtcct...gcgctggtgaaacccacacagaccctcacactgacctgcaccttctctgggttctcactcagc......actagtggaatgcgtgtgagctggatccgtcagcccccagggaaggccctggagtggattgcacgcattgattgggat.........gatgataaatactacagcacatctctgaag...accaggctcaccatctccaaggacacctccaaaaaccaggtggtccttacaatgaccaacatggaccctgtggacacagccacgtattactgtgcacggatac >IGHV2-70*11 cgggtcaccttgagggagtctggtcct...gcgctggtgaaacccacacagaccctcacactgacctgcaccttctctgggttctcactcagc......actagtggaatgtgtgtgagctggatccgtcagcccccagggaaggccctggagtggcttgcacgcattgattgggat.........gatgataaatactacagcacatctctgaag...accaggctcaccatctccaaggacacctccaaaaaccaggtggtccttacaatgaccaacatggaccctgtggacacagccacgtattactgtgcacggatac >IGHV2-70*12 cagatcaccttgaaggagtctggtcct...acgctggtgaaacccacacagaccctcacgctgacctgcaccttctctgggttctcactcagc......actagtggaatgtgtgtgagctggatccgtcagcccccagggaaggccctggagtggcttgcactcattgattgggat.........gatgataaatactacagcacatctctgaag...accaggctcaccatctccaaggacacctccaaaaaccaggtggtccttacaatgaccaacatggaccctgtggacacagccacatattactgtgcacacagac >IGHV2-70*13 caggtcaccttgagggagtctggtcct...gcgctggtgaaacccacacagaccctcacactgacctgcaccttctctgggttctcactcagc......actagtggaatgtgtgtgagctggatccgtcagcccccagggaaggccctggagtggcttgcactcattgattgggat.........gatgataaatactacagcacatctctgaag...accaggctcaccatctccaaggacacctccaaaaaccaggtggtccttacaatgaccaacatggaccctgtggacacagccacgtattattgtgcacggatac >IGHV2-70D*04 caggtcaccttgaaggagtctggtcct...gcgctggtgaaacccacacagaccctcacactgacctgcaccttctctgggttctcactcagc......actagtggaatgcgtgtgagctggatccgtcagcccccagggaaggccctggagtggcttgcacgcattgattgggat.........gatgataaattctacagcacatctctgaag...accaggctcaccatctccaaggacacctccaaaaaccaggtggtccttacaatgaccaacatggaccctgtggacacagccacgtattactgtgcacggatac >IGHV2-70D*14 caggtcaccttgaaggagtctggtcct...gcgctggtgaaacccacacagaccctcacactgacctgcaccttctctgggttctcactcagc......actagtggaatgcgtgtgagctggatccgtcagcccccaggtaaggccctggagtggcttgcacgcattgattgggat.........gatgataaattctacagcacatctctgaag...accaggctcaccatctccaaggacacctccaaaaaccaggtggtccttacaatgaccaacatggaccctgtggacacagccacgtattactgtgcacggatac >IGHV2/OR16-5*01 caggtcaccttgaaggagtctggtcct...gcgctggtgaaacccacagagaccctcacgctgacctgcactctctctgggttctcactcagc......acttctggaatgggtatgagctggatccgtcagcccccagggaaggccctggagtggcttgctcacatttttttgaat.........gacaaaaaatcctacagcacgtctctgaag...aacaggctcatcatctccaaggacacctccaaaagccaggtggtccttaccatgaccaacatggaccctgtggacacagccacgtattactgtgcatggagag >IGHV3-11*01 caggtgcagctggtggagtctggggga...ggcttggtcaagcctggagggtccctgagactctcctgtgcagcctctggattcaccttc............agtgactactacatgagctggatccgccaggctccagggaaggggctggagtgggtttcatacattagtagtagt......ggtagtaccatatactacgcagactctgtgaag...ggccgattcaccatctccagggacaacgccaagaactcactgtatctgcaaatgaacagcctgagagccgaggacacggccgtgtattactgtgcgagaga >IGHV3-11*03 caggtgcagctgttggagtctggggga...ggcttggtcaagcctggagggtccctgagactctcctgtgcagcctctggattcaccttc............agtgactactacatgagctggatccgccaggctccagggaaggggctggagtgggtttcatacattagtagtagt......agtagttacacaaactacgcagactctgtgaag...ggccgattcaccatctccagagacaacgccaagaactcactgtatctgcaaatgaacagcctgagagccgaggacacggccgtgtattactgtgcgaga >IGHV3-11*04 caggtgcagctggtggagtctggggga...ggcttggtcaagcctggagggtccctgagactctcctgtgcagcctctggattcaccttc............agtgactactacatgagctggatccgccaggctccagggaaggggctggagtgggtttcatacattagtagtagt......ggtagtaccatatactacgcagactctgtgaag...ggccgattcaccatctccagggacaacgccaagaactcactgtatctgcaaatgaacagcctgagagccgaggacacggctgtgtattactgtgcgagaga >IGHV3-11*05 caggtgcagctggtggagtctggggga...ggcttggtcaagcctggagggtccctgagactctcctgtgcagcctctggattcaccttc............agtgactactacatgagctggatccgccaggctccagggaaggggctggagtgggtttcatacattagtagtagt......agtagttacacaaactacgcagactctgtgaag...ggccgattcaccatctccagagacaacgccaagaactcactgtatctgcaaatgaacagcctgagagccgaggacacggccgtgtattactgtgcgagaga >IGHV3-11*06 caggtgcagctggtggagtctggggga...ggcttggtcaagcctggagggtccctgagactctcctgtgcagcctctggattcaccttc............agtgactactacatgagctggatccgccaggctccagggaaggggctggagtgggtttcatacattagtagtagt......agtagttacacaaactacgcagactctgtgaag...ggccgattcaccatctccagagacaacgccaagaactcactgtatctgcaaatgaacagcctgagagccgaggacacggctgtgtattactgtgcgagaga >IGHV3-13*01 gaggtgcagctggtggagtctggggga...ggcttggtacagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctacgacatgcactgggtccgccaagctacaggaaaaggtctggagtgggtctcagctattggtactgct.........ggtgacacatactatccaggctccgtgaag...ggccgattcaccatctccagagaaaatgccaagaactccttgtatcttcaaatgaacagcctgagagccggggacacggctgtgtattactgtgcaagaga >IGHV3-13*02 gaggtgcatctggtggagtctggggga...ggcttggtacagcctgggggggccctgagactctcctgtgcagcctctggattcaccttc............agtaactacgacatgcactgggtccgccaagctacaggaaaaggtctggagtgggtctcagccaatggtactgct.........ggtgacacatactatccaggctccgtgaag...gggcgattcaccatctccagagaaaatgccaagaactccttgtatcttcaaatgaacagcctgagagccggggacacggctgtgtattactgtgcaagaga >IGHV3-13*03 gaggtgcagctggtggagtctggggga...ggcttggtacagcctggggggtccctgagactctcctgtgcagcctgtggattcaccttc............agtagctacgacatgcactgggtccgccaagctacaggaaaaggtctggagtgggtctcagctattggtactgct.........ggtgacacatactatccaggctccgtgaag...ggccaattcaccatctccagagaaaatgccaagaactccttgtatcttcaaatgaacagcctgagagccggggacacggctgtgtattactgtgcaaga >IGHV3-13*04 gaggtgcagctggtggagtctggggga...ggcttggtacagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctacgacatgcactgggtccgccaagctacaggaaaaggtctggaatgggtctcagctattggtactgct.........ggtgacacatactatccaggctccgtgaag...ggccgattcaccatctccagagaaaatgccaagaactccttgtatcttcaaatgaacagcctgagagccggggacacggctgtgtattactgtgcaagaga >IGHV3-13*05 gaggtgcagctggtggagtctggggga...ggcttggtacagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctacgacatgcactgggtccgccaagctacaggaaaaggtctggagtgggtctcagctattggtactgct.........ggtgacccatactatccaggctccgtgaag...ggccgattcaccatctccagagaaaatgccaagaactccttgtatcttcaaatgaacagcctgagagccggggacacggctgtgtattactgtgcaagaga >IGHV3-15*01 gaggtgcagctggtggagtctggggga...ggcttggtaaagcctggggggtcccttagactctcctgtgcagcctctggattcactttc............agtaacgcctggatgagctgggtccgccaggctccagggaaggggctggagtgggttggccgtattaaaagcaaaactgatggtgggacaacagactacgctgcacccgtgaaa...ggcagattcaccatctcaagagatgattcaaaaaacacgctgtatctgcaaatgaacagcctgaaaaccgaggacacagccgtgtattactgtaccacaga >IGHV3-15*02 gaggtgcagctggtggagtctggggga...gccttggtaaagcctggggggtcccttagactctcctgtgcagcctctggattcactttc............agtaacgcctggatgagctgggtccgccaggctccagggaaggggctggagtgggttggccgtattaaaagcaaaactgatggtgggacaacagactacgctgcacccgtgaaa...ggcagattcaccatctcaagagatgattcaaaaaacacgctgtatctgcaaatgaacagcctgaaaaccgaggacacagccgtgtattactgtaccacaga >IGHV3-15*03 gaggtgcagctggtggagtctgccgga...gccttggtacagcctggggggtcccttagactctcctgtgcagcctctggattcacttgc............agtaacgcctggatgagctgggtccgccaggctccagggaaggggctggagtgggttggccgtattaaaagcaaagctaatggtgggacaacagactacgctgcacctgtgaaa...ggcagattcaccatctcaagagttgattcaaaaaacacgctgtatctgcaaatgaacagcctgaaaaccgaggacacagccgtgtattactgtaccacaga >IGHV3-15*04 gaggtgcagctggtggagtctggggga...ggcttggtaaagcctggggggtcccttagactctcctgtgcagcctctggattcactttc............agtaacgcctggatgagctgggtccgccaggctccagggaaggggctggagtgggttggccgtattgaaagcaaaactgatggtgggacaacagactacgctgcacccgtgaaa...ggcagattcaccatctcaagagatgattcaaaaaacacgctgtatctgcaaatgaacagcctgaaaaccgaggacacagccgtgtattactgtaccacaga >IGHV3-15*05 gaggtgcagctggtggagtctggggga...ggcttggtaaagcctggggggtcccttagactctcctgtgcagcctctggattcactttc............agtaacgcctggatgagctgggtccgccaggctccagggaaggggctggagtgggttggccgtattaaaagcaaaactgatggtgggacaacagactacgctgcacccgtgaaa...ggcagattcaccatctcaagagatgattcaaaaaacacgctgtatctgcaaatgaacagtctgaaaaccgaggacacagccgtgtattactgtaccacaga >IGHV3-15*06 gaggtgcagctggtggagtctggggga...ggcttggtaaagcctggggggtcccttagactctcctgtgcagcctctggattcactttc............agtaacgcctggatgagctgggtccgccaggctccagggaaggggctggagtgggtcggccgtattaaaagcaaaactgatggtgggacaacaaactacgctgcacccgtgaaa...ggcagattcaccatctcaagagatgattcaaaaaacacgctgtatctgcaaatgaacagcctgaaaaccgaggacacagccgtgtattactgtaccacaga >IGHV3-15*07 gaggtgcagctggtggagtctggggga...ggcttggtaaagcctggggggtcccttagactctcctgtgcagcctctggtttcactttc............agtaacgcctggatgaactgggtccgccaggctccagggaaggggctggagtgggtcggccgtattaaaagcaaaactgatggtgggacaacagactacgctgcacccgtgaaa...ggcagattcaccatctcaagagatgattcaaaaaacacgctgtatctgcaaatgaacagcctgaaaaccgaggacacagccgtgtattactgtaccacaga >IGHV3-15*08 gaggtgcagctggtggagtctgcggga...ggcttggtacagcctggggggtcccttagactctcctgtgcagcctctggattcacttgc............agtaacgcctggatgagctgggtccgccaggctccagggaaggggctggagtgggttggctgtattaaaagcaaagctaatggtgggacaacagactacgctgcacctgtgaaa...ggcagattcaccatctcaagagatgattcaaaaaacacgctgtatctgcaaatgatcagcctgaaaaccgaggacacggccgtgtattactgtaccacagg >IGHV3-16*01 gaggtacaactggtggagtctggggga...ggcttggtacagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agtaacagtgacatgaactgggcccgcaaggctccaggaaaggggctggagtgggtatcgggtgttagttggaat......ggcagtaggacgcactatgtggactccgtgaag...cgccgattcatcatctccagagacaattccaggaactccctgtatctgcaaaagaacagacggagagccgaggacatggctgtgtattactgtgtgagaaa >IGHV3-16*02 gaggtgcagctggtggagtctggggga...ggcttggtacagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agtaacagtgacatgaactgggcccgcaaggctccaggaaaggggctggagtgggtatcgggtgttagttggaat......ggcagtaggacgcactatgtggactccgtgaag...cgccgattcatcatctccagagacaattccaggaactccctgtatctgcaaaagaacagacggagagccgaggacatggctgtgtattactgtgtgagaaa >IGHV3-19*01 acagtgcagctggtggagtctggggga...ggcttggtagagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agtaacagtgacatgaactgggtccgccaggctccaggaaaggggctggagtgggtatcgggtgttagttggaat......ggcagtaggacgcactatgcagactctgtgaag...ggccgattcatcatctccagagacaattccaggaacttcctgtatcagcaaatgaacagcctgaggcccgaggacatggctgtgtattactgtgtgagaaa >IGHV3-20*01 gaggtgcagctggtggagtctggggga...ggtgtggtacggcctggggggtccctgagactctcctgtgcagcctctggattcaccttt............gatgattatggcatgagctgggtccgccaagctccagggaaggggctggagtgggtctctggtattaattggaat......ggtggtagcacaggttatgcagactctgtgaag...ggccgattcaccatctccagagacaacgccaagaactccctgtatctgcaaatgaacagtctgagagccgaggacacggccttgtatcactgtgcgagaga >IGHV3-20*02 gaggtgcagctggtggagtctggggga...ggtgtggtacggcctggggggtccctgagactctcctttgcagcctctggattcaccttt............gatgattatggcatgagctgggtccgccaagctccagggaaggggctggagtgggtctctggtattaattggaat......ggtggtagcacaggttatgcagactctgtgaag...ggccgattcaccatctccagagacaacgccaagaactccctgtatctgcaaatgaacagtctgagagccgaggacacggccttgtatcactgtgcgagaga >IGHV3-21*01 gaggtgcagctggtggagtctggggga...ggcctggtcaagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatagcatgaactgggtccgccaggctccagggaaggggctggagtgggtctcatccattagtagtagt......agtagttacatatactacgcagactcagtgaag...ggccgattcaccatctccagagacaacgccaagaactcactgtatctgcaaatgaacagcctgagagccgaggacacggctgtgtattactgtgcgagaga >IGHV3-21*02 gaggtgcaactggtggagtctggggga...ggcctggtcaagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatagcatgaactgggtccgccaggctccagggaaggggctggagtgggtctcatccattagtagtagt......agtagttacatatactacgcagactcagtgaag...ggccgattcaccatctccagagacaacgccaagaactcactgtatctgcaaatgaacagcctgagagccgaggacacggctgtgtattactgtgcgagaga >IGHV3-21*03 gaggtgcagctggtggagtctggggga...ggcctggtcaagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatagcatgaactgggtccgccaggctccagggaaggggctggagtgggtctcatccattagtagtagt......agtagttacatatactacgcagactcagtgaag...ggccgattcaccatctccagagacaacgccaagaactcactgtatctgcaaatgaacagcctgagagccgaggacacagctgtgtattactgtgcgagaga >IGHV3-21*04 gaggtgcagctggtggagtctggggga...ggcctggtcaagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatagcatgaactgggtccgccaggctccagggaaggggctggagtgggtctcatccattagtagtagt......agtagttacatatactacgcagactcagtgaag...ggccgattcaccatctccagagacaacgccaagaactcactgtatctgcaaatgaacagcctgagagccgaggacacggccgtgtattactgtgcgagaga >IGHV3-22*01 gaggtgcatctggtggagtctggggga...gccttggtacagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agttactactacatgagcggggtccgccaggctcccgggaaggggctggaatgggtaggtttcattagaaacaaagctaatggtgggacaacagaatagaccacgtctgtgaaa...ggcagattcacaatctcaagagatgattccaaaagcatcacctatctgcaaatgaagagcctgaaaaccgaggacacggccgtgtattactgttccagaga >IGHV3-22*02 gaggtgcagctggtggagtctggggga...ggcttggtacagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agttactactacatgagcggggtccgccaggctcccgggaaggggctggaatgggtaggtttcattagaaacaaagctaatggtgggacaacagaatagaccacgtctgtgaaa...ggcagattcacaatctcaagagatgattccaaaagcatcacctatctgcaaatgaagagcctgaaaaccgaggacacggccgtgtattactgttccagaga >IGHV3-23*01 gaggtgcagctgttggagtctggggga...ggcttggtacagcctggggggtccctgagactctcctgtgcagcctctggattcaccttt............agcagctatgccatgagctgggtccgccaggctccagggaaggggctggagtgggtctcagctattagtggtagt......ggtggtagcacatactacgcagactccgtgaag...ggccggttcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagccgaggacacggccgtatattactgtgcgaaaga >IGHV3-23*02 gaggtgcagctgttggagtctggggga...ggcttggtacagcctggggggtccctgagactctcctgtgcagcctctggattcaccttt............agcagctatgccatgagctgggtccgccaggctccagggaaggggctggagtgggtctcagctattagtggtagt......ggtggtagcacatactacggagactccgtgaag...ggccggttcaccatctcaagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagccgaggacacggccgtatattactgtgcgaaaga >IGHV3-23*03 gaggtgcagctgttggagtctggggga...ggcttggtacagcctggggggtccctgagactctcctgtgcagcctctggattcaccttt............agcagctatgccatgagctgggtccgccaggctccagggaaggggctggagtgggtctcagttatttatagcggt......ggtagtagcacatactatgcagactccgtgaag...ggccggttcaccatctccagagataattccaagaacacgctgtatctgcaaatgaacagcctgagagccgaggacacggccgtatattactgtgcgaaaga >IGHV3-23*04 gaggtgcagctggtggagtctggggga...ggcttggtacagcctggggggtccctgagactctcctgtgcagcctctggattcaccttt............agcagctatgccatgagctgggtccgccaggctccagggaaggggctggagtgggtctcagctattagtggtagt......ggtggtagcacatactacgcagactccgtgaag...ggccggttcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagccgaggacacggccgtatattactgtgcgaaaga >IGHV3-23*05 gaggtgcagctgttggagtctggggga...ggcttggtacagcctggggggtccctgagactctcctgtgcagcctctggattcaccttt............agcagctatgccatgagctgggtccgccaggctccagggaaggggctggagtgggtctcagctatttatagcagt......ggtagtagcacatactatgcagactccgtgaag...ggccggttcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagccgaggacacggccgtatattactgtgcgaaa >IGHV3-23D*01 gaggtgcagctgttggagtctggggga...ggcttggtacagcctggggggtccctgagactctcctgtgcagcctctggattcaccttt............agcagctatgccatgagctgggtccgccaggctccagggaaggggctggagtgggtctcagctattagtggtagt......ggtggtagcacatactacgcagactccgtgaag...ggccggttcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagccgaggacacggccgtatattactgtgcgaaaga >IGHV3-25*01 gagatgcagctggtggagtctggggga...ggcttgcaaaagcctgcgtggtccccgagactctcctgtgcagcctctcaattcaccttc............agtagctactacatgaactgtgtccgccaggctccagggaatgggctggagttggtttgacaagttaatcctaat......gggggtagcacatacctcatagactccggtaag...gaccgattcaatacctccagagataacgccaagaacacacttcatctgcaaatgaacagcctgaaaaccgaggacacggccctctattagtgtaccagaga >IGHV3-25*02 gagatgcagctggtggagtctggggga...ggcttggcaaagcctgcgtggtccccgagactctcctgtgcagcctctcaattcaccttc............agtagctactacatgaactgtgtccgccaggctccagggaatgggctggagttggtttgacaagttaatcctaat......gggggtagcacatacctcatagactccggtaag...gaccgattcaatacctccagagataacgccaagaacacacttcatctgcaaatgaacagcctgaaaaccgaggacacggccctctattagtgtaccagaga >IGHV3-25*03 gagatgcagctggtggagtctggggga...ggcttggcaaagcctgcgtggtccccgagactctcctgtgcagcctctcaattcaccttc............agtagctactacatgaactgtgtccgccaggctccagggaatgggctggagttggttggacaagttaatcctaat......gggggtagcacatacctcatagactccggtaag...gaccgattcaatacctccagagataacgccaagaacacacttcatctgcaaatgaacagcctgaaaaccgaggacacggccctgtattagtgtaccaga >IGHV3-25*04 gagacgcagctggtggagtctggggga...ggcttggcaaagcctgggcggtccccgagactctcctgtgcagcctctcaattcaccttc............agtagctactacatgaactgtgtccgccaggctccagggaatgggctggagttggttggacaagttaatcctaat......gggggtagcacatacctcatagactccggtaag...gaccgattcaatacctccagagataacgccaagaacacacttcatctgcaaatgaacagcctgaaaaccgaggacacggccctgtattactgtaccagaga >IGHV3-25*05 gagatgcagctggtggagtctggggga...ggcttggcaaagcctgcgtggtccccgagactctcctgtgcagcctctcaattcaccttc............agtagctactacatgaactgtgtccgccaggctccagggaatgggctggagttggttggacaagttaatcctaat......gggggtagcacatacctcatagactccggtaag...gaccgattcaatacctccagagataacgccaagaacacacttcatctgcaaatgaacagcctgaaaaccgaggacacggccctctattagtgtaccagaga >IGHV3-29*01 gaggtggagctgatagagcccacagag...gacctgagacaacctgggaagttcctgagactctcctgtgtagcctctagattcgccttc............agtagcttctgaatgagcccagttcaccagtctgcaggcaaggggctggagtgagtaatagatataaaagatgat......ggaagtcagatacaccatgcagactctgtgaag...ggcagattctccatctccaaagacaatgctaagaactctctgtatctgcaaatgaacagtcagagaactgaggacatggctgtgtatggctgtacataaggtt >IGHV3-30*01 caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatgctatgcactgggtccgccaggctccaggcaaggggctagagtgggtggcagttatatcatatgat......ggaagtaataaatactacgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgcgagaga >IGHV3-30*02 caggtgcagctggtggagtctggggga...ggcgtggtccagcctggggggtccctgagactctcctgtgcagcgtctggattcaccttc............agtagctatggcatgcactgggtccgccaggctccaggcaaggggctggagtgggtggcatttatacggtatgat......ggaagtaataaatactatgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgcgaaaga >IGHV3-30*03 caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatggcatgcactgggtccgccaggctccaggcaaggggctggagtgggtggcagttatatcatatgat......ggaagtaataaatactatgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgcgagaga >IGHV3-30*04 caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatgctatgcactgggtccgccaggctccaggcaaggggctggagtgggtggcagttatatcatatgat......ggaagtaataaatactacgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgcgagaga >IGHV3-30*05 caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatggcatgcactgggtccgccaggctccaggcaaggggctagagtgggtggcagttatatcatatgat......ggaagtaataaatactacgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagctgagggcacggctgtgtattactgtgcgagaga >IGHV3-30*06 caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcgtctggattcaccttc............agtagctatggcatgcactgggtccgccaggctccaggcaaggggctagagtgggtggcagttatatcatatgat......ggaagtaataaatactacgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgcgagaga >IGHV3-30*07 caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatgctatgcactgggtccgccaggctccaggcaaggggctagagtgggtggcagttatatcatatgat......ggaagtaataaatactacgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagccgaggacacggctgtgtattactgtgcgagaga >IGHV3-30*08 caggtgcagctggtggactctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcctctgcattcaccttc............agtagctatgctatgcactgggtccgccaggctccaggcaaggggctagagtgggtggcagttatatcatatgat......ggaagtaataaatactacgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgcgaga >IGHV3-30*09 caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatgctatgcactgggtccgccaggctccaggcaaggggctggagtgggtggcagttatatcatatgat......ggaagtaataaatactacgcagactccgtgaag...ggccgattcgccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgcgagaga >IGHV3-30*10 caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatgctatgcactgggtccgccaggctccaggcaaggggctagagtgggtggcagttatatcatatgat......ggaagtaataaatactacacagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgcgagaga >IGHV3-30*11 caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcgtctggattcaccttc............agtagctatgctatgcactgggtccgccaggctccaggcaaggggctagagtgggtggcagttatatcatatgat......ggaagtaataaatactacgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgcgagaga >IGHV3-30*12 caggtgcagctggtggagtctgggggg...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcgtctggattcaccttc............agtagctatggcatgcactgggtccgccaggctccaggcaaggggctagagtgggtggcagttatatcatatgat......ggaagtaataaatactacgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagccgaggacacggctgtgtattactgtgcgagaga >IGHV3-30*13 caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatggcatgcactgggtccgccaggctccaggcaaggggctagagtgggtggcagttatatcatatgat......ggaagtaataaatactacgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacaggctgtatctgcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgcgagaga >IGHV3-30*14 caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatgctatgcactgggtccgccaggctccaggcaaggggctggagtgggtggcagttatatcatatgat......ggaagtaataaatactacgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatcttcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgcgagaga >IGHV3-30*15 caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatgctatgcactgggtccgccaggctccaggcaaggggctagagtgggtggcagttatatcatatgat......ggaagtaataaatactacgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgagcagcctgagagctgaggacacggctgtgtattactgtgcgagaga >IGHV3-30*16 caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatgctatgcactgggtccgccaggccccaggcaaggggctagagtgggtggcagttatatcatatgat......ggaagtaataaatactacgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgcgagaga >IGHV3-30*17 caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatgctatgcactgggtccgccaggctccgggcaaggggctagagtgggtggcagttatatcatatgat......ggaagtaataaatactacgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgcgagaga >IGHV3-30*18 caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatggcatgcactgggtccgccaggctccaggcaaggggctggagtgggtggcagttatatcatatgat......ggaagtaataaatactatgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgcgaaaga >IGHV3-30*19 caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcgtctggattcaccttc............agtagctatggcatgcactgggtccgccaggctccaggcaaggggctggagtgggtggcagttatatcatatgat......ggaagtaataaatactacgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgcgagaga >IGHV3-30-2*01 gaggtacagctcgtggagtccggagag...gacccaagacaacctgggggatccctgagactctcctgtgcagactctggattaaccttc............agtagctactgaaggaactcggtttcccaggctccagggaaggggctggagtgagtagtagatatacagtgtgat......ggaagtcagatatgttatgcataatctttgaag...agcaaattcaccatctccaaagaaaatgccaagaactcactgtatttgctaatgaacagtctgagagcagcgggcacagctgtgtgttactgtatgtgaggca >IGHV3-30-22*01 gaggtggagctgatagagtccatagag...gacctgagacaacctgggaagttcctgagactctcctgtgtagcctctagattcgccttc............agtagcttctgaatgagccgagttcaccagtctccaggcaaggggctggagtgagtaatagatataaaagatgat......ggaagtcagatacaccatgcagactctgtgaag...ggcagattctccatctccaaagacaatgctaagaactctctgtatctgcaaatgaacagtcagagagctgaggacatggacgtgtatggctgtacataaggtc >IGHV3-30-3*01 caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatgctatgcactgggtccgccaggctccaggcaaggggctggagtgggtggcagttatatcatatgat......ggaagcaataaatactacgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgcgagaga >IGHV3-30-3*02 caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcgtctggattcaccttc............agtagctatgctatgcactgggtccgccaggctccaggcaaggggctggagtgggtggcagttatatcatatgat......ggaagcaataaatactacgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgcgaaaga >IGHV3-30-3*03 caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatgctatgcactgggtccgccaggctccaggcaaggggctggagtgggtggcagttatatcatatgat......ggaagtaataaatactacgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgcgagaga >IGHV3-30-33*01 gaggtacagctcgtggagtccggagag...gacccaagacaacctgggggatccctgagactctcctgtgcagactctggattaaccttc............agtagctactgaaggagctcggtttcccaggctccagggaaggggctggagtgagtagtagatatacagtgtgat......ggaagtcagatatgttatgcataatctttgaag...agcaaattcaccatctccaaagaaaatgccaagaactcactgtatttgctaatgaacagtctgagagcagagggcacagctgtgtgttactgtatgtgagg >IGHV3-30-42*01 gaggtggagctgatagagcccacagag...gacctgagacaacctgggaagttcctgagactctcctgtgtagcctctagattcgccttc............agtagcttctgaatgagcccagttcaccagtctgcaggcaaggggctggagtgagtaatagatataaaagatgat......ggaagtcagatacaccatgcagactctgtgaag...ggcagattctccatctccaaagacaatgctaagaactctctgtatctgcaaatgaacagtcagagaactgaggacatggctgtgtatggctgtacataaggtt >IGHV3-30-5*01 caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatggcatgcactgggtccgccaggctccaggcaaggggctggagtgggtggcagttatatcatatgat......ggaagtaataaatactatgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgcgaaaga >IGHV3-30-5*02 caggtgcagctggtggagtctggggga...ggcgtggtccagcctggggggtccctgagactctcctgtgcagcgtctggattcaccttc............agtagctatggcatgcactgggtccgccaggctccaggcaaggggctggagtgggtggcatttatacggtatgat......ggaagtaataaatactatgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgcgaaaga >IGHV3-30-52*01 gaggtacagctcgtggagtccggagag...gacccaagacaacctgggggatccctgagactctcctgtgcagactctggattaaccttc............agtagctactgaaggaactcggtttcccaggctccagggaaggggctggagtgagtagtagatatacagtgtgat......ggaagtcagatatgttatgcataatctttgaag...agcaaattcaccatctccaaagaaaatgccaagaactcactgtatttgctaatgaacagtctgagagcagcgggcacagctgtgtgttactgtatgtgagg >IGHV3-32*01 gaggtggagctgatagagtccatagag...gacctgagacaacctgggaagttcctgagactctcctgtgtagcctctagattcgccttc............agtagcttctgaatgagccgagttcaccagtctccaggcaaggggctggagtgagtaatagatataaaagatgat......ggaagtcagatacaccatgcagactctgtgaag...ggcagattctccatctccaaagacaatgctaagaactctctgtatctgcaaatgaacactcagagagctgaggacgtggccgtgtatggctatacataaggtc >IGHV3-33*01 caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcgtctggattcaccttc............agtagctatggcatgcactgggtccgccaggctccaggcaaggggctggagtgggtggcagttatatggtatgat......ggaagtaataaatactatgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagccgaggacacggctgtgtattactgtgcgagaga >IGHV3-33*02 caggtacagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcgtctggattcaccttc............agtagctatggcatgcactgggtccgccaggctccaggcaaggggctggagtgggtggcagttatatggtatgat......ggaagtaataaatactatgcagactccgcgaag...ggccgattcaccatctccagagacaattccacgaacacgctgtttctgcaaatgaacagcctgagagccgaggacacggctgtgtattactgtgcgagaga >IGHV3-33*03 caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcgtctggattcaccttc............agtagctatggcatgcactgggtccgccaggctccaggcaaggggctggagtgggtggcagttatatggtatgat......ggaagtaataaatactatgcagactccgtgaag...ggccgattcaccatctccagagacaactccaagaacacgctgtatctgcaaatgaacagcctgagagccgaggacacggctgtgtattactgtgcgaaaga >IGHV3-33*04 caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcgtctggattcaccttc............agtagctatggcatgcactgggtccgccaggctccaggcaaggggctagagtgggtggcagttatatggtatgac......ggaagtaataaatactatgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagccgaggacacggctgtgtattactgtgcgagaga >IGHV3-33*05 caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcgtctggattcaccttc............agtagctatggcatgcactgggtccgccaggctccaggcaaggggctggagtgggtggcagttatatcatatgat......ggaagtaataaatactatgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagccgaggacacggctgtgtattactgtgcgagaga >IGHV3-33*06 caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcgtctggattcaccttc............agtagctatggcatgcactgggtccgccaggctccaggcaaggggctggagtgggtggcagttatatggtatgat......ggaagtaataaatactatgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagccgaggacacggctgtgtattactgtgcgaaaga >IGHV3-33-2*01 gaggtacagctcgtggagtccggagag...gacccaagacaacctgggggatccttgagactctcctgtgcagactctggattaaccttc............agtagctactgaatgagctcggtttcccaggctccagggaaggggctggagtgagtagtagatatacagtgtgat......ggaagtcagatatgttatgcccaatctgtgaag...agcaaattcaccatctccaaagaaaatgccaagaactcactgtatttgcaaatgaacagtctgagagcagagggcacagctgtgtgttactgtatgtgaggca >IGHV3-35*01 gaggtgcagctggtggagtctggggga...ggcttggtacagcctgggggatccctgagactctcctgtgcagcctctggattcaccttc............agtaacagtgacatgaactgggtccatcaggctccaggaaaggggctggagtgggtatcgggtgttagttggaat......ggcagtaggacgcactatgcagactctgtgaag...ggccgattcatcatctccagagacaattccaggaacaccctgtatctgcaaacgaatagcctgagggccgaggacacggctgtgtattactgtgtgagaaa >IGHV3-38*01 gaggtgcagctggtggagtctggggga...ggcttggtacagcctagggggtccctgagactctcctgtgcagcctctggattcaccgtc............agtagcaatgagatgagctggatccgccaggctccagggaaggggctggagtgggtctcatccattagtggt............ggtagcacatactacgcagactccaggaag...ggcagattcaccatctccagagacaattccaagaacacgctgtatcttcaaatgaacaacctgagagctgagggcacggccgcgtattactgtgccagatata >IGHV3-38*02 gaggtgcagctggtggagtctggggga...ggcttggtacagcctagggggtccctgagactctcctgtgcagcctctggattcaccgtc............agtagcaatgagatgagctggatccgccaggctccagggaaggggctggagtgggtctcatccattagtggt............ggtagcacatactacgcagactccaggaag...ggcagattcaccatctccagagacaattccaagaacacgctgtatcttcaaatgaacaacctgagagctgagggcacggccgtgtattactgtgccagatata >IGHV3-38*03 gaggtgcagctggtggagtctggggga...ggcttggtacagcctagggggtccctgagactctcctgtgcagcctctggattcaccgtc............agtagcaatgagatgagctggatccgccaggctccagggaagggtctggagtgggtctcatccattagtggt............ggtagcacatactacgcagactccaggaag...ggcagattcaccatctccagagacaattccaagaacacgctgtatcttcaaatgaacaacctgagagctgagggcacggccgtgtattactgtgccagatata >IGHV3-38-3*01 gaggtgcagctggtggagtctcgggga...gtcttggtacagcctggggggtccctgagactctcctgtgcagcctctggattcaccgtc............agtagcaatgagatgagctgggtccgccaggctccagggaagggtctggagtgggtctcatccattagtggt............ggtagcacatactacgcagactccaggaag...ggcagattcaccatctccagagacaattccaagaacacgctgcatcttcaaatgaacagcctgagagctgaggacacggctgtgtattactgtaagaaaga >IGHV3-43*01 gaagtgcagctggtggagtctggggga...gtcgtggtacagcctggggggtccctgagactctcctgtgcagcctctggattcaccttt............gatgattataccatgcactgggtccgtcaagctccggggaagggtctggagtgggtctctcttattagttgggat......ggtggtagcacatactatgcagactctgtgaag...ggccgattcaccatctccagagacaacagcaaaaactccctgtatctgcaaatgaacagtctgagaactgaggacaccgccttgtattactgtgcaaaagata >IGHV3-43*02 gaagtgcagctggtggagtctggggga...ggcgtggtacagcctggggggtccctgagactctcctgtgcagcctctggattcaccttt............gatgattatgccatgcactgggtccgtcaagctccagggaagggtctggagtgggtctctcttattagtggggat......ggtggtagcacatactatgcagactctgtgaag...ggccgattcaccatctccagagacaacagcaaaaactccctgtatctgcaaatgaacagtctgagaactgaggacaccgccttgtattactgtgcaaaagata >IGHV3-43D*01 gaagtgcagctggtggagtctggggga...gtcgtggtacagcctggggggtccctgagactctcctgtgcagcctctggattcaccttt............gatgattatgccatgcactgggtccgtcaagctccggggaagggtctggagtgggtctctcttattagttgggat......ggtggtagcacctactatgcagactctgtgaag...ggtcgattcaccatctccagagacaacagcaaaaactccctgtatctgcaaatgaacagtctgagagctgaggacaccgccttgtattactgtgcaaaagata >IGHV3-47*01 gaggatcagctggtggagtctggggga...ggcttggtacagcctggggggtccctgcgaccctcctgtgcagcctctggattcgccttc............agtagctatgctctgcactgggttcgccgggctccagggaagggtctggagtgggtatcagctattggtactggt.........ggtgatacatactatgcagactccgtgatg...ggccgattcaccatctccagagacaacgccaagaagtccttgtatcttcatatgaacagcctgatagctgaggacatggctgtgtattattgtgcaaga >IGHV3-47*02 gaggatcagctggtggagtctggggga...ggcttggtacagcctggggggtccctgagaccctcctgtgcagcctctggattcgccttc............agtagctatgttctgcactgggttcgccgggctccagggaagggtccggagtgggtatcagctattggtactggt.........ggtgatacatactatgcagactccgtgatg...ggccgattcaccatctccagagacaacgccaagaagtccttgtatcttcaaatgaacagcctgatagctgaggacatggctgtgtattattgtgcaagaga >IGHV3-48*01 gaggtgcagctggtggagtctggggga...ggcttggtacagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatagcatgaactgggtccgccaggctccagggaaggggctggagtgggtttcatacattagtagtagt......agtagtaccatatactacgcagactctgtgaag...ggccgattcaccatctccagagacaatgccaagaactcactgtatctgcaaatgaacagcctgagagccgaggacacggctgtgtattactgtgcgagaga >IGHV3-48*02 gaggtgcagctggtggagtctggggga...ggcttggtacagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatagcatgaactgggtccgccaggctccagggaaggggctggagtgggtttcatacattagtagtagt......agtagtaccatatactacgcagactctgtgaag...ggccgattcaccatctccagagacaatgccaagaactcactgtatctgcaaatgaacagcctgagagacgaggacacggctgtgtattactgtgcgagaga >IGHV3-48*03 gaggtgcagctggtggagtctggggga...ggcttggtacagcctggagggtccctgagactctcctgtgcagcctctggattcaccttc............agtagttatgaaatgaactgggtccgccaggctccagggaaggggctggagtgggtttcatacattagtagtagt......ggtagtaccatatactacgcagactctgtgaag...ggccgattcaccatctccagagacaacgccaagaactcactgtatctgcaaatgaacagcctgagagccgaggacacggctgtttattactgtgcgagaga >IGHV3-48*04 gaggtgcagctggtggagtctggggga...ggcttggtacagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatagcatgaactgggtccgccaggctccagggaaggggctggagtgggtttcatacattagtagtagt......agtagtaccatatactacgcagactctgtgaag...ggccgattcaccatctccagagacaacgccaagaactcactgtatctgcaaatgaacagcctgagagccgaggacacggctgtgtattactgtgcgagaga >IGHV3-49*01 gaggtgcagctggtggagtctggggga...ggcttggtacagccagggcggtccctgagactctcctgtacagcttctggattcaccttt............ggtgattatgctatgagctggttccgccaggctccagggaaggggctggagtgggtaggtttcattagaagcaaagcttatggtgggacaacagaatacaccgcgtctgtgaaa...ggcagattcaccatctcaagagatggttccaaaagcatcgcctatctgcaaatgaacagcctgaaaaccgaggacacagccgtgtattactgtactagaga >IGHV3-49*02 gaggtgcagctggtggagtctggggga...ggcttggtacagccagggccgtccctgagactctcctgtacagcttctggattcaccttt............gggtattatcctatgagctgggtccgccaggctccagggaaggggctggagtgggtaggtttcattagaagcaaagcttatggtgggacaacagaatacgccgcgtctgtgaaa...ggcagattcaccatctcaagagatgattccaaaagcatcgcctatctgcaaatgaacagcctgaaaaccgaggacacagccgtgtattactgtactagaga >IGHV3-49*03 gaggtgcagctggtggagtctggggga...ggcttggtacagccagggcggtccctgagactctcctgtacagcttctggattcaccttt............ggtgattatgctatgagctggttccgccaggctccagggaaggggctggagtgggtaggtttcattagaagcaaagcttatggtgggacaacagaatacgccgcgtctgtgaaa...ggcagattcaccatctcaagagatgattccaaaagcatcgcctatctgcaaatgaacagcctgaaaaccgaggacacagccgtgtattactgtactagaga >IGHV3-49*04 gaggtgcagctggtggagtctggggga...ggcttggtacagccagggcggtccctgagactctcctgtacagcttctggattcaccttt............ggtgattatgctatgagctgggtccgccaggctccagggaaggggctggagtgggtaggtttcattagaagcaaagcttatggtgggacaacagaatacgccgcgtctgtgaaa...ggcagattcaccatctcaagagatgattccaaaagcatcgcctatctgcaaatgaacagcctgaaaaccgaggacacagccgtgtattactgtactagaga >IGHV3-49*05 gaggtgcagctggtggagtctggggga...ggcttggtaaagccagggcggtccctgagactctcctgtacagcttctggattcaccttt............ggtgattatgctatgagctggttccgccaggctccagggaaggggctggagtgggtaggtttcattagaagcaaagcttatggtgggacaacagaatacgccgcgtctgtgaaa...ggcagattcaccatctcaagagatgattccaaaagcatcgcctatctgcaaatgaacagcctgaaaaccgaggacacagccgtgtattactgtactagaga >IGHV3-52*01 gaggtgcagctggtggagtctgggtga...ggcttggtacagcctggagggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctcctggatgcactgggtctgccaggctccggagaaggggctggagtgggtggccgacataaagtgtgac......ggaagtgagaaatactatgtagactctgtgaag...ggccgattgaccatctccagagacaatgccaagaactccctctatctgcaagtgaacagcctgagagctgaggacatgaccgtgtattactgtgtgagagg >IGHV3-52*02 gaggtgcagctggtggagtctgggtga...ggcttggtacagcctggagggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctcctggatgcactgggtctgccaggctccggagaaggggcaggagtgggtggccgacataaagtgtgac......ggaagtgagaaatactatgtagactctgtgaag...ggccgattgaccatctccagagacaatgccaagaactccctctatctgcaagtgaacagcctgagagctgaggacatgaccgtgtattactgtgtgaga >IGHV3-52*03 gaggtgcagctggtcgagtctgggtga...ggcttggtacagcctggagggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctcctggatgcactgggtctgccaggctccggagaaggggctggagtgggtggccgacataaagtgtgac......ggaagtgagaaatactatgtagactctgtgaag...ggccgattgaccatctccagagacaatgccaagaactccctctatctgcaagtgaacagcctgagagctgaggacatgaccgtgtattactgtgtgaga >IGHV3-53*01 gaggtgcagctggtggagtctggagga...ggcttgatccagcctggggggtccctgagactctcctgtgcagcctctgggttcaccgtc............agtagcaactacatgagctgggtccgccaggctccagggaaggggctggagtgggtctcagttatttatagcggt.........ggtagcacatactacgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatcttcaaatgaacagcctgagagccgaggacacggccgtgtattactgtgcgagaga >IGHV3-53*02 gaggtgcagctggtggagactggagga...ggcttgatccagcctggggggtccctgagactctcctgtgcagcctctgggttcaccgtc............agtagcaactacatgagctgggtccgccaggctccagggaaggggctggagtgggtctcagttatttatagcggt.........ggtagcacatactacgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatcttcaaatgaacagcctgagagccgaggacacggccgtgtattactgtgcgagaga >IGHV3-53*03 gaggtgcagctggtggagtctggagga...ggcttgatccagcctggggggtccctgagactctcctgtgcagcctctgggttcaccgtc............agtagcaactacatgagctgggtccgccagcctccagggaaggggctggagtgggtctcagttatttatagcggt.........ggtagcacatactacgcagactctgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatcttcaaatgaacagcctgagagccgaggacacggccgtgtattactgtgctaggga >IGHV3-53*04 gaggtgcagctggtggagtctggagga...ggcttggtccagcctggggggtccctgagactctcctgtgcagcctctgggttcaccgtc............agtagcaactacatgagctgggtccgccaggctccagggaaggggctggagtgggtctcagttatttatagcggt.........ggtagcacatactacgcagactccgtgaag...ggccgattcaccatctccagacacaattccaagaacacgctgtatcttcaaatgaacagcctgagagctgaggacacggccgtgtattactgtgcgagaga >IGHV3-54*01 gaggtacagctggtggagtctgaagaa...aaccaaagacaacttgggggatccctgagactctcctgtgcagactctggattaaccttc............agtagctactgaatgagctcagattcccaagctccagggaaggggctggagtgagtagtagatatatagtaggat......agaagtcagctatgttatgcacaatctgtgaag...agcagattcaccatctccaaagaaaatgccaagaactcactctgtttgcaaatgaacagtctgagagcagagggcacggccgtgtattactgtatgtgagt >IGHV3-54*02 gaggtacagctggtggagtctgaagaa...aaccaaagacaacttgggggatccctgagactctcctgtgcagactctggattaaccttc............agtagctactgaatgagctcagattcccaggctccagggaaggggctggagtgagtagtagatatatagtacgat......agaagtcagatatgttatgcacaatctgtgaag...agcagattcaccatctccaaagaaaatgccaagaactcactccgtttgcaaatgaacagtctgagagcagagggcacggccgtgtattactgtatgtgagg >IGHV3-54*04 gaggtacagctggtggagtctgaagaa...aaccaaagacaacttgggggatccctgagactctcctgtgcagactctggattaaccttc............agtagctactgaatgagctcagattcccaggctccagggaaggggctggagtgagtagtagatatatagtaggat......agaagtcagctatgttatgcacaatctgtgaag...agcagattcaccatctccaaagaaaatgccaagaactcactctgtttgcaaatgaacagtctgagagcagagggcacggccgtgtattactgtatgtgagt >IGHV3-62*01 gaggtgcagctggtggagtctggggaa...ggcttggtccagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctctgctatgcactgggtccgccaggctccaagaaagggtttgtagtgggtctcagttattagtacaagt......ggtgataccgtactctacacagactctgtgaag...ggccgattcaccatctccagagacaatgcccagaattcactgtctctgcaaatgaacagcctgagagccgagggcacagttgtgtactactgtgtgaaaga >IGHV3-63*01 gaggtggagctgatagagtccatagag...ggcctgagacaacttgggaagttcctgagactctcctgtgtagcctctggattcaccttc............agtagctactgaatgagctgggtcaatgagactctagggaaggggctggagggagtaatagatgtaaaatatgat......ggaagtcagatataccatgcagactctgtgaag...ggcagattcaccatctccaaagacaatgctaagaactcaccgtatctccaaacgaacagtctgagagctgaggacatgaccatgcatggctgtacataaggtt >IGHV3-63*02 gaggtggagctgatagagtccatagag...ggcctgagacaacttgggaagttcctgagactctcctgtgtagcctctggattcaccttc............agtagctactgaatgagctgggtcaatgagactctagggaaggggctggagggagtaatagatgtaaaatatgat......ggaagtcagatataccatgcagactctgtgaag...ggcagattcaccatctccaaagacaatgctaagaactcaccgtatctgcaaacgaacagtctgagagctgaggacatgaccatgcatggctgtacataa >IGHV3-64*01 gaggtgcagctggtggagtctggggga...ggcttggtccagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatgctatgcactgggtccgccaggctccagggaagggactggaatatgtttcagctattagtagtaat......gggggtagcacatattatgcaaactctgtgaag...ggcagattcaccatctccagagacaattccaagaacacgctgtatcttcaaatgggcagcctgagagctgaggacatggctgtgtattactgtgcgagaga >IGHV3-64*02 gaggtgcagctggtggagtctggggaa...ggcttggtccagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatgctatgcactgggtccgccaggctccagggaagggactggaatatgtttcagctattagtagtaat......gggggtagcacatattatgcagactctgtgaag...ggcagattcaccatctccagagacaattccaagaacacgctgtatcttcaaatgggcagcctgagagctgaggacatggctgtgtattactgtgcgagaga >IGHV3-64*03 gaggtgcagctggtggagtctggggga...ggcttggtccagcctggggggtccctgagactctcctgttcagcctctggattcaccttc............agtagctatgctatgcactgggtccgccaggctccagggaagggactggaatatgtttcagctattagtagtaat......gggggtagcacatactacgcagactcagtgaag...ggcagattcaccatctccagagacaattccaagaacacgctgtatgtccaaatgagcagtctgagagctgaggacacggctgtgtattactgtgtgaaaga >IGHV3-64*04 caggtgcagctggtggagtctggggga...ggcttggtccagcctggggggtccctgagactctcctgttcagcctctggattcaccttc............agtagctatgctatgcactgggtccgccaggctccagggaagggactggaatatgtttcagctattagtagtaat......gggggtagcacatactacgcagactcagtgaag...ggcagattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgcgagaga >IGHV3-64*05 gaggtgcagctggtggagtctggggga...ggcttggtccagcctggggggtccctgagactctcctgttcagcctctggattcaccttc............agtagctatgctatgcactgggtccgccaggctccagggaagggactggaatatgtttcagctattagtagtaat......gggggtagcacatactacgcagactcagtgaag...ggcagattcaccatctccagagacaattccaagaacacgctgtatgttcaaatgagcagtctgagagctgaggacacggctgtgtattactgtgtgaaaga >IGHV3-64D*06 gaggtgcagctggtggagtctggggga...ggcttggtccagcctggggggtccctgagactctcctgttcagcctctggattcaccttc............agtagctatgctatgcactgggtccgccaggctccagggaagggactggaatatgtttcagctattagtagtaat......gggggtagcacatactacgcagactccgtgaag...ggcagattcaccatctccagagacaattccaagaacacgctgtatcttcaaatgagcagtctgagagctgaggacacggctgtgtattactgtgtgaaaga >IGHV3-66*01 gaggtgcagctggtggagtctggggga...ggcttggtccagcctggggggtccctgagactctcctgtgcagcctctggattcaccgtc............agtagcaactacatgagctgggtccgccaggctccagggaaggggctggagtgggtctcagttatttatagcggt.........ggtagcacatactacgcagactccgtgaag...ggcagattcaccatctccagagacaattccaagaacacgctgtatcttcaaatgaacagcctgagagccgaggacacggctgtgtattactgtgcgagaga >IGHV3-66*02 gaggtgcagctggtggagtctggggga...ggcttggtccagcctggggggtccctgagactctcctgtgcagcctctggattcaccgtc............agtagcaactacatgagctgggtccgccaggctccagggaaggggctggagtgggtctcagttatttatagcggt.........ggtagcacatactacgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatcttcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgcgaga >IGHV3-66*03 gaggtgcagctggtggagtctggagga...ggcttgatccagcctggggggtccctgagactctcctgtgcagcctctgggttcaccgtc............agtagcaactacatgagctgggtccgccaggctccagggaaggggctggagtgggtctcagttatttatagctgt.........ggtagcacatactacgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatcttcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgcgagaga >IGHV3-66*04 gaggtgcagctggtggagtctggggga...ggcttggtccagcctggggggtccctgagactctcctgtgcagcctctggattcaccgtc............agtagcaactacatgagctgggtccgccaggctccagggaaggggctggagtgggtctcagttatttatagcggt.........ggtagcacatactacgcagactccgtgaag...ggcagattcaccatctccagagacaattccaagaacacgctgtatcttcaaatgaacagcctgagagccgaggacacggctgtgtattactgtgcgagaca >IGHV3-69-1*01 gaggtgcagctggtggagtctggggga...ggcttggtaaagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agtgactactacatgaactgggtccgccaggctccagggaaggggctggagtgggtctcatccattagtagtagt.........agtaccatatactacgcagactctgtgaag...ggccgattcaccatctccagagacaacgccaagaactcactgtatctgcaaatgaacagcctgagagccgaggacacggctgtgtattactgtgcgagaga >IGHV3-69-1*02 gaggtgcagctggtggagtctggggga...ggcttggtaaagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agtgactactacatgaactgggtccgccaggctccagggaaggggctggagtgggtctcatccattagtagtagt.........agtaccatatactacgcagactctgtgaag...ggccgattcaccatctccagagacaacgccaagaactcactgtatctgcaaatgaacagcctgagagccgaggacacggctgtttattactgtgcgagaga >IGHV3-7*01 gaggtgcagctggtggagtctggggga...ggcttggtccagcctggggggtccctgagactctcctgtgcagcctctggattcaccttt............agtagctattggatgagctgggtccgccaggctccagggaaggggctggagtgggtggccaacataaagcaagat......ggaagtgagaaatactatgtggactctgtgaag...ggccgattcaccatctccagagacaacgccaagaactcactgtatctgcaaatgaacagcctgagagccgaggacacggctgtgtattactgtgcgagaga >IGHV3-7*02 gaggtgcagctggtggagtctggggga...ggcttggtccagcctggggggtccctgagactctcctgtgcagcctctggattcaccttt............agtagctattggatgagctgggtccgccaggctccagggaaagggctggagtgggtggccaacataaagcaagat......ggaagtgagaaatactatgtggactctgtgaag...ggccgattcaccatctccagagacaacgccaagaactcactgtatctgcaaatgaacagcctgagagccgaggacacggctgtgtattactgtgcgaga >IGHV3-7*03 gaggtgcagctggtggagtctggggga...ggcttggtccagcctggggggtccctgagactctcctgtgcagcctctggattcaccttt............agtagctattggatgagctgggtccgccaggctccagggaaggggctggagtgggtggccaacataaagcaagat......ggaagtgagaaatactatgtggactctgtgaag...ggccgattcaccatctccagagacaacgccaagaactcactgtatctgcaaatgaacagcctgagagccgaggacacggccgtgtattactgtgcgagaga >IGHV3-71*01 gaggtgcagctggtggagtccggggga...ggcttggtccagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agtgactactacatgagctgggtccgccaggctcccgggaaggggctggagtgggtaggtttcattagaaacaaagctaatggtgggacaacagaatagaccacgtctgtgaaa...ggcagattcacaatctcaagagatgattccaaaagcatcacctatctgcaaatgaacagcctgagagccgaggacacggccgtgtattactgtgcgagaga >IGHV3-71*02 gaggtgcagctggtggagtctggggga...ggcttggtccagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agtgactactacatgagctgggtccgccaggctcccgggaaggggctggagtgggtaggtttcattagaaacaaagctaatggtgggacaacagaatagaccacgtctgtgaaa...ggcagattcacaatctcaagagatgattccaaaagcatcacctatctgcaaatgaacagcctgagagccgaggacatggctgtgtattactgtgcgagaga >IGHV3-71*03 gaggtgcagctggtggagtctggggga...ggcttggtccagcctggggggtccctgagactctcctgtgcagcctctggtttcaccttc............agtgactactacatgagctgggtccgccaggctcccgggaaggggctggagtgggtaggtttcattagaaacaaagctaatggtgggacaacagaatagaccacgtctgtgaaa...ggcagattcacaatctcaagagatgattccaaaagcatcacctatctgcaaatgaacagcctgagagccgaggacacggctgtgtattactgtgcgagaga >IGHV3-72*01 gaggtgcagctggtggagtctggggga...ggcttggtccagcctggagggtccctgagactctcctgtgcagcctctggattcaccttc............agtgaccactacatggactgggtccgccaggctccagggaaggggctggagtgggttggccgtactagaaacaaagctaacagttacaccacagaatacgccgcgtctgtgaaa...ggcagattcaccatctcaagagatgattcaaagaactcactgtatctgcaaatgaacagcctgaaaaccgaggacacggccgtgtattactgtgctagaga >IGHV3-72*02 ....................................................................................accttc............agtgaccactacatggactgggtccgccaggctccagggaaggggctggagtgggttggccgtactagaaacaaagctaacagctacaccacagaatacgccgcgtctgtgaaa...ggcagattcaccatctcaagagatgattcaaagaactcactgtat >IGHV3-73*01 gaggtgcagctggtggagtctggggga...ggcttggtccagcctggggggtccctgaaactctcctgtgcagcctctgggttcaccttc............agtggctctgctatgcactgggtccgccaggcttccgggaaagggctggagtgggttggccgtattagaagcaaagctaacagttacgcgacagcatatgctgcgtcggtgaaa...ggcaggttcaccatctccagagatgattcaaagaacacggcgtatctgcaaatgaacagcctgaaaaccgaggacacggccgtgtattactgtactagaca >IGHV3-73*02 gaggtgcagctggtggagtccggggga...ggcttggtccagcctggggggtccctgaaactctcctgtgcagcctctgggttcaccttc............agtggctctgctatgcactgggtccgccaggcttccgggaaagggctggagtgggttggccgtattagaagcaaagctaacagttacgcgacagcatatgctgcgtcggtgaaa...ggcaggttcaccatctccagagatgattcaaagaacacggcgtatctgcaaatgaacagcctgaaaaccgaggacacggccgtgtattactgtactagaca >IGHV3-74*01 gaggtgcagctggtggagtccggggga...ggcttagttcagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctactggatgcactgggtccgccaagctccagggaaggggctggtgtgggtctcacgtattaatagtgat......gggagtagcacaagctacgcggactccgtgaag...ggccgattcaccatctccagagacaacgccaagaacacgctgtatctgcaaatgaacagtctgagagccgaggacacggctgtgtattactgtgcaagaga >IGHV3-74*02 gaggtgcagctggtggagtctggggga...ggcttagttcagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctactggatgcactgggtccgccaagctccagggaaggggctggtgtgggtctcacgtattaatagtgat......gggagtagcacaagctacgcggactccgtgaag...ggccgattcaccatctccagagacaacgccaagaacacgctgtatctgcaaatgaacagtctgagagccgaggacacggctgtgtattactgtgcaaga >IGHV3-74*03 gaggtgcagctggtggagtccggggga...ggcttagttcagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctactggatgcactgggtccgccaagctccagggaaggggctggtgtgggtctcacgtattaatagtgat......gggagtagcacaacgtacgcggactccgtgaag...ggccgattcaccatctccagagacaacgccaagaacacgctgtatctgcaaatgaacagtctgagagccgaggacacggctgtgtattactgtgcaagaga >IGHV3-9*01 gaagtgcagctggtggagtctggggga...ggcttggtacagcctggcaggtccctgagactctcctgtgcagcctctggattcaccttt............gatgattatgccatgcactgggtccggcaagctccagggaagggcctggagtgggtctcaggtattagttggaat......agtggtagcataggctatgcggactctgtgaag...ggccgattcaccatctccagagacaacgccaagaactccctgtatctgcaaatgaacagtctgagagctgaggacacggccttgtattactgtgcaaaagata >IGHV3-9*02 gaagtgcagctggtggagtctggggga...ggcttggtacagcctggcaggtccctgagactctcctgtgcagcctctggattcacctct............gatgattatgccatgcactgggtccggcaagctccagggaagggcctggagtgggtctcaggtattagttggaat......agtggtagcataggctatgcggactctgtgaag...ggccgattcaccatctccagagacaacgccaagaactccctgtatctgcaaatgaacagtctgagagctgaggacacggccttgtattactgtgcaaaagata >IGHV3-9*03 gaagtgcagctggtggagtctggggga...ggcttggtacagcctggcaggtccctgagactctcctgtgcagcctctggattcaccttt............gatgattatgccatgcactgggtccggcaagctccagggaagggcctggagtgggtctcaggtattagttggaat......agtggtagcataggctatgcggactctgtgaag...ggccgattcaccatctccagagacaacgccaagaactccctgtatctgcaaatgaacagtctgagagctgaggacatggccttgtattactgtgcaaaagata >IGHV3-NL1*01 caggtgcagctggtggagtctggggga...ggcgtggtccagcctggggggtccctgagactctcctgtgcagcgtctggattcaccttc............agtagctatggcatgcactgggtccgccaggctccaggcaaggggctggagtgggtctcagttatttatagcggt......ggtagtagcacatactatgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgcgaaaga >IGHV3/OR15-7*01 gaggtgcagctggtggagtctggggga...ggcttggtccagcctgggggttctctgagactctcatgtgcagcctctggattcaccttc............agtgaccactacatgagctgggtccgccaggctcaagggaaagggctagagttggtaggtttaataagaaacaaagctaacagttacacgacagaatatgctgcgtctgtgaaa...ggcagacttaccatctcaagagaggattcaaagaacacgatgtatctgcaaatgagcaacctgaaaaccgaggacttggccgtgtattactgtgctaga >IGHV3/OR15-7*02 gaggtgcagctgttggagtctggggga...ggcttggtccagcctgggggttctctgagactctcatgtgctgcctctggattcaccttc............agtgaccactacatgagctgggtccgccaggctcaagggaaagggctagagttggtaggtttaataagaaacaaagctaacagttacacgacagaatatgctgcgtctgtgaaa...ggcagacttaccatctcaagagaggattcaaagaacacgctgtatctgcaaatgagcagcctgaaaaccgaggacttggccgtgtattactgtgctaga >IGHV3/OR15-7*03 gaggtgcagctggtggagtctggggga...ggcttggtccagcctgggggttctctgagactctcatgtgcagcctctggattcaccttc............agtgaccactacatgagctgggtccgccaggctcaagggaaagggctagagttggtaggtttaataagaaacaaagctaacagttacacgacagaatatgctgcgtctgtgaaa...ggcagacttaccatctcaagagaggattcaaagaacacgctgtatctgcaaatgagcagcctgaaaaccgaggacttggccgtgtattactgtgctaga >IGHV3/OR15-7*05 gaggtgcagctggtggagtctggggga...ggcttggtccagcctgggggttctctgagactctcatgtgcagcctctggattcaccttc............agtgaccactacatgagctgggtccgccaggctcaagggaaagggctagagttggtaggtttaataagaaacaaagctaacagttacacgacagaatatgctgcgtctgtgaaa...ggcagacttaccatctcaagagaggattcaaagaacacgctgtatctgcaaatgagcaacctgaaaaccgaggacttggccgtgtattactgtgctagaga >IGHV3/OR16-10*01 gaggttcagctggtgcagtctggggga...ggcttggtacatcctggggggtccctgagactctcctgtgcaggctctggattcaccttc............agtagctatgctatgcactgggttcgccaggctccaggaaaaggtctggagtgggtatcagctattggtactggt.........ggtggcacatactatgcagactccgtgaag...ggccgattcaccatctccagagacaatgccaagaactccttgtatcttcaaatgaacagcctgagagccgaggacatggctgtgtattactgtgcaaga >IGHV3/OR16-10*02 gaggttcagctggtgcagtctggggga...ggcttggtacagcctggggggtccctgagactctcctgtgcaggctctggattcaccttc............agtagctatgctatgcactgggttcgccaggctccaggaaaaggtctggagtgggtatcagctattggtactggt.........ggtggcacatactatgcagactccgtgaag...ggccgattcaccatctccagagacaatgccaagaactccttgtatcttcaaatgaacagcctgagagccgaggacatggctgtgtattactgtgcaaga >IGHV3/OR16-10*03 gaggtgcagctggtggagtctggggga...ggcttggtacagcctggggggtccctgagactctcctgtgcaggctctggattcaccttc............agtagctatgctatgcactgggttcgccaggctccaggaaaaggtctggagtgggtatcagctattggtactggt.........ggtggcacatactatgcagactccgtgaag...ggccgattcaccatctccagagacaatgccaagaactccttgtatcttcaaatgaacagcctgagagccgaggacatggctgtgtattactgtgcaagaga >IGHV3/OR16-12*01 gaggtgcagctggtagagtctgggaga...ggcttggcccagcctggggggtacctaaaactctccggtgcagcctctggattcaccgtc............ggtagctggtacatgagctggatccaccaggctccagggaagggtctggagtgggtctcatacattagtagtagt......ggttgtagcacaaactacgcagactctgtgaag...ggcagattcaccatctccacagacaactcaaagaacacgctctacctgcaaatgaacagcctgagagtggaggacacggccgtgtattactgtgcaaga >IGHV3/OR16-13*01 gaggtgcagctggtggagtctggggga...ggcttagtacagcctggagggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctactggatgcactgggtccgccaagctccagggaaggggctggtgtgggtctcacgtattaatagtgat......gggagtagcacaagctacgcagactccatgaag...ggccaattcaccatctccagagacaatgctaagaacacgctgtatctgcaaatgaacagtctgagagctgaggacatggctgtgtattactgtactaga >IGHV3/OR16-14*01 gaggtgcagctggaggagtctggggga...ggcttagtacagcctggagggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctactggatgcactgggtccgccaatctccagggaaggggctggtgtgagtctcacgtattaatagtgat......gggagtagcacaagctacgcagactccttgaag...ggccaattcaccatctccagagacaatgctaagaacacgctgtatctgcaaatgaacagtctgagagctgaggacatggctgtgtattactgtactaga >IGHV3/OR16-15*01 gaagtgcagctggtggagtctggggga...ggcttggtccagcctggggggtccctgagactctcctgtgcagcctctgtattcaccttc............agtaacagtgacataaactgggtcctctaggctccaggaaaggggctggagtgggtctcgggtattagttggaat......ggcggtaagacgcactatgtggactccgtgaag...ggccaattttccatctccagagacaattccagcaagtccctgtatctgcaaaagaacagacagagagccaaggacatggccgtgtattactgtgtgagaaa >IGHV3/OR16-15*02 gaggtgcagctggtggagtctggggga...ggcttggtccagcctggggggtccctgagacactcctgtgcagcctctggattcaccttc............agtaacagtgacatgaactgggtcctctaggctccaggaaaggggctggagtgggtctcgggtattagttggaat......ggcggtaagacgcactatgtggactccgtgaag...ggccaatttaccatctccagagacaattccagcaagtccctgtatctgcaaaagaacagacagagagccaaagacatggccgtgtattactgtgtgaga >IGHV3/OR16-16*01 gaggtgcagctggtggagtctggggga...ggcttggtccagcctggggggtccctgagacactcctgtgcagcctctggattcaccttc............agtaacagtgacatgaactgggtcctctaggctccaggaaaggggctggagtgggtctcggatattagttggaat......ggcggtaagacgcactatgtggactccgtgaag...ggccaatttaccatctccagagacaattccagcaagtccctgtatctgcaaaagaacagacagagagccaaggacatggccgtgtattactgtgtgaga >IGHV3/OR16-6*02 gaggtgcagctggtggagtctgcggga...ggccttggtacagcctgggggtcccttagactctcctgtgcagcctctggattcacttgc............agtaacgcctggatgagctgggtccgccaggctccagggaaggggctggagtgggttggctgtattaaaagcaaagctaatggtgggacaacagactacgctgcacctgtgaaa...ggcagattcaccatctcaagagatgattcaaaaaacacgctgtatctgcaaatgatcagcctgaaaaccgaggacacggccgtgtattactgtaccacagg >IGHV3/OR16-8*01 gaggtgcagctggtggagtctggggga...ggcttggtacagcctggggggtccctgagactgtcctgtccagcctctggattcaccttc............agtaaccactacatgagctgggtccgccaggctccagggaagggactggagtgggtttcatacattagtggtgat......agtggttacacaaactacgcagactctgtgaag...ggccgattcaccatctccagggacaacgccaataactcaccgtatctgcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgtgaaa >IGHV3/OR16-8*02 gaggtgcagctggtggagtctggggga...ggcttggtacagcctggggggtccctgagactgtcctgtccagactctggattcaccttc............agtaaccactacatgagctgggtccgccaggctccagggaagggactggagtggatttcatacattagtggtgat......agtggttacacaaactacgcagactctgtgaag...ggccgattcaccatctccagggacaacgccaataactcaccgtatctgcaaatgaacagcttgagagctgaggacacggctgtgtattactgtgtgaaaca >IGHV3/OR16-9*01 gaggtgcagctggtggagtctggagga...ggcttggtacagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agtaaccactacacgagctgggtccgccaggctccagggaagggactggagtgggtttcatacagtagtggtaat......agtggttacacaaactacgcagactctgtgaaa...ggccgattcaccatctccagggacaacgccaagaactcactgtatctgcaaatgaacagcctgagagccgaggacacggctgtgtattactgtgtgaaa >IGHV4-28*01 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggacaccctgtccctcacctgcgctgtctctggttactccatcagc.........agtagtaactggtggggctggatccggcagcccccagggaagggactggagtggattgggtacatctattatagt.........gggagcacctactacaacccgtccctcaag...agtcgagtcaccatgtcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgtggacacggccgtgtattactgtgcgagaaa >IGHV4-28*02 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcacagaccctgtccctcacctgcgctgtctctggttactccatcagc.........agtagtaactggtggggctggatccggcagcccccagggaagggactggagtggattgggtacatctattatagt.........gggagcatctactacaacccgtccctcaag...agtcgagtcaccatgtcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgtggacacggccgtgtattactgtgcgagaaa >IGHV4-28*03 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggacaccctgtccctcacctgcgctgtctctggttactccatcagc.........agtagtaactggtggggctggatccggcagcccccagggaagggactggagtggattgggtacatctattatagt.........gggagcacctactacaacccgtccctcaag...agtcgagtcaccatgtcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgtggacacggccgtgtattactgtgcgagaga >IGHV4-28*04 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggacaccctgtccctcacctgcgctgtctctggttactccatcagc.........agtagtaactggtggggctggatccggcagcccccagggaagggactggagtggattgggtacatctattatagt.........gggagcacctactacaacccgtccctcaag...agtcgagtcaccatgtcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgtggacaccggcgtgtattactgtgcgaga >IGHV4-28*05 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggacaccctgtccctcacctgcgctgtctctggttactccatcagc.........agtagtaactggtggggctggatccggcagcccccagggaagggactggagtggattgggtacatctattatagt.........gggagcatctactacaacccgtccctcaag...agtcgagtcaccatgtcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgtggacacggccgtgtattactgtgcgagaaa >IGHV4-28*06 caggtgcagctacaggagtcgggccca...ggactggtgaagccttcggacaccctgtccctcacctgcgctgtctctggttactccatcagc.........agtagtaactggtggggctggatccggcagcccccagggaagggactggagtggattgggtacatctattatagt.........gggagcaccaactacaacccgtccctcaag...agtcgagtcaccatgtcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccttggacacggccgtgtattactgtgcgagaaa >IGHV4-28*07 caggtacagctgcaggagtcgggccca...ggactggtgaagccttcggacaccctgtccctcacctgcgctgtctctggttactccatcagc.........agtagtaactggtggggctggatccggcagcccccagggaagggactggagtggattgggtacatctattatagt.........gggagcacctactacaacccgtccctcaag...agtcgagtcaccatgtcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgtggacacggccgtgtattactgtgcgagaaa >IGHV4-30-2*01 cagctgcagctgcaggagtccggctca...ggactggtgaagccttcacagaccctgtccctcacctgcgctgtctctggtggctccatcagc......agtggtggttactcctggagctggatccggcagccaccagggaagggcctggagtggattgggtacatctatcatagt.........gggagcacctactacaacccgtccctcaag...agtcgagtcaccatatcagtagacaggtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggccgtgtattactgtgccagaga >IGHV4-30-2*02 cagctgcagctgcaggagtccggctca...ggactggtgaagccttcacagaccctgtccctcacctgcgctgtctctggtggctccatcagc......agtggtggttactcctggagctggatccggcagccaccagggaagggcctggagtggattgggtacatctatcatagt.........gggagcacctactacaacccgtccctcaag...agtcgagtcaccatatcagtagacaggtccaagaaccagttctccctgaagctgagctctgtgaccgctgcggacacggccgtgtattactgtgcg >IGHV4-30-2*03 cagctgcagctgcaggagtccggctca...ggactggtgaagccttcacagaccctgtccctcacctgcgctgtctctggtggctccatcagc......agtggtggttactcctggagctggatccggcagccaccagggaagggcctggagtggattgggagtatctattatagt.........gggagcacctactacaacccgtccctcaag...agtcgagtcaccatatccgtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgctgcagacacggctgtgtattactgtgcgagaca >IGHV4-30-2*04 ...........................................................................tctggtggctccatcagc......agtggtggttactcctggagctggatccggcagccaccagggaagggcctggagtggattgggtacatctatcatagt.........gggagcacctactacaacccgtccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcagacacggccgtgtattactgtgcgagaga >IGHV4-30-2*05 cagctgcagctgcaggagtccggctca...ggactggtgaagccttcacagaccctgtccctcacctgcgctgtctctggtggctccatcagc......agtggtggttactcctggagctggatccggcagccaccagggaagggcctggagtggattgggtacatctatcatagt.........gggagcacctactacaacccgtccctcaag...agtcgagttaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgactgccgcagacacggccgtgtattactgtgccagaga >IGHV4-30-2*06 cagctgcagctgcaggagtccggctca...ggactggtgaagccttcacagaccctgtccctcacctgcgctgtctctggtggctccatcagc......agtggtggttactcctggagctggatccggcagtcaccagggaagggcctggagtggattgggtacatctatcatagt.........gggagcacctactacaacccgtccctcaag...agtcgagtcaccatatcagtagacaggtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggccgtgtattactgtgccagaga >IGHV4-30-4*01 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcacagaccctgtccctcacctgcactgtctctggtggctccatcagc......agtggtgattactactggagttggatccgccagcccccagggaagggcctggagtggattgggtacatctattacagt.........gggagcacctactacaacccgtccctcaag...agtcgagttaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgactgccgcagacacggccgtgtattactgtgccagaga >IGHV4-30-4*02 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggacaccctgtccctcacctgcactgtctctggtggctccatcagc......agtggtgattactactggagttggatccgccagcccccagggaagggcctggagtggattgggtacatctattacagt.........gggagcacctactacaacccgtccctcaag...agtcgagttaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgactgcagcagacacggccgtgtattactgtgccagaga >IGHV4-30-4*03 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcacagaccctgtccctcacctgcactgtctctggtggctccatcagc......agtggtgattactactggagttggatccgccagcccccagggaagggcctggagtggattgggtacatctattacagt.........gggagcacctactacaacccgtccctcaag...agtcgagttaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgactgccgcggacacggccgtgtattactg >IGHV4-30-4*04 caggtgcagctgcaggactcgggccca...ggactggtgaagccttcacagaccctgtccctcacctgcactgtctctggtggctccatcagc......agtggtgattactactggagttggatccgccagcccccagggaagggcctggagtggattgggtacttctattacagt.........gggagcacctactacaacccgtccctcaag...agtcgagttaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgactgccgcagacacggccgtgtattactg >IGHV4-30-4*05 ..........................................................................ctctggtggctccatcagc......agtggtgattactactggagttggatccgccagcncccagggaagggcctggagtggattgggtacatctattacagt.........gggagcacctactacaacccgtccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgactgccgcagacacggccgtgtattactgtgccagaga >IGHV4-30-4*06 ...........................................................................tctggtggctccatcagc......agtggtgattactactggagttggatccgccagcacccagggaagggcctggagtggattgggtacatctattacagt.........gggagcacctactacaacccgtccctcaag...agtcgagttaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgactgccgcagacacggccgtgtattactgtgccagaga >IGHV4-30-4*07 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcacagaccctgtccctcacctgcgctgtctctggtggctccatcagc......agtggtggttactcctggagctggatccggcagccaccagggaagggactggagtggattgggtatatctattacagt.........gggagcacctactacaacccgtccctcaag...agtcgagttaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggccgtgtattactgtgccagaga >IGHV4-31*01 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcacagaccctgtccctcacctgcactgtctctggtggctccatcagc......agtggtggttactactggagctggatccgccagcacccagggaagggcctggagtggattgggtacatctattacagt.........gggagcacctactacaacccgtccctcaag...agtctagttaccatatcagtagacacgtctaagaaccagttctccctgaagctgagctctgtgactgccgcggacacggccgtgtattactgtgcgagaga >IGHV4-31*02 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcacagaccctgtccctcacctgtactgtctctggtggctccatcagc......agtggtggttactactggagctggatccgccagcacccagggaagggcctggagtggattgggtacatctattacagt.........gggagcacctactacaacccgtccctcaag...agtcgagttaccatatcagtagacacgtctaagaaccagttctccctgaagctgagctctgtgactgccgcggacacggccgtgtattactgtgcgagaga >IGHV4-31*03 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcacagaccctgtccctcacctgcactgtctctggtggctccatcagc......agtggtggttactactggagctggatccgccagcacccagggaagggcctggagtggattgggtacatctattacagt.........gggagcacctactacaacccgtccctcaag...agtcgagttaccatatcagtagacacgtctaagaaccagttctccctgaagctgagctctgtgactgccgcggacacggccgtgtattactgtgcgagaga >IGHV4-31*04 caggtgcggctgcaggagtcgggccca...ggactggtgaagccttcacagaccctgtccctcacctgcactgtctctggtggctccatcagc......agtggtggttactactggagctggatccgccagcacccagggaagggcctggagtggattgggtacatctattacagt.........gggagcacctactacaacccgtccctcaag...agtcgagttaccatatcagtagacacgtctaagaaccagttctccctgaagctgagctctgtgactgccgcggacacggccgtgtattactgtgcg >IGHV4-31*05 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcacagaccctgtccctcacctgcactgtctctggtggctccatcagc......agtggtggttactactggagctggatccgccagcacccagggaagggcctggagtggattgggtacatctattacagt.........gggagcacctactacaacccgtccctcaag...agtcgagttaccatatcagtagacacgtctaagaaccagttctccctgaagctgagctctgtgacc...gcggacgcggccgtgtattactgtgcg >IGHV4-31*06 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcacagaccctgtccctcacctgcactgtctctggtggctccatcagc......agtggtagttactactggagctggatccgccagcacccagggaagggcctggagtggattgggtacatctattacagt.........gggagcacctactacaacccgtccctcaag...agtcgagttaccatatcagtagacacgtctaagaaccagttctccctgaagctgagctctgtgactgccgcggacacggccgtgtattactg >IGHV4-31*07 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcacagaccctgtccctcacctgcactgtctctggtggatccatcagc......agtggtggttactactggagctggatccgccagcacccagggaagggcctggagtggattgggtacatctattacagt.........gggagcacctactacaacccgtccctcaag...agtcgagttaccatatcagtagacacgtctaagaaccagttctccctgaagctgagctctgtgactgccgcggacacggccgtgtattactg >IGHV4-31*08 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcacagaccctgtccctcacctgcactgtctctggtggctccatcagc......agtggtggttactactggagctggatccgccagcacccagggaagggcctggagtggattgggtacatctattacagt.........gggagcacctactacaacccgtccctcaag...agtcgagttaccatatccgtagacacgtccaagaaccagttctccctgaagctgagctctgtgactgccgcggacacggccgtgtattactg >IGHV4-31*09 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcacagaccctgtccctcacctgcactgtctctggtggctccatcagc......agtggtggttactactggagctggatccgccagcacccagggaagggcctggagtggattgggtacatctattacagt.........gggagcacctactacaacccgtccctcaag...agtcgagttaccatatcagtagacaagtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggccgtgtattactg >IGHV4-31*10 caggtgcagctgcaggagtcgggccca...ggactgttgaagccttcacagaccctgtccctcacctgcactgtctctggtggctccatcagc......agtggtggttactactggagctggatccgccagcacccagggaagggcctggagtggattgggtgcatctattacagt.........gggagcacctactacaacccgtccctcaag...agtcgagttaccatatcagtagacccgtccaagaaccagttctccctgaagccgagctctgtgactgccgcggacacggccgtggattactgtgcgagaga >IGHV4-34*01 caggtgcagctacagcagtggggcgca...ggactgttgaagccttcggagaccctgtccctcacctgcgctgtctatggtgggtccttc............agtggttactactggagctggatccgccagcccccagggaaggggctggagtggattggggaaatcaatcatagt.........ggaagcaccaactacaacccgtccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggctgtgtattactgtgcgagagg >IGHV4-34*02 caggtgcagctacaacagtggggcgca...ggactgttgaagccttcggagaccctgtccctcacctgcgctgtctatggtgggtccttc............agtggttactactggagctggatccgccagcccccagggaaggggctggagtggattggggaaatcaatcatagt.........ggaagcaccaactacaacccgtccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggctgtgtattactgtgcgagagg >IGHV4-34*03 caggtgcagctacagcagtggggcgca...ggactgttgaagccttcggagaccctgtccctcacctgcgctgtctatggtgggtccttc............agtggttactactggagctggatccgccagcccccagggaaggggctggagtggattggggaaatcaatcatagt.........ggaagcaccaactacaacccgtccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggccgtgtattactg >IGHV4-34*04 caggtgcagctacagcagtggggcgca...ggactgttgaagccttcggagaccctgtccctcacctgcgctgtctatggtgggtccttc............agtggttactactggagctggatccgccagcccccagggaaggggctggagtggattggggaaatcaatcatagt.........ggaagcaccaacaacaacccgtccctcaag...agtcgagccaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggctgtgtattactgtgcgagagg >IGHV4-34*05 caggtgcagctacagcagtggggcgca...ggactgttgaagccttcggagaccctgtccctcacctgcgctgtctatggtgggtccttc............agtggttactactggtgctggatccgccagcccctagggaaggggctggagtggattggggaaatcaatcatagt.........ggaagcaccaacaacaacccgtccctcaag...agtcgagccaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggctgtgtattactgtgcgagagg >IGHV4-34*06 caggtgcagctacagcagtggggcgca...ggactgttgaagccttcggagaccctgtccctcacctgcgctgtctatggtgggtccttc............agtggttactactggagctggatccgccagcccccagggaaggggctggagtggattggggaaatcaatcatagt.........ggaagcaccaactacaacccgtccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttctccctgaagctgggctctgtgaccgccgcggacacggccgtgtattactg >IGHV4-34*07 caggtgcagctacagcagtggggcgca...ggactgttgaagccttcggagaccctgtccctcacctgcgctgtctatggtgggtccttc............agtggttactactggagctggatccgccagcccccagggaaggggctggagtggattggggaaatcaaccatagt.........ggaagcaccaactacaacccgtccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggccgtgtattactg >IGHV4-34*08 caggtgcagctacagcagtggggcgca...ggactgttgaagccttcggagaccctgtccctcacctgcgctgtctatggtgggaccttc............agtggttactactggagctggatccgccagcccccagggaaggggctggagtggattggggaaatcaatcatagt.........ggaagcaccaactacaacccgtccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggctgtgtattactgtgcg >IGHV4-34*09 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcacagaccctgtccctcacctgcgctgtctatggtgggtccttc............agtggttactactggagctggatccgccagcccccagggaagggactggagtggattggggaaatcaatcatagt.........ggaagcaccaactacaacccgtccctcaag...agtcgagttaccatatcagtagacacgtctaagaaccagttctccctgaagctgagctctgtgactgccgcggacacggccgtgtattactgtgcgagaga >IGHV4-34*10 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcgctgtctatggtgggtccttc............agtggttactactggagctggatccgccagcccccagggaagggactggagtggattggggaaatcaatcatagt.........ggaagcaccaactacaacccgtccctcaag...agtcgaatcaccatgtcagtagacacgtccaagaaccagttctacctgaagctgagctctgtgaccgccgcggacacggccgtgtattactgtgcgagata >IGHV4-34*11 caggtgcagctacagcagtggggcgca...ggactgttgaagccttcggagaccctgtccctcacctgcgctgtctatggtgggtccgtc............agtggttactactggagctggatccggcagcccccagggaaggggctggagtggattgggtatatctattatagt.........gggagcaccaacaacaacccctccctcaag...agtcgagccaccatatcagtagacacgtccaagaaccagttctccctgaacctgagctctgtgaccgccgcggacacggccgtgtattgctgtgcgagaga >IGHV4-34*12 caggtgcagctacagcagtggggcgca...ggactgttgaagccttcggagaccctgtccctcacctgcgctgtctatggtgggtccttc............agtggttactactggagctggatccgccagcccccagggaaggggctggagtggattggggaaatcattcatagt.........ggaagcaccaactacaacccgtccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggctgtgtattactgtgcgaga >IGHV4-34*13 ...........................................................................tatggtgggtccttc............agtggttactactggagctggatccgccagcccccagggaaggggctggagtggattggggaaatcaatcatagt.........ggaagcaccaactacaacccctccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggctgtgtattactgtgcgagagg >IGHV4-38-2*01 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcgctgtctctggttactccatcagc.........agtggttactactggggctggatccggcagcccccagggaaggggctggagtggattgggagtatctatcatagt.........gggagcacctactacaacccgtccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcagacacggccgtgtattactgtgcgaga >IGHV4-38-2*02 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcactgtctctggttactccatcagc.........agtggttactactggggctggatccggcagcccccagggaaggggctggagtggattgggagtatctatcatagt.........gggagcacctactacaacccgtccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcagacacggccgtgtattactgtgcgagaga >IGHV4-39*01 cagctgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcactgtctctggtggctccatcagc......agtagtagttactactggggctggatccgccagcccccagggaaggggctggagtggattgggagtatctattatagt.........gggagcacctactacaacccgtccctcaag...agtcgagtcaccatatccgtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcagacacggctgtgtattactgtgcgagaca >IGHV4-39*02 cagctgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcactgtctctggtggctccatcagc......agtagtagttactactggggctggatccgccagcccccagggaaggggctggagtggattgggagtatctattatagt.........gggagcacctactacaacccgtccctcaag...agtcgagtcaccatatccgtagacacgtccaagaaccacttctccctgaagctgagctctgtgaccgccgcagacacggctgtgtattactgtgcgagaga >IGHV4-39*03 cagctgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcactgtctctggtggctccatcagc......agtagtagttactactggggctggatccgccagcccccagggaaggggctggagtggattgggagtatctattatagt.........gggagcacctactacaacccgtccctcaag...agtcgagtcaccatatccgtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcagacacggccgtgtattactg >IGHV4-39*04 ..................................................................................gctccatcagc......agtagtagttactactggggctggatccgccagcccccagggaaggggctggagtggattgggagtatctattatagt.........gggagcacctactacaacccgtccctcaag...agtcgagtcaccatatccgtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacac >IGHV4-39*05 cagctgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccccgtccctcacctgcactgtctctggtggctccatcagc......agtagtagttactactggggctggatccgccagcccccagggaaggggctggagtggattgggagtatctattatagt.........gggagcacctactacaacccgtccctcaag...agtcgagtcaccatatccgtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcagacacggctgtgtattactgtgcg >IGHV4-39*06 cggctgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcactgtctctggtggctccatcagc......agtagtagttactactggggctggatccgccagcccccagggaaggggctggagtggattgggagtatctattatagt.........gggagcacctactacaacccgtccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttccccctgaagctgagctctgtgaccgccgcggacacggccgtgtattactgtgcgagaga >IGHV4-39*07 cagctgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcactgtctctggtggctccatcagc......agtagtagttactactggggctggatccgccagcccccagggaaggggctggagtggattgggagtatctattatagt.........gggagcacctactacaacccgtccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggccgtgtattactgtgcgagaga >IGHV4-4*01 caggtgcagctgcaggagtcgggccca...ggactggtgaagcctccggggaccctgtccctcacctgcgctgtctctggtggctccatcagc.........agtagtaactggtggagttgggtccgccagcccccagggaaggggctggagtggattggggaaatctatcatagt.........gggagcaccaactacaacccgtccctcaag...agtcgagtcaccatatcagtagacaagtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggccgtgtattgctgtgcgagaga >IGHV4-4*02 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggggaccctgtccctcacctgcgctgtctctggtggctccatcagc.........agtagtaactggtggagttgggtccgccagcccccagggaaggggctggagtggattggggaaatctatcatagt.........gggagcaccaactacaacccgtccctcaag...agtcgagtcaccatatcagtagacaagtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggccgtgtattactgtgcgagaga >IGHV4-4*03 caggtgcagctgcaggagtcgggccca...ggactggtgaagcctccggggaccctgtccctcacctgcgctgtctctggtggctccatcagc.........agtagtaactggtggagttgggtccgccagcccccagggaaggggctggagtggattggggaaatctatcatagt.........gggagcaccaactacaacccgtccctcaag...agtcgagtcaccatatcagtagacaagtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggccgtgtattactg >IGHV4-4*04 caggtgcagctgcaggagtcgggccca...ggactggtgaagcctccggggaccctgtccctcacctgcgctatctctggtggctccatcagc.........agtagtaactggtggagttgggtccgccagcccccagggaaggggctggagtggattggggaaatctatcatagt.........gggagcaccaactacaacccgtccctcaag...agtcgagtcaccatatcagtagacaagtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggccgtgtattactg >IGHV4-4*05 caggtgcagctgcaggagttgggccca...ggactggtgaagcctccggggaccctgtccctcacctgcgctgtctctggtggctccatcagc.........agtagtaactggtggagttgggtccgccagcccccagggaaggggctggagtggattggggaaatctatcatagt.........gggagcaccaactacaacccgtccctcaag...agtcgagtcaccatatcagtagacaagtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggccgtgtattactg >IGHV4-4*06 ...........................................................................tctggtggctccatcagc.........agtagtaactggtggagttgggtccgccagcccccagggannnggctggagtggattggggaaatctatcatagt.........gggagcaccaactacaacccgtccctcaag...agtcgagtcaccatgtcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggccgtgtattactgtgcgagaga >IGHV4-4*07 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcactgtctctggtggctccatc............agtagttactactggagctggatccggcagcccgccgggaagggactggagtggattgggcgtatctataccagt.........gggagcaccaactacaacccctccctcaag...agtcgagtcaccatgtcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggccgtgtattactgtgcgagaga >IGHV4-4*08 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcactgtctctggtggctccatc............agtagttactactggagctggatccggcagcccccagggaagggactggagtggattgggtatatctataccagt.........gggagcaccaactacaacccctccctcaag...agtcgagtcaccatatccgtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcagacacggccgtgtattactgtgcgagaga >IGHV4-55*01 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcatctgcgctgtctctggtgactccatcagc.........agtggtaactggtgaatctgggtccgccagcccccagggaaggggctggagtggattggggaaatccatcatagt.........gggagcacctactacaacccgtccctcaag...agtcgaatcaccatgtccgtagacacgtccaagaaccagttctacctgaagctgagctctgtgaccgccgcggacacggccgtgtattactgtgcgagata >IGHV4-55*02 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcatctgcgctgtctctggtgactccatcagc.........agtggtaactggtgaatctgggtccgccagcccccagggaaggggctggagtggattggggaaatccatcatagt.........gggagcacctactacaacccgtccctcaag...agtcgaatcaccatgtcagtagacacgtccaagaaccagttctacctgaagctgagctctgtgaccgccgcggacacggccgtgtattactgtgcgagata >IGHV4-55*03 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcatctgcgctgtctctggtgactccatcagc.........agtggtaactggtgaatctgggtccgccagcccccagggaaggggctggagtggattggggaaatccatcatagt.........gggagcacctactacaacccgtccctcaag...agtcgaatcaccatgtcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggccgtgtattactg >IGHV4-55*04 caggtgcagctgcaggagtcgggccca...ggactggtgaagctttcggagaccctgtccctcatctgcgctgtctctggtgactccatcagc.........agtggtaactggtgaatctgggtccgccagcccccagggaaggggctggagtggattggggaaatccatcatagt.........gggagcacctactacaacccgtccctcaag...agtcgaatcaccatgtcagtagacacgtccaagaaccagttctacctgaagctgagctctgtgaccgccgcggacacggccgtgtattactg >IGHV4-55*05 caggtgcagctgcaggagtcgggccca...ggactggtgaagctttcggagaccctgtccctcatctgcgctgtctctggtgactccatcagc.........agtggtaactggtgaatctgggtccgccagcccccagggaaggggctggagtggattggggaaatccatcatagt.........gggagcacctactacaacccgtccctcaag...agtcgaatcaccatgtccgtagacacgtccaagaaccagttctacctgaagctgagctctgtgaccgccgcggacacggccgtgtattactg >IGHV4-55*06 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcatctgcgctgtctctggtgactccatcagc.........agtggtaactggtgaatctgggtccgccagcccccagggaaggggctggagtggattggggaaatccatcatagt.........gggagcacctactacaacccgtccctcaag...agtcgaatcaccatgtccgtagacacgtccaagaagcagttctacctgaagctgagctctgtgaccgctgcggacacggccgtgtattactg >IGHV4-55*07 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcatctgcgctgtctctggtgactccatcagc.........agtggtaactggtgaatctgggtccgccagcccccagggaaggggctggagtggattggggaaatccatcatagt.........gggagcacctactacaacccgtccctcaag...agtcgaatcaccatgtccgtagacacgtccaggaaccagttctccctgaagctgagctctgtgaccgccgcagacacggccgtgtattactg >IGHV4-55*08 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcatctgcgctgtctctggtgactccatcagc.........agtggtaactggtgaatctgggtccgccagcccccagggaaggggctggagtggattggggaaatccatcatagt.........gggagcacctactacaacccgtccctcaag...agtcgaatcaccatgtcagtagacacgtccaagaaccagttctacctgaagctgagctctgtgaccgccgcggacacggccgtgtattactgtgcgagaga >IGHV4-55*09 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcatctgcgctgtctctggtgactccatcagc.........agtggtaactggtgaatctgggtccgccagcccccagggaaggggctggagtggattggggaaatccatcatagt.........gggagcacctactacaacccgtccctcaag...agtcgaatcaccatgtccgtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgtggacacggccgtgtattactgtgcgagaaa >IGHV4-59*01 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcactgtctctggtggctccatc............agtagttactactggagctggatccggcagcccccagggaagggactggagtggattgggtatatctattacagt.........gggagcaccaactacaacccctccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgctgcggacacggccgtgtattactgtgcgagaga >IGHV4-59*02 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcactgtctctggtggctccgtc............agtagttactactggagctggatccggcagcccccagggaagggactggagtggattgggtatatctattacagt.........gggagcaccaactacaacccctccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgctgcggacacggccgtgtattactgtgcgagaga >IGHV4-59*03 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcactgtctctggtggctccatc............agtagttactactggagctggatccggcagcccccagggaagggactggagtggattgggtatatctattacagt.........gggagcaccaactacaacccctccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccaattctccctgaagctgagctctgtgaccgctgcggacacggccgtgtattactgtgcg >IGHV4-59*04 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcactgtctctggtggctccatc............agtagttactactggagctggatccggcagcccccagggaagggactggagtggattgggtatatctattatagt.........gggagcacctactacaacccgtccctcaag...agtcgagtcaccatgtcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcagacacggctgtgtattactgtgcg >IGHV4-59*05 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcactgtctctggtggctccatc............agtagttactactggagctggatccggcagccgccggggaagggactggagtggattgggcgtatctattatagt.........gggagcacctactacaacccgtccctcaag...agtcgagtcaccatatccgtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcagacacggctgtgtattactgtgcg >IGHV4-59*06 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcactgtcactggtggctccatc............agtagttactactggagctggatccggcagcccgctgggaagggcctggagtggattgggtacatctattacagt.........gggagcacctactacaacccgtccctcaag...agtcgagttaccatatcagtagacacgtctaagaaccagttctccctgaagctgagctctgtgactgccgcggacacggccgtgtattactgtgcg >IGHV4-59*07 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggacaccctgtccctcacctgcactgtctctggtggctccatc............agtagttactactggagctggatccggcagcccccagggaagggactggagtggattgggtatatctattacagt.........gggagcaccaactacaacccctccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgctgcggacacggccgtgtattactgtgcgaga >IGHV4-59*08 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcactgtctctggtggctccatc............agtagttactactggagctggatccggcagcccccagggaagggactggagtggattgggtatatctattacagt.........gggagcaccaactacaacccctccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcagacacggccgtgtattactgtgcgagaca >IGHV4-59*09 ...........................................................................tctggtggctccatc............agtagttactactggagctggatccggcagcccccaggnannngactggagtggattgggtatatctattacagt.........gggagcaccaactacaacccctccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgctgcggacacggccgtgtattactgtgcgagagg >IGHV4-59*10 caggtgcagctacagcagtggggcgca...ggactgttgaagccttcggagaccctgtccctcacctgcgctgtctatggtggctccatc............agtagttactactggagctggatccggcagcccgccgggaaggggctggagtggattgggcgtatctataccagt.........gggagcaccaactacaacccctccctcaag...agtcgagtcaccatgtcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggccgtgtattactgtgcgagata >IGHV4-61*01 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcactgtctctggtggctccgtcagc......agtggtagttactactggagctggatccggcagcccccagggaagggactggagtggattgggtatatctattacagt.........gggagcaccaactacaacccctccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgctgcggacacggccgtgtattactgtgcgagaga >IGHV4-61*02 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcacagaccctgtccctcacctgcactgtctctggtggctccatcagc......agtggtagttactactggagctggatccggcagcccgccgggaagggactggagtggattgggcgtatctataccagt.........gggagcaccaactacaacccctccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcagacacggccgtgtattactgtgcgagaga >IGHV4-61*03 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcactgtctctggtggctccgtcagc......agtggtagttactactggagctggatccggcagcccccagggaagggactggagtggattgggtatatctattacagt.........gggagcaccaactacaacccctccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccacttctccctgaagctgagctctgtgaccgctgcggacacggccgtgtattactgtgcgagaga >IGHV4-61*04 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcactgtctctggtggctccgtcagc......agtggtagttactactggagctggatccggcagcccccagggaagggactggagtggattggatatatctattacagt.........gggagcaccaactacaacccctccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgct...gacacggccgtgtattactg >IGHV4-61*05 cagctgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcactgtctctggtggctccatcagc......agtagtagttactactggggctggatccggcagcccccagggaagggactggagtggattgggtatatctattacagt.........gggagcaccaactacaacccctccctcaag...agtcgagtcaccatatcagtagacaagtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggccgtgtattactgtgcgaga >IGHV4-61*06 ...........................................................................tctggtggctccgtcagc......agtggtagttactactggagctggatccggcagcccccagggaagggactggagtggattgggtatatctattacagt.........gggagcaccaactacaacccctccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggccgtgtattactgtgccagaga >IGHV4-61*07 ...........................................................................tctggtggctccgtcagc......agtggtagttactactggagctggatccggcagcccccagggaagggactggagtggattgggtatatctattacagt.........gggagcaccaactacaacccctccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgctgcggacacggccgtgtattactgtgcgagaca >IGHV4-61*08 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcactgtctctggtggctccgtcagc......agtggtggttactactggagctggatccggcagcccccagggaagggactggagtggattgggtatatctattacagt.........gggagcaccaactacaacccctccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgctgcggacacggccgtgtattactgtgcgagaga >IGHV4/OR15-8*01 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcgttgtctctggtggctccatcagc.........agtagtaactggtggagctgggtccgccagcccccagggaaggggctggagtggattggggaaatctatcatagt.........gggagccccaactacaacccgtccctcaag...agtcgagtcaccatatcagtagacaagtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggccgtgtattactgtgcgagaga >IGHV4/OR15-8*02 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcgttgtctctggtggctccatcagc.........agtagtaactggtggagctgggtccgccagcccccagggaaggggctggagtggattggggaaatctatcatagt.........gggaaccccaactacaacccgtccctcaag...agtcgagtcaccatatcaatagacaagtccaagaaccaattctccctgaagctgagctctgtgaccgccgcggacacggccgtgtattactgtgcgagaga >IGHV4/OR15-8*03 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcgttgtctctggtggctccatcagc.........agtagtaactggtggagctgggtccgccagcccccagggaaggggctggagtggattggggaaatctatcatagt.........gggagccccaactacaacccatccctcaag...agtcgagtcaccatatcagtagacaagtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggccgtgtattactgtgcgagaga >IGHV5-10-1*01 gaagtgcagctggtgcagtctggagca...gaggtgaaaaagcccggggagtctctgaggatctcctgtaagggttctggatacagcttt............accagctactggatcagctgggtgcgccagatgcccgggaaaggcctggagtggatggggaggattgatcctagt......gactcttataccaactacagcccgtccttccaa...ggccacgtcaccatctcagctgacaagtccatcagcactgcctacctgcagtggagcagcctgaaggcctcggacaccgccatgtattactgtgcgaga >IGHV5-10-1*02 gaagtgcagctggtgcagtctggagca...gaggtgaaaaagcccggggagtctctgaggatctcctgtaagggttctggatacagcttt............accagctactggatcagctgggtgcgccagatgcccgggaaaggcttggagtggatggggaggattgatcctagt......gactcttataccaactacagcccgtccttccaa...ggccacgtcaccatctcagctgacaagtccatcagcactgcctacctgcagtggagcagcctgaaggc.tcggacaccgccatgtattactgtgcgagaca >IGHV5-10-1*03 gaagtgcagctggtgcagtccggagca...gaggtgaaaaagcccggggagtctctgaggatctcctgtaagggttctggatacagcttt............accagctactggatcagctgggtgcgccagatgcccgggaaaggcctggagtggatggggaggattgatcctagt......gactcttataccaactacagcccgtccttccaa...ggccacgtcaccatctcagctgacaagtccatcagcactgcctacctgcagtggagcagcctgaaggcctcggacaccgccatgtattactgtgcgaga >IGHV5-10-1*04 gaagtgcagctggtgcagtctggagca...gaggtgaaaaagcccggggagtctctgaggatctcctgtaagggttctggatacagcttt............accagctactggatcagctgggtgcgccagatgcccgggaaaggcctggagtggatggggaggattgatcctagt......gactcttataccaactacagcccgtccttccaa...ggccaggtcaccatctcagctgacaagtccatcagcactgcctacctgcagtggagcagcctgaaggcctcggacaccgccatgtattactgtgcgaga >IGHV5-51*01 gaggtgcagctggtgcagtctggagca...gaggtgaaaaagcccggggagtctctgaagatctcctgtaagggttctggatacagcttt............accagctactggatcggctgggtgcgccagatgcccgggaaaggcctggagtggatggggatcatctatcctggt......gactctgataccagatacagcccgtccttccaa...ggccaggtcaccatctcagccgacaagtccatcagcaccgcctacctgcagtggagcagcctgaaggcctcggacaccgccatgtattactgtgcgagaca >IGHV5-51*02 gaggtgcagctggtgcagtctggagca...gaggtgaaaaagcccggggagtctctgaagatctcctgtaagggttctggatacagcttt............accagctactggaccggctgggtgcgccagatgcccgggaaaggcttggagtggatggggatcatctatcctggt......gactctgataccagatacagcccgtccttccaa...ggccaggtcaccatctcagccgacaagtccatcagcaccgcctacctgcagtggagcagcctgaaggcctcggacaccgccatgtattactgtgcgagaca >IGHV5-51*03 gaggtgcagctggtgcagtctggagca...gaggtgaaaaagccgggggagtctctgaagatctcctgtaagggttctggatacagcttt............accagctactggatcggctgggtgcgccagatgcccgggaaaggcctggagtggatggggatcatctatcctggt......gactctgataccagatacagcccgtccttccaa...ggccaggtcaccatctcagccgacaagtccatcagcaccgcctacctgcagtggagcagcctgaaggcctcggacaccgccatgtattactgtgcgaga >IGHV5-51*04 gaggtgcagctggtgcagtctggagca...gaggtgaaaaagccgggggagtctctgaagatctcctgtaagggttctggatacagcttt............accagctactggatcggctgggtgcgccagatgcccgggaaaggcctggagtggatggggatcatctatcctggt......gactctgataccagatacagcccgtccttccaa...ggccaggtcaccatctcagccgacaagcccatcagcaccgcctacctgcagtggagcagcctgaaggcctcggacaccgccatgtattactgtgcgaga >IGHV5-51*05 .....................................aaaagcccggggagtctctgaagatctcctgtaagggttctggatacagcttt............accagctactggatcggctgggtgcgccagatgcccaggaaaggcctggagtggatggggatcatctatcctggt......gactctgataccagatacagcccgtccttccaa...ggccaggtcaccatctcagccgacaagtccatcagcaccgcctacctgcagtggagcagcctgaaggcctcggacaccgccatg >IGHV5-78*01 gaggtgcagctgttgcagtctgcagca...gaggtgaaaagacccggggagtctctgaggatctcctgtaagacttctggatacagcttt............accagctactggatccactgggtgcgccagatgcccgggaaagaactggagtggatggggagcatctatcctggg......aactctgataccagatacagcccatccttccaa...ggccacgtcaccatctcagccgacagctccagcagcaccgcctacctgcagtggagcagcctgaaggcctcggacgccgccatgtattattgtgtgaga >IGHV6-1*01 caggtacagctgcagcagtcaggtcca...ggactggtgaagccctcgcagaccctctcactcacctgtgccatctccggggacagtgtctct......agcaacagtgctgcttggaactggatcaggcagtccccatcgagaggccttgagtggctgggaaggacatactacaggtcc...aagtggtataatgattatgcagtatctgtgaaa...agtcgaataaccatcaacccagacacatccaagaaccagttctccctgcagctgaactctgtgactcccgaggacacggctgtgtattactgtgcaagaga >IGHV6-1*02 caggtacagctgcagcagtcaggtccg...ggactggtgaagccctcgcagaccctctcactcacctgtgccatctccggggacagtgtctct......agcaacagtgctgcttggaactggatcaggcagtccccatcgagaggccttgagtggctgggaaggacatactacaggtcc...aagtggtataatgattatgcagtatctgtgaaa...agtcgaataaccatcaacccagacacatccaagaaccagttctccctgcagctgaactctgtgactcccgaggacacggctgtgtattactgtgcaagaga >IGHV7-34-1*01 ...ctgcagctggtgcagtctgggcct...gaggtgaagaagcctggggcctcagtgaaggtctcctataagtcttctggttacaccttc............accatctatggtatgaattgggtatgatagacccctggacagggctttgagtggatgtgatggatcatcacctac......actgggaacccaacgtatacccacggcttcaca...ggatggtttgtcttctccatggacacgtctgtcagcacggcgtgtcttcagatcagcagcctaaaggctgaggacacggccgagtattactgtgcgaagta >IGHV7-34-1*02 ...ctgcagctggtgcagtctgggcct...gaggtgaagaagcctggggcctcagtgaaggtctcctataagtcttctggttacaccttc............accatctatggtatgaattgggtatgatagacccctggacagggctttgagtggatgtgatggatcatcacctac......aatgggaacccaacgtatacccacggcttcaca...ggatggtttgtcttctccatggacacgtctgtcagcacggcgtgtcttcagatcagcagcctaaaggctgaggacacggccgagtattactgtgcgaagta >IGHV7-4-1*01 caggtgcagctggtgcaatctgggtct...gagttgaagaagcctggggcctcagtgaaggtttcctgcaaggcttctggatacaccttc............actagctatgctatgaattgggtgcgacaggcccctggacaagggcttgagtggatgggatggatcaacaccaac......actgggaacccaacgtatgcccagggcttcaca...ggacggtttgtcttctccttggacacctctgtcagcacggcatatctgcagatctgcagcctaaaggctgaggacactgccgtgtattactgtgcgaga >IGHV7-4-1*02 caggtgcagctggtgcaatctgggtct...gagttgaagaagcctggggcctcagtgaaggtttcctgcaaggcttctggatacaccttc............actagctatgctatgaattgggtgcgacaggcccctggacaagggcttgagtggatgggatggatcaacaccaac......actgggaacccaacgtatgcccagggcttcaca...ggacggtttgtcttctccttggacacctctgtcagcacggcatatctgcagatcagcagcctaaaggctgaggacactgccgtgtattactgtgcgagaga >IGHV7-4-1*03 caggtgcagctggtgcaatctgggtct...gagttgaagaagcctggggcctcagtgaaggtttcctgcaaggcttctggatacaccttc............actagctatgctatgaattgggtgcgacaggcccctggacaagggcttgagtggatgggatggatcaacaccaac......actgggaacccaacgtatgcccagggcttcaca...ggacggtttgtcttctccttggacacctctgtcagcacggcatatctgcagatcagcacgctaaaggctgaggacactg >IGHV7-4-1*04 caggtgcagctggtgcaatctgggtct...gagttgaagaagcctggggcctcagtgaaggtttcctgcaaggcttctggatacaccttc............actagctatgctatgaattgggtgcgacaggcccctggacaagggcttgagtggatgggatggatcaacaccaac......actgggaacccaacgtatgcccagggcttcaca...ggacggtttgtcttctccttggacacctctgtcagcatggcatatctgcagatcagcagcctaaaggctgaggacactgccgtgtattactgtgcgagaga >IGHV7-4-1*05 caggtgcagctggtgcaatctgggtct...gagttgaagaagcctggggcctcagtgaaggtttcctgcaaggcttctggatacaccttc............actagctatgctatgaattgggtgcgacaggcccctggacaagggcttgagtggatgggatggatcaacaccaac......actgggaacccaacgtatgcccagggcttcaca...ggacggtttgtcttctccttggacacctctgtcagcatggcatatctgcagatcagcagcctaaaggctgaggacactgccgtgtgttactgtgcgagaga >IGHV7-40*03 ttttcaatagaaaagtcaaataatcta...agtgtcaatcagtggatgattagataaaatatgatatatgtaaatcatggaatactatgc............agccagtatggtatgaattcagtgtgaccagcccctggacaagggcttgagtggatgggatggatcatcacctac......actgggaacccaacatataccaacggcttcaca...ggacggtttctattctccatggacacctctgtcagcatggcgtatctgcagatcagcagcctaaaggctgaggacacggccgtgtatgactgtatgagaga >IGHV7-81*01 caggtgcagctggtgcagtctggccat...gaggtgaagcagcctggggcctcagtgaaggtctcctgcaaggcttctggttacagtttc............accacctatggtatgaattgggtgccacaggcccctggacaagggcttgagtggatgggatggttcaacacctac......actgggaacccaacatatgcccagggcttcaca...ggacggtttgtcttctccatggacacctctgccagcacagcatacctgcagatcagcagcctaaaggctgaggacatggccatgtattactgtgcgagata
--- a/baseline/baseline_url.txt Tue Sep 01 16:03:44 2020 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,1 +0,0 @@ -http://selection.med.yale.edu/baseline/ \ No newline at end of file
--- a/baseline/comparePDFs.r Tue Sep 01 16:03:44 2020 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,225 +0,0 @@ -options("warn"=-1) - -#from http://selection.med.yale.edu/baseline/Archive/Baseline%20Version%201.3/Baseline_Functions_Version1.3.r -# Compute p-value of two distributions -compareTwoDistsFaster <-function(sigma_S=seq(-20,20,length.out=4001), N=10000, dens1=runif(4001,0,1), dens2=runif(4001,0,1)){ -#print(c(length(dens1),length(dens2))) -if(length(dens1)>1 & length(dens2)>1 ){ - dens1<-dens1/sum(dens1) - dens2<-dens2/sum(dens2) - cum2 <- cumsum(dens2)-dens2/2 - tmp<- sum(sapply(1:length(dens1),function(i)return(dens1[i]*cum2[i]))) - #print(tmp) - if(tmp>0.5)tmp<-tmp-1 - return( tmp ) - } - else { - return(NA) - } - #return (sum(sapply(1:N,function(i)(sample(sigma_S,1,prob=dens1)>sample(sigma_S,1,prob=dens2))))/N) -} - - -require("grid") -arg <- commandArgs(TRUE) -#arg <- c("300143","4","5") -arg[!arg=="clonal"] -input <- arg[1] -output <- arg[2] -rowIDs <- as.numeric( sapply(arg[3:(max(3,length(arg)))],function(x){ gsub("chkbx","",x) } ) ) - -numbSeqs = length(rowIDs) - -if ( is.na(rowIDs[1]) | numbSeqs>10 ) { - stop( paste("Error: Please select between one and 10 seqeunces to compare.") ) -} - -#load( paste("output/",sessionID,".RData",sep="") ) -load( input ) -#input - -xMarks = seq(-20,20,length.out=4001) - -plot_grid_s<-function(pdf1,pdf2,Sample=100,cex=1,xlim=NULL,xMarks = seq(-20,20,length.out=4001)){ - yMax = max(c(abs(as.numeric(unlist(listPDFs[pdf1]))),abs(as.numeric(unlist(listPDFs[pdf2]))),0),na.rm=T) * 1.1 - - if(length(xlim==2)){ - xMin=xlim[1] - xMax=xlim[2] - } else { - xMin_CDR = xMarks[listPDFs[pdf1][[1]][["CDR"]]>0.001][1] - xMin_FWR = xMarks[listPDFs[pdf1][[1]][["FWR"]]>0.001][1] - xMax_CDR = xMarks[listPDFs[pdf1][[1]][["CDR"]]>0.001][length(xMarks[listPDFs[pdf1][[1]][["CDR"]]>0.001])] - xMax_FWR = xMarks[listPDFs[pdf1][[1]][["FWR"]]>0.001][length(xMarks[listPDFs[pdf1][[1]][["FWR"]]>0.001])] - - xMin_CDR2 = xMarks[listPDFs[pdf2][[1]][["CDR"]]>0.001][1] - xMin_FWR2 = xMarks[listPDFs[pdf2][[1]][["FWR"]]>0.001][1] - xMax_CDR2 = xMarks[listPDFs[pdf2][[1]][["CDR"]]>0.001][length(xMarks[listPDFs[pdf2][[1]][["CDR"]]>0.001])] - xMax_FWR2 = xMarks[listPDFs[pdf2][[1]][["FWR"]]>0.001][length(xMarks[listPDFs[pdf2][[1]][["FWR"]]>0.001])] - - xMin=min(c(xMin_CDR,xMin_FWR,xMin_CDR2,xMin_FWR2,0),na.rm=TRUE) - xMax=max(c(xMax_CDR,xMax_FWR,xMax_CDR2,xMax_FWR2,0),na.rm=TRUE) - } - - sigma<-approx(xMarks,xout=seq(xMin,xMax,length.out=Sample))$x - grid.rect(gp = gpar(col=gray(0.6),fill="white",cex=cex)) - x <- sigma - pushViewport(viewport(x=0.175,y=0.175,width=0.825,height=0.825,just=c("left","bottom"),default.units="npc")) - #pushViewport(plotViewport(c(1.8, 1.8, 0.25, 0.25)*cex)) - pushViewport(dataViewport(x, c(yMax,-yMax),gp = gpar(cex=cex),extension=c(0.05))) - grid.polygon(c(0,0,1,1),c(0,0.5,0.5,0),gp=gpar(col=grey(0.95),fill=grey(0.95)),default.units="npc") - grid.polygon(c(0,0,1,1),c(1,0.5,0.5,1),gp=gpar(col=grey(0.9),fill=grey(0.9)),default.units="npc") - grid.rect() - grid.xaxis(gp = gpar(cex=cex/1.1)) - yticks = pretty(c(-yMax,yMax),8) - yticks = yticks[yticks>(-yMax) & yticks<(yMax)] - grid.yaxis(at=yticks,label=abs(yticks),gp = gpar(cex=cex/1.1)) - if(length(listPDFs[pdf1][[1]][["CDR"]])>1){ - ycdr<-approx(xMarks,listPDFs[pdf1][[1]][["CDR"]],xout=seq(xMin,xMax,length.out=Sample),yleft=0,yright=0)$y - grid.lines(unit(x,"native"), unit(ycdr,"native"),gp=gpar(col=2,lwd=2)) - } - if(length(listPDFs[pdf1][[1]][["FWR"]])>1){ - yfwr<-approx(xMarks,listPDFs[pdf1][[1]][["FWR"]],xout=seq(xMin,xMax,length.out=Sample),yleft=0,yright=0)$y - grid.lines(unit(x,"native"), unit(-yfwr,"native"),gp=gpar(col=4,lwd=2)) - } - - if(length(listPDFs[pdf2][[1]][["CDR"]])>1){ - ycdr2<-approx(xMarks,listPDFs[pdf2][[1]][["CDR"]],xout=seq(xMin,xMax,length.out=Sample),yleft=0,yright=0)$y - grid.lines(unit(x,"native"), unit(ycdr2,"native"),gp=gpar(col=2,lwd=2,lty=2)) - } - if(length(listPDFs[pdf2][[1]][["FWR"]])>1){ - yfwr2<-approx(xMarks,listPDFs[pdf2][[1]][["FWR"]],xout=seq(xMin,xMax,length.out=Sample),yleft=0,yright=0)$y - grid.lines(unit(x,"native"), unit(-yfwr2,"native"),gp=gpar(col=4,lwd=2,lty=2)) - } - - grid.lines(unit(c(0,1),"npc"), unit(c(0.5,0.5),"npc"),gp=gpar(col=1)) - grid.lines(unit(c(0,0),"native"), unit(c(0,1),"npc"),gp=gpar(col=1,lwd=1,lty=3)) - - grid.text("All", x = unit(-2.5, "lines"), rot = 90,gp = gpar(cex=cex)) - grid.text( expression(paste("Selection Strength (", Sigma, ")", sep="")) , y = unit(-2.5, "lines"),gp = gpar(cex=cex)) - - if(pdf1==pdf2 & length(listPDFs[pdf2][[1]][["FWR"]])>1 & length(listPDFs[pdf2][[1]][["CDR"]])>1 ){ - pCDRFWR = compareTwoDistsFaster(sigma_S=xMarks, N=10000, dens1=listPDFs[[pdf1]][["CDR"]], dens2=listPDFs[[pdf1]][["FWR"]]) - pval = formatC(as.numeric(pCDRFWR),digits=3) - grid.text( substitute(expression(paste(P[CDR/FWR], "=", x, sep="")),list(x=pval))[[2]] , x = unit(0.02, "npc"),y = unit(0.98, "npc"),just=c("left", "top"),gp = gpar(cex=cex*1.2)) - } - grid.text(paste("CDR"), x = unit(0.98, "npc"),y = unit(0.98, "npc"),just=c("right", "top"),gp = gpar(cex=cex*1.5)) - grid.text(paste("FWR"), x = unit(0.98, "npc"),y = unit(0.02, "npc"),just=c("right", "bottom"),gp = gpar(cex=cex*1.5)) - popViewport(2) -} -#plot_grid_s(1) - - -p2col<-function(p=0.01){ - breaks=c(-.51,-0.1,-.05,-0.01,-0.005,0,0.005,0.01,0.05,0.1,0.51) - i<-findInterval(p,breaks) - cols = c( rgb(0.8,1,0.8), rgb(0.6,1,0.6), rgb(0.4,1,0.4), rgb(0.2,1,0.2) , rgb(0,1,0), - rgb(1,0,0), rgb(1,.2,.2), rgb(1,.4,.4), rgb(1,.6,.6) , rgb(1,.8,.8) ) - return(cols[i]) -} - - -plot_pvals<-function(pdf1,pdf2,cex=1,upper=TRUE){ - if(upper){ - pCDR1FWR2 = compareTwoDistsFaster(sigma_S=xMarks, N=10000, dens1=listPDFs[[pdf1]][["CDR"]], dens2=listPDFs[[pdf2]][["FWR"]]) - pFWR1FWR2 = compareTwoDistsFaster(sigma_S=xMarks, N=10000, dens1=listPDFs[[pdf1]][["FWR"]], dens2=listPDFs[[pdf2]][["FWR"]]) - pFWR1CDR2 = compareTwoDistsFaster(sigma_S=xMarks, N=10000, dens2=listPDFs[[pdf2]][["CDR"]], dens1=listPDFs[[pdf1]][["FWR"]]) - pCDR1CDR2 = compareTwoDistsFaster(sigma_S=xMarks, N=10000, dens2=listPDFs[[pdf2]][["CDR"]], dens1=listPDFs[[pdf1]][["CDR"]]) - grid.polygon(c(0.5,0.5,1,1),c(0,0.5,0.5,0),gp=gpar(col=p2col(pFWR1FWR2),fill=p2col(pFWR1FWR2)),default.units="npc") - grid.polygon(c(0.5,0.5,1,1),c(1,0.5,0.5,1),gp=gpar(col=p2col(pCDR1FWR2),fill=p2col(pCDR1FWR2)),default.units="npc") - grid.polygon(c(0.5,0.5,0,0),c(1,0.5,0.5,1),gp=gpar(col=p2col(pCDR1CDR2),fill=p2col(pCDR1CDR2)),default.units="npc") - grid.polygon(c(0.5,0.5,0,0),c(0,0.5,0.5,0),gp=gpar(col=p2col(pFWR1CDR2),fill=p2col(pFWR1CDR2)),default.units="npc") - - grid.lines(c(0,1),0.5,gp=gpar(lty=2,col=gray(0.925))) - grid.lines(0.5,c(0,1),gp=gpar(lty=2,col=gray(0.925))) - - grid.text(formatC(as.numeric(pFWR1FWR2),digits=3), x = unit(0.75, "npc"),y = unit(0.25, "npc"),just=c("center", "center"),gp = gpar(cex=cex)) - grid.text(formatC(as.numeric(pCDR1FWR2),digits=3), x = unit(0.75, "npc"),y = unit(0.75, "npc"),just=c("center", "center"),gp = gpar(cex=cex)) - grid.text(formatC(as.numeric(pCDR1CDR2),digits=3), x = unit(0.25, "npc"),y = unit(0.75, "npc"),just=c("center", "center"),gp = gpar(cex=cex)) - grid.text(formatC(as.numeric(pFWR1CDR2),digits=3), x = unit(0.25, "npc"),y = unit(0.25, "npc"),just=c("center", "center"),gp = gpar(cex=cex)) - - - # grid.text(paste("P = ",formatC(pCDRFWR,digits=3)), x = unit(0.5, "npc"),y = unit(0.98, "npc"),just=c("center", "top"),gp = gpar(cex=cex)) - # grid.text(paste("P = ",formatC(pFWRFWR,digits=3)), x = unit(0.5, "npc"),y = unit(0.02, "npc"),just=c("center", "bottom"),gp = gpar(cex=cex)) - } - else{ - } -} - - -################################################################################## -################## The whole OCD's matrix ######################################## -################################################################################## - -#pdf(width=4*numbSeqs+1/3,height=4*numbSeqs+1/3) -pdf( output ,width=4*numbSeqs+1/3,height=4*numbSeqs+1/3) - -pushViewport(viewport(x=0.02,y=0.02,just = c("left", "bottom"),w =0.96,height=0.96,layout = grid.layout(numbSeqs+1,numbSeqs+1,widths=unit.c(unit(rep(1,numbSeqs),"null"),unit(4,"lines")),heights=unit.c(unit(4,"lines"),unit(rep(1,numbSeqs),"null"))))) - -for( seqOne in 1:numbSeqs+1){ - pushViewport(viewport(layout.pos.col = seqOne-1, layout.pos.row = 1)) - if(seqOne>2){ - grid.polygon(c(0,0,0.5,0.5),c(0,0.5,0.5,0),gp=gpar(col=grey(0.5),fill=grey(0.9)),default.units="npc") - grid.polygon(c(1,1,0.5,0.5),c(0,0.5,0.5,0),gp=gpar(col=grey(0.5),fill=grey(0.95)),default.units="npc") - grid.polygon(c(0,0,1,1),c(1,0.5,0.5,1),gp=gpar(col=grey(0.5)),default.units="npc") - - grid.text(y=.25,x=0.75,"FWR",gp = gpar(cex=1.5),just="center") - grid.text(y=.25,x=0.25,"CDR",gp = gpar(cex=1.5),just="center") - } - grid.rect(gp = gpar(col=grey(0.9))) - grid.text(y=.75,substr(paste(names(listPDFs)[rowIDs[seqOne-1]]),1,16),gp = gpar(cex=2),just="center") - popViewport(1) -} - -for( seqOne in 1:numbSeqs+1){ - pushViewport(viewport(layout.pos.row = seqOne, layout.pos.col = numbSeqs+1)) - if(seqOne<=numbSeqs){ - grid.polygon(c(0,0.5,0.5,0),c(0,0,0.5,0.5),gp=gpar(col=grey(0.5),fill=grey(0.95)),default.units="npc") - grid.polygon(c(0,0.5,0.5,0),c(1,1,0.5,0.5),gp=gpar(col=grey(0.5),fill=grey(0.9)),default.units="npc") - grid.polygon(c(1,0.5,0.5,1),c(0,0,1,1),gp=gpar(col=grey(0.5)),default.units="npc") - grid.text(x=.25,y=0.75,"CDR",gp = gpar(cex=1.5),just="center",rot=270) - grid.text(x=.25,y=0.25,"FWR",gp = gpar(cex=1.5),just="center",rot=270) - } - grid.rect(gp = gpar(col=grey(0.9))) - grid.text(x=0.75,substr(paste(names(listPDFs)[rowIDs[seqOne-1]]),1,16),gp = gpar(cex=2),rot=270,just="center") - popViewport(1) -} - -for( seqOne in 1:numbSeqs+1){ - for(seqTwo in 1:numbSeqs+1){ - pushViewport(viewport(layout.pos.col = seqTwo-1, layout.pos.row = seqOne)) - if(seqTwo>seqOne){ - plot_pvals(rowIDs[seqOne-1],rowIDs[seqTwo-1],cex=2) - grid.rect() - } - popViewport(1) - } -} - - -xMin=0 -xMax=0.01 -for(pdf1 in rowIDs){ - xMin_CDR = xMarks[listPDFs[pdf1][[1]][["CDR"]]>0.001][1] - xMin_FWR = xMarks[listPDFs[pdf1][[1]][["FWR"]]>0.001][1] - xMax_CDR = xMarks[listPDFs[pdf1][[1]][["CDR"]]>0.001][length(xMarks[listPDFs[pdf1][[1]][["CDR"]]>0.001])] - xMax_FWR = xMarks[listPDFs[pdf1][[1]][["FWR"]]>0.001][length(xMarks[listPDFs[pdf1][[1]][["FWR"]]>0.001])] - xMin=min(c(xMin_CDR,xMin_FWR,xMin),na.rm=TRUE) - xMax=max(c(xMax_CDR,xMax_FWR,xMax),na.rm=TRUE) -} - - - -for(i in 1:numbSeqs+1){ - for(j in (i-1):numbSeqs){ - pushViewport(viewport(layout.pos.col = i-1, layout.pos.row = j+1)) - grid.rect() - plot_grid_s(rowIDs[i-1],rowIDs[j],cex=1) - popViewport(1) - } -} - -dev.off() - -cat("Success", paste(rowIDs,collapse="_"),sep=":") -
--- a/baseline/filter.r Tue Sep 01 16:03:44 2020 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,55 +0,0 @@ -arg = commandArgs(TRUE) -summaryfile = arg[1] -gappedfile = arg[2] -selection = arg[3] -output = arg[4] -print(paste("selection = ", selection)) - - -summarydat = read.table(summaryfile, header=T, sep="\t", fill=T, stringsAsFactors=F, quote = "") -gappeddat = read.table(gappedfile, header=T, sep="\t", fill=T, stringsAsFactors=F, quote = "") - -fix_column_names = function(df){ - if("V.DOMAIN.Functionality" %in% names(df)){ - names(df)[names(df) == "V.DOMAIN.Functionality"] = "Functionality" - print("found V.DOMAIN.Functionality, changed") - } - if("V.DOMAIN.Functionality.comment" %in% names(df)){ - names(df)[names(df) == "V.DOMAIN.Functionality.comment"] = "Functionality.comment" - print("found V.DOMAIN.Functionality.comment, changed") - } - return(df) -} - -gappeddat = fix_column_names(gappeddat) - -#dat = data.frame(merge(gappeddat, summarydat, by="Sequence.ID", all.x=T)) - -dat = cbind(gappeddat, summarydat$AA.JUNCTION) - -colnames(dat)[length(dat)] = "AA.JUNCTION" - -dat$VGene = gsub("^Homsap ", "", dat$V.GENE.and.allele) -dat$VGene = gsub("[*].*", "", dat$VGene) - -dat$DGene = gsub("^Homsap ", "", dat$D.GENE.and.allele) -dat$DGene = gsub("[*].*", "", dat$DGene) - -dat$JGene = gsub("^Homsap ", "", dat$J.GENE.and.allele) -dat$JGene = gsub("[*].*", "", dat$JGene) - -print(str(dat)) - -dat$past = do.call(paste, c(dat[unlist(strsplit(selection, ","))], sep = ":")) - -dat = dat[!duplicated(dat$past), ] - -print(paste("Sequences remaining after duplicate filter:", nrow(dat))) - -dat = dat[dat$Functionality != "No results" & dat$Functionality != "unproductive",] - -print(paste("Sequences remaining after functionality filter:", nrow(dat))) - -print(paste("Sequences remaining:", nrow(dat))) - -write.table(x=dat, file=output, sep="\t",quote=F,row.names=F,col.names=T)
--- a/baseline/script_imgt.py Tue Sep 01 16:03:44 2020 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,86 +0,0 @@ -#import xlrd #avoid dep -import argparse -import re - -parser = argparse.ArgumentParser() -parser.add_argument("--input", help="Excel input file containing one or more sheets where column G has the gene annotation, H has the sequence id and J has the sequence") -parser.add_argument("--ref", help="Reference file") -parser.add_argument("--output", help="Output file") -parser.add_argument("--id", help="ID to be used at the '>>>' line in the output") - -args = parser.parse_args() - -print "script_imgt.py" -print "input:", args.input -print "ref:", args.ref -print "output:", args.output -print "id:", args.id - -refdic = dict() -with open(args.ref, 'rU') as ref: - currentSeq = "" - currentId = "" - for line in ref: - if line.startswith(">"): - if currentSeq is not "" and currentId is not "": - refdic[currentId[1:]] = currentSeq - currentId = line.rstrip() - currentSeq = "" - else: - currentSeq += line.rstrip() - refdic[currentId[1:]] = currentSeq - -print "Have", str(len(refdic)), "reference sequences" - -vPattern = [r"(IGHV[0-9]-[0-9ab]+-?[0-9]?D?\*\d{1,2})"]#, -# r"(TRBV[0-9]{1,2}-?[0-9]?-?[123]?)", -# r"(IGKV[0-3]D?-[0-9]{1,2})", -# r"(IGLV[0-9]-[0-9]{1,2})", -# r"(TRAV[0-9]{1,2}(-[1-46])?(/DV[45678])?)", -# r"(TRGV[234589])", -# r"(TRDV[1-3])"] - -#vPattern = re.compile(r"|".join(vPattern)) -vPattern = re.compile("|".join(vPattern)) - -def filterGene(s, pattern): - if type(s) is not str: - return None - res = pattern.search(s) - if res: - return res.group(0) - return None - - - -currentSeq = "" -currentId = "" -first=True -with open(args.input, 'r') as i: - with open(args.output, 'a') as o: - o.write(">>>" + args.id + "\n") - outputdic = dict() - for line in i: - if first: - first = False - continue - linesplt = line.split("\t") - ref = filterGene(linesplt[1], vPattern) - if not ref or not linesplt[2].rstrip(): - continue - if ref in outputdic: - outputdic[ref] += [(linesplt[0].replace(">", ""), linesplt[2].replace(">", "").rstrip())] - else: - outputdic[ref] = [(linesplt[0].replace(">", ""), linesplt[2].replace(">", "").rstrip())] - #print outputdic - - for k in outputdic.keys(): - if k in refdic: - o.write(">>" + k + "\n") - o.write(refdic[k] + "\n") - for seq in outputdic[k]: - #print seq - o.write(">" + seq[0] + "\n") - o.write(seq[1] + "\n") - else: - print k + " not in reference, skipping " + k
--- a/baseline/script_xlsx.py Tue Sep 01 16:03:44 2020 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,58 +0,0 @@ -import xlrd -import argparse - -parser = argparse.ArgumentParser() -parser.add_argument("--input", help="Excel input file containing one or more sheets where column G has the gene annotation, H has the sequence id and J has the sequence") -parser.add_argument("--ref", help="Reference file") -parser.add_argument("--output", help="Output file") - -args = parser.parse_args() - -gene_column = 6 -id_column = 7 -seq_column = 8 -LETTERS = [x for x in "ABCDEFGHIJKLMNOPQRSTUVWXYZ"] - - -refdic = dict() -with open(args.ref, 'r') as ref: - currentSeq = "" - currentId = "" - for line in ref.readlines(): - if line[0] is ">": - if currentSeq is not "" and currentId is not "": - refdic[currentId[1:]] = currentSeq - currentId = line.rstrip() - currentSeq = "" - else: - currentSeq += line.rstrip() - refdic[currentId[1:]] = currentSeq - -currentSeq = "" -currentId = "" -with xlrd.open_workbook(args.input, 'r') as wb: - with open(args.output, 'a') as o: - for sheet in wb.sheets(): - if sheet.cell(1,gene_column).value.find("IGHV") < 0: - print "Genes not in column " + LETTERS[gene_column] + ", skipping sheet " + sheet.name - continue - o.write(">>>" + sheet.name + "\n") - outputdic = dict() - for rowindex in range(1, sheet.nrows): - ref = sheet.cell(rowindex, gene_column).value.replace(">", "") - if ref in outputdic: - outputdic[ref] += [(sheet.cell(rowindex, id_column).value.replace(">", ""), sheet.cell(rowindex, seq_column).value)] - else: - outputdic[ref] = [(sheet.cell(rowindex, id_column).value.replace(">", ""), sheet.cell(rowindex, seq_column).value)] - #print outputdic - - for k in outputdic.keys(): - if k in refdic: - o.write(">>" + k + "\n") - o.write(refdic[k] + "\n") - for seq in outputdic[k]: - #print seq - o.write(">" + seq[0] + "\n") - o.write(seq[1] + "\n") - else: - print k + " not in reference, skipping " + k
--- a/baseline/wrapper.sh Tue Sep 01 16:03:44 2020 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,92 +0,0 @@ -#!/bin/bash -dir="$(cd "$(dirname "$0")" && pwd)" - -testID=$1 -species=$2 -substitutionModel=$3 -mutabilityModel=$4 -clonal=$5 -fixIndels=$6 -region=$7 -inputs=$8 -inputs=($inputs) -IDs=$9 -IDs=($IDs) -ref=${10} -output=${11} -selection=${12} -output_table=${13} -outID="result" - -echo "$PWD" - -echo "testID = $testID" -echo "species = $species" -echo "substitutionModel = $substitutionModel" -echo "mutabilityModel = $mutabilityModel" -echo "clonal = $clonal" -echo "fixIndels = $fixIndels" -echo "region = $region" -echo "inputs = ${inputs[@]}" -echo "IDs = ${IDs[@]}" -echo "ref = $ref" -echo "output = $output" -echo "outID = $outID" - -fasta="$PWD/baseline.fasta" - - -count=0 -for current in ${inputs[@]} -do - f=$(file $current) - zipType="Zip archive" - if [[ "$f" == *"Zip archive"* ]] || [[ "$f" == *"XZ compressed data"* ]] - then - id=${IDs[$count]} - echo "id=$id" - if [[ "$f" == *"Zip archive"* ]] ; then - echo "Zip archive" - echo "unzip $input -d $PWD/files/" - unzip $current -d "$PWD/$id/" - elif [[ "$f" == *"XZ compressed data"* ]] ; then - echo "ZX archive" - echo "tar -xJf $input -C $PWD/files/" - mkdir -p "$PWD/$id/files" - tar -xJf $current -C "$PWD/$id/files/" - fi - filtered="$PWD/filtered_${id}.txt" - imgt_1_file="`find $PWD/$id -name '1_*.txt'`" - imgt_2_file="`find $PWD/$id -name '2_*.txt'`" - echo "1_Summary file: ${imgt_1_file}" - echo "2_IMGT-gapped file: ${imgt_2_file}" - echo "filter.r for $id" - Rscript $dir/filter.r ${imgt_1_file} ${imgt_2_file} "$selection" $filtered 2>&1 - - final="$PWD/final_${id}.txt" - cat $filtered | cut -f2,4,7 > $final - python $dir/script_imgt.py --input $final --ref $ref --output $fasta --id $id - else - python $dir/script_xlsx.py --input $current --ref $ref --output $fasta - fi - count=$((count+1)) -done -workdir="$PWD" -cd $dir -echo "file: ${inputs[0]}" -#Rscript --verbose $dir/Baseline_Main.r $testID $species $substitutionModel $mutabilityModel $clonal $fixIndels $region ${inputs[0]} $workdir/ $outID 2>&1 -Rscript --verbose $dir/Baseline_Main.r $testID $species $substitutionModel $mutabilityModel $clonal $fixIndels $region $fasta $workdir/ $outID 2>&1 - -echo "$workdir/${outID}.txt" - -rows=`tail -n +2 $workdir/${outID}.txt | grep -v "All sequences combined" | grep -n 'Group' | grep -Eoh '^[0-9]+' | tr '\n' ' '` -rows=($rows) -#unset rows[${#rows[@]}-1] - -cd $dir -Rscript --verbose $dir/comparePDFs.r $workdir/${outID}.RData $output ${rows[@]} 2>&1 -cp $workdir/result.txt ${output_table} - - - -
--- a/change_o/change_o_url.txt Tue Sep 01 16:03:44 2020 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,1 +0,0 @@ -https://changeo.readthedocs.io/en/version-0.4.4/ \ No newline at end of file
--- a/change_o/define_clones.r Tue Sep 01 16:03:44 2020 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,15 +0,0 @@ -args <- commandArgs(trailingOnly = TRUE) - -input=args[1] -output=args[2] - -change.o = read.table(input, header=T, sep="\t", quote="", stringsAsFactors=F) - -freq = data.frame(table(change.o$CLONE)) -freq2 = data.frame(table(freq$Freq)) - -freq2$final = as.numeric(freq2$Freq) * as.numeric(as.character(freq2$Var1)) - -names(freq2) = c("Clone size", "Nr of clones", "Nr of sequences") - -write.table(x=freq2, file=output, sep="\t",quote=F,row.names=F,col.names=T)
--- a/change_o/define_clones.sh Tue Sep 01 16:03:44 2020 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,39 +0,0 @@ -#!/bin/bash -dir="$(cd "$(dirname "$0")" && pwd)" - -#define_clones.sh $input $noparse $scores $regions $out_file - -type=$1 -input=$2 - -mkdir -p $PWD/outdir - -cp $input $PWD/input.tab #file has to have a ".tab" extension - -if [ "bygroup" == "$type" ] ; then - mode=$3 - act=$4 - model=$5 - norm=$6 - sym=$7 - link=$8 - dist=$9 - output=${10} - output2=${11} - - DefineClones.py -d $PWD/input.tab --nproc 4 --outdir $PWD/outdir --outname output --mode $mode --act $act --model $model --dist $dist --norm $norm --sym $sym --link $link - - Rscript $dir/define_clones.r $PWD/outdir/output_clone-pass.tab $output2 2>&1 -else - method=$3 - output=$4 - output2=$5 - - DefineClones.py hclust -d $PWD/input.tab --nproc 4 --outdir $PWD/outdir --outname output --method $method - - Rscript $dir/define_clones.r $PWD/outdir/output_clone-pass.tab $output2 2>&1 -fi - -cp $PWD/outdir/output_clone-pass.tab $output - -rm -rf $PWD/outdir/
--- a/change_o/makedb.sh Tue Sep 01 16:03:44 2020 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,36 +0,0 @@ -#!/bin/bash -dir="$(cd "$(dirname "$0")" && pwd)" - -input=$1 -noparse=$2 -scores=$3 -regions=$4 -output=$5 - -if [ "true" == "$noparse" ] ; then - noparse="--noparse" -else - noparse="" -fi - -if [ "true" == "$scores" ] ; then - scores="--scores" -else - scores="" -fi - -if [ "true" == "$regions" ] ; then - regions="--regions" -else - regions="" -fi - -mkdir $PWD/outdir - -echo "makedb: $PWD/outdir" - -MakeDb.py imgt -i $input --outdir $PWD/outdir --outname output $noparse $scores $regions - -mv $PWD/outdir/output_db-pass.tab $output - -rm -rf $PWD/outdir/
--- a/change_o/select_first_in_clone.r Tue Sep 01 16:03:44 2020 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,16 +0,0 @@ -args <- commandArgs(trailingOnly = TRUE) - -input.file = args[1] -output.file = args[2] - -print("select_in_first_clone.r") -print(input.file) -print(output.file) - -input = read.table(input.file, header=T, sep="\t", fill=T, stringsAsFactors=F, quote="") - -input = input[!duplicated(input$CLONE),] - -names(input)[1] = "Sequence.ID" - -write.table(input, output.file, quote=F, sep="\t", row.names=F, col.names=T, na="")
--- a/check_unique_id.r Tue Sep 01 16:03:44 2020 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,25 +0,0 @@ -args <- commandArgs(trailingOnly = TRUE) #first argument must be the summary file so it can grab the - -current_file = args[1] - -current = read.table(current_file, header=T, sep="\t", fill=T, stringsAsFactors=F, quote="", check.names=F) - -if(!("Sequence number" %in% names(current))){ - stop("First argument doesn't contain the 'Sequence number' column") -} - -tbl = table(current[,"Sequence ID"]) -l_tbl = length(tbl) -check = any(tbl > 1) - -#if(l_tbl != nrow(current)){ # non unique IDs? -if(check){ - print("Sequence.ID is not unique for every sequence, adding sequence number to IDs") - for(i in 1:length(args)){ - current_file = args[i] - print(paste("Appending 'Sequence number' column to 'Sequence ID' column in", current_file)) - current = read.table(current_file, header=T, sep="\t", fill=T, stringsAsFactors=F, quote="", check.names=F) - current[,"Sequence ID"] = paste(current[,"Sequence ID"], current[,"Sequence number"], sep="_") - write.table(x = current, file = current_file, quote = F, sep = "\t", na = "", row.names = F, col.names = T) - } -}
--- a/datatypes_conf.xml Tue Sep 01 16:03:44 2020 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,6 +0,0 @@ -<?xml version="1.0"?> -<datatypes> - <registration> - <datatype extension="imgt_archive" type="galaxy.datatypes.binary:CompressedArchive" display_in_upload="True" subclass="True"/> - </registration> -</datatypes>
--- a/gene_identification.py Tue Sep 01 16:03:44 2020 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,226 +0,0 @@ -import re -import argparse -import time -starttime= int(time.time() * 1000) - -parser = argparse.ArgumentParser() -parser.add_argument("--input", help="The 1_Summary file from an IMGT zip file") -parser.add_argument("--output", help="The annotated output file to be merged back with the summary file") - -args = parser.parse_args() - -infile = args.input -#infile = "test_VH-Ca_Cg_25nt/1_Summary_test_VH-Ca_Cg_25nt_241013.txt" -output = args.output -#outfile = "identified.txt" - -dic = dict() -total = 0 - - -first = True -IDIndex = 0 -seqIndex = 0 - -with open(infile, 'r') as f: #read all sequences into a dictionary as key = ID, value = sequence - for line in f: - total += 1 - linesplt = line.split("\t") - if first: - print "linesplt", linesplt - IDIndex = linesplt.index("Sequence ID") - seqIndex = linesplt.index("Sequence") - first = False - continue - - ID = linesplt[IDIndex] - if len(linesplt) < 28: #weird rows without a sequence - dic[ID] = "" - else: - dic[ID] = linesplt[seqIndex] - -print "Number of input sequences:", len(dic) - -#old cm sequence: gggagtgcatccgccccaacccttttccccctcgtctcctgtgagaattccc -#old cg sequence: ctccaccaagggcccatcggtcttccccctggcaccctcctccaagagcacctctgggggcacagcggccctgggctgcctggtcaaggactacttccccgaaccggtgacggtgtcgtggaactcaggcgccctgaccag - -#lambda/kappa reference sequence -searchstrings = {"ca": "catccccgaccagccccaaggtcttcccgctgagcctctgcagcacccagccagatgggaacgtggtcatcgcctgcctgg", - "cg": "ctccaccaagggcccatcggtcttccccctggcaccctcctccaagagcacctctgggggcacagcggcc", - "ce": "gcctccacacagagcccatccgtcttccccttgacccgctgctgcaaaaacattccctcc", - "cm": "gggagtgcatccgccccaacc"} #new (shorter) cm sequence - -compiledregex = {"ca": [], - "cg": [], - "ce": [], - "cm": []} - -#lambda/kappa reference sequence variable nucleotides -ca1 = {38: 't', 39: 'g', 48: 'a', 49: 'g', 51: 'c', 68: 'a', 73: 'c'} -ca2 = {38: 'g', 39: 'a', 48: 'c', 49: 'c', 51: 'a', 68: 'g', 73: 'a'} -cg1 = {0: 'c', 33: 'a', 38: 'c', 44: 'a', 54: 't', 56: 'g', 58: 'g', 66: 'g', 132: 'c'} -cg2 = {0: 'c', 33: 'g', 38: 'g', 44: 'g', 54: 'c', 56: 'a', 58: 'a', 66: 'g', 132: 't'} -cg3 = {0: 't', 33: 'g', 38: 'g', 44: 'g', 54: 't', 56: 'g', 58: 'g', 66: 'g', 132: 'c'} -cg4 = {0: 't', 33: 'g', 38: 'g', 44: 'g', 54: 'c', 56: 'a', 58: 'a', 66: 'c', 132: 'c'} - -#remove last snp for shorter cg sequence --- note, also change varsInCG -del cg1[132] -del cg2[132] -del cg3[132] -del cg4[132] - -#reference sequences are cut into smaller parts of 'chunklength' length, and with 'chunklength' / 2 overlap -chunklength = 8 - -#create the chunks of the reference sequence with regular expressions for the variable nucleotides -for i in range(0, len(searchstrings["ca"]) - chunklength, chunklength / 2): - pos = i - chunk = searchstrings["ca"][i:i+chunklength] - result = "" - varsInResult = 0 - for c in chunk: - if pos in ca1.keys(): - varsInResult += 1 - result += "[" + ca1[pos] + ca2[pos] + "]" - else: - result += c - pos += 1 - compiledregex["ca"].append((re.compile(result), varsInResult)) - -for i in range(0, len(searchstrings["cg"]) - chunklength, chunklength / 2): - pos = i - chunk = searchstrings["cg"][i:i+chunklength] - result = "" - varsInResult = 0 - for c in chunk: - if pos in cg1.keys(): - varsInResult += 1 - result += "[" + "".join(set([cg1[pos], cg2[pos], cg3[pos], cg4[pos]])) + "]" - else: - result += c - pos += 1 - compiledregex["cg"].append((re.compile(result), varsInResult)) - -for i in range(0, len(searchstrings["cm"]) - chunklength, chunklength / 2): - compiledregex["cm"].append((re.compile(searchstrings["cm"][i:i+chunklength]), False)) - -for i in range(0, len(searchstrings["ce"]) - chunklength + 1, chunklength / 2): - compiledregex["ce"].append((re.compile(searchstrings["ce"][i:i+chunklength]), False)) - -def removeAndReturnMaxIndex(x): #simplifies a list comprehension - m = max(x) - index = x.index(m) - x[index] = 0 - return index - - -start_location = dict() -hits = dict() -alltotal = 0 -for key in compiledregex.keys(): #for ca/cg/cm/ce - regularexpressions = compiledregex[key] #get the compiled regular expressions - for ID in dic.keys()[0:]: #for every ID - if ID not in hits.keys(): #ensure that the dictionairy that keeps track of the hits for every gene exists - hits[ID] = {"ca_hits": 0, "cg_hits": 0, "cm_hits": 0, "ce_hits": 0, "ca1": 0, "ca2": 0, "cg1": 0, "cg2": 0, "cg3": 0, "cg4": 0} - currentIDHits = hits[ID] - seq = dic[ID] - lastindex = 0 - start_zero = len(searchstrings[key]) #allows the reference sequence to start before search sequence (start_locations of < 0) - start = [0] * (len(seq) + start_zero) - for i, regexp in enumerate(regularexpressions): #for every regular expression - relativeStartLocation = lastindex - (chunklength / 2) * i - if relativeStartLocation >= len(seq): - break - regex, hasVar = regexp - matches = regex.finditer(seq[lastindex:]) - for match in matches: #for every match with the current regex, only uses the first hit because of the break at the end of this loop - lastindex += match.start() - start[relativeStartLocation + start_zero] += 1 - if hasVar: #if the regex has a variable nt in it - chunkstart = chunklength / 2 * i #where in the reference does this chunk start - chunkend = chunklength / 2 * i + chunklength #where in the reference does this chunk end - if key == "ca": #just calculate the variable nt score for 'ca', cheaper - currentIDHits["ca1"] += len([1 for x in ca1 if chunkstart <= x < chunkend and ca1[x] == seq[lastindex + x - chunkstart]]) - currentIDHits["ca2"] += len([1 for x in ca2 if chunkstart <= x < chunkend and ca2[x] == seq[lastindex + x - chunkstart]]) - elif key == "cg": #just calculate the variable nt score for 'cg', cheaper - currentIDHits["cg1"] += len([1 for x in cg1 if chunkstart <= x < chunkend and cg1[x] == seq[lastindex + x - chunkstart]]) - currentIDHits["cg2"] += len([1 for x in cg2 if chunkstart <= x < chunkend and cg2[x] == seq[lastindex + x - chunkstart]]) - currentIDHits["cg3"] += len([1 for x in cg3 if chunkstart <= x < chunkend and cg3[x] == seq[lastindex + x - chunkstart]]) - currentIDHits["cg4"] += len([1 for x in cg4 if chunkstart <= x < chunkend and cg4[x] == seq[lastindex + x - chunkstart]]) - else: #key == "cm" #no variable regions in 'cm' or 'ce' - pass - break #this only breaks when there was a match with the regex, breaking means the 'else:' clause is skipped - else: #only runs if there were no hits - continue - #print "found ", regex.pattern , "at", lastindex, "adding one to", (lastindex - chunklength / 2 * i), "to the start array of", ID, "gene", key, "it's now:", start[lastindex - chunklength / 2 * i] - currentIDHits[key + "_hits"] += 1 - start_location[ID + "_" + key] = str([(removeAndReturnMaxIndex(start) + 1 - start_zero) for x in range(5) if len(start) > 0 and max(start) > 1]) - #start_location[ID + "_" + key] = str(start.index(max(start))) - - -varsInCA = float(len(ca1.keys()) * 2) -varsInCG = float(len(cg1.keys()) * 2) - 2 # -2 because the sliding window doesn't hit the first and last nt twice -varsInCM = 0 -varsInCE = 0 - -def round_int(val): - return int(round(val)) - -first = True -seq_write_count=0 -with open(infile, 'r') as f: #read all sequences into a dictionary as key = ID, value = sequence - with open(output, 'w') as o: - for line in f: - total += 1 - if first: - o.write("Sequence ID\tbest_match\tnt_hit_percentage\tchunk_hit_percentage\tstart_locations\n") - first = False - continue - linesplt = line.split("\t") - if linesplt[2] == "No results": - pass - ID = linesplt[1] - currentIDHits = hits[ID] - possibleca = float(len(compiledregex["ca"])) - possiblecg = float(len(compiledregex["cg"])) - possiblecm = float(len(compiledregex["cm"])) - possiblece = float(len(compiledregex["ce"])) - cahits = currentIDHits["ca_hits"] - cghits = currentIDHits["cg_hits"] - cmhits = currentIDHits["cm_hits"] - cehits = currentIDHits["ce_hits"] - if cahits >= cghits and cahits >= cmhits and cahits >= cehits: #its a ca gene - ca1hits = currentIDHits["ca1"] - ca2hits = currentIDHits["ca2"] - if ca1hits >= ca2hits: - o.write(ID + "\tIGA1\t" + str(round_int(ca1hits / varsInCA * 100)) + "\t" + str(round_int(cahits / possibleca * 100)) + "\t" + start_location[ID + "_ca"] + "\n") - else: - o.write(ID + "\tIGA2\t" + str(round_int(ca2hits / varsInCA * 100)) + "\t" + str(round_int(cahits / possibleca * 100)) + "\t" + start_location[ID + "_ca"] + "\n") - elif cghits >= cahits and cghits >= cmhits and cghits >= cehits: #its a cg gene - cg1hits = currentIDHits["cg1"] - cg2hits = currentIDHits["cg2"] - cg3hits = currentIDHits["cg3"] - cg4hits = currentIDHits["cg4"] - if cg1hits >= cg2hits and cg1hits >= cg3hits and cg1hits >= cg4hits: #cg1 gene - o.write(ID + "\tIGG1\t" + str(round_int(cg1hits / varsInCG * 100)) + "\t" + str(round_int(cghits / possiblecg * 100)) + "\t" + start_location[ID + "_cg"] + "\n") - elif cg2hits >= cg1hits and cg2hits >= cg3hits and cg2hits >= cg4hits: #cg2 gene - o.write(ID + "\tIGG2\t" + str(round_int(cg2hits / varsInCG * 100)) + "\t" + str(round_int(cghits / possiblecg * 100)) + "\t" + start_location[ID + "_cg"] + "\n") - elif cg3hits >= cg1hits and cg3hits >= cg2hits and cg3hits >= cg4hits: #cg3 gene - o.write(ID + "\tIGG3\t" + str(round_int(cg3hits / varsInCG * 100)) + "\t" + str(round_int(cghits / possiblecg * 100)) + "\t" + start_location[ID + "_cg"] + "\n") - else: #cg4 gene - o.write(ID + "\tIGG4\t" + str(round_int(cg4hits / varsInCG * 100)) + "\t" + str(round_int(cghits / possiblecg * 100)) + "\t" + start_location[ID + "_cg"] + "\n") - else: #its a cm or ce gene - if cmhits >= cehits: - o.write(ID + "\tIGM\t100\t" + str(round_int(cmhits / possiblecm * 100)) + "\t" + start_location[ID + "_cm"] + "\n") - else: - o.write(ID + "\tIGE\t100\t" + str(round_int(cehits / possiblece * 100)) + "\t" + start_location[ID + "_ce"] + "\n") - seq_write_count += 1 - -print "Time: %i" % (int(time.time() * 1000) - starttime) - -print "Number of sequences written to file:", seq_write_count - - - - -
--- a/imgt_loader.r Tue Sep 01 16:03:44 2020 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,98 +0,0 @@ -args <- commandArgs(trailingOnly = TRUE) - -summ.file = args[1] -aa.file = args[2] -junction.file = args[3] -out.file = args[4] - -summ = read.table(summ.file, sep="\t", header=T, quote="", fill=T) -aa = read.table(aa.file, sep="\t", header=T, quote="", fill=T) -junction = read.table(junction.file, sep="\t", header=T, quote="", fill=T) - -fix_column_names = function(df){ - if("V.DOMAIN.Functionality" %in% names(df)){ - names(df)[names(df) == "V.DOMAIN.Functionality"] = "Functionality" - print("found V.DOMAIN.Functionality, changed") - } - if("V.DOMAIN.Functionality.comment" %in% names(df)){ - names(df)[names(df) == "V.DOMAIN.Functionality.comment"] = "Functionality.comment" - print("found V.DOMAIN.Functionality.comment, changed") - } - return(df) -} - -summ = fix_column_names(summ) -aa = fix_column_names(aa) -junction = fix_column_names(junction) - -old_summary_columns=c('Sequence.ID','JUNCTION.frame','V.GENE.and.allele','D.GENE.and.allele','J.GENE.and.allele','CDR1.IMGT.length','CDR2.IMGT.length','CDR3.IMGT.length','Orientation') -old_sequence_columns=c('CDR1.IMGT','CDR2.IMGT','CDR3.IMGT') -old_junction_columns=c('JUNCTION') - -added_summary_columns=c('Functionality','V.REGION.identity..','V.REGION.identity.nt','D.REGION.reading.frame','AA.JUNCTION','Functionality.comment','Sequence') -added_sequence_columns=c('FR1.IMGT','FR2.IMGT','FR3.IMGT','CDR3.IMGT','JUNCTION','J.REGION','FR4.IMGT') - -added_junction_columns=c('P3.V.nt.nb','N.REGION.nt.nb','N1.REGION.nt.nb','P5.D.nt.nb','P3.D.nt.nb','N2.REGION.nt.nb','P5.J.nt.nb','X3.V.REGION.trimmed.nt.nb','X5.D.REGION.trimmed.nt.nb','X3.D.REGION.trimmed.nt.nb','X5.J.REGION.trimmed.nt.nb','N.REGION','N1.REGION','N2.REGION') -added_junction_columns=c(added_junction_columns, 'P5.D1.nt.nb', 'P3.D1.nt.nb', 'N2.REGION.nt.nb', 'P5.D2.nt.nb', 'P3.D2.nt.nb', 'N3.REGION.nt.nb', 'P5.D3.nt.nb', 'P3.D2.nt.nb', 'N4.REGION.nt.nb', 'X5.D1.REGION.trimmed.nt.nb', 'X3.D1.REGION.trimmed.nt.nb', 'X5.D2.REGION.trimmed.nt.nb', 'X3.D2.REGION.trimmed.nt.nb', 'X5.D3.REGION.trimmed.nt.nb', 'X3.D3.REGION.trimmed.nt.nb', 'D.REGION.nt.nb', 'D1.REGION.nt.nb', 'D2.REGION.nt.nb', 'D3.REGION.nt.nb') - -out=summ[,c("Sequence.ID","JUNCTION.frame","V.GENE.and.allele","D.GENE.and.allele","J.GENE.and.allele")] - -out[,"CDR1.Seq"] = aa[,"CDR1.IMGT"] -out[,"CDR1.Length"] = summ[,"CDR1.IMGT.length"] - -out[,"CDR2.Seq"] = aa[,"CDR2.IMGT"] -out[,"CDR2.Length"] = summ[,"CDR2.IMGT.length"] - -out[,"CDR3.Seq"] = aa[,"CDR3.IMGT"] -out[,"CDR3.Length"] = summ[,"CDR3.IMGT.length"] - -out[,"CDR3.Seq.DNA"] = junction[,"JUNCTION"] -out[,"CDR3.Length.DNA"] = nchar(as.character(junction[,"JUNCTION"])) -out[,"Strand"] = summ[,"Orientation"] -out[,"CDR3.Found.How"] = "a" - -out[,added_summary_columns] = summ[,added_summary_columns] - -out[,added_sequence_columns] = aa[,added_sequence_columns] - -out[,added_junction_columns] = junction[,added_junction_columns] - -out[,"Top V Gene"] = gsub(".* ", "", gsub("\\*.*", "", summ[,"V.GENE.and.allele"])) -out[,"Top D Gene"] = gsub(".* ", "", gsub("\\*.*", "", summ[,"D.GENE.and.allele"])) -out[,"Top J Gene"] = gsub(".* ", "", gsub("\\*.*", "", summ[,"J.GENE.and.allele"])) - -out = out[,c('Sequence.ID','JUNCTION.frame','Top V Gene','Top D Gene','Top J Gene','CDR1.Seq','CDR1.Length','CDR2.Seq','CDR2.Length','CDR3.Seq','CDR3.Length','CDR3.Seq.DNA','CDR3.Length.DNA','Strand','CDR3.Found.How','Functionality','V.REGION.identity..','V.REGION.identity.nt','D.REGION.reading.frame','AA.JUNCTION','Functionality.comment','Sequence','FR1.IMGT','FR2.IMGT','FR3.IMGT','CDR3.IMGT','JUNCTION','J.REGION','FR4.IMGT','P3.V.nt.nb','N.REGION.nt.nb','N1.REGION.nt.nb','P5.D.nt.nb','P3.D.nt.nb','N2.REGION.nt.nb','P5.J.nt.nb','X3.V.REGION.trimmed.nt.nb','X5.D.REGION.trimmed.nt.nb','X3.D.REGION.trimmed.nt.nb','X5.J.REGION.trimmed.nt.nb','N.REGION','N1.REGION','N2.REGION', 'P5.D1.nt.nb', 'P3.D1.nt.nb', 'N2.REGION.nt.nb', 'P5.D2.nt.nb', 'P3.D2.nt.nb', 'N3.REGION.nt.nb', 'P5.D3.nt.nb', 'P3.D2.nt.nb', 'N4.REGION.nt.nb', 'X5.D1.REGION.trimmed.nt.nb', 'X3.D1.REGION.trimmed.nt.nb', 'X5.D2.REGION.trimmed.nt.nb', 'X3.D2.REGION.trimmed.nt.nb', 'X5.D3.REGION.trimmed.nt.nb', 'X3.D3.REGION.trimmed.nt.nb', 'D.REGION.nt.nb', 'D1.REGION.nt.nb', 'D2.REGION.nt.nb', 'D3.REGION.nt.nb')] - -names(out) = c('ID','VDJ Frame','Top V Gene','Top D Gene','Top J Gene','CDR1 Seq','CDR1 Length','CDR2 Seq','CDR2 Length','CDR3 Seq','CDR3 Length','CDR3 Seq DNA','CDR3 Length DNA','Strand','CDR3 Found How','Functionality','V-REGION identity %','V-REGION identity nt','D-REGION reading frame','AA JUNCTION','Functionality comment','Sequence','FR1-IMGT','FR2-IMGT','FR3-IMGT','CDR3-IMGT','JUNCTION','J-REGION','FR4-IMGT','P3V-nt nb','N-REGION-nt nb','N1-REGION-nt nb','P5D-nt nb','P3D-nt nb','N2-REGION-nt nb','P5J-nt nb','3V-REGION trimmed-nt nb','5D-REGION trimmed-nt nb','3D-REGION trimmed-nt nb','5J-REGION trimmed-nt nb','N-REGION','N1-REGION','N2-REGION', 'P5.D1.nt.nb', 'P3.D1.nt.nb', 'N2.REGION.nt.nb', 'P5.D2.nt.nb', 'P3.D2.nt.nb', 'N3.REGION.nt.nb', 'P5.D3.nt.nb', 'P3.D2.nt.nb', 'N4.REGION.nt.nb', 'X5.D1.REGION.trimmed.nt.nb', 'X3.D1.REGION.trimmed.nt.nb', 'X5.D2.REGION.trimmed.nt.nb', 'X3.D2.REGION.trimmed.nt.nb', 'X5.D3.REGION.trimmed.nt.nb', 'X3.D3.REGION.trimmed.nt.nb', 'D.REGION.nt.nb', 'D1.REGION.nt.nb', 'D2.REGION.nt.nb', 'D3.REGION.nt.nb') - -out[,"VDJ Frame"] = as.character(out[,"VDJ Frame"]) - -fltr = out[,"VDJ Frame"] == "in-frame" -if(any(fltr, na.rm = T)){ - out[fltr, "VDJ Frame"] = "In-frame" -} - -fltr = out[,"VDJ Frame"] == "null" -if(any(fltr, na.rm = T)){ - out[fltr, "VDJ Frame"] = "Out-of-frame" -} - -fltr = out[,"VDJ Frame"] == "out-of-frame" -if(any(fltr, na.rm = T)){ - out[fltr, "VDJ Frame"] = "Out-of-frame" -} - -fltr = out[,"VDJ Frame"] == "" -if(any(fltr, na.rm = T)){ - out[fltr, "VDJ Frame"] = "Out-of-frame" -} - -for(col in c('Top V Gene','Top D Gene','Top J Gene')){ - out[,col] = as.character(out[,col]) - fltr = out[,col] == "" - if(any(fltr, na.rm = T)){ - out[fltr,col] = "NA" - } -} - -write.table(out, out.file, sep="\t", quote=F, row.names=F, col.names=T)
--- a/merge.r Tue Sep 01 16:03:44 2020 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,27 +0,0 @@ -args <- commandArgs(trailingOnly = TRUE) - -input.1 = args[1] -input.2 = args[2] - -fields.1 = args[3] -fields.2 = args[4] - -field.1 = args[5] -field.2 = args[6] - -output = args[7] - -dat1 = read.table(input.1, header=T, sep="\t", quote="", stringsAsFactors=F, fill=T, row.names=NULL) -if(fields.1 != "all"){ - fields.1 = unlist(strsplit(fields.1, ",")) - dat1 = dat1[,fields.1] -} -dat2 = read.table(input.2, header=T, sep="\t", quote="", stringsAsFactors=F, fill=T, row.names=NULL) -if(fields.2 != "all"){ - fields.2 = unlist(strsplit(fields.2, ",")) - dat2 = dat2[,fields.2] -} - -dat3 = merge(dat1, dat2, by.x=field.1, by.y=field.2) - -write.table(dat3, output, sep="\t",quote=F,row.names=F,col.names=T)
--- a/merge_and_filter.r Tue Sep 01 16:03:44 2020 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,304 +0,0 @@ -args <- commandArgs(trailingOnly = TRUE) - - -summaryfile = args[1] -sequencesfile = args[2] -mutationanalysisfile = args[3] -mutationstatsfile = args[4] -hotspotsfile = args[5] -aafile = args[6] -gene_identification_file= args[7] -output = args[8] -before.unique.file = args[9] -unmatchedfile = args[10] -method=args[11] -functionality=args[12] -unique.type=args[13] -filter.unique=args[14] -filter.unique.count=as.numeric(args[15]) -class.filter=args[16] -empty.region.filter=args[17] - -print(paste("filter.unique.count:", filter.unique.count)) - -summ = read.table(summaryfile, header=T, sep="\t", fill=T, stringsAsFactors=F, quote="") -sequences = read.table(sequencesfile, header=T, sep="\t", fill=T, stringsAsFactors=F, quote="") -mutationanalysis = read.table(mutationanalysisfile, header=T, sep="\t", fill=T, stringsAsFactors=F, quote="") -mutationstats = read.table(mutationstatsfile, header=T, sep="\t", fill=T, stringsAsFactors=F, quote="") -hotspots = read.table(hotspotsfile, header=T, sep="\t", fill=T, stringsAsFactors=F, quote="") -AAs = read.table(aafile, header=T, sep="\t", fill=T, stringsAsFactors=F, quote="") -gene_identification = read.table(gene_identification_file, header=T, sep="\t", fill=T, stringsAsFactors=F, quote="") - -fix_column_names = function(df){ - if("V.DOMAIN.Functionality" %in% names(df)){ - names(df)[names(df) == "V.DOMAIN.Functionality"] = "Functionality" - print("found V.DOMAIN.Functionality, changed") - } - if("V.DOMAIN.Functionality.comment" %in% names(df)){ - names(df)[names(df) == "V.DOMAIN.Functionality.comment"] = "Functionality.comment" - print("found V.DOMAIN.Functionality.comment, changed") - } - return(df) -} - -fix_non_unique_ids = function(df){ - df$Sequence.ID = paste(df$Sequence.ID, 1:nrow(df)) - return(df) -} - -summ = fix_column_names(summ) -sequences = fix_column_names(sequences) -mutationanalysis = fix_column_names(mutationanalysis) -mutationstats = fix_column_names(mutationstats) -hotspots = fix_column_names(hotspots) -AAs = fix_column_names(AAs) - -if(method == "blastn"){ - #"qseqid\tsseqid\tpident\tlength\tmismatch\tgapopen\tqstart\tqend\tsstart\tsend\tevalue\tbitscore" - gene_identification = gene_identification[!duplicated(gene_identification$qseqid),] - ref_length = data.frame(sseqid=c("ca1", "ca2", "cg1", "cg2", "cg3", "cg4", "cm"), ref.length=c(81,81,141,141,141,141,52)) - gene_identification = merge(gene_identification, ref_length, by="sseqid", all.x=T) - gene_identification$chunk_hit_percentage = (gene_identification$length / gene_identification$ref.length) * 100 - gene_identification = gene_identification[,c("qseqid", "chunk_hit_percentage", "pident", "qstart", "sseqid")] - colnames(gene_identification) = c("Sequence.ID", "chunk_hit_percentage", "nt_hit_percentage", "start_locations", "best_match") -} - -#print("Summary analysis files columns") -#print(names(summ)) - - - -input.sequence.count = nrow(summ) -print(paste("Number of sequences in summary file:", input.sequence.count)) - -filtering.steps = data.frame(character(0), numeric(0)) - -filtering.steps = rbind(filtering.steps, c("Input", input.sequence.count)) - -filtering.steps[,1] = as.character(filtering.steps[,1]) -filtering.steps[,2] = as.character(filtering.steps[,2]) -#filtering.steps[,3] = as.numeric(filtering.steps[,3]) - -#print("summary files columns") -#print(names(summ)) - -summ = merge(summ, gene_identification, by="Sequence.ID") - -print(paste("Number of sequences after merging with gene identification:", nrow(summ))) - -summ = summ[summ$Functionality != "No results",] - -print(paste("Number of sequences after 'No results' filter:", nrow(summ))) - -filtering.steps = rbind(filtering.steps, c("After 'No results' filter", nrow(summ))) - -if(functionality == "productive"){ - summ = summ[summ$Functionality == "productive (see comment)" | summ$Functionality == "productive",] -} else if (functionality == "unproductive"){ - summ = summ[summ$Functionality == "unproductive (see comment)" | summ$Functionality == "unproductive",] -} else if (functionality == "remove_unknown"){ - summ = summ[summ$Functionality != "No results" & summ$Functionality != "unknown (see comment)" & summ$Functionality != "unknown",] -} - -print(paste("Number of sequences after functionality filter:", nrow(summ))) - -filtering.steps = rbind(filtering.steps, c("After functionality filter", nrow(summ))) - -if(F){ #to speed up debugging - set.seed(1) - summ = summ[sample(nrow(summ), floor(nrow(summ) * 0.03)),] - print(paste("Number of sequences after sampling 3%:", nrow(summ))) - - filtering.steps = rbind(filtering.steps, c("Number of sequences after sampling 3%", nrow(summ))) -} - -print("mutation analysis files columns") -print(names(mutationanalysis[,!(names(mutationanalysis) %in% names(summ)[-1])])) - -result = merge(summ, mutationanalysis[,!(names(mutationanalysis) %in% names(summ)[-1])], by="Sequence.ID") - -print(paste("Number of sequences after merging with mutation analysis file:", nrow(result))) - -#print("mutation stats files columns") -#print(names(mutationstats[,!(names(mutationstats) %in% names(result)[-1])])) - -result = merge(result, mutationstats[,!(names(mutationstats) %in% names(result)[-1])], by="Sequence.ID") - -print(paste("Number of sequences after merging with mutation stats file:", nrow(result))) - -print("hotspots files columns") -print(names(hotspots[,!(names(hotspots) %in% names(result)[-1])])) - -result = merge(result, hotspots[,!(names(hotspots) %in% names(result)[-1])], by="Sequence.ID") - -print(paste("Number of sequences after merging with hotspots file:", nrow(result))) - -print("sequences files columns") -print(c("FR1.IMGT", "CDR1.IMGT", "FR2.IMGT", "CDR2.IMGT", "FR3.IMGT", "CDR3.IMGT")) - -sequences = sequences[,c("Sequence.ID", "FR1.IMGT", "CDR1.IMGT", "FR2.IMGT", "CDR2.IMGT", "FR3.IMGT", "CDR3.IMGT")] -names(sequences) = c("Sequence.ID", "FR1.IMGT.seq", "CDR1.IMGT.seq", "FR2.IMGT.seq", "CDR2.IMGT.seq", "FR3.IMGT.seq", "CDR3.IMGT.seq") -result = merge(result, sequences, by="Sequence.ID", all.x=T) - -AAs = AAs[,c("Sequence.ID", "CDR3.IMGT")] -names(AAs) = c("Sequence.ID", "CDR3.IMGT.AA") -result = merge(result, AAs, by="Sequence.ID", all.x=T) - -print(paste("Number of sequences in result after merging with sequences:", nrow(result))) - -result$VGene = gsub("^Homsap ", "", result$V.GENE.and.allele) -result$VGene = gsub("[*].*", "", result$VGene) -result$DGene = gsub("^Homsap ", "", result$D.GENE.and.allele) -result$DGene = gsub("[*].*", "", result$DGene) -result$JGene = gsub("^Homsap ", "", result$J.GENE.and.allele) -result$JGene = gsub("[*].*", "", result$JGene) - -splt = strsplit(class.filter, "_")[[1]] -chunk_hit_threshold = as.numeric(splt[1]) -nt_hit_threshold = as.numeric(splt[2]) - -higher_than=(result$chunk_hit_percentage >= chunk_hit_threshold & result$nt_hit_percentage >= nt_hit_threshold) - -if(!all(higher_than, na.rm=T)){ #check for no unmatched - result[!higher_than,"best_match"] = paste("unmatched,", result[!higher_than,"best_match"]) -} - -if(class.filter == "101_101"){ - result$best_match = "all" -} - -write.table(x=result, file=gsub("merged.txt$", "before_filters.txt", output), sep="\t",quote=F,row.names=F,col.names=T) - -print(paste("Number of empty CDR1 sequences:", sum(result$CDR1.IMGT.seq == "", na.rm=T))) -print(paste("Number of empty FR2 sequences:", sum(result$FR2.IMGT.seq == "", na.rm=T))) -print(paste("Number of empty CDR2 sequences:", sum(result$CDR2.IMGT.seq == "", na.rm=T))) -print(paste("Number of empty FR3 sequences:", sum(result$FR3.IMGT.seq == "", na.rm=T))) - -if(empty.region.filter == "leader"){ - result = result[result$FR1.IMGT.seq != "" & result$CDR1.IMGT.seq != "" & result$FR2.IMGT.seq != "" & result$CDR2.IMGT.seq != "" & result$FR3.IMGT.seq != "", ] -} else if(empty.region.filter == "FR1"){ - result = result[result$CDR1.IMGT.seq != "" & result$FR2.IMGT.seq != "" & result$CDR2.IMGT.seq != "" & result$FR3.IMGT.seq != "", ] -} else if(empty.region.filter == "CDR1"){ - result = result[result$FR2.IMGT.seq != "" & result$CDR2.IMGT.seq != "" & result$FR3.IMGT.seq != "", ] -} else if(empty.region.filter == "FR2"){ - result = result[result$CDR2.IMGT.seq != "" & result$FR3.IMGT.seq != "", ] -} - -print(paste("After removal sequences that are missing a gene region:", nrow(result))) -filtering.steps = rbind(filtering.steps, c("After removal sequences that are missing a gene region", nrow(result))) - -if(empty.region.filter == "leader"){ - result = result[!(grepl("n|N", result$FR1.IMGT.seq) | grepl("n|N", result$FR2.IMGT.seq) | grepl("n|N", result$FR3.IMGT.seq) | grepl("n|N", result$CDR1.IMGT.seq) | grepl("n|N", result$CDR2.IMGT.seq) | grepl("n|N", result$CDR3.IMGT.seq)),] -} else if(empty.region.filter == "FR1"){ - result = result[!(grepl("n|N", result$FR2.IMGT.seq) | grepl("n|N", result$FR3.IMGT.seq) | grepl("n|N", result$CDR1.IMGT.seq) | grepl("n|N", result$CDR2.IMGT.seq) | grepl("n|N", result$CDR3.IMGT.seq)),] -} else if(empty.region.filter == "CDR1"){ - result = result[!(grepl("n|N", result$FR2.IMGT.seq) | grepl("n|N", result$FR3.IMGT.seq) | grepl("n|N", result$CDR2.IMGT.seq) | grepl("n|N", result$CDR3.IMGT.seq)),] -} else if(empty.region.filter == "FR2"){ - result = result[!(grepl("n|N", result$FR3.IMGT.seq) | grepl("n|N", result$CDR2.IMGT.seq) | grepl("n|N", result$CDR3.IMGT.seq)),] -} - -print(paste("Number of sequences in result after n filtering:", nrow(result))) -filtering.steps = rbind(filtering.steps, c("After N filter", nrow(result))) - -cleanup_columns = c("FR1.IMGT.Nb.of.mutations", - "CDR1.IMGT.Nb.of.mutations", - "FR2.IMGT.Nb.of.mutations", - "CDR2.IMGT.Nb.of.mutations", - "FR3.IMGT.Nb.of.mutations") - -for(col in cleanup_columns){ - result[,col] = gsub("\\(.*\\)", "", result[,col]) - result[,col] = as.numeric(result[,col]) - result[is.na(result[,col]),] = 0 -} - -write.table(result, before.unique.file, sep="\t", quote=F,row.names=F,col.names=T) - - -if(filter.unique != "no"){ - clmns = names(result) - if(filter.unique == "remove_vjaa"){ - result$unique.def = paste(result$VGene, result$JGene, result$CDR3.IMGT.AA) - } else if(empty.region.filter == "leader"){ - result$unique.def = paste(result$FR1.IMGT.seq, result$CDR1.IMGT.seq, result$FR2.IMGT.seq, result$CDR2.IMGT.seq, result$FR3.IMGT.seq, result$CDR3.IMGT.seq) - } else if(empty.region.filter == "FR1"){ - result$unique.def = paste(result$CDR1.IMGT.seq, result$FR2.IMGT.seq, result$CDR2.IMGT.seq, result$FR3.IMGT.seq, result$CDR3.IMGT.seq) - } else if(empty.region.filter == "CDR1"){ - result$unique.def = paste(result$FR2.IMGT.seq, result$CDR2.IMGT.seq, result$FR3.IMGT.seq, result$CDR3.IMGT.seq) - } else if(empty.region.filter == "FR2"){ - result$unique.def = paste(result$CDR2.IMGT.seq, result$FR3.IMGT.seq, result$CDR3.IMGT.seq) - } - - if(grepl("remove", filter.unique)){ - result = result[duplicated(result$unique.def) | duplicated(result$unique.def, fromLast=T),] - unique.defs = data.frame(table(result$unique.def)) - unique.defs = unique.defs[unique.defs$Freq >= filter.unique.count,] - result = result[result$unique.def %in% unique.defs$Var1,] - } - - if(filter.unique != "remove_vjaa"){ - result$unique.def = paste(result$unique.def, gsub(",.*", "", result$best_match)) #keep the unique sequences that are in multiple classes, gsub so the unmatched don't have a class after it - } - - result = result[!duplicated(result$unique.def),] -} - -write.table(result, gsub("before_unique_filter.txt", "after_unique_filter.txt", before.unique.file), sep="\t", quote=F,row.names=F,col.names=T) - -filtering.steps = rbind(filtering.steps, c("After filter unique sequences", nrow(result))) - -print(paste("Number of sequences in result after unique filtering:", nrow(result))) - -if(nrow(summ) == 0){ - stop("No data remaining after filter") -} - -result$best_match_class = gsub(",.*", "", result$best_match) #gsub so the unmatched don't have a class after it - -#result$past = "" -#cls = unlist(strsplit(unique.type, ",")) -#for (i in 1:nrow(result)){ -# result[i,"past"] = paste(result[i,cls], collapse=":") -#} - - - -result$past = do.call(paste, c(result[unlist(strsplit(unique.type, ","))], sep = ":")) - -result.matched = result[!grepl("unmatched", result$best_match),] -result.unmatched = result[grepl("unmatched", result$best_match),] - -result = rbind(result.matched, result.unmatched) - -result = result[!(duplicated(result$past)), ] - -result = result[,!(names(result) %in% c("past", "best_match_class"))] - -print(paste("Number of sequences in result after", unique.type, "filtering:", nrow(result))) - -filtering.steps = rbind(filtering.steps, c("After remove duplicates based on filter", nrow(result))) - -unmatched = result[grepl("^unmatched", result$best_match),c("Sequence.ID", "chunk_hit_percentage", "nt_hit_percentage", "start_locations", "best_match")] - -print(paste("Number of rows in result:", nrow(result))) -print(paste("Number of rows in unmatched:", nrow(unmatched))) - -matched.sequences = result[!grepl("^unmatched", result$best_match),] - -write.table(x=matched.sequences, file=gsub("merged.txt$", "filtered.txt", output), sep="\t",quote=F,row.names=F,col.names=T) - -matched.sequences.count = nrow(matched.sequences) -unmatched.sequences.count = sum(grepl("^unmatched", result$best_match)) -if(matched.sequences.count <= unmatched.sequences.count){ - print("WARNING NO MATCHED (SUB)CLASS SEQUENCES!!") -} - -filtering.steps = rbind(filtering.steps, c("Number of matched sequences", matched.sequences.count)) -filtering.steps = rbind(filtering.steps, c("Number of unmatched sequences", unmatched.sequences.count)) -filtering.steps[,2] = as.numeric(filtering.steps[,2]) -filtering.steps$perc = round(filtering.steps[,2] / input.sequence.count * 100, 2) - -write.table(x=filtering.steps, file=gsub("unmatched", "filtering_steps", unmatchedfile), sep="\t",quote=F,row.names=F,col.names=F) - -write.table(x=result, file=output, sep="\t",quote=F,row.names=F,col.names=T) -write.table(x=unmatched, file=unmatchedfile, sep="\t",quote=F,row.names=F,col.names=T)
--- a/mutation_column_checker.py Tue Sep 01 16:03:44 2020 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,27 +0,0 @@ -import re - -mutationMatcher = re.compile("^([nactg])(\d+).([nactg]),?[ ]?([A-Z])?(\d+)?[>]?([A-Z;])?(.*)?") - -with open("7_V-REGION-mutation-and-AA-change-table.txt", 'r') as file_handle: - first = True - fr3_index = -1 - for i, line in enumerate(file_handle): - line_split = line.split("\t") - if first: - fr3_index = line_split.index("FR3-IMGT") - first = False - continue - - if len(line_split) < fr3_index: - continue - - fr3_data = line_split[fr3_index] - if len(fr3_data) > 5: - try: - test = [mutationMatcher.match(x).groups() for x in fr3_data.split("|") if x] - except: - print(line_split[1]) - print("Something went wrong at line {line} with:".format(line=line_split[0])) - #print([x for x in fr3_data.split("|") if not mutationMatcher.match(x)]) - if i % 100000 == 0: - print(i)
--- a/naive_output.r Tue Sep 01 16:03:44 2020 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,45 +0,0 @@ -args <- commandArgs(trailingOnly = TRUE) - -naive.file = args[1] -shm.file = args[2] -output.file.ca = args[3] -output.file.cg = args[4] -output.file.cm = args[5] - -naive = read.table(naive.file, sep="\t", header=T, quote="", fill=T) -shm.merge = read.table(shm.file, sep="\t", header=T, quote="", fill=T) - - -final = merge(naive, shm.merge[,c("Sequence.ID", "best_match")], by.x="ID", by.y="Sequence.ID") -print(paste("nrow final:", nrow(final))) -names(final)[names(final) == "best_match"] = "Sample" -final.numeric = final[,sapply(final, is.numeric)] -final.numeric[is.na(final.numeric)] = 0 -final[,sapply(final, is.numeric)] = final.numeric - -final.ca = final[grepl("^ca", final$Sample),] -final.cg = final[grepl("^cg", final$Sample),] -final.cm = final[grepl("^cm", final$Sample),] - -if(nrow(final.ca) > 0){ - final.ca$Replicate = 1 -} - -if(nrow(final.cg) > 0){ - final.cg$Replicate = 1 -} - -if(nrow(final.cm) > 0){ - final.cm$Replicate = 1 -} - -#print(paste("nrow final:", nrow(final))) -#final2 = final -#final2$Sample = gsub("[0-9]", "", final2$Sample) -#final = rbind(final, final2) -#final$Replicate = 1 - -write.table(final.ca, output.file.ca, quote=F, sep="\t", row.names=F, col.names=T) -write.table(final.cg, output.file.cg, quote=F, sep="\t", row.names=F, col.names=T) -write.table(final.cm, output.file.cm, quote=F, sep="\t", row.names=F, col.names=T) -
--- a/new_imgt.r Tue Sep 01 16:03:44 2020 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,40 +0,0 @@ -args <- commandArgs(trailingOnly = TRUE) - -imgt.dir = args[1] -merged.file = args[2] -gene = args[3] - -merged = read.table(merged.file, header=T, sep="\t", fill=T, stringsAsFactors=F, comment.char="", quote="") - -if(!("Sequence.ID" %in% names(merged))){ #change-o db - print("Change-O DB changing 'SEQUENCE_ID' to 'Sequence.ID'") - names(merged)[which(names[merged] == "SEQUENCE_ID")] = "Sequence.ID" -} - -if(gene != "-"){ - merged = merged[grepl(paste("^", gene, sep=""), merged$best_match),] -} - -if("best_match" %in% names(merged)){ - merged = merged[!grepl("unmatched", merged$best_match),] -} - -nrow_dat = 0 - -for(f in list.files(imgt.dir, pattern="*.txt$")){ - #print(paste("filtering", f)) - path = file.path(imgt.dir, f) - dat = read.table(path, header=T, sep="\t", fill=T, quote="", stringsAsFactors=F, check.names=FALSE, comment.char="") - - dat = dat[dat[,"Sequence ID"] %in% merged$Sequence.ID,] - - nrow_dat = nrow(dat) - - if(nrow(dat) > 0 & grepl("^8_", f)){ #change the FR1 columns to 0 in the "8_..." file - dat[,grepl("^FR1", names(dat))] = 0 - } - - write.table(dat, path, quote=F, sep="\t", row.names=F, col.names=T, na="") -} - -print(paste("Creating new zip for ", gene, "with", nrow_dat, "sequences"))
--- a/pattern_plots.r Tue Sep 01 16:03:44 2020 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,178 +0,0 @@ -library(ggplot2) -library(reshape2) -library(scales) - -args <- commandArgs(trailingOnly = TRUE) - -input.file = args[1] #the data that's get turned into the "SHM overview" table in the html report "data_sum.txt" - -plot1.path = args[2] -plot1.png = paste(plot1.path, ".png", sep="") -plot1.txt = paste(plot1.path, ".txt", sep="") -plot1.pdf = paste(plot1.path, ".pdf", sep="") - -plot2.path = args[3] -plot2.png = paste(plot2.path, ".png", sep="") -plot2.txt = paste(plot2.path, ".txt", sep="") -plot2.pdf = paste(plot2.path, ".pdf", sep="") - -plot3.path = args[4] -plot3.png = paste(plot3.path, ".png", sep="") -plot3.txt = paste(plot3.path, ".txt", sep="") -plot3.pdf = paste(plot3.path, ".pdf", sep="") - -clean.output = args[5] - -dat = read.table(input.file, header=F, sep=",", quote="", stringsAsFactors=F, fill=T, row.names=1) - -classes = c("IGA", "IGA1", "IGA2", "IGG", "IGG1", "IGG2", "IGG3", "IGG4", "IGM", "IGE") -xyz = c("x", "y", "z") -new.names = c(paste(rep(classes, each=3), xyz, sep="."), paste("un", xyz, sep="."), paste("all", xyz, sep=".")) - -names(dat) = new.names - -clean.dat = dat -clean.dat = clean.dat[,c(paste(rep(classes, each=3), xyz, sep="."), paste("all", xyz, sep="."), paste("un", xyz, sep="."))] - -write.table(clean.dat, clean.output, quote=F, sep="\t", na="", row.names=T, col.names=NA) - -dat["RGYW.WRCY",] = colSums(dat[c(13,14),], na.rm=T) -dat["TW.WA",] = colSums(dat[c(15,16),], na.rm=T) - -data1 = dat[c("RGYW.WRCY", "TW.WA"),] - -data1 = data1[,names(data1)[grepl(".z", names(data1))]] -names(data1) = gsub("\\..*", "", names(data1)) - -data1 = melt(t(data1)) - -names(data1) = c("Class", "Type", "value") - -chk = is.na(data1$value) -if(any(chk)){ - data1[chk, "value"] = 0 -} - -data1 = data1[order(data1$Type),] - -write.table(data1, plot1.txt, quote=F, sep="\t", na="", row.names=F, col.names=T) - -p = ggplot(data1, aes(Class, value)) + geom_bar(aes(fill=Type), stat="identity", position="dodge", colour = "black") + ylab("% of mutations") + guides(fill=guide_legend(title=NULL)) + ggtitle("Percentage of mutations in AID and pol eta motives") -p = p + theme(panel.background = element_rect(fill = "white", colour="black"),text = element_text(size=15, colour="black"), axis.text.x = element_text(angle = 45, hjust = 1)) + scale_fill_manual(values=c("RGYW.WRCY" = "white", "TW.WA" = "blue4")) -#p = p + scale_colour_manual(values=c("RGYW.WRCY" = "black", "TW.WA" = "blue4")) -png(filename=plot1.png, width=510, height=300) -print(p) -dev.off() - -ggsave(plot1.pdf, p) - -data2 = dat[c(1, 5:8),] - -data2 = data2[,names(data2)[grepl("\\.x", names(data2))]] -names(data2) = gsub(".x", "", names(data2)) - -data2["A/T",] = dat["Targeting of A T (%)",names(dat)[grepl("\\.z", names(dat))]] - -data2["G/C transitions",] = round(data2["Transitions at G C (%)",] / data2["Number of Mutations (%)",] * 100, 1) - -data2["mutation.at.gc",] = dat["Transitions at G C (%)",names(dat)[grepl("\\.y", names(dat))]] -data2["G/C transversions",] = round((data2["mutation.at.gc",] - data2["Transitions at G C (%)",]) / data2["Number of Mutations (%)",] * 100, 1) - -data2["G/C transversions",is.nan(unlist(data2["G/C transversions",]))] = 0 -data2["G/C transversions",is.infinite(unlist(data2["G/C transversions",]))] = 0 -data2["G/C transitions",is.nan(unlist(data2["G/C transitions",]))] = 0 -data2["G/C transitions",is.infinite(unlist(data2["G/C transitions",]))] = 0 - -data2 = melt(t(data2[c("A/T","G/C transitions","G/C transversions"),])) - -names(data2) = c("Class", "Type", "value") - -chk = is.na(data2$value) -if(any(chk)){ - data2[chk, "value"] = 0 -} - -data2 = data2[order(data2$Type),] - -write.table(data2, plot2.txt, quote=F, sep="\t", na="", row.names=F, col.names=T) - -p = ggplot(data2, aes(x=Class, y=value, fill=Type)) + geom_bar(position="fill", stat="identity", colour = "black") + scale_y_continuous(labels=percent_format()) + guides(fill=guide_legend(title=NULL)) + ylab("% of mutations") + ggtitle("Relative mutation patterns") -p = p + theme(panel.background = element_rect(fill = "white", colour="black"), text = element_text(size=15, colour="black"), axis.text.x = element_text(angle = 45, hjust = 1)) + scale_fill_manual(values=c("A/T" = "blue4", "G/C transversions" = "gray74", "G/C transitions" = "white")) -#p = p + scale_colour_manual(values=c("A/T" = "blue4", "G/C transversions" = "gray74", "G/C transitions" = "black")) -png(filename=plot2.png, width=480, height=300) -print(p) -dev.off() - -ggsave(plot2.pdf, p) - -data3 = dat[c(5, 6, 8, 17:20),] -data3 = data3[,names(data3)[grepl("\\.x", names(data3))]] -names(data3) = gsub(".x", "", names(data3)) - -data3["G/C transitions",] = round(data3["Transitions at G C (%)",] / (data3["C",] + data3["G",]) * 100, 1) - -data3["G/C transversions",] = round((data3["Targeting of G C (%)",] - data3["Transitions at G C (%)",]) / (data3["C",] + data3["G",]) * 100, 1) - -data3["A/T",] = round(data3["Targeting of A T (%)",] / (data3["A",] + data3["T",]) * 100, 1) - -data3["G/C transitions",is.nan(unlist(data3["G/C transitions",]))] = 0 -data3["G/C transitions",is.infinite(unlist(data3["G/C transitions",]))] = 0 - -data3["G/C transversions",is.nan(unlist(data3["G/C transversions",]))] = 0 -data3["G/C transversions",is.infinite(unlist(data3["G/C transversions",]))] = 0 - -data3["A/T",is.nan(unlist(data3["A/T",]))] = 0 -data3["A/T",is.infinite(unlist(data3["A/T",]))] = 0 - -data3 = melt(t(data3[8:10,])) -names(data3) = c("Class", "Type", "value") - -chk = is.na(data3$value) -if(any(chk)){ - data3[chk, "value"] = 0 -} - -data3 = data3[order(data3$Type),] - -write.table(data3, plot3.txt, quote=F, sep="\t", na="", row.names=F, col.names=T) - -p = ggplot(data3, aes(Class, value)) + geom_bar(aes(fill=Type), stat="identity", position="dodge", colour = "black") + ylab("% of nucleotides") + guides(fill=guide_legend(title=NULL)) + ggtitle("Absolute mutation patterns") -p = p + theme(panel.background = element_rect(fill = "white", colour="black"), text = element_text(size=15, colour="black"), axis.text.x = element_text(angle = 45, hjust = 1)) + scale_fill_manual(values=c("A/T" = "blue4", "G/C transversions" = "gray74", "G/C transitions" = "white")) -#p = p + scale_colour_manual(values=c("A/T" = "blue4", "G/C transversions" = "gray74", "G/C transitions" = "black")) -png(filename=plot3.png, width=480, height=300) -print(p) -dev.off() - -ggsave(plot3.pdf, p) - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
--- a/plot_pdf.r Tue Sep 01 16:03:44 2020 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,17 +0,0 @@ -library(ggplot2) - -args <- commandArgs(trailingOnly = TRUE) -print(args) - -input = args[1] -outputdir = args[2] -setwd(outputdir) - -load(input) - -print(names(pdfplots)) - -for(n in names(pdfplots)){ - print(paste("n:", n)) - ggsave(pdfplots[[n]], file=n) -}
--- a/sequence_overview.r Tue Sep 01 16:03:44 2020 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,363 +0,0 @@ -library(reshape2) - -args <- commandArgs(trailingOnly = TRUE) - -before.unique.file = args[1] -merged.file = args[2] -outputdir = args[3] -gene.classes = unlist(strsplit(args[4], ",")) -hotspot.analysis.sum.file = args[5] -NToverview.file = paste(outputdir, "ntoverview.txt", sep="/") -NTsum.file = paste(outputdir, "ntsum.txt", sep="/") -main.html = "index.html" -empty.region.filter = args[6] - - -setwd(outputdir) - -before.unique = read.table(before.unique.file, header=T, sep="\t", fill=T, stringsAsFactors=F, quote="") -merged = read.table(merged.file, header=T, sep="\t", fill=T, stringsAsFactors=F, quote="") -hotspot.analysis.sum = read.table(hotspot.analysis.sum.file, header=F, sep=",", fill=T, stringsAsFactors=F, quote="") - -#before.unique = before.unique[!grepl("unmatched", before.unique$best_match),] - -if(empty.region.filter == "leader"){ - before.unique$seq_conc = paste(before.unique$FR1.IMGT.seq, before.unique$CDR1.IMGT.seq, before.unique$FR2.IMGT.seq, before.unique$CDR2.IMGT.seq, before.unique$FR3.IMGT.seq, before.unique$CDR3.IMGT.seq) -} else if(empty.region.filter == "FR1"){ - before.unique$seq_conc = paste(before.unique$CDR1.IMGT.seq, before.unique$FR2.IMGT.seq, before.unique$CDR2.IMGT.seq, before.unique$FR3.IMGT.seq, before.unique$CDR3.IMGT.seq) -} else if(empty.region.filter == "CDR1"){ - before.unique$seq_conc = paste(before.unique$FR2.IMGT.seq, before.unique$CDR2.IMGT.seq, before.unique$FR3.IMGT.seq, before.unique$CDR3.IMGT.seq) -} else if(empty.region.filter == "FR2"){ - before.unique$seq_conc = paste(before.unique$CDR2.IMGT.seq, before.unique$FR3.IMGT.seq, before.unique$CDR3.IMGT.seq) -} - -IDs = before.unique[,c("Sequence.ID", "seq_conc", "best_match", "Functionality")] -IDs$best_match = as.character(IDs$best_match) - -dat = data.frame(table(before.unique$seq_conc)) - -names(dat) = c("seq_conc", "Freq") - -dat$seq_conc = factor(dat$seq_conc) - -dat = dat[order(as.character(dat$seq_conc)),] - -#writing html from R... -get.bg.color = function(val){ - if(val %in% c("TRUE", "FALSE", "T", "F")){ #if its a logical value, give the background a green/red color - return(ifelse(val,"#eafaf1","#f9ebea")) - } else if (!is.na(as.numeric(val))) { #if its a numerical value, give it a grey tint if its >0 - return(ifelse(val > 0,"#eaecee","white")) - } else { - return("white") - } -} -td = function(val) { - return(paste("<td bgcolor='", get.bg.color(val), "'>", val, "</td>", sep="")) -} -tr = function(val) { - return(paste(c("<tr>", sapply(val, td), "</tr>"), collapse="")) -} - -make.link = function(id, clss, val) { - paste("<a href='", clss, "_", id, ".html'>", val, "</a>", sep="") -} -tbl = function(df) { - res = "<table border='1'>" - for(i in 1:nrow(df)){ - res = paste(res, tr(df[i,]), sep="") - } - res = paste(res, "</table>") -} - -cat("<center><img src='data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAA8AAAAPCAYAAAA71pVKAAAAzElEQVQoka2TwQ2CQBBFpwTshw4ImW8ogJMlUIMmhNCDxgasAi50oSXA8XlAjCG7aqKTzGX/vsnM31mzR0gk7tTudO5MEizpzvQ4ryUSe408J3Xn+grE0p1rnpOamVmWsZG4rS+dzzAMsN8Hi9yyjI1JNGtxu4VxBJgLRLpoTKIPiW0LlwtUVRTubW2OBGUJu92cZRmdfbKQMAw8o+vi5v0fLorZ7Y9waGYJjsf38DJz0O1PsEQffOcv4Sa6YYfDDJ5Obzbsp93+5VfdATueO1fdLdI0AAAAAElFTkSuQmCC'> Please note that this tab is based on all sequences before filter unique sequences and the remove duplicates based on filters are applied. In this table only sequences occuring more than once are included. </center>", file=main.html, append=F) -cat("<table border='1' class='pure-table pure-table-striped'>", file=main.html, append=T) - -if(empty.region.filter == "leader"){ - cat("<caption>FR1+CDR1+FR2+CDR2+FR3+CDR3 sequences that show up more than once</caption>", file=main.html, append=T) -} else if(empty.region.filter == "FR1"){ - cat("<caption>CDR1+FR2+CDR2+FR3+CDR3 sequences that show up more than once</caption>", file=main.html, append=T) -} else if(empty.region.filter == "CDR1"){ - cat("<caption>FR2+CDR2+FR3+CDR3 sequences that show up more than once</caption>", file=main.html, append=T) -} else if(empty.region.filter == "FR2"){ - cat("<caption>CDR2+FR3+CDR3 sequences that show up more than once</caption>", file=main.html, append=T) -} - -cat("<tr>", file=main.html, append=T) -cat("<th>Sequence</th><th>Functionality</th><th>IGA1</th><th>IGA2</th><th>IGG1</th><th>IGG2</th><th>IGG3</th><th>IGG4</th><th>IGM</th><th>IGE</th><th>UN</th>", file=main.html, append=T) -cat("<th>total IGA</th><th>total IGG</th><th>total IGM</th><th>total IGE</th><th>number of subclasses</th><th>present in both IGA and IGG</th><th>present in IGA, IGG and IGM</th><th>present in IGA, IGG and IGE</th><th>present in IGA, IGG, IGM and IGE</th><th>IGA1+IGA2</th>", file=main.html, append=T) -cat("<th>IGG1+IGG2</th><th>IGG1+IGG3</th><th>IGG1+IGG4</th><th>IGG2+IGG3</th><th>IGG2+IGG4</th><th>IGG3+IGG4</th>", file=main.html, append=T) -cat("<th>IGG1+IGG2+IGG3</th><th>IGG2+IGG3+IGG4</th><th>IGG1+IGG2+IGG4</th><th>IGG1+IGG3+IGG4</th><th>IGG1+IGG2+IGG3+IGG4</th>", file=main.html, append=T) -cat("</tr>", file=main.html, append=T) - - - -single.sequences=0 #sequence only found once, skipped -in.multiple=0 #same sequence across multiple subclasses -multiple.in.one=0 #same sequence multiple times in one subclass -unmatched=0 #all of the sequences are unmatched -some.unmatched=0 #one or more sequences in a clone are unmatched -matched=0 #should be the same als matched sequences - -sequence.id.page="by_id.html" - -for(i in 1:nrow(dat)){ - - ca1 = IDs[IDs$seq_conc == dat[i,c("seq_conc")] & grepl("^IGA1", IDs$best_match),] - ca2 = IDs[IDs$seq_conc == dat[i,c("seq_conc")] & grepl("^IGA2", IDs$best_match),] - - cg1 = IDs[IDs$seq_conc == dat[i,c("seq_conc")] & grepl("^IGG1", IDs$best_match),] - cg2 = IDs[IDs$seq_conc == dat[i,c("seq_conc")] & grepl("^IGG2", IDs$best_match),] - cg3 = IDs[IDs$seq_conc == dat[i,c("seq_conc")] & grepl("^IGG3", IDs$best_match),] - cg4 = IDs[IDs$seq_conc == dat[i,c("seq_conc")] & grepl("^IGG4", IDs$best_match),] - - cm = IDs[IDs$seq_conc == dat[i,c("seq_conc")] & grepl("^IGM", IDs$best_match),] - - ce = IDs[IDs$seq_conc == dat[i,c("seq_conc")] & grepl("^IGE", IDs$best_match),] - - un = IDs[IDs$seq_conc == dat[i,c("seq_conc")] & grepl("^unmatched", IDs$best_match),] - - allc = rbind(ca1, ca2, cg1, cg2, cg3, cg4, cm, ce, un) - - ca1.n = nrow(ca1) - ca2.n = nrow(ca2) - - cg1.n = nrow(cg1) - cg2.n = nrow(cg2) - cg3.n = nrow(cg3) - cg4.n = nrow(cg4) - - cm.n = nrow(cm) - - ce.n = nrow(ce) - - un.n = nrow(un) - - classes = c(ca1.n, ca2.n, cg1.n, cg2.n, cg3.n, cg4.n, cm.n, ce.n, un.n) - - classes.sum = sum(classes) - - if(classes.sum == 1){ - single.sequences = single.sequences + 1 - next - } - - if(un.n == classes.sum){ - unmatched = unmatched + 1 - next - } - - classes.no.un = classes[-length(classes)] - - in.classes = sum(classes.no.un > 0) - - matched = matched + in.classes #count in how many subclasses the sequence occurs. - - if(any(classes == classes.sum)){ - multiple.in.one = multiple.in.one + 1 - } else if (un.n > 0) { - some.unmatched = some.unmatched + 1 - } else { - in.multiple = in.multiple + 1 - } - - id = as.numeric(dat[i,"seq_conc"]) - - functionality = paste(unique(allc[,"Functionality"]), collapse=",") - - by.id.row = c() - - if(ca1.n > 0){ - cat(tbl(ca1), file=paste("IGA1_", id, ".html", sep="")) - } - - if(ca2.n > 0){ - cat(tbl(ca2), file=paste("IGA2_", id, ".html", sep="")) - } - - if(cg1.n > 0){ - cat(tbl(cg1), file=paste("IGG1_", id, ".html", sep="")) - } - - if(cg2.n > 0){ - cat(tbl(cg2), file=paste("IGG2_", id, ".html", sep="")) - } - - if(cg3.n > 0){ - cat(tbl(cg3), file=paste("IGG3_", id, ".html", sep="")) - } - - if(cg4.n > 0){ - cat(tbl(cg4), file=paste("IGG4_", id, ".html", sep="")) - } - - if(cm.n > 0){ - cat(tbl(cm), file=paste("IGM_", id, ".html", sep="")) - } - - if(ce.n > 0){ - cat(tbl(ce), file=paste("IGE_", id, ".html", sep="")) - } - - if(un.n > 0){ - cat(tbl(un), file=paste("un_", id, ".html", sep="")) - } - - ca1.html = make.link(id, "IGA1", ca1.n) - ca2.html = make.link(id, "IGA2", ca2.n) - - cg1.html = make.link(id, "IGG1", cg1.n) - cg2.html = make.link(id, "IGG2", cg2.n) - cg3.html = make.link(id, "IGG3", cg3.n) - cg4.html = make.link(id, "IGG4", cg4.n) - - cm.html = make.link(id, "IGM", cm.n) - - ce.html = make.link(id, "IGE", ce.n) - - un.html = make.link(id, "un", un.n) - - #extra columns - ca.n = ca1.n + ca2.n - - cg.n = cg1.n + cg2.n + cg3.n + cg4.n - - #in.classes - - in.ca.cg = (ca.n > 0 & cg.n > 0) - - in.ca.cg.cm = (ca.n > 0 & cg.n > 0 & cm.n > 0) - - in.ca.cg.ce = (ca.n > 0 & cg.n > 0 & ce.n > 0) - - in.ca.cg.cm.ce = (ca.n > 0 & cg.n > 0 & cm.n > 0 & ce.n > 0) - - in.ca1.ca2 = (ca1.n > 0 & ca2.n > 0) - - in.cg1.cg2 = (cg1.n > 0 & cg2.n > 0) - in.cg1.cg3 = (cg1.n > 0 & cg3.n > 0) - in.cg1.cg4 = (cg1.n > 0 & cg4.n > 0) - in.cg2.cg3 = (cg2.n > 0 & cg3.n > 0) - in.cg2.cg4 = (cg2.n > 0 & cg4.n > 0) - in.cg3.cg4 = (cg3.n > 0 & cg4.n > 0) - - in.cg1.cg2.cg3 = (cg1.n > 0 & cg2.n > 0 & cg3.n > 0) - in.cg2.cg3.cg4 = (cg2.n > 0 & cg3.n > 0 & cg4.n > 0) - in.cg1.cg2.cg4 = (cg1.n > 0 & cg2.n > 0 & cg4.n > 0) - in.cg1.cg3.cg4 = (cg1.n > 0 & cg3.n > 0 & cg4.n > 0) - - in.cg.all = (cg1.n > 0 & cg2.n > 0 & cg3.n > 0 & cg4.n > 0) - - #rw = c(as.character(dat[i,"seq_conc"]), functionality, ca1.html, ca2.html, cg1.html, cg2.html, cg3.html, cg4.html, cm.html, un.html) - rw = c(as.character(dat[i,"seq_conc"]), functionality, ca1.html, ca2.html, cg1.html, cg2.html, cg3.html, cg4.html, cm.html, ce.html, un.html) - rw = c(rw, ca.n, cg.n, cm.n, ce.n, in.classes, in.ca.cg, in.ca.cg.cm, in.ca.cg.ce, in.ca.cg.cm.ce, in.ca1.ca2, in.cg1.cg2, in.cg1.cg3, in.cg1.cg4, in.cg2.cg3, in.cg2.cg4, in.cg3.cg4, in.cg1.cg2.cg3, in.cg2.cg3.cg4, in.cg1.cg2.cg4, in.cg1.cg3.cg4, in.cg.all) - - - - cat(tr(rw), file=main.html, append=T) - - - for(i in 1:nrow(allc)){ #generate html by id - html = make.link(id, allc[i,"best_match"], allc[i,"Sequence.ID"]) - cat(paste(html, "<br />"), file=sequence.id.page, append=T) - } -} - -cat("</table>", file=main.html, append=T) - -print(paste("Single sequences:", single.sequences)) -print(paste("Sequences in multiple subclasses:", in.multiple)) -print(paste("Multiple sequences in one subclass:", multiple.in.one)) -print(paste("Matched with unmatched:", some.unmatched)) -print(paste("Count that should match 'matched' sequences:", matched)) - -#ACGT overview - -#NToverview = merged[!grepl("^unmatched", merged$best_match),] -NToverview = merged - -if(empty.region.filter == "leader"){ - NToverview$seq = paste(NToverview$FR1.IMGT.seq, NToverview$CDR1.IMGT.seq, NToverview$FR2.IMGT.seq, NToverview$CDR2.IMGT.seq, NToverview$FR3.IMGT.seq) -} else if(empty.region.filter == "FR1"){ - NToverview$seq = paste(NToverview$CDR1.IMGT.seq, NToverview$FR2.IMGT.seq, NToverview$CDR2.IMGT.seq, NToverview$FR3.IMGT.seq) -} else if(empty.region.filter == "CDR1"){ - NToverview$seq = paste(NToverview$FR2.IMGT.seq, NToverview$CDR2.IMGT.seq, NToverview$FR3.IMGT.seq) -} else if(empty.region.filter == "FR2"){ - NToverview$seq = paste(NToverview$CDR2.IMGT.seq, NToverview$FR3.IMGT.seq) -} - -NToverview$A = nchar(gsub("[^Aa]", "", NToverview$seq)) -NToverview$C = nchar(gsub("[^Cc]", "", NToverview$seq)) -NToverview$G = nchar(gsub("[^Gg]", "", NToverview$seq)) -NToverview$T = nchar(gsub("[^Tt]", "", NToverview$seq)) - -#Nsum = data.frame(Sequence.ID="-", best_match="Sum", seq="-", A = sum(NToverview$A), C = sum(NToverview$C), G = sum(NToverview$G), T = sum(NToverview$T)) - -#NToverview = rbind(NToverview, NTsum) - -NTresult = data.frame(nt=c("A", "C", "T", "G")) - -for(clazz in gene.classes){ - print(paste("class:", clazz)) - NToverview.sub = NToverview[grepl(paste("^", clazz, sep=""), NToverview$best_match),] - print(paste("nrow:", nrow(NToverview.sub))) - new.col.x = c(sum(NToverview.sub$A), sum(NToverview.sub$C), sum(NToverview.sub$T), sum(NToverview.sub$G)) - new.col.y = sum(new.col.x) - new.col.z = round(new.col.x / new.col.y * 100, 2) - - tmp = names(NTresult) - NTresult = cbind(NTresult, data.frame(new.col.x, new.col.y, new.col.z)) - names(NTresult) = c(tmp, paste(clazz, c("x", "y", "z"), sep="")) -} - -NToverview.tmp = NToverview[,c("Sequence.ID", "best_match", "seq", "A", "C", "G", "T")] - -names(NToverview.tmp) = c("Sequence.ID", "best_match", "Sequence of the analysed region", "A", "C", "G", "T") - -write.table(NToverview.tmp, NToverview.file, quote=F, sep="\t", row.names=F, col.names=T) - -NToverview = NToverview[!grepl("unmatched", NToverview$best_match),] - -new.col.x = c(sum(NToverview$A), sum(NToverview$C), sum(NToverview$T), sum(NToverview$G)) -new.col.y = sum(new.col.x) -new.col.z = round(new.col.x / new.col.y * 100, 2) - -tmp = names(NTresult) -NTresult = cbind(NTresult, data.frame(new.col.x, new.col.y, new.col.z)) -names(NTresult) = c(tmp, paste("all", c("x", "y", "z"), sep="")) - -names(hotspot.analysis.sum) = names(NTresult) - -hotspot.analysis.sum = rbind(hotspot.analysis.sum, NTresult) - -write.table(hotspot.analysis.sum, hotspot.analysis.sum.file, quote=F, sep=",", row.names=F, col.names=F, na="0") - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
--- a/shm_clonality.htm Tue Sep 01 16:03:44 2020 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,144 +0,0 @@ -<html> - -<head> -<meta http-equiv=Content-Type content="text/html; charset=windows-1252"> -<meta name=Generator content="Microsoft Word 14 (filtered)"> -<style> -<!-- - /* Font Definitions */ - @font-face - {font-family:Calibri; - panose-1:2 15 5 2 2 2 4 3 2 4;} -@font-face - {font-family:Tahoma; - panose-1:2 11 6 4 3 5 4 4 2 4;} - /* Style Definitions */ - p.MsoNormal, li.MsoNormal, div.MsoNormal - {margin-top:0in; - margin-right:0in; - margin-bottom:10.0pt; - margin-left:0in; - line-height:115%; - font-size:11.0pt; - font-family:"Calibri","sans-serif";} -a:link, span.MsoHyperlink - {color:blue; - text-decoration:underline;} -a:visited, span.MsoHyperlinkFollowed - {color:purple; - text-decoration:underline;} -p - {margin-right:0in; - margin-left:0in; - font-size:12.0pt; - font-family:"Times New Roman","serif";} -p.MsoAcetate, li.MsoAcetate, div.MsoAcetate - {mso-style-link:"Balloon Text Char"; - margin:0in; - margin-bottom:.0001pt; - font-size:8.0pt; - font-family:"Tahoma","sans-serif";} -p.msochpdefault, li.msochpdefault, div.msochpdefault - {mso-style-name:msochpdefault; - margin-right:0in; - margin-left:0in; - font-size:12.0pt; - font-family:"Calibri","sans-serif";} -p.msopapdefault, li.msopapdefault, div.msopapdefault - {mso-style-name:msopapdefault; - margin-right:0in; - margin-bottom:10.0pt; - margin-left:0in; - line-height:115%; - font-size:12.0pt; - font-family:"Times New Roman","serif";} -span.apple-converted-space - {mso-style-name:apple-converted-space;} -span.BalloonTextChar - {mso-style-name:"Balloon Text Char"; - mso-style-link:"Balloon Text"; - font-family:"Tahoma","sans-serif";} -.MsoChpDefault - {font-size:10.0pt; - font-family:"Calibri","sans-serif";} -.MsoPapDefault - {margin-bottom:10.0pt; - line-height:115%;} -@page WordSection1 - {size:8.5in 11.0in; - margin:1.0in 1.0in 1.0in 1.0in;} -div.WordSection1 - {page:WordSection1;} ---> -</style> - -</head> - -<body lang=EN-US link=blue vlink=purple> - -<div class=WordSection1> - -<p style='margin-top:0in;margin-right:0in;margin-bottom:6.4pt;margin-left:0in; -text-align:justify;background:white'><b><span lang=EN-GB style='color:black'>References</span></b></p> - -<p style='margin-top:0in;margin-right:0in;margin-bottom:6.4pt;margin-left:0in; -text-align:justify;background:white'><span lang=EN-GB style='color:black'>Gupta, -Namita T. and Vander Heiden, Jason A. and Uduman, Mohamed and Gadala-Maria, -Daniel and Yaari, Gur and Kleinstein, Steven H. (2015). <a name="OLE_LINK106"></a><a -name="OLE_LINK107"></a>Change-O: a toolkit for analyzing large-scale B cell -immunoglobulin repertoire sequencing data: Table 1. In<span -class=apple-converted-space> </span><em>Bioinformatics, 31 (20), pp. -3356–3358.</em><span class=apple-converted-space><i> </i></span>[</span><a -href="http://dx.doi.org/10.1093/bioinformatics/btv359" target="_blank"><span -lang=EN-GB style='color:#303030'>doi:10.1093/bioinformatics/btv359</span></a><span -lang=EN-GB style='color:black'>][</span><a -href="http://dx.doi.org/10.1093/bioinformatics/btv359" target="_blank"><span -lang=EN-GB style='color:#303030'>Link</span></a><span lang=EN-GB -style='color:black'>]</span></p> - -<p style='margin-top:0in;margin-right:0in;margin-bottom:6.4pt;margin-left:0in; -text-align:justify;background:white'><span lang=EN-GB style='color:black'> </span></p> - -<p style='margin-top:0in;margin-right:0in;margin-bottom:6.4pt;margin-left:0in; -text-align:justify;background:white'><a name="OLE_LINK110"><u><span lang=EN-GB -style='color:black'>All, IGA, IGG, IGM and IGE tabs</span></u></a></p> - -<p style='margin-top:0in;margin-right:0in;margin-bottom:6.4pt;margin-left:0in; -text-align:justify;background:white'><span lang=EN-GB style='color:black'>In -these tabs information on the clonal relation of transcripts can be found. To -calculate clonal relation Change-O is used (Gupta et al, PMID: 26069265). -Transcripts are considered clonally related if they have maximal three nucleotides -difference in their CDR3 sequence and the same first V segment (as assigned by -IMGT). Results are represented in a table format showing the clone size and the -number of clones or sequences with this clone size. Change-O settings used are -the </span><span lang=EN-GB>nucleotide hamming distance substitution model with -a complete distance of maximal three. For clonal assignment the first gene -segments were used, and the distances were not normalized. In case of -asymmetric distances, the minimal distance was used.<span style='color:black'> </span></span></p> - -<p style='margin-top:0in;margin-right:0in;margin-bottom:6.4pt;margin-left:0in; -text-align:justify;background:white'><span lang=EN-GB style='color:black'> </span></p> - -<p style='margin-top:0in;margin-right:0in;margin-bottom:6.4pt;margin-left:0in; -text-align:justify;background:white'><u><span lang=EN-GB style='color:black'>Overlap -tab</span></u><span lang=EN-GB style='color:black'> </span></p> - -<p style='margin-top:0in;margin-right:0in;margin-bottom:6.4pt;margin-left:0in; -text-align:justify;background:white'><span lang=EN-GB style='color:black'>This -tab gives information on with which (sub)classe(s) each unique analyzed region -(based on the exact nucleotide sequence of the analyzes region and the CDR3 -nucleotide sequence) is found with. This gives information if the combination -of the exact same nucleotide sequence of the analyzed region and the CDR3 -sequence can be found in multiple (sub)classes.</span></p> - -<p style='margin-top:0in;margin-right:0in;margin-bottom:6.4pt;margin-left:0in; -text-align:justify;background:white'><span style='color:black'><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAA8AAAAPCAYAAAA71pVKAAAAzElEQVQoka2TwQ2CQBBFpwTshw4ImW8ogJMlUIMmhNCDxgasAi50oSXA8XlAjCG7aqKTzGX/vsnM31mzR0gk7tTudO5MEizpzvQ4ryUSe408J3Xn+grE0p1rnpOamVmWsZG4rS+dzzAMsN8Hi9yyjI1JNGtxu4VxBJgLRLpoTKIPiW0LlwtUVRTubW2OBGUJu92cZRmdfbKQMAw8o+vi5v0fLorZ7Y9waGYJjsf38DJz0O1PsEQffOcv4Sa6YYfDDJ5Obzbsp93+5VfdATueO1fdLdI0AAAAAElFTkSuQmCC"> Please note that this tab is based on all -sequences before filter unique sequences and the remove duplicates based on -filters are applied. In this table only sequences occuring more than once are -included. </span></p> - -</div> - -</body> - -</html>
--- a/shm_csr.htm Tue Sep 01 16:03:44 2020 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,95 +0,0 @@ -<html> - -<head> -<meta http-equiv=Content-Type content="text/html; charset=windows-1252"> -<meta name=Generator content="Microsoft Word 14 (filtered)"> -<style> -<!-- - /* Font Definitions */ - @font-face - {font-family:Calibri; - panose-1:2 15 5 2 2 2 4 3 2 4;} - /* Style Definitions */ - p.MsoNormal, li.MsoNormal, div.MsoNormal - {margin-top:0in; - margin-right:0in; - margin-bottom:10.0pt; - margin-left:0in; - line-height:115%; - font-size:11.0pt; - font-family:"Calibri","sans-serif";} -a:link, span.MsoHyperlink - {color:blue; - text-decoration:underline;} -a:visited, span.MsoHyperlinkFollowed - {color:purple; - text-decoration:underline;} -span.apple-converted-space - {mso-style-name:apple-converted-space;} -.MsoChpDefault - {font-family:"Calibri","sans-serif";} -.MsoPapDefault - {margin-bottom:10.0pt; - line-height:115%;} -@page WordSection1 - {size:8.5in 11.0in; - margin:1.0in 1.0in 1.0in 1.0in;} -div.WordSection1 - {page:WordSection1;} ---> -</style> - -</head> - -<body lang=EN-US link=blue vlink=purple> - -<div class=WordSection1> - -<p class=MsoNormalCxSpFirst style='text-align:justify'><span lang=EN-GB -style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>The -graphs in this tab give insight into the subclass distribution of IGG and IGA -transcripts. </span><span lang=EN-GB style='font-size:12.0pt;line-height:115%; -font-family:"Times New Roman","serif"'>Human Cµ, Cα, Cγ and Cε -constant genes are assigned using a </span><span lang=EN-GB style='font-size: -12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>custom script -specifically designed for human (sub)class assignment in repertoire data as -described in van Schouwenburg and IJspeert et al, submitted for publication. In -this script the reference sequences for the subclasses are divided in 8 -nucleotide chunks which overlap by 4 nucleotides. These overlapping chunks are -then individually aligned in the right order to each input sequence. The -percentage of the chunks identified in each rearrangement is calculated in the -‘chunk hit percentage’. </span><span lang=EN-GB style='font-size:12.0pt; -line-height:115%;font-family:"Times New Roman","serif"'>Cα and Cγ -subclasses are very homologous and only differ in a few nucleotides. To assign -subclasses the </span><span lang=EN-GB style='font-size:12.0pt;line-height: -115%;font-family:"Times New Roman","serif"'>‘nt hit percentage’ is calculated. -This percentage indicates how well the chunks covering the subclass specific -nucleotide match with the different subclasses. </span><span lang=EN-GB -style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>Information -on normal distribution of subclasses in healthy individuals of different ages -can be found in IJspeert and van Schouwenburg et al, PMID: 27799928.</span></p> - -<p class=MsoNormalCxSpMiddle style='text-align:justify'><a name="OLE_LINK100"></a><a -name="OLE_LINK99"></a><a name="OLE_LINK25"><u><span lang=EN-GB -style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>IGA -subclass distribution</span></u></a></p> - -<p class=MsoNormalCxSpMiddle style='text-align:justify'><span lang=EN-GB -style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>Pie -chart showing the relative distribution of IGA1 and IGA2 transcripts in the -sample.</span></p> - -<p class=MsoNormalCxSpMiddle style='text-align:justify'><u><span lang=EN-GB -style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>IGG -subclass distribution</span></u></p> - -<p class=MsoNormalCxSpLast style='text-align:justify'><span lang=EN-GB -style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>Pie -chart showing the relative distribution of IGG1, IGG2, IGG3 and IGG4 -transcripts in the sample.</span></p> - -</div> - -</body> - -</html>
--- a/shm_csr.py Tue Sep 01 16:03:44 2020 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,501 +0,0 @@ -import argparse -import logging -import sys -import os -import re - -from collections import defaultdict - -def main(): - parser = argparse.ArgumentParser() - parser.add_argument("--input", help="The '7_V-REGION-mutation-and-AA-change-table' and '10_V-REGION-mutation-hotspots' merged together, with an added 'best_match' annotation") - parser.add_argument("--genes", help="The genes available in the 'best_match' column") - parser.add_argument("--empty_region_filter", help="Where does the sequence start?", choices=['leader', 'FR1', 'CDR1', 'FR2']) - parser.add_argument("--output", help="Output file") - - args = parser.parse_args() - - infile = args.input - genes = str(args.genes).split(",") - empty_region_filter = args.empty_region_filter - outfile = args.output - - genedic = dict() - - mutationdic = dict() - mutationMatcher = re.compile("^(.)(\d+).(.),?[ ]?(.)?(\d+)?.?(.)?(.?.?.?.?.?)?") - mutationMatcher = re.compile("^([actg])(\d+).([actg]),?[ ]?([A-Z])?(\d+)?.?([A-Z])?(.*)?") - mutationMatcher = re.compile("^([actg])(\d+).([actg]),?[ ]?([A-Z])?(\d+)?[>]?([A-Z;])?(.*)?") - mutationMatcher = re.compile("^([nactg])(\d+).([nactg]),?[ ]?([A-Z])?(\d+)?[>]?([A-Z;])?(.*)?") - NAMatchResult = (None, None, None, None, None, None, '') - geneMatchers = {gene: re.compile("^" + gene + ".*") for gene in genes} - linecount = 0 - - IDIndex = 0 - best_matchIndex = 0 - fr1Index = 0 - cdr1Index = 0 - fr2Index = 0 - cdr2Index = 0 - fr3Index = 0 - first = True - IDlist = [] - mutationList = [] - mutationListByID = {} - cdr1LengthDic = {} - cdr2LengthDic = {} - - fr1LengthDict = {} - fr2LengthDict = {} - fr3LengthDict = {} - - cdr1LengthIndex = 0 - cdr2LengthIndex = 0 - - fr1SeqIndex = 0 - fr2SeqIndex = 0 - fr3SeqIndex = 0 - - tandem_sum_by_class = defaultdict(int) - expected_tandem_sum_by_class = defaultdict(float) - - with open(infile, 'ru') as i: - for line in i: - if first: - linesplt = line.split("\t") - IDIndex = linesplt.index("Sequence.ID") - best_matchIndex = linesplt.index("best_match") - fr1Index = linesplt.index("FR1.IMGT") - cdr1Index = linesplt.index("CDR1.IMGT") - fr2Index = linesplt.index("FR2.IMGT") - cdr2Index = linesplt.index("CDR2.IMGT") - fr3Index = linesplt.index("FR3.IMGT") - cdr1LengthIndex = linesplt.index("CDR1.IMGT.seq") - cdr2LengthIndex = linesplt.index("CDR2.IMGT.seq") - fr1SeqIndex = linesplt.index("FR1.IMGT.seq") - fr2SeqIndex = linesplt.index("FR2.IMGT.seq") - fr3SeqIndex = linesplt.index("FR3.IMGT.seq") - first = False - continue - linecount += 1 - linesplt = line.split("\t") - ID = linesplt[IDIndex] - genedic[ID] = linesplt[best_matchIndex] - - mutationdic[ID + "_FR1"] = [] - if len(linesplt[fr1Index]) > 5 and empty_region_filter == "leader": - mutationdic[ID + "_FR1"] = [mutationMatcher.match(x).groups() for x in linesplt[fr1Index].split("|") if x] - - mutationdic[ID + "_CDR1"] = [] - if len(linesplt[cdr1Index]) > 5 and empty_region_filter in ["leader", "FR1"]: - mutationdic[ID + "_CDR1"] = [mutationMatcher.match(x).groups() for x in linesplt[cdr1Index].split("|") if x] - - mutationdic[ID + "_FR2"] = [] - if len(linesplt[fr2Index]) > 5 and empty_region_filter in ["leader", "FR1", "CDR1"]: - mutationdic[ID + "_FR2"] = [mutationMatcher.match(x).groups() for x in linesplt[fr2Index].split("|") if x] - - mutationdic[ID + "_CDR2"] = [] - if len(linesplt[cdr2Index]) > 5: - mutationdic[ID + "_CDR2"] = [mutationMatcher.match(x).groups() for x in linesplt[cdr2Index].split("|") if x] - - mutationdic[ID + "_FR2-CDR2"] = mutationdic[ID + "_FR2"] + mutationdic[ID + "_CDR2"] - - mutationdic[ID + "_FR3"] = [] - if len(linesplt[fr3Index]) > 5: - mutationdic[ID + "_FR3"] = [mutationMatcher.match(x).groups() for x in linesplt[fr3Index].split("|") if x] - - mutationList += mutationdic[ID + "_FR1"] + mutationdic[ID + "_CDR1"] + mutationdic[ID + "_FR2"] + mutationdic[ID + "_CDR2"] + mutationdic[ID + "_FR3"] - mutationListByID[ID] = mutationdic[ID + "_FR1"] + mutationdic[ID + "_CDR1"] + mutationdic[ID + "_FR2"] + mutationdic[ID + "_CDR2"] + mutationdic[ID + "_FR3"] - - cdr1Length = len(linesplt[cdr1LengthIndex]) - cdr2Length = len(linesplt[cdr2LengthIndex]) - - #print linesplt[fr2SeqIndex] - fr1Length = len(linesplt[fr1SeqIndex]) if empty_region_filter == "leader" else 0 - fr2Length = len(linesplt[fr2SeqIndex]) if empty_region_filter in ["leader", "FR1", "CDR1"] else 0 - fr3Length = len(linesplt[fr3SeqIndex]) - - cdr1LengthDic[ID] = cdr1Length - cdr2LengthDic[ID] = cdr2Length - - fr1LengthDict[ID] = fr1Length - fr2LengthDict[ID] = fr2Length - fr3LengthDict[ID] = fr3Length - - IDlist += [ID] - print "len(mutationdic) =", len(mutationdic) - - with open(os.path.join(os.path.dirname(os.path.abspath(infile)), "mutationdict.txt"), 'w') as out_handle: - for ID, lst in mutationdic.iteritems(): - for mut in lst: - out_handle.write("{0}\t{1}\n".format(ID, "\t".join([str(x) for x in mut]))) - - #tandem mutation stuff - tandem_frequency = defaultdict(int) - mutation_frequency = defaultdict(int) - - mutations_by_id_dic = {} - first = True - mutation_by_id_file = os.path.join(os.path.dirname(outfile), "mutation_by_id.txt") - with open(mutation_by_id_file, 'r') as mutation_by_id: - for l in mutation_by_id: - if first: - first = False - continue - splt = l.split("\t") - mutations_by_id_dic[splt[0]] = int(splt[1]) - - tandem_file = os.path.join(os.path.dirname(outfile), "tandems_by_id.txt") - with open(tandem_file, 'w') as o: - highest_tandem_length = 0 - - o.write("Sequence.ID\tnumber_of_mutations\tnumber_of_tandems\tregion_length\texpected_tandems\tlongest_tandem\ttandems\n") - for ID in IDlist: - mutations = mutationListByID[ID] - if len(mutations) == 0: - continue - last_mut = max(mutations, key=lambda x: int(x[1])) - - last_mut_pos = int(last_mut[1]) - - mut_positions = [False] * (last_mut_pos + 1) - - for mutation in mutations: - frm, where, to, frmAA, whereAA, toAA, thing = mutation - where = int(where) - mut_positions[where] = True - - tandem_muts = [] - tandem_start = -1 - tandem_length = 0 - for i in range(len(mut_positions)): - if mut_positions[i]: - if tandem_start == -1: - tandem_start = i - tandem_length += 1 - #print "".join(["1" if x else "0" for x in mut_positions[:i+1]]) - else: - if tandem_length > 1: - tandem_muts.append((tandem_start, tandem_length)) - #print "{0}{1} {2}:{3}".format(" " * (i - tandem_length), "^" * tandem_length, tandem_start, tandem_length) - tandem_start = -1 - tandem_length = 0 - if tandem_length > 1: # if the sequence ends with a tandem mutation - tandem_muts.append((tandem_start, tandem_length)) - - if len(tandem_muts) > 0: - if highest_tandem_length < len(tandem_muts): - highest_tandem_length = len(tandem_muts) - - region_length = fr1LengthDict[ID] + cdr1LengthDic[ID] + fr2LengthDict[ID] + cdr2LengthDic[ID] + fr3LengthDict[ID] - longest_tandem = max(tandem_muts, key=lambda x: x[1]) if len(tandem_muts) else (0, 0) - num_mutations = mutations_by_id_dic[ID] # len(mutations) - f_num_mutations = float(num_mutations) - num_tandem_muts = len(tandem_muts) - expected_tandem_muts = f_num_mutations * (f_num_mutations - 1.0) / float(region_length) - o.write("{0}\t{1}\t{2}\t{3}\t{4}\t{5}\t{6}\n".format(ID, - str(num_mutations), - str(num_tandem_muts), - str(region_length), - str(round(expected_tandem_muts, 2)), - str(longest_tandem[1]), - str(tandem_muts))) - gene = genedic[ID] - if gene.find("unmatched") == -1: - tandem_sum_by_class[gene] += num_tandem_muts - expected_tandem_sum_by_class[gene] += expected_tandem_muts - - tandem_sum_by_class["all"] += num_tandem_muts - expected_tandem_sum_by_class["all"] += expected_tandem_muts - - gene = gene[:3] - if gene in ["IGA", "IGG"]: - tandem_sum_by_class[gene] += num_tandem_muts - expected_tandem_sum_by_class[gene] += expected_tandem_muts - else: - tandem_sum_by_class["unmatched"] += num_tandem_muts - expected_tandem_sum_by_class["unmatched"] += expected_tandem_muts - - - for tandem_mut in tandem_muts: - tandem_frequency[str(tandem_mut[1])] += 1 - #print "\t".join([ID, str(len(tandem_muts)), str(longest_tandem[1]) , str(tandem_muts)]) - - tandem_freq_file = os.path.join(os.path.dirname(outfile), "tandem_frequency.txt") - with open(tandem_freq_file, 'w') as o: - for frq in sorted([int(x) for x in tandem_frequency.keys()]): - o.write("{0}\t{1}\n".format(frq, tandem_frequency[str(frq)])) - - tandem_row = [] - genes_extra = list(genes) - genes_extra.append("all") - for x, y, in zip([tandem_sum_by_class[x] for x in genes_extra], [expected_tandem_sum_by_class[x] for x in genes_extra]): - if y != 0: - tandem_row += [x, round(y, 2), round(x / y, 2)] - else: - tandem_row += [x, round(y, 2), 0] - - tandem_freq_file = os.path.join(os.path.dirname(outfile), "shm_overview_tandem_row.txt") - with open(tandem_freq_file, 'w') as o: - o.write("Tandems/Expected (ratio),{0}\n".format(",".join([str(x) for x in tandem_row]))) - - #print mutationList, linecount - - AALength = (int(max(mutationList, key=lambda i: int(i[4]) if i[4] and i[5] != ";" else 0)[4]) + 1) # [4] is the position of the AA mutation, None if silent - if AALength < 60: - AALength = 64 - - AA_mutation = [0] * AALength - AA_mutation_dic = {"IGA": AA_mutation[:], "IGG": AA_mutation[:], "IGM": AA_mutation[:], "IGE": AA_mutation[:], "unm": AA_mutation[:], "all": AA_mutation[:]} - AA_mutation_empty = AA_mutation[:] - - print "AALength:", AALength - aa_mutations_by_id_file = outfile[:outfile.rindex("/")] + "/aa_id_mutations.txt" - with open(aa_mutations_by_id_file, 'w') as o: - o.write("ID\tbest_match\t" + "\t".join([str(x) for x in range(1,AALength)]) + "\n") - for ID in mutationListByID.keys(): - AA_mutation_for_ID = AA_mutation_empty[:] - for mutation in mutationListByID[ID]: - if mutation[4] and mutation[5] != ";": - AA_mutation_position = int(mutation[4]) - try: - AA_mutation[AA_mutation_position] += 1 - AA_mutation_for_ID[AA_mutation_position] += 1 - except Exception as e: - print e - print mutation - sys.exit() - clss = genedic[ID][:3] - AA_mutation_dic[clss][AA_mutation_position] += 1 - o.write(ID + "\t" + genedic[ID] + "\t" + "\t".join([str(x) for x in AA_mutation_for_ID[1:]]) + "\n") - - - - #absent AA stuff - absentAACDR1Dic = defaultdict(list) - absentAACDR1Dic[5] = range(29,36) - absentAACDR1Dic[6] = range(29,35) - absentAACDR1Dic[7] = range(30,35) - absentAACDR1Dic[8] = range(30,34) - absentAACDR1Dic[9] = range(31,34) - absentAACDR1Dic[10] = range(31,33) - absentAACDR1Dic[11] = [32] - - absentAACDR2Dic = defaultdict(list) - absentAACDR2Dic[0] = range(55,65) - absentAACDR2Dic[1] = range(56,65) - absentAACDR2Dic[2] = range(56,64) - absentAACDR2Dic[3] = range(57,64) - absentAACDR2Dic[4] = range(57,63) - absentAACDR2Dic[5] = range(58,63) - absentAACDR2Dic[6] = range(58,62) - absentAACDR2Dic[7] = range(59,62) - absentAACDR2Dic[8] = range(59,61) - absentAACDR2Dic[9] = [60] - - absentAA = [len(IDlist)] * (AALength-1) - for k, cdr1Length in cdr1LengthDic.iteritems(): - for c in absentAACDR1Dic[cdr1Length]: - absentAA[c] -= 1 - - for k, cdr2Length in cdr2LengthDic.iteritems(): - for c in absentAACDR2Dic[cdr2Length]: - absentAA[c] -= 1 - - - aa_mutations_by_id_file = outfile[:outfile.rindex("/")] + "/absent_aa_id.txt" - with open(aa_mutations_by_id_file, 'w') as o: - o.write("ID\tcdr1length\tcdr2length\tbest_match\t" + "\t".join([str(x) for x in range(1,AALength)]) + "\n") - for ID in IDlist: - absentAAbyID = [1] * (AALength-1) - cdr1Length = cdr1LengthDic[ID] - for c in absentAACDR1Dic[cdr1Length]: - absentAAbyID[c] -= 1 - - cdr2Length = cdr2LengthDic[ID] - for c in absentAACDR2Dic[cdr2Length]: - absentAAbyID[c] -= 1 - o.write(ID + "\t" + str(cdr1Length) + "\t" + str(cdr2Length) + "\t" + genedic[ID] + "\t" + "\t".join([str(x) for x in absentAAbyID]) + "\n") - - if linecount == 0: - print "No data, exiting" - with open(outfile, 'w') as o: - o.write("RGYW (%)," + ("0,0,0\n" * len(genes))) - o.write("WRCY (%)," + ("0,0,0\n" * len(genes))) - o.write("WA (%)," + ("0,0,0\n" * len(genes))) - o.write("TW (%)," + ("0,0,0\n" * len(genes))) - import sys - - sys.exit() - - hotspotMatcher = re.compile("[actg]+,(\d+)-(\d+)\((.*)\)") - RGYWCount = {} - WRCYCount = {} - WACount = {} - TWCount = {} - - #IDIndex = 0 - ataIndex = 0 - tatIndex = 0 - aggctatIndex = 0 - atagcctIndex = 0 - first = True - with open(infile, 'ru') as i: - for line in i: - if first: - linesplt = line.split("\t") - ataIndex = linesplt.index("X.a.t.a") - tatIndex = linesplt.index("t.a.t.") - aggctatIndex = linesplt.index("X.a.g.g.c.t..a.t.") - atagcctIndex = linesplt.index("X.a.t..a.g.c.c.t.") - first = False - continue - linesplt = line.split("\t") - gene = linesplt[best_matchIndex] - ID = linesplt[IDIndex] - RGYW = [(int(x), int(y), z) for (x, y, z) in - [hotspotMatcher.match(x).groups() for x in linesplt[aggctatIndex].split("|") if x]] - WRCY = [(int(x), int(y), z) for (x, y, z) in - [hotspotMatcher.match(x).groups() for x in linesplt[atagcctIndex].split("|") if x]] - WA = [(int(x), int(y), z) for (x, y, z) in - [hotspotMatcher.match(x).groups() for x in linesplt[ataIndex].split("|") if x]] - TW = [(int(x), int(y), z) for (x, y, z) in - [hotspotMatcher.match(x).groups() for x in linesplt[tatIndex].split("|") if x]] - RGYWCount[ID], WRCYCount[ID], WACount[ID], TWCount[ID] = 0, 0, 0, 0 - - with open(os.path.join(os.path.dirname(os.path.abspath(infile)), "RGYW.txt"), 'a') as out_handle: - for hotspot in RGYW: - out_handle.write("{0}\t{1}\n".format(ID, "\t".join([str(x) for x in hotspot]))) - - mutationList = mutationdic[ID + "_FR1"] + mutationdic[ID + "_CDR1"] + mutationdic[ID + "_FR2"] + mutationdic[ID + "_CDR2"] + mutationdic[ID + "_FR3"] - for mutation in mutationList: - frm, where, to, AAfrm, AAwhere, AAto, junk = mutation - mutation_in_RGYW = any(((start <= int(where) <= end) for (start, end, region) in RGYW)) - mutation_in_WRCY = any(((start <= int(where) <= end) for (start, end, region) in WRCY)) - mutation_in_WA = any(((start <= int(where) <= end) for (start, end, region) in WA)) - mutation_in_TW = any(((start <= int(where) <= end) for (start, end, region) in TW)) - - in_how_many_motifs = sum([mutation_in_RGYW, mutation_in_WRCY, mutation_in_WA, mutation_in_TW]) - - if in_how_many_motifs > 0: - RGYWCount[ID] += (1.0 * int(mutation_in_RGYW)) / in_how_many_motifs - WRCYCount[ID] += (1.0 * int(mutation_in_WRCY)) / in_how_many_motifs - WACount[ID] += (1.0 * int(mutation_in_WA)) / in_how_many_motifs - TWCount[ID] += (1.0 * int(mutation_in_TW)) / in_how_many_motifs - - mutations_in_motifs_file = os.path.join(os.path.dirname(os.path.abspath(infile)), "mutation_in_motifs.txt") - if not os.path.exists(mutation_by_id_file): - with open(mutations_in_motifs_file, 'w') as out_handle: - out_handle.write("{0}\n".format("\t".join([ - "Sequence.ID", - "mutation_position", - "region", - "from_nt", - "to_nt", - "mutation_position_AA", - "from_AA", - "to_AA", - "motif", - "motif_start_nt", - "motif_end_nt", - "rest" - ]))) - - with open(mutations_in_motifs_file, 'a') as out_handle: - motif_dic = {"RGYW": RGYW, "WRCY": WRCY, "WA": WA, "TW": TW} - for mutation in mutationList: - frm, where, to, AAfrm, AAwhere, AAto, junk = mutation - for motif in motif_dic.keys(): - - for start, end, region in motif_dic[motif]: - if start <= int(where) <= end: - out_handle.write("{0}\n".format( - "\t".join([ - ID, - where, - region, - frm, - to, - str(AAwhere), - str(AAfrm), - str(AAto), - motif, - str(start), - str(end), - str(junk) - ]) - )) - - - - def mean(lst): - return (float(sum(lst)) / len(lst)) if len(lst) > 0 else 0.0 - - - def median(lst): - lst = sorted(lst) - l = len(lst) - if l == 0: - return 0 - if l == 1: - return lst[0] - - l = int(l / 2) - - if len(lst) % 2 == 0: - return float(lst[l] + lst[(l - 1)]) / 2.0 - else: - return lst[l] - - funcs = {"mean": mean, "median": median, "sum": sum} - - directory = outfile[:outfile.rfind("/") + 1] - value = 0 - valuedic = dict() - - for fname in funcs.keys(): - for gene in genes: - with open(directory + gene + "_" + fname + "_value.txt", 'r') as v: - valuedic[gene + "_" + fname] = float(v.readlines()[0].rstrip()) - with open(directory + "all_" + fname + "_value.txt", 'r') as v: - valuedic["total_" + fname] = float(v.readlines()[0].rstrip()) - - - def get_xyz(lst, gene, f, fname): - x = round(round(f(lst), 1)) - y = valuedic[gene + "_" + fname] - z = str(round(x / float(y) * 100, 1)) if y != 0 else "0" - return (str(x), str(y), z) - - dic = {"RGYW": RGYWCount, "WRCY": WRCYCount, "WA": WACount, "TW": TWCount} - arr = ["RGYW", "WRCY", "WA", "TW"] - - for fname in funcs.keys(): - func = funcs[fname] - foutfile = outfile[:outfile.rindex("/")] + "/hotspot_analysis_" + fname + ".txt" - with open(foutfile, 'w') as o: - for typ in arr: - o.write(typ + " (%)") - curr = dic[typ] - for gene in genes: - geneMatcher = geneMatchers[gene] - if valuedic[gene + "_" + fname] is 0: - o.write(",0,0,0") - else: - x, y, z = get_xyz([curr[x] for x in [y for y, z in genedic.iteritems() if geneMatcher.match(z)]], gene, func, fname) - o.write("," + x + "," + y + "," + z) - x, y, z = get_xyz([y for x, y in curr.iteritems() if not genedic[x].startswith("unmatched")], "total", func, fname) - #x, y, z = get_xyz([y for x, y in curr.iteritems()], "total", func, fname) - o.write("," + x + "," + y + "," + z + "\n") - - - # for testing - seq_motif_file = outfile[:outfile.rindex("/")] + "/motif_per_seq.txt" - with open(seq_motif_file, 'w') as o: - o.write("ID\tRGYW\tWRCY\tWA\tTW\n") - for ID in IDlist: - #o.write(ID + "\t" + str(round(RGYWCount[ID], 2)) + "\t" + str(round(WRCYCount[ID], 2)) + "\t" + str(round(WACount[ID], 2)) + "\t" + str(round(TWCount[ID], 2)) + "\n") - o.write(ID + "\t" + str(RGYWCount[ID]) + "\t" + str(WRCYCount[ID]) + "\t" + str(WACount[ID]) + "\t" + str(TWCount[ID]) + "\n") - -if __name__ == "__main__": - main()
--- a/shm_csr.r Tue Sep 01 16:03:44 2020 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,561 +0,0 @@ -library(data.table) -library(ggplot2) -library(reshape2) - -args <- commandArgs(trailingOnly = TRUE) - -input = args[1] -genes = unlist(strsplit(args[2], ",")) -outputdir = args[3] -empty.region.filter = args[4] -setwd(outputdir) - -#dat = read.table(input, header=T, sep="\t", fill=T, stringsAsFactors=F) - -dat = data.frame(fread(input, sep="\t", header=T, stringsAsFactors=F)) #fread because read.table suddenly skips certain rows... - -if(length(dat$Sequence.ID) == 0){ - setwd(outputdir) - result = data.frame(x = rep(0, 5), y = rep(0, 5), z = rep(NA, 5)) - row.names(result) = c("Number of Mutations (%)", "Transition (%)", "Transversions (%)", "Transitions at G C (%)", "Targeting of G C (%)") - write.table(x=result, file="mutations.txt", sep=",",quote=F,row.names=T,col.names=F) - transitionTable = data.frame(A=rep(0, 4),C=rep(0, 4),G=rep(0, 4),T=rep(0, 4)) - row.names(transitionTable) = c("A", "C", "G", "T") - transitionTable["A","A"] = NA - transitionTable["C","C"] = NA - transitionTable["G","G"] = NA - transitionTable["T","T"] = NA - - write.table(x=transitionTable, file="transitions.txt", sep=",",quote=F,row.names=T,col.names=NA) - cat("0", file="n.txt") - stop("No data") -} - -cleanup_columns = c("FR1.IMGT.c.a", - "FR2.IMGT.g.t", - "CDR1.IMGT.Nb.of.nucleotides", - "CDR2.IMGT.t.a", - "FR1.IMGT.c.g", - "CDR1.IMGT.c.t", - "FR2.IMGT.a.c", - "FR2.IMGT.Nb.of.mutations", - "FR2.IMGT.g.c", - "FR2.IMGT.a.g", - "FR3.IMGT.t.a", - "FR3.IMGT.t.c", - "FR2.IMGT.g.a", - "FR3.IMGT.c.g", - "FR1.IMGT.Nb.of.mutations", - "CDR1.IMGT.g.a", - "CDR1.IMGT.t.g", - "CDR1.IMGT.g.c", - "CDR2.IMGT.Nb.of.nucleotides", - "FR2.IMGT.a.t", - "CDR1.IMGT.Nb.of.mutations", - "CDR3.IMGT.Nb.of.nucleotides", - "CDR1.IMGT.a.g", - "FR3.IMGT.a.c", - "FR1.IMGT.g.a", - "FR3.IMGT.a.g", - "FR1.IMGT.a.t", - "CDR2.IMGT.a.g", - "CDR2.IMGT.Nb.of.mutations", - "CDR2.IMGT.g.t", - "CDR2.IMGT.a.c", - "CDR1.IMGT.t.c", - "FR3.IMGT.g.c", - "FR1.IMGT.g.t", - "FR3.IMGT.g.t", - "CDR1.IMGT.a.t", - "FR1.IMGT.a.g", - "FR3.IMGT.a.t", - "FR3.IMGT.Nb.of.nucleotides", - "FR2.IMGT.t.c", - "CDR2.IMGT.g.a", - "FR2.IMGT.t.a", - "CDR1.IMGT.t.a", - "FR2.IMGT.t.g", - "FR3.IMGT.t.g", - "FR2.IMGT.Nb.of.nucleotides", - "FR1.IMGT.t.a", - "FR1.IMGT.t.g", - "FR3.IMGT.c.t", - "FR1.IMGT.t.c", - "CDR2.IMGT.a.t", - "FR2.IMGT.c.t", - "CDR1.IMGT.g.t", - "CDR2.IMGT.t.g", - "FR1.IMGT.Nb.of.nucleotides", - "CDR1.IMGT.c.g", - "CDR2.IMGT.t.c", - "FR3.IMGT.g.a", - "CDR1.IMGT.a.c", - "FR2.IMGT.c.a", - "FR3.IMGT.Nb.of.mutations", - "FR2.IMGT.c.g", - "CDR2.IMGT.g.c", - "FR1.IMGT.g.c", - "CDR2.IMGT.c.t", - "FR3.IMGT.c.a", - "CDR1.IMGT.c.a", - "CDR2.IMGT.c.g", - "CDR2.IMGT.c.a", - "FR1.IMGT.c.t", - "FR1.IMGT.Nb.of.silent.mutations", - "FR2.IMGT.Nb.of.silent.mutations", - "FR3.IMGT.Nb.of.silent.mutations", - "FR1.IMGT.Nb.of.nonsilent.mutations", - "FR2.IMGT.Nb.of.nonsilent.mutations", - "FR3.IMGT.Nb.of.nonsilent.mutations") - -print("Cleaning up columns") - -for(col in cleanup_columns){ - dat[,col] = gsub("\\(.*\\)", "", dat[,col]) - #dat[dat[,col] == "",] = "0" - dat[,col] = as.numeric(dat[,col]) - dat[is.na(dat[,col]),col] = 0 -} - -regions = c("FR1", "CDR1", "FR2", "CDR2", "FR3") -if(empty.region.filter == "FR1") { - regions = c("CDR1", "FR2", "CDR2", "FR3") -} else if (empty.region.filter == "CDR1") { - regions = c("FR2", "CDR2", "FR3") -} else if (empty.region.filter == "FR2") { - regions = c("CDR2", "FR3") -} - -pdfplots = list() #save() this later to create the pdf plots in another script (maybe avoids the "address (nil), cause memory not mapped") - -sum_by_row = function(x, columns) { sum(as.numeric(x[columns]), na.rm=T) } - -print("aggregating data into new columns") - -VRegionMutations_columns = paste(regions, ".IMGT.Nb.of.mutations", sep="") -dat$VRegionMutations = apply(dat, FUN=sum_by_row, 1, columns=VRegionMutations_columns) - -VRegionNucleotides_columns = paste(regions, ".IMGT.Nb.of.nucleotides", sep="") -dat$FR3.IMGT.Nb.of.nucleotides = nchar(dat$FR3.IMGT.seq) -dat$VRegionNucleotides = apply(dat, FUN=sum_by_row, 1, columns=VRegionNucleotides_columns) - -transitionMutations_columns = paste(rep(regions, each=4), c(".IMGT.a.g", ".IMGT.g.a", ".IMGT.c.t", ".IMGT.t.c"), sep="") -dat$transitionMutations = apply(dat, FUN=sum_by_row, 1, columns=transitionMutations_columns) - -transversionMutations_columns = paste(rep(regions, each=8), c(".IMGT.a.c",".IMGT.c.a",".IMGT.a.t",".IMGT.t.a",".IMGT.g.c",".IMGT.c.g",".IMGT.g.t",".IMGT.t.g"), sep="") -dat$transversionMutations = apply(dat, FUN=sum_by_row, 1, columns=transversionMutations_columns) - -transitionMutationsAtGC_columns = paste(rep(regions, each=2), c(".IMGT.g.a",".IMGT.c.t"), sep="") -dat$transitionMutationsAtGC = apply(dat, FUN=sum_by_row, 1, columns=transitionMutationsAtGC_columns) - -totalMutationsAtGC_columns = paste(rep(regions, each=6), c(".IMGT.c.g",".IMGT.c.t",".IMGT.c.a",".IMGT.g.c",".IMGT.g.a",".IMGT.g.t"), sep="") -#totalMutationsAtGC_columns = paste(rep(regions, each=6), c(".IMGT.g.a",".IMGT.c.t",".IMGT.c.a",".IMGT.c.g",".IMGT.g.t"), sep="") -dat$totalMutationsAtGC = apply(dat, FUN=sum_by_row, 1, columns=totalMutationsAtGC_columns) - -transitionMutationsAtAT_columns = paste(rep(regions, each=2), c(".IMGT.a.g",".IMGT.t.c"), sep="") -dat$transitionMutationsAtAT = apply(dat, FUN=sum_by_row, 1, columns=transitionMutationsAtAT_columns) - -totalMutationsAtAT_columns = paste(rep(regions, each=6), c(".IMGT.a.g",".IMGT.a.c",".IMGT.a.t",".IMGT.t.g",".IMGT.t.c",".IMGT.t.a"), sep="") -#totalMutationsAtAT_columns = paste(rep(regions, each=5), c(".IMGT.a.g",".IMGT.t.c",".IMGT.a.c",".IMGT.g.c",".IMGT.t.g"), sep="") -dat$totalMutationsAtAT = apply(dat, FUN=sum_by_row, 1, columns=totalMutationsAtAT_columns) - -FRRegions = regions[grepl("FR", regions)] -CDRRegions = regions[grepl("CDR", regions)] - -FR_silentMutations_columns = paste(FRRegions, ".IMGT.Nb.of.silent.mutations", sep="") -dat$silentMutationsFR = apply(dat, FUN=sum_by_row, 1, columns=FR_silentMutations_columns) - -CDR_silentMutations_columns = paste(CDRRegions, ".IMGT.Nb.of.silent.mutations", sep="") -dat$silentMutationsCDR = apply(dat, FUN=sum_by_row, 1, columns=CDR_silentMutations_columns) - -FR_nonSilentMutations_columns = paste(FRRegions, ".IMGT.Nb.of.nonsilent.mutations", sep="") -dat$nonSilentMutationsFR = apply(dat, FUN=sum_by_row, 1, columns=FR_nonSilentMutations_columns) - -CDR_nonSilentMutations_columns = paste(CDRRegions, ".IMGT.Nb.of.nonsilent.mutations", sep="") -dat$nonSilentMutationsCDR = apply(dat, FUN=sum_by_row, 1, columns=CDR_nonSilentMutations_columns) - -mutation.sum.columns = c("Sequence.ID", "VRegionMutations", "VRegionNucleotides", "transitionMutations", "transversionMutations", "transitionMutationsAtGC", "transitionMutationsAtAT", "silentMutationsFR", "nonSilentMutationsFR", "silentMutationsCDR", "nonSilentMutationsCDR") -write.table(dat[,mutation.sum.columns], "mutation_by_id.txt", sep="\t",quote=F,row.names=F,col.names=T) - -setwd(outputdir) - -write.table(dat, input, sep="\t",quote=F,row.names=F,col.names=T) - -base.order.x = data.frame(base=c("A", "C", "G", "T"), order.x=1:4) -base.order.y = data.frame(base=c("T", "G", "C", "A"), order.y=1:4) - -calculate_result = function(i, gene, dat, matrx, f, fname, name){ - tmp = dat[grepl(paste("^", gene, ".*", sep=""), dat$best_match),] - - j = i - 1 - x = (j * 3) + 1 - y = (j * 3) + 2 - z = (j * 3) + 3 - - if(nrow(tmp) > 0){ - if(fname == "sum"){ - matrx[1,x] = round(f(tmp$VRegionMutations, na.rm=T), digits=1) - matrx[1,y] = round(f(tmp$VRegionNucleotides, na.rm=T), digits=1) - matrx[1,z] = round(f(matrx[1,x] / matrx[1,y]) * 100, digits=1) - } else { - matrx[1,x] = round(f(tmp$VRegionMutations, na.rm=T), digits=1) - matrx[1,y] = round(f(tmp$VRegionNucleotides, na.rm=T), digits=1) - matrx[1,z] = round(f(tmp$VRegionMutations / tmp$VRegionNucleotides) * 100, digits=1) - } - - matrx[2,x] = round(f(tmp$transitionMutations, na.rm=T), digits=1) - matrx[2,y] = round(f(tmp$VRegionMutations, na.rm=T), digits=1) - matrx[2,z] = round(matrx[2,x] / matrx[2,y] * 100, digits=1) - - matrx[3,x] = round(f(tmp$transversionMutations, na.rm=T), digits=1) - matrx[3,y] = round(f(tmp$VRegionMutations, na.rm=T), digits=1) - matrx[3,z] = round(matrx[3,x] / matrx[3,y] * 100, digits=1) - - matrx[4,x] = round(f(tmp$transitionMutationsAtGC, na.rm=T), digits=1) - matrx[4,y] = round(f(tmp$totalMutationsAtGC, na.rm=T), digits=1) - matrx[4,z] = round(matrx[4,x] / matrx[4,y] * 100, digits=1) - - matrx[5,x] = round(f(tmp$totalMutationsAtGC, na.rm=T), digits=1) - matrx[5,y] = round(f(tmp$VRegionMutations, na.rm=T), digits=1) - matrx[5,z] = round(matrx[5,x] / matrx[5,y] * 100, digits=1) - - matrx[6,x] = round(f(tmp$transitionMutationsAtAT, na.rm=T), digits=1) - matrx[6,y] = round(f(tmp$totalMutationsAtAT, na.rm=T), digits=1) - matrx[6,z] = round(matrx[6,x] / matrx[6,y] * 100, digits=1) - - matrx[7,x] = round(f(tmp$totalMutationsAtAT, na.rm=T), digits=1) - matrx[7,y] = round(f(tmp$VRegionMutations, na.rm=T), digits=1) - matrx[7,z] = round(matrx[7,x] / matrx[7,y] * 100, digits=1) - - matrx[8,x] = round(f(tmp$nonSilentMutationsFR, na.rm=T), digits=1) - matrx[8,y] = round(f(tmp$silentMutationsFR, na.rm=T), digits=1) - matrx[8,z] = round(matrx[8,x] / matrx[8,y], digits=1) - - matrx[9,x] = round(f(tmp$nonSilentMutationsCDR, na.rm=T), digits=1) - matrx[9,y] = round(f(tmp$silentMutationsCDR, na.rm=T), digits=1) - matrx[9,z] = round(matrx[9,x] / matrx[9,y], digits=1) - - if(fname == "sum"){ - - regions.fr = regions[grepl("FR", regions)] - regions.fr = paste(regions.fr, ".IMGT.Nb.of.nucleotides", sep="") - regions.cdr = regions[grepl("CDR", regions)] - regions.cdr = paste(regions.cdr, ".IMGT.Nb.of.nucleotides", sep="") - - if(length(regions.fr) > 1){ #in case there is only on FR region (rowSums needs >1 column) - matrx[10,x] = round(f(rowSums(tmp[,regions.fr], na.rm=T)), digits=1) - } else { - matrx[10,x] = round(f(tmp[,regions.fr], na.rm=T), digits=1) - } - matrx[10,y] = round(f(tmp$VRegionNucleotides, na.rm=T), digits=1) - matrx[10,z] = round(matrx[10,x] / matrx[10,y] * 100, digits=1) - - if(length(regions.cdr) > 1){ #in case there is only on CDR region - matrx[11,x] = round(f(rowSums(tmp[,regions.cdr], na.rm=T)), digits=1) - } else { - matrx[11,x] = round(f(tmp[,regions.cdr], na.rm=T), digits=1) - } - matrx[11,y] = round(f(tmp$VRegionNucleotides, na.rm=T), digits=1) - matrx[11,z] = round(matrx[11,x] / matrx[11,y] * 100, digits=1) - } - } - - transitionTable = data.frame(A=zeros,C=zeros,G=zeros,T=zeros) - row.names(transitionTable) = c("A", "C", "G", "T") - transitionTable["A","A"] = NA - transitionTable["C","C"] = NA - transitionTable["G","G"] = NA - transitionTable["T","T"] = NA - - if(nrow(tmp) > 0){ - for(nt1 in nts){ - for(nt2 in nts){ - if(nt1 == nt2){ - next - } - NT1 = LETTERS[letters == nt1] - NT2 = LETTERS[letters == nt2] - FR1 = paste("FR1.IMGT.", nt1, ".", nt2, sep="") - CDR1 = paste("CDR1.IMGT.", nt1, ".", nt2, sep="") - FR2 = paste("FR2.IMGT.", nt1, ".", nt2, sep="") - CDR2 = paste("CDR2.IMGT.", nt1, ".", nt2, sep="") - FR3 = paste("FR3.IMGT.", nt1, ".", nt2, sep="") - if (empty.region.filter == "leader"){ - transitionTable[NT1,NT2] = sum(tmp[,c(FR1, CDR1, FR2, CDR2, FR3)]) - } else if (empty.region.filter == "FR1") { - transitionTable[NT1,NT2] = sum(tmp[,c(CDR1, FR2, CDR2, FR3)]) - } else if (empty.region.filter == "CDR1") { - transitionTable[NT1,NT2] = sum(tmp[,c(FR2, CDR2, FR3)]) - } else if (empty.region.filter == "FR2") { - transitionTable[NT1,NT2] = sum(tmp[,c(CDR2, FR3)]) - } - } - } - transition = transitionTable - transition$id = names(transition) - - transition2 = melt(transition, id.vars="id") - - transition2 = merge(transition2, base.order.x, by.x="id", by.y="base") - - transition2 = merge(transition2, base.order.y, by.x="variable", by.y="base") - - transition2[is.na(transition2$value),]$value = 0 - - if(any(transition2$value != 0)){ #having a transition table filled with 0 is bad - print("Plotting heatmap and transition") - png(filename=paste("transitions_stacked_", name, ".png", sep="")) - p = ggplot(transition2, aes(factor(reorder(id, order.x)), y=value, fill=factor(reorder(variable, order.y)))) + geom_bar(position="fill", stat="identity", colour="black") #stacked bar - p = p + xlab("From base") + ylab("") + ggtitle("Bargraph transition information") + guides(fill=guide_legend(title=NULL)) - p = p + theme(panel.background = element_rect(fill = "white", colour="black"), text = element_text(size=16, colour="black")) + scale_fill_manual(values=c("A" = "blue4", "G" = "lightblue1", "C" = "olivedrab3", "T" = "olivedrab4")) - #p = p + scale_colour_manual(values=c("A" = "black", "G" = "black", "C" = "black", "T" = "black")) - print(p) - dev.off() - - pdfplots[[paste("transitions_stacked_", name, ".pdf", sep="")]] <<- p - - png(filename=paste("transitions_heatmap_", name, ".png", sep="")) - p = ggplot(transition2, aes(factor(reorder(variable, -order.y)), factor(reorder(id, -order.x)))) + geom_tile(aes(fill = value)) + scale_fill_gradient(low="white", high="steelblue") #heatmap - p = p + xlab("To base") + ylab("From Base") + ggtitle("Heatmap transition information") + theme(panel.background = element_rect(fill = "white", colour="black"), text = element_text(size=16, colour="black")) - print(p) - dev.off() - - pdfplots[[paste("transitions_heatmap_", name, ".pdf", sep="")]] <<- p - } else { - #print("No data to plot") - } - } - - #print(paste("writing value file: ", name, "_", fname, "_value.txt" ,sep="")) - write.table(x=transitionTable, file=paste("transitions_", name ,"_", fname, ".txt", sep=""), sep=",",quote=F,row.names=T,col.names=NA) - write.table(x=tmp[,c("Sequence.ID", "best_match", "chunk_hit_percentage", "nt_hit_percentage", "start_locations")], file=paste("matched_", name , "_", fname, ".txt", sep=""), sep="\t",quote=F,row.names=F,col.names=T) - cat(matrx[1,x], file=paste(name, "_", fname, "_value.txt" ,sep="")) - cat(nrow(tmp), file=paste(name, "_", fname, "_n.txt" ,sep="")) - #print(paste(fname, name, nrow(tmp))) - matrx -} -nts = c("a", "c", "g", "t") -zeros=rep(0, 4) -funcs = c(median, sum, mean) -fnames = c("median", "sum", "mean") - -print("Creating result tables") - -for(i in 1:length(funcs)){ - func = funcs[[i]] - fname = fnames[[i]] - - print(paste("Creating table for", fname)) - - rows = 9 - if(fname == "sum"){ - rows = 11 - } - matrx = matrix(data = 0, ncol=((length(genes) + 1) * 3),nrow=rows) - for(i in 1:length(genes)){ - matrx = calculate_result(i, genes[i], dat, matrx, func, fname, genes[i]) - } - matrx = calculate_result(i + 1, ".*", dat[!grepl("unmatched", dat$best_match),], matrx, func, fname, name="all") - - result = data.frame(matrx) - if(fname == "sum"){ - row.names(result) = c("Number of Mutations (%)", "Transitions (%)", "Transversions (%)", "Transitions at G C (%)", "Targeting of G C (%)", "Transitions at A T (%)", "Targeting of A T (%)", "FR R/S (ratio)", "CDR R/S (ratio)", "nt in FR", "nt in CDR") - } else { - row.names(result) = c("Number of Mutations (%)", "Transitions (%)", "Transversions (%)", "Transitions at G C (%)", "Targeting of G C (%)", "Transitions at A T (%)", "Targeting of A T (%)", "FR R/S (ratio)", "CDR R/S (ratio)") - } - write.table(x=result, file=paste("mutations_", fname, ".txt", sep=""), sep=",",quote=F,row.names=T,col.names=F) -} - -print("Adding median number of mutations to sum table") -sum.table = read.table("mutations_sum.txt", sep=",", header=F) -median.table = read.table("mutations_median.txt", sep=",", header=F) - -new.table = sum.table[1,] -new.table[2,] = median.table[1,] -new.table[3:12,] = sum.table[2:11,] -new.table[,1] = as.character(new.table[,1]) -new.table[2,1] = "Median of Number of Mutations (%)" - -#sum.table = sum.table[c("Number of Mutations (%)", "Median of Number of Mutations (%)", "Transition (%)", "Transversions (%)", "Transitions at G C (%)", "Targeting of G C (%)", "Transitions at A T (%)", "Targeting of A T (%)", "FR R/S (ratio)", "CDR R/S (ratio)", "nt in FR", "nt in CDR"),] - -write.table(x=new.table, file="mutations_sum.txt", sep=",",quote=F,row.names=F,col.names=F) - -print("Plotting IGA piechart") - -dat = dat[!grepl("^unmatched", dat$best_match),] - -#blegh - -genesForPlot = dat[grepl("IGA", dat$best_match),]$best_match - -if(length(genesForPlot) > 0){ - genesForPlot = data.frame(table(genesForPlot)) - colnames(genesForPlot) = c("Gene","Freq") - genesForPlot$label = paste(genesForPlot$Gene, "-", genesForPlot$Freq) - - pc = ggplot(genesForPlot, aes(x = factor(1), y=Freq, fill=Gene)) - pc = pc + geom_bar(width = 1, stat = "identity") + scale_fill_manual(labels=genesForPlot$label, values=c("IGA1" = "lightblue1", "IGA2" = "blue4")) - pc = pc + coord_polar(theta="y") + scale_y_continuous(breaks=NULL) - pc = pc + theme(panel.background = element_rect(fill = "white", colour="black"), text = element_text(size=16, colour="black"), axis.title=element_blank(), axis.text=element_blank(), axis.ticks=element_blank()) - pc = pc + xlab(" ") + ylab(" ") + ggtitle(paste("IGA subclass distribution", "( n =", sum(genesForPlot$Freq), ")")) - write.table(genesForPlot, "IGA_pie.txt", sep="\t",quote=F,row.names=F,col.names=T) - - png(filename="IGA.png") - print(pc) - dev.off() - - pdfplots[["IGA.pdf"]] <- pc -} - -print("Plotting IGG piechart") - -genesForPlot = dat[grepl("IGG", dat$best_match),]$best_match - -if(length(genesForPlot) > 0){ - genesForPlot = data.frame(table(genesForPlot)) - colnames(genesForPlot) = c("Gene","Freq") - genesForPlot$label = paste(genesForPlot$Gene, "-", genesForPlot$Freq) - - pc = ggplot(genesForPlot, aes(x = factor(1), y=Freq, fill=Gene)) - pc = pc + geom_bar(width = 1, stat = "identity") + scale_fill_manual(labels=genesForPlot$label, values=c("IGG1" = "olivedrab3", "IGG2" = "red", "IGG3" = "gold", "IGG4" = "darkred")) - pc = pc + coord_polar(theta="y") + scale_y_continuous(breaks=NULL) - pc = pc + theme(panel.background = element_rect(fill = "white", colour="black"), text = element_text(size=16, colour="black"), axis.title=element_blank(), axis.text=element_blank(), axis.ticks=element_blank()) - pc = pc + xlab(" ") + ylab(" ") + ggtitle(paste("IGG subclass distribution", "( n =", sum(genesForPlot$Freq), ")")) - write.table(genesForPlot, "IGG_pie.txt", sep="\t",quote=F,row.names=F,col.names=T) - - png(filename="IGG.png") - print(pc) - dev.off() - - pdfplots[["IGG.pdf"]] <- pc -} - -print("Plotting scatterplot") - -dat$percentage_mutations = round(dat$VRegionMutations / dat$VRegionNucleotides * 100, 2) -dat.clss = dat - -dat.clss$best_match = substr(dat.clss$best_match, 0, 3) - -dat.clss = rbind(dat, dat.clss) - -p = ggplot(dat.clss, aes(best_match, percentage_mutations)) -p = p + geom_point(aes(colour=best_match), position="jitter") + geom_boxplot(aes(middle=mean(percentage_mutations)), alpha=0.1, outlier.shape = NA) -p = p + xlab("Subclass") + ylab("Frequency") + ggtitle("Frequency scatter plot") + theme(panel.background = element_rect(fill = "white", colour="black"), text = element_text(size=16, colour="black")) -p = p + scale_fill_manual(values=c("IGA" = "blue4", "IGA1" = "lightblue1", "IGA2" = "blue4", "IGG" = "olivedrab3", "IGG1" = "olivedrab3", "IGG2" = "red", "IGG3" = "gold", "IGG4" = "darkred", "IGM" = "darkviolet", "IGE" = "darkorange", "all" = "blue4")) -p = p + scale_colour_manual(guide = guide_legend(title = "Subclass"), values=c("IGA" = "blue4", "IGA1" = "lightblue1", "IGA2" = "blue4", "IGG" = "olivedrab3", "IGG1" = "olivedrab3", "IGG2" = "red", "IGG3" = "gold", "IGG4" = "darkred", "IGM" = "darkviolet", "IGE" = "darkorange", "all" = "blue4")) - -png(filename="scatter.png") -print(p) -dev.off() - -pdfplots[["scatter.pdf"]] <- p - -write.table(dat[,c("Sequence.ID", "best_match", "VRegionMutations", "VRegionNucleotides", "percentage_mutations")], "scatter.txt", sep="\t",quote=F,row.names=F,col.names=T) - -print("Plotting frequency ranges plot") - -dat$best_match_class = substr(dat$best_match, 0, 3) -freq_labels = c("0", "0-2", "2-5", "5-10", "10-15", "15-20", "20") -dat$frequency_bins = cut(dat$percentage_mutations, breaks=c(-Inf, 0, 2,5,10,15,20, Inf), labels=freq_labels) - -frequency_bins_sum = data.frame(data.table(dat)[, list(class_sum=sum(.N)), by=c("best_match_class")]) - -frequency_bins_data = data.frame(data.table(dat)[, list(frequency_count=.N), by=c("best_match_class", "frequency_bins")]) - -frequency_bins_data = merge(frequency_bins_data, frequency_bins_sum, by="best_match_class") - -frequency_bins_data$frequency = round(frequency_bins_data$frequency_count / frequency_bins_data$class_sum * 100, 2) - -p = ggplot(frequency_bins_data, aes(frequency_bins, frequency)) -p = p + geom_bar(aes(fill=best_match_class), stat="identity", position="dodge") + theme(panel.background = element_rect(fill = "white", colour="black"), text = element_text(size=16, colour="black")) -p = p + xlab("Frequency ranges") + ylab("Frequency") + ggtitle("Mutation Frequencies by class") + scale_fill_manual(guide = guide_legend(title = "Class"), values=c("IGA" = "blue4", "IGG" = "olivedrab3", "IGM" = "darkviolet", "IGE" = "darkorange", "all" = "blue4")) - -png(filename="frequency_ranges.png") -print(p) -dev.off() - -pdfplots[["frequency_ranges.pdf"]] <- p - -save(pdfplots, file="pdfplots.RData") - -frequency_bins_data_by_class = frequency_bins_data - -frequency_bins_data_by_class = frequency_bins_data_by_class[order(frequency_bins_data_by_class$best_match_class, frequency_bins_data_by_class$frequency_bins),] - -frequency_bins_data_by_class$frequency_bins = gsub("-", " to ", frequency_bins_data_by_class$frequency_bins) -frequency_bins_data_by_class[frequency_bins_data_by_class$frequency_bins == "20", c("frequency_bins")] = "20 or higher" -frequency_bins_data_by_class[frequency_bins_data_by_class$frequency_bins == "0", c("frequency_bins")] = "0 or lower" - -write.table(frequency_bins_data_by_class, "frequency_ranges_classes.txt", sep="\t",quote=F,row.names=F,col.names=T) - -frequency_bins_data = data.frame(data.table(dat)[, list(frequency_count=.N), by=c("best_match", "best_match_class", "frequency_bins")]) - -frequency_bins_sum = data.frame(data.table(dat)[, list(class_sum=sum(.N)), by=c("best_match")]) - -frequency_bins_data = merge(frequency_bins_data, frequency_bins_sum, by="best_match") - -frequency_bins_data$frequency = round(frequency_bins_data$frequency_count / frequency_bins_data$class_sum * 100, 2) - -frequency_bins_data = frequency_bins_data[order(frequency_bins_data$best_match, frequency_bins_data$frequency_bins),] -frequency_bins_data$frequency_bins = gsub("-", " to ", frequency_bins_data$frequency_bins) -frequency_bins_data[frequency_bins_data$frequency_bins == "20", c("frequency_bins")] = "20 or higher" -frequency_bins_data[frequency_bins_data$frequency_bins == "0", c("frequency_bins")] = "0 or lower" - -write.table(frequency_bins_data, "frequency_ranges_subclasses.txt", sep="\t",quote=F,row.names=F,col.names=T) - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
--- a/shm_csr.xml Tue Sep 01 16:03:44 2020 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,240 +0,0 @@ -<tool id="shm_csr" name="SHM & CSR pipeline" version="1.0"> - <description></description> - <requirements> - <requirement type="package" version="2.7">python</requirement> - <requirement type="package" version="1.16.0">numpy</requirement> - <requirement type="package" version="1.2.0">xlrd</requirement> - <requirement type="package" version="3.0.0">r-ggplot2</requirement> - <requirement type="package" version="1.4.3">r-reshape2</requirement> - <requirement type="package" version="0.5.0">r-scales</requirement> - <requirement type="package" version="3.4_5">r-seqinr</requirement> - <requirement type="package" version="1.11.4">r-data.table</requirement> - </requirements> - <command interpreter="bash"> - #if str ( $filter_unique.filter_unique_select ) == "remove": - wrapper.sh $in_file custom $out_file $out_file.files_path "${in_file.name}" "-" $functionality $unique $naive_output_cond.naive_output $naive_output_ca $naive_output_cg $naive_output_cm $naive_output_ce $naive_output_all $filter_unique.filter_unique_select $filter_unique.filter_unique_clone_count $class_filter_cond.class_filter $empty_region_filter $fast - #else: - wrapper.sh $in_file custom $out_file $out_file.files_path "${in_file.name}" "-" $functionality $unique $naive_output_cond.naive_output $naive_output_ca $naive_output_cg $naive_output_cm $naive_output_ce $naive_output_all $filter_unique.filter_unique_select 2 $class_filter_cond.class_filter $empty_region_filter $fast - #end if - </command> - <inputs> - <param name="in_file" type="data" format="data" label="IMGT zip file to be analysed" /> - <param name="empty_region_filter" type="select" label="Sequence starts at" help="" > - <option value="leader" selected="true">Leader: include FR1, CDR1, FR2, CDR2, FR3 in filters</option> - <option value="FR1" selected="true">FR1: include CDR1,FR2,CDR2,FR3 in filters</option> - <option value="CDR1">CDR1: include FR2,CDR2,FR3 in filters</option> - <option value="FR2">FR2: include CDR2,FR3 in filters</option> - </param> - <param name="functionality" type="select" label="Functionality filter" help="" > - <option value="productive" selected="true">Productive (Productive and Productive see comment)</option> - <option value="unproductive">Unproductive (Unproductive and Unproductive see comment)</option> - <option value="remove_unknown">Productive and Unproductive (Productive, Productive see comment, Unproductive, Unproductive and Unproductive see comment)</option> - </param> - <conditional name="filter_unique"> - <param name="filter_unique_select" type="select" label="Filter unique sequences" help="See below for an example."> - <option value="remove" selected="true">Remove uniques (Based on nucleotide sequence + C)</option> - <option value="remove_vjaa">Remove uniques (Based on V+J+CDR3 (AA))</option> - <option value="keep">Keep uniques (Based on nucleotide sequence + C)</option> - <option value="no">No</option> - </param> - <when value="remove"> - <param name="filter_unique_clone_count" size="4" type="integer" label="How many sequences should be in a group to keep 1 of them" value="2" min="2"/> - </when> - <when value="keep"></when> - <when value="no"></when> - </conditional> - <param name="unique" type="select" label="Remove duplicates based on" help="" > - <option value="VGene,CDR3.IMGT.AA,best_match_class">Top.V.Gene, CDR3 (AA), C region</option> - <option value="VGene,CDR3.IMGT.AA">Top.V.Gene, CDR3 (AA)</option> - <option value="CDR3.IMGT.AA,best_match_class">CDR3 (AA), C region</option> - <option value="CDR3.IMGT.AA">CDR3 (AA)</option> - - <option value="VGene,CDR3.IMGT.seq,best_match_class">Top.V.Gene, CDR3 (nt), C region</option> - <option value="VGene,CDR3.IMGT.seq">Top.V.Gene, CDR3 (nt)</option> - <option value="CDR3.IMGT.seq,best_match_class">CDR3 (nt), C region</option> - <option value="CDR3.IMGT.seq">CDR3 (nt)</option> - <option value="Sequence.ID" selected="true">Don't remove duplicates</option> - </param> - <conditional name="class_filter_cond"> - <param name="class_filter" type="select" label="Human Class/Subclass filter" help="" > - <option value="70_70" selected="true">>70% class and >70% subclass</option> - <option value="60_55">>60% class and >55% subclass</option> - <option value="70_0">>70% class</option> - <option value="60_0">>60% class</option> - <option value="19_0">>19% class</option> - <option value="101_101">Do not assign (sub)class</option> - </param> - <when value="70_70"></when> - <when value="60_55"></when> - <when value="70_0"></when> - <when value="60_0"></when> - <when value="19_0"></when> - <when value="101_101"></when> - </conditional> - <conditional name="naive_output_cond"> - <param name="naive_output" type="select" label="Output new IMGT archives per class into your history?"> - <option value="yes">Yes</option> - <option value="no" selected="true">No</option> - </param> - <when value="yes"></when> - <when value="no"></when> - </conditional> - <param name="fast" type="select" label="Fast" help="Skips generating the new ZIP files and Change-O/Baseline" > - <option value="yes">Yes</option> - <option value="no" selected="true">No</option> - </param> - </inputs> - <outputs> - <data format="html" name="out_file" label = "SHM & CSR on ${in_file.name}"/> - <data format="imgt_archive" name="naive_output_ca" label = "Filtered IMGT IGA: ${in_file.name}" > - <filter>naive_output_cond['naive_output'] == "yes"</filter> - <filter>class_filter_cond['class_filter'] != "101_101"</filter> - </data> - <data format="imgt_archive" name="naive_output_cg" label = "Filtered IMGT IGG: ${in_file.name}" > - <filter>naive_output_cond['naive_output'] == "yes"</filter> - <filter>class_filter_cond['class_filter'] != "101_101"</filter> - </data> - <data format="imgt_archive" name="naive_output_cm" label = "Filtered IMGT IGM: ${in_file.name}" > - <filter>naive_output_cond['naive_output'] == "yes"</filter> - <filter>class_filter_cond['class_filter'] != "101_101"</filter> - </data> - <data format="imgt_archive" name="naive_output_ce" label = "Filtered IMGT IGE: ${in_file.name}" > - <filter>naive_output_cond['naive_output'] == "yes"</filter> - <filter>class_filter_cond['class_filter'] != "101_101"</filter> - </data> - <data format="imgt_archive" name="naive_output_all" label = "Filtered IMGT all: ${in_file.name}" > - <filter>naive_output_cond['naive_output'] == "yes"</filter> - <filter>class_filter_cond['class_filter'] == "101_101"</filter> - </data> - </outputs> - <tests> - <test> - <param name="fast" value="yes"/> - <output name="out_file" file="test1.html"/> - </test> - </tests> - <help> -<![CDATA[ -**References** - -Yaari, G. and Uduman, M. and Kleinstein, S. H. (2012). Quantifying selection in high-throughput Immunoglobulin sequencing data sets. In *Nucleic Acids Research, 40 (17), pp. e134–e134.* [`doi:10.1093/nar/gks457`_] - -.. _doi:10.1093/nar/gks457: http://dx.doi.org/10.1093/nar/gks457 - -Gupta, Namita T. and Vander Heiden, Jason A. and Uduman, Mohamed and Gadala-Maria, Daniel and Yaari, Gur and Kleinstein, Steven H. (2015). Change-O: a toolkit for analyzing large-scale B cell immunoglobulin repertoire sequencing data: Table 1. *In Bioinformatics, 31 (20), pp. 3356–3358.* [`doi:10.1093/bioinformatics/btv359`_] - -.. _doi:10.1093/bioinformatics/btv359: http://dx.doi.org/10.1093/bioinformatics/btv359 - ------ - -**Input files** - -IMGT/HighV-QUEST .zip and .txz are accepted as input files. The file to be analysed can be selected using the dropdown menu. - -.. class:: infomark - -Note: Files can be uploaded by using “get data†and “upload file†and selecting “IMGT archive“ as a file type. Special characters should be prevented in the file names of the uploaded samples as these can give errors when running the immune repertoire pipeline. Underscores are allowed in the file names. - ------ - -**Sequence starts at** - -Identifies the region which will be included in the analysis (analysed region) - -- Sequences which are missing a gene region (FR1/CDR1 etc) in the analysed region are excluded. -- Sequences containing an ambiguous base in the analysed region or the CDR3 are excluded. -- All other filtering/analysis is based on the analysed region. - ------ - -**Functionality filter** - -Allows filtering on productive rearrangements, unproductive rearrangements or both based on the assignment provided by IMGT. - -**Filter unique sequences** - -*Remove unique:* - - -This filter consists of two different steps. - -Step 1: removes all sequences of which the nucleotide sequence in the “analysed region†and the CDR3 (see sequence starts at filter) occurs only once. (Sub)classes are not taken into account in this filter step. - -Step 2: removes all duplicate sequences (sequences with the exact same nucleotide sequence in the analysed region, the CDR3 and the same (sub)class). - -.. class:: infomark - -This means that sequences with the same nucleotide sequence but a different (sub)class will be included in the results of both (sub)classes. - -*Keep unique:* - -Removes all duplicate sequences (sequences with the exact same nucleotide sequence in the analysed region and the same (sub)class). - -Example of the sequences that are included using either the “remove unique filter†or the “keep unique filter†- -+--------------------------+ -| unique filter | -+--------+--------+--------+ -| values | remove | keep | -+--------+--------+--------+ -| A | A | A | -+--------+--------+--------+ -| A | B | B | -+--------+--------+--------+ -| B | D | C | -+--------+--------+--------+ -| B | | D | -+--------+--------+--------+ -| C | | | -+--------+--------+--------+ -| D | | | -+--------+--------+--------+ -| D | | | -+--------+--------+--------+ - ------ - -**Remove duplicates based on** - -Allows the selection of a single sequence per clone. Different definitions of a clone can be chosen. - -.. class:: infomark - -Note: The first sequence (in the data set) of each clone is always included in the analysis. When the first matched sequence is unmatched (no subclass assigned) the first matched sequence will be included. This means that altering the data order (by for instance sorting) can change the sequence which is included in the analysis and therefore slightly influences the results. - ------ - -**Human Class/Subclass filter** - -.. class:: warningmark - -Note: This filter should only be applied when analysing human IGH data in which a (sub)class specific sequence is present. Otherwise please select the do not assign (sub)class option to prevent errors when running the pipeline. - -The class percentage is based on the ‘chunk hit percentage’ (see below). The subclass percentage is based on the ‘nt hit percentage’ (see below). - -The SHM & CSR pipeline identifies human Cµ, Cα, Cγ and Cε constant genes by dividing the reference sequences for the subclasses (NG_001019) in 8 nucleotide chunks which overlap by 4 nucleotides. These overlapping chunks are then individually aligned in the right order to each input sequence. This alignment is used to calculate the chunck hit percentage and the nt hit percentage. - -*Chunk hit percentage*: The percentage of the chunks that is aligned - -*Nt hit percentage*: The percentage of chunks covering the subclass specific nucleotide match with the different subclasses. The most stringent filter for the subclass is 70% ‘nt hit percentage’ which means that 5 out of 7 subclass specific nucleotides for Cα or 6 out of 8 subclass specific nucleotides of Cγ should match with the specific subclass. -The option “>25% class†can be chosen when you only are interested in the class (Cα/Cγ/Cµ/Cɛ) of your sequences and the length of your sequence is not long enough to assign the subclasses. - ------ - -**Output new IMGT archives per class into your history?** - -If yes is selected, additional output files (one for each class) will be added to the history which contain information of the sequences that passed the selected filtering criteria. These files are in the same format as the IMGT/HighV-QUEST output files and therefore are also compatible with many other analysis programs, such as the Immune repertoire pipeline. - ------ - -**Execute** - -Upon pressing execute a new analysis is added to your history (right side of the page). Initially this analysis will be grey, after initiating the analysis colour of the analysis in the history will change to yellow. When the analysis is finished it will turn green in the history. Now the analysis can be opened by clicking on the eye icon on the analysis of interest. When an analysis turns red an error has occurred when running the analysis. If you click on the analysis title additional information can be found on the analysis. In addition a bug icon appears. Here more information on the error can be found. - -]]> - </help> - <citations> - <citation type="doi">10.1093/nar/gks457</citation> - <citation type="doi">10.1093/bioinformatics/btv359</citation> - </citations> -</tool>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/shm_csr/.gitattributes Fri Feb 19 15:08:51 2021 +0000 @@ -0,0 +1,2 @@ +# Auto detect text files and perform LF normalization +* text=auto
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/shm_csr/.gitignore Fri Feb 19 15:08:51 2021 +0000 @@ -0,0 +1,4 @@ + +shm_csr\.tar\.gz + +\.vscode/settings\.json
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/shm_csr/LICENSE Fri Feb 19 15:08:51 2021 +0000 @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2019 david + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/shm_csr/README.md Fri Feb 19 15:08:51 2021 +0000 @@ -0,0 +1,13 @@ +# SHM CSR + +Somatic hypermutation and class switch recombination pipeline. +The docker version can be found [here](https://github.com/ErasmusMC-Bioinformatics/ARGalaxy-docker). + +# Dependencies +-------------------- +[Python 2.7](https://www.python.org/) +[Change-O](https://changeo.readthedocs.io/en/version-0.4.4/) +[Baseline](http://selection.med.yale.edu/baseline/) +[R data.table](https://cran.r-project.org/web/packages/data.table/data.table.pdf) +[R ggplot2](https://cran.r-project.org/web/packages/ggplot2/ggplot2.pdf) +[R reshape2](https://cran.r-project.org/web/packages/reshape/reshape.pdf)
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/shm_csr/aa_histogram.r Fri Feb 19 15:08:51 2021 +0000 @@ -0,0 +1,69 @@ +library(ggplot2) + +args <- commandArgs(trailingOnly = TRUE) + +mutations.by.id.file = args[1] +absent.aa.by.id.file = args[2] +genes = strsplit(args[3], ",")[[1]] +genes = c(genes, "") +outdir = args[4] + + +print("---------------- read input ----------------") + +mutations.by.id = read.table(mutations.by.id.file, sep="\t", fill=T, header=T, quote="") +absent.aa.by.id = read.table(absent.aa.by.id.file, sep="\t", fill=T, header=T, quote="") + +for(gene in genes){ + graph.title = paste(gene, "AA mutation frequency") + if(gene == ""){ + mutations.by.id.gene = mutations.by.id[!grepl("unmatched", mutations.by.id$best_match),] + absent.aa.by.id.gene = absent.aa.by.id[!grepl("unmatched", absent.aa.by.id$best_match),] + + graph.title = "AA mutation frequency all" + } else { + mutations.by.id.gene = mutations.by.id[grepl(paste("^", gene, sep=""), mutations.by.id$best_match),] + absent.aa.by.id.gene = absent.aa.by.id[grepl(paste("^", gene, sep=""), absent.aa.by.id$best_match),] + } + print(paste("nrow", gene, nrow(absent.aa.by.id.gene))) + if(nrow(mutations.by.id.gene) == 0){ + next + } + + mutations.at.position = colSums(mutations.by.id.gene[,-c(1,2)]) + aa.at.position = colSums(absent.aa.by.id.gene[,-c(1,2,3,4)]) + + dat_freq = mutations.at.position / aa.at.position + dat_freq[is.na(dat_freq)] = 0 + dat_dt = data.frame(i=1:length(dat_freq), freq=dat_freq) + + + print("---------------- plot ----------------") + + m = ggplot(dat_dt, aes(x=i, y=freq)) + theme(axis.text.x = element_text(angle = 90, hjust = 1), text = element_text(size=13, colour="black")) + m = m + geom_bar(stat="identity", colour = "black", fill = "darkgrey", alpha=0.8) + scale_x_continuous(breaks=dat_dt$i, labels=dat_dt$i) + m = m + annotate("segment", x = 0.5, y = -0.05, xend=26.5, yend=-0.05, colour="darkgreen", size=1) + annotate("text", x = 13, y = -0.1, label="FR1") + m = m + annotate("segment", x = 26.5, y = -0.07, xend=38.5, yend=-0.07, colour="darkblue", size=1) + annotate("text", x = 32.5, y = -0.15, label="CDR1") + m = m + annotate("segment", x = 38.5, y = -0.05, xend=55.5, yend=-0.05, colour="darkgreen", size=1) + annotate("text", x = 47, y = -0.1, label="FR2") + m = m + annotate("segment", x = 55.5, y = -0.07, xend=65.5, yend=-0.07, colour="darkblue", size=1) + annotate("text", x = 60.5, y = -0.15, label="CDR2") + m = m + annotate("segment", x = 65.5, y = -0.05, xend=104.5, yend=-0.05, colour="darkgreen", size=1) + annotate("text", x = 85, y = -0.1, label="FR3") + m = m + expand_limits(y=c(-0.1,1)) + xlab("AA position") + ylab("Frequency") + ggtitle(graph.title) + m = m + theme(panel.background = element_rect(fill = "white", colour="black"), panel.grid.major.y = element_line(colour = "black"), panel.grid.major.x = element_blank()) + #m = m + scale_colour_manual(values=c("black")) + + print("---------------- write/print ----------------") + + + dat.sums = data.frame(index=1:length(mutations.at.position), mutations.at.position=mutations.at.position, aa.at.position=aa.at.position) + + write.table(dat.sums, paste(outdir, "/aa_histogram_sum_", gene, ".txt", sep=""), sep="\t",quote=F,row.names=F,col.names=T) + write.table(mutations.by.id.gene, paste(outdir, "/aa_histogram_count_", gene, ".txt", sep=""), sep="\t",quote=F,row.names=F,col.names=T) + write.table(absent.aa.by.id.gene, paste(outdir, "/aa_histogram_absent_", gene, ".txt", sep=""), sep="\t",quote=F,row.names=F,col.names=T) + write.table(dat_dt, paste(outdir, "/aa_histogram_", gene, ".txt", sep=""), sep="\t",quote=F,row.names=F,col.names=T) + + png(filename=paste(outdir, "/aa_histogram_", gene, ".png", sep=""), width=1280, height=720) + print(m) + dev.off() + + ggsave(paste(outdir, "/aa_histogram_", gene, ".pdf", sep=""), m, width=14, height=7) +}
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/shm_csr/baseline/Baseline_Functions.r Fri Feb 19 15:08:51 2021 +0000 @@ -0,0 +1,2287 @@ +######################################################################################### +# License Agreement +# +# THIS WORK IS PROVIDED UNDER THE TERMS OF THIS CREATIVE COMMONS PUBLIC LICENSE +# ("CCPL" OR "LICENSE"). THE WORK IS PROTECTED BY COPYRIGHT AND/OR OTHER +# APPLICABLE LAW. ANY USE OF THE WORK OTHER THAN AS AUTHORIZED UNDER THIS LICENSE +# OR COPYRIGHT LAW IS PROHIBITED. +# +# BY EXERCISING ANY RIGHTS TO THE WORK PROVIDED HERE, YOU ACCEPT AND AGREE TO BE +# BOUND BY THE TERMS OF THIS LICENSE. TO THE EXTENT THIS LICENSE MAY BE CONSIDERED +# TO BE A CONTRACT, THE LICENSOR GRANTS YOU THE RIGHTS CONTAINED HERE IN +# CONSIDERATION OF YOUR ACCEPTANCE OF SUCH TERMS AND CONDITIONS. +# +# BASELIne: Bayesian Estimation of Antigen-Driven Selection in Immunoglobulin Sequences +# Coded by: Mohamed Uduman & Gur Yaari +# Copyright 2012 Kleinstein Lab +# Version: 1.3 (01/23/2014) +######################################################################################### + +# Global variables + + FILTER_BY_MUTATIONS = 1000 + + # Nucleotides + NUCLEOTIDES = c("A","C","G","T") + + # Amino Acids + AMINO_ACIDS <- c("F", "F", "L", "L", "S", "S", "S", "S", "Y", "Y", "*", "*", "C", "C", "*", "W", "L", "L", "L", "L", "P", "P", "P", "P", "H", "H", "Q", "Q", "R", "R", "R", "R", "I", "I", "I", "M", "T", "T", "T", "T", "N", "N", "K", "K", "S", "S", "R", "R", "V", "V", "V", "V", "A", "A", "A", "A", "D", "D", "E", "E", "G", "G", "G", "G") + names(AMINO_ACIDS) <- c("TTT", "TTC", "TTA", "TTG", "TCT", "TCC", "TCA", "TCG", "TAT", "TAC", "TAA", "TAG", "TGT", "TGC", "TGA", "TGG", "CTT", "CTC", "CTA", "CTG", "CCT", "CCC", "CCA", "CCG", "CAT", "CAC", "CAA", "CAG", "CGT", "CGC", "CGA", "CGG", "ATT", "ATC", "ATA", "ATG", "ACT", "ACC", "ACA", "ACG", "AAT", "AAC", "AAA", "AAG", "AGT", "AGC", "AGA", "AGG", "GTT", "GTC", "GTA", "GTG", "GCT", "GCC", "GCA", "GCG", "GAT", "GAC", "GAA", "GAG", "GGT", "GGC", "GGA", "GGG") + names(AMINO_ACIDS) <- names(AMINO_ACIDS) + + #Amino Acid Traits + #"*" "A" "C" "D" "E" "F" "G" "H" "I" "K" "L" "M" "N" "P" "Q" "R" "S" "T" "V" "W" "Y" + #B = "Hydrophobic/Burried" N = "Intermediate/Neutral" S="Hydrophilic/Surface") + TRAITS_AMINO_ACIDS_CHOTHIA98 <- c("*","N","B","S","S","B","N","N","B","S","B","B","S","N","S","S","N","N","B","B","N") + names(TRAITS_AMINO_ACIDS_CHOTHIA98) <- sort(unique(AMINO_ACIDS)) + TRAITS_AMINO_ACIDS <- array(NA,21) + + # Codon Table + CODON_TABLE <- as.data.frame(matrix(NA,ncol=64,nrow=12)) + + # Substitution Model: Smith DS et al. 1996 + substitution_Literature_Mouse <- matrix(c(0, 0.156222928, 0.601501588, 0.242275484, 0.172506739, 0, 0.241239892, 0.586253369, 0.54636291, 0.255795364, 0, 0.197841727, 0.290240811, 0.467680608, 0.24207858, 0),nrow=4,byrow=T,dimnames=list(NUCLEOTIDES,NUCLEOTIDES)) + substitution_Flu_Human <- matrix(c(0,0.2795596,0.5026927,0.2177477,0.1693210,0,0.3264723,0.5042067,0.4983549,0.3328321,0,0.1688130,0.2021079,0.4696077,0.3282844,0),4,4,byrow=T,dimnames=list(NUCLEOTIDES,NUCLEOTIDES)) + substitution_Flu25_Human <- matrix(c(0,0.2580641,0.5163685,0.2255674,0.1541125,0,0.3210224,0.5248651,0.5239281,0.3101292,0,0.1659427,0.1997207,0.4579444,0.3423350,0),4,4,byrow=T,dimnames=list(NUCLEOTIDES,NUCLEOTIDES)) + load("FiveS_Substitution.RData") + + # Mutability Models: Shapiro GS et al. 2002 + triMutability_Literature_Human <- matrix(c(0.24, 1.2, 0.96, 0.43, 2.14, 2, 1.11, 1.9, 0.85, 1.83, 2.36, 1.31, 0.82, 0.52, 0.89, 1.33, 1.4, 0.82, 1.83, 0.73, 1.83, 1.62, 1.53, 0.57, 0.92, 0.42, 0.42, 1.47, 3.44, 2.58, 1.18, 0.47, 0.39, 1.12, 1.8, 0.68, 0.47, 2.19, 2.35, 2.19, 1.05, 1.84, 1.26, 0.28, 0.98, 2.37, 0.66, 1.58, 0.67, 0.92, 1.76, 0.83, 0.97, 0.56, 0.75, 0.62, 2.26, 0.62, 0.74, 1.11, 1.16, 0.61, 0.88, 0.67, 0.37, 0.07, 1.08, 0.46, 0.31, 0.94, 0.62, 0.57, 0.29, NA, 1.44, 0.46, 0.69, 0.57, 0.24, 0.37, 1.1, 0.99, 1.39, 0.6, 2.26, 1.24, 1.36, 0.52, 0.33, 0.26, 1.25, 0.37, 0.58, 1.03, 1.2, 0.34, 0.49, 0.33, 2.62, 0.16, 0.4, 0.16, 0.35, 0.75, 1.85, 0.94, 1.61, 0.85, 2.09, 1.39, 0.3, 0.52, 1.33, 0.29, 0.51, 0.26, 0.51, 3.83, 2.01, 0.71, 0.58, 0.62, 1.07, 0.28, 1.2, 0.74, 0.25, 0.59, 1.09, 0.91, 1.36, 0.45, 2.89, 1.27, 3.7, 0.69, 0.28, 0.41, 1.17, 0.56, 0.93, 3.41, 1, 1, NA, 5.9, 0.74, 2.51, 2.24, 2.24, 1.95, 3.32, 2.34, 1.3, 2.3, 1, 0.66, 0.73, 0.93, 0.41, 0.65, 0.89, 0.65, 0.32, NA, 0.43, 0.85, 0.43, 0.31, 0.31, 0.23, 0.29, 0.57, 0.71, 0.48, 0.44, 0.76, 0.51, 1.7, 0.85, 0.74, 2.23, 2.08, 1.16, 0.51, 0.51, 1, 0.5, NA, NA, 0.71, 2.14), nrow=64,byrow=T) + triMutability_Literature_Mouse <- matrix(c(1.31, 1.35, 1.42, 1.18, 2.02, 2.02, 1.02, 1.61, 1.99, 1.42, 2.01, 1.03, 2.02, 0.97, 0.53, 0.71, 1.19, 0.83, 0.96, 0.96, 0, 1.7, 2.22, 0.59, 1.24, 1.07, 0.51, 1.68, 3.36, 3.36, 1.14, 0.29, 0.33, 0.9, 1.11, 0.63, 1.08, 2.07, 2.27, 1.74, 0.22, 1.19, 2.37, 1.15, 1.15, 1.56, 0.81, 0.34, 0.87, 0.79, 2.13, 0.49, 0.85, 0.97, 0.36, 0.82, 0.66, 0.63, 1.15, 0.94, 0.85, 0.25, 0.93, 1.19, 0.4, 0.2, 0.44, 0.44, 0.88, 1.06, 0.77, 0.39, 0, 0, 0, 0, 0, 0, 0.43, 0.43, 0.86, 0.59, 0.59, 0, 1.18, 0.86, 2.9, 1.66, 0.4, 0.2, 1.54, 0.43, 0.69, 1.71, 0.68, 0.55, 0.91, 0.7, 1.71, 0.09, 0.27, 0.63, 0.2, 0.45, 1.01, 1.63, 0.96, 1.48, 2.18, 1.2, 1.31, 0.66, 2.13, 0.49, 0, 0, 0, 2.97, 2.8, 0.79, 0.4, 0.5, 0.4, 0.11, 1.68, 0.42, 0.13, 0.44, 0.93, 0.71, 1.11, 1.19, 2.71, 1.08, 3.43, 0.4, 0.67, 0.47, 1.02, 0.14, 1.56, 1.98, 0.53, 0.33, 0.63, 2.06, 1.77, 1.46, 3.74, 2.93, 2.1, 2.18, 0.78, 0.73, 2.93, 0.63, 0.57, 0.17, 0.85, 0.52, 0.31, 0.31, 0, 0, 0.51, 0.29, 0.83, 0.54, 0.28, 0.47, 0.9, 0.99, 1.24, 2.47, 0.73, 0.23, 1.13, 0.24, 2.12, 0.24, 0.33, 0.83, 1.41, 0.62, 0.28, 0.35, 0.77, 0.17, 0.72, 0.58, 0.45, 0.41), nrow=64,byrow=T) + triMutability_Names <- c("AAA", "AAC", "AAG", "AAT", "ACA", "ACC", "ACG", "ACT", "AGA", "AGC", "AGG", "AGT", "ATA", "ATC", "ATG", "ATT", "CAA", "CAC", "CAG", "CAT", "CCA", "CCC", "CCG", "CCT", "CGA", "CGC", "CGG", "CGT", "CTA", "CTC", "CTG", "CTT", "GAA", "GAC", "GAG", "GAT", "GCA", "GCC", "GCG", "GCT", "GGA", "GGC", "GGG", "GGT", "GTA", "GTC", "GTG", "GTT", "TAA", "TAC", "TAG", "TAT", "TCA", "TCC", "TCG", "TCT", "TGA", "TGC", "TGG", "TGT", "TTA", "TTC", "TTG", "TTT") + load("FiveS_Mutability.RData") + +# Functions + + # Translate codon to amino acid + translateCodonToAminoAcid<-function(Codon){ + return(AMINO_ACIDS[Codon]) + } + + # Translate amino acid to trait change + translateAminoAcidToTraitChange<-function(AminoAcid){ + return(TRAITS_AMINO_ACIDS[AminoAcid]) + } + + # Initialize Amino Acid Trait Changes + initializeTraitChange <- function(traitChangeModel=1,species=1,traitChangeFileName=NULL){ + if(!is.null(traitChangeFileName)){ + tryCatch( + traitChange <- read.delim(traitChangeFileName,sep="\t",header=T) + , error = function(ex){ + cat("Error|Error reading trait changes. Please check file name/path and format.\n") + q() + } + ) + }else{ + traitChange <- TRAITS_AMINO_ACIDS_CHOTHIA98 + } + TRAITS_AMINO_ACIDS <<- traitChange + } + + # Read in formatted nucleotide substitution matrix + initializeSubstitutionMatrix <- function(substitutionModel,species,subsMatFileName=NULL){ + if(!is.null(subsMatFileName)){ + tryCatch( + subsMat <- read.delim(subsMatFileName,sep="\t",header=T) + , error = function(ex){ + cat("Error|Error reading substitution matrix. Please check file name/path and format.\n") + q() + } + ) + if(sum(apply(subsMat,1,sum)==1)!=4) subsMat = t(apply(subsMat,1,function(x)x/sum(x))) + }else{ + if(substitutionModel==1)subsMat <- substitution_Literature_Mouse + if(substitutionModel==2)subsMat <- substitution_Flu_Human + if(substitutionModel==3)subsMat <- substitution_Flu25_Human + + } + + if(substitutionModel==0){ + subsMat <- matrix(1,4,4) + subsMat[,] = 1/3 + subsMat[1,1] = 0 + subsMat[2,2] = 0 + subsMat[3,3] = 0 + subsMat[4,4] = 0 + } + + + NUCLEOTIDESN = c(NUCLEOTIDES,"N", "-") + if(substitutionModel==5){ + subsMat <- FiveS_Substitution + return(subsMat) + }else{ + subsMat <- rbind(subsMat,rep(NA,4),rep(NA,4)) + return( matrix(data.matrix(subsMat),6,4,dimnames=list(NUCLEOTIDESN,NUCLEOTIDES) ) ) + } + } + + + # Read in formatted Mutability file + initializeMutabilityMatrix <- function(mutabilityModel=1, species=1,mutabilityMatFileName=NULL){ + if(!is.null(mutabilityMatFileName)){ + tryCatch( + mutabilityMat <- read.delim(mutabilityMatFileName,sep="\t",header=T) + , error = function(ex){ + cat("Error|Error reading mutability matrix. Please check file name/path and format.\n") + q() + } + ) + }else{ + mutabilityMat <- triMutability_Literature_Human + if(species==2) mutabilityMat <- triMutability_Literature_Mouse + } + + if(mutabilityModel==0){ mutabilityMat <- matrix(1,64,3)} + + if(mutabilityModel==5){ + mutabilityMat <- FiveS_Mutability + return(mutabilityMat) + }else{ + return( matrix( data.matrix(mutabilityMat), 64, 3, dimnames=list(triMutability_Names,1:3)) ) + } + } + + # Read FASTA file formats + # Modified from read.fasta from the seqinR package + baseline.read.fasta <- + function (file = system.file("sequences/sample.fasta", package = "seqinr"), + seqtype = c("DNA", "AA"), as.string = FALSE, forceDNAtolower = TRUE, + set.attributes = TRUE, legacy.mode = TRUE, seqonly = FALSE, + strip.desc = FALSE, sizeof.longlong = .Machine$sizeof.longlong, + endian = .Platform$endian, apply.mask = TRUE) + { + seqtype <- match.arg(seqtype) + + lines <- readLines(file) + + if (legacy.mode) { + comments <- grep("^;", lines) + if (length(comments) > 0) + lines <- lines[-comments] + } + + + ind_groups<-which(substr(lines, 1L, 3L) == ">>>") + lines_mod<-lines + + if(!length(ind_groups)){ + lines_mod<-c(">>>All sequences combined",lines) + } + + ind_groups<-which(substr(lines_mod, 1L, 3L) == ">>>") + + lines <- array("BLA",dim=(length(ind_groups)+length(lines_mod))) + id<-sapply(1:length(ind_groups),function(i)ind_groups[i]+i-1)+1 + lines[id] <- "THIS IS A FAKE SEQUENCE" + lines[-id] <- lines_mod + rm(lines_mod) + + ind <- which(substr(lines, 1L, 1L) == ">") + nseq <- length(ind) + if (nseq == 0) { + stop("no line starting with a > character found") + } + start <- ind + 1 + end <- ind - 1 + + while( any(which(ind%in%end)) ){ + ind=ind[-which(ind%in%end)] + nseq <- length(ind) + if (nseq == 0) { + stop("no line starting with a > character found") + } + start <- ind + 1 + end <- ind - 1 + } + + end <- c(end[-1], length(lines)) + sequences <- lapply(seq_len(nseq), function(i) paste(lines[start[i]:end[i]], collapse = "")) + if (seqonly) + return(sequences) + nomseq <- lapply(seq_len(nseq), function(i) { + + #firstword <- strsplit(lines[ind[i]], " ")[[1]][1] + substr(lines[ind[i]], 2, nchar(lines[ind[i]])) + + }) + if (seqtype == "DNA") { + if (forceDNAtolower) { + sequences <- as.list(tolower(chartr(".","-",sequences))) + }else{ + sequences <- as.list(toupper(chartr(".","-",sequences))) + } + } + if (as.string == FALSE) + sequences <- lapply(sequences, s2c) + if (set.attributes) { + for (i in seq_len(nseq)) { + Annot <- lines[ind[i]] + if (strip.desc) + Annot <- substr(Annot, 2L, nchar(Annot)) + attributes(sequences[[i]]) <- list(name = nomseq[[i]], + Annot = Annot, class = switch(seqtype, AA = "SeqFastaAA", + DNA = "SeqFastadna")) + } + } + names(sequences) <- nomseq + return(sequences) + } + + + # Replaces non FASTA characters in input files with N + replaceNonFASTAChars <-function(inSeq="ACGTN-AApA"){ + gsub('[^ACGTNacgt[:punct:]-[:punct:].]','N',inSeq,perl=TRUE) + } + + # Find the germlines in the FASTA list + germlinesInFile <- function(seqIDs){ + firstChar = sapply(seqIDs,function(x){substr(x,1,1)}) + secondChar = sapply(seqIDs,function(x){substr(x,2,2)}) + return(firstChar==">" & secondChar!=">") + } + + # Find the groups in the FASTA list + groupsInFile <- function(seqIDs){ + sapply(seqIDs,function(x){substr(x,1,2)})==">>" + } + + # In the process of finding germlines/groups, expand from the start to end of the group + expandTillNext <- function(vecPosToID){ + IDs = names(vecPosToID) + posOfInterests = which(vecPosToID) + + expandedID = rep(NA,length(IDs)) + expandedIDNames = gsub(">","",IDs[posOfInterests]) + startIndexes = c(1,posOfInterests[-1]) + stopIndexes = c(posOfInterests[-1]-1,length(IDs)) + expandedID = unlist(sapply(1:length(startIndexes),function(i){ + rep(i,stopIndexes[i]-startIndexes[i]+1) + })) + names(expandedID) = unlist(sapply(1:length(startIndexes),function(i){ + rep(expandedIDNames[i],stopIndexes[i]-startIndexes[i]+1) + })) + return(expandedID) + } + + # Process FASTA (list) to return a matrix[input, germline) + processInputAdvanced <- function(inputFASTA){ + + seqIDs = names(inputFASTA) + numbSeqs = length(seqIDs) + posGermlines1 = germlinesInFile(seqIDs) + numbGermlines = sum(posGermlines1) + posGroups1 = groupsInFile(seqIDs) + numbGroups = sum(posGroups1) + consDef = NA + + if(numbGermlines==0){ + posGermlines = 2 + numbGermlines = 1 + } + + glPositionsSum = cumsum(posGermlines1) + glPositions = table(glPositionsSum) + #Find the position of the conservation row + consDefPos = as.numeric(names(glPositions[names(glPositions)!=0 & glPositions==1]))+1 + if( length(consDefPos)> 0 ){ + consDefID = match(consDefPos, glPositionsSum) + #The coservation rows need to be pulled out and stores seperately + consDef = inputFASTA[consDefID] + inputFASTA = inputFASTA[-consDefID] + + seqIDs = names(inputFASTA) + numbSeqs = length(seqIDs) + posGermlines1 = germlinesInFile(seqIDs) + numbGermlines = sum(posGermlines1) + posGroups1 = groupsInFile(seqIDs) + numbGroups = sum(posGroups1) + if(numbGermlines==0){ + posGermlines = 2 + numbGermlines = 1 + } + } + + posGroups <- expandTillNext(posGroups1) + posGermlines <- expandTillNext(posGermlines1) + posGermlines[posGroups1] = 0 + names(posGermlines)[posGroups1] = names(posGroups)[posGroups1] + posInput = rep(TRUE,numbSeqs) + posInput[posGroups1 | posGermlines1] = FALSE + + matInput = matrix(NA, nrow=sum(posInput), ncol=2) + rownames(matInput) = seqIDs[posInput] + colnames(matInput) = c("Input","Germline") + + vecInputFASTA = unlist(inputFASTA) + matInput[,1] = vecInputFASTA[posInput] + matInput[,2] = vecInputFASTA[ which( names(inputFASTA)%in%paste(">",names(posGermlines)[posInput],sep="") )[ posGermlines[posInput]] ] + + germlines = posGermlines[posInput] + groups = posGroups[posInput] + + return( list("matInput"=matInput, "germlines"=germlines, "groups"=groups, "conservationDefinition"=consDef )) + } + + + # Replace leading and trailing dashes in the sequence + replaceLeadingTrailingDashes <- function(x,readEnd){ + iiGap = unlist(gregexpr("-",x[1])) + ggGap = unlist(gregexpr("-",x[2])) + #posToChange = intersect(iiGap,ggGap) + + + seqIn = replaceLeadingTrailingDashesHelper(x[1]) + seqGL = replaceLeadingTrailingDashesHelper(x[2]) + seqTemplate = rep('N',readEnd) + seqIn <- c(seqIn,seqTemplate[(length(seqIn)+1):readEnd]) + seqGL <- c(seqGL,seqTemplate[(length(seqGL)+1):readEnd]) +# if(posToChange!=-1){ +# seqIn[posToChange] = "-" +# seqGL[posToChange] = "-" +# } + + seqIn = c2s(seqIn[1:readEnd]) + seqGL = c2s(seqGL[1:readEnd]) + + lenGL = nchar(seqGL) + if(lenGL<readEnd){ + seqGL = paste(seqGL,c2s(rep("N",readEnd-lenGL)),sep="") + } + + lenInput = nchar(seqIn) + if(lenInput<readEnd){ + seqIn = paste(seqIn,c2s(rep("N",readEnd-lenInput)),sep="") + } + return( c(seqIn,seqGL) ) + } + + replaceLeadingTrailingDashesHelper <- function(x){ + grepResults = gregexpr("-*",x) + grepResultsPos = unlist(grepResults) + grepResultsLen = attr(grepResults[[1]],"match.length") + #print(paste("x = '", x, "'", sep="")) + x = s2c(x) + if(x[1]=="-"){ + x[1:grepResultsLen[1]] = "N" + } + if(x[length(x)]=="-"){ + x[(length(x)-grepResultsLen[length(grepResultsLen)]+1):length(x)] = "N" + } + return(x) + } + + + + + # Check sequences for indels + checkForInDels <- function(matInputP){ + insPos <- checkInsertion(matInputP) + delPos <- checkDeletions(matInputP) + return(list("Insertions"=insPos, "Deletions"=delPos)) + } + + # Check sequences for insertions + checkInsertion <- function(matInputP){ + insertionCheck = apply( matInputP,1, function(x){ + inputGaps <- as.vector( gregexpr("-",x[1])[[1]] ) + glGaps <- as.vector( gregexpr("-",x[2])[[1]] ) + return( is.finite( match(FALSE, glGaps%in%inputGaps ) ) ) + }) + return(as.vector(insertionCheck)) + } + # Fix inserstions + fixInsertions <- function(matInputP){ + insPos <- checkInsertion(matInputP) + sapply((1:nrow(matInputP))[insPos],function(rowIndex){ + x <- matInputP[rowIndex,] + inputGaps <- gregexpr("-",x[1])[[1]] + glGaps <- gregexpr("-",x[2])[[1]] + posInsertions <- glGaps[!(glGaps%in%inputGaps)] + inputInsertionToN <- s2c(x[2]) + inputInsertionToN[posInsertions]!="-" + inputInsertionToN[posInsertions] <- "N" + inputInsertionToN <- c2s(inputInsertionToN) + matInput[rowIndex,2] <<- inputInsertionToN + }) + return(insPos) + } + + # Check sequences for deletions + checkDeletions <-function(matInputP){ + deletionCheck = apply( matInputP,1, function(x){ + inputGaps <- as.vector( gregexpr("-",x[1])[[1]] ) + glGaps <- as.vector( gregexpr("-",x[2])[[1]] ) + return( is.finite( match(FALSE, inputGaps%in%glGaps ) ) ) + }) + return(as.vector(deletionCheck)) + } + # Fix sequences with deletions + fixDeletions <- function(matInputP){ + delPos <- checkDeletions(matInputP) + sapply((1:nrow(matInputP))[delPos],function(rowIndex){ + x <- matInputP[rowIndex,] + inputGaps <- gregexpr("-",x[1])[[1]] + glGaps <- gregexpr("-",x[2])[[1]] + posDeletions <- inputGaps[!(inputGaps%in%glGaps)] + inputDeletionToN <- s2c(x[1]) + inputDeletionToN[posDeletions] <- "N" + inputDeletionToN <- c2s(inputDeletionToN) + matInput[rowIndex,1] <<- inputDeletionToN + }) + return(delPos) + } + + + # Trim DNA sequence to the last codon + trimToLastCodon <- function(seqToTrim){ + seqLen = nchar(seqToTrim) + trimmedSeq = s2c(seqToTrim) + poi = seqLen + tailLen = 0 + + while(trimmedSeq[poi]=="-" || trimmedSeq[poi]=="."){ + tailLen = tailLen + 1 + poi = poi - 1 + } + + trimmedSeq = c2s(trimmedSeq[1:(seqLen-tailLen)]) + seqLen = nchar(trimmedSeq) + # Trim sequence to last codon + if( getCodonPos(seqLen)[3] > seqLen ) + trimmedSeq = substr(seqToTrim,1, ( (getCodonPos(seqLen)[1])-1 ) ) + + return(trimmedSeq) + } + + # Given a nuclotide position, returns the pos of the 3 nucs that made the codon + # e.g. nuc 86 is part of nucs 85,86,87 + getCodonPos <- function(nucPos){ + codonNum = (ceiling(nucPos/3))*3 + return( (codonNum-2):codonNum) + } + + # Given a nuclotide position, returns the codon number + # e.g. nuc 86 = codon 29 + getCodonNumb <- function(nucPos){ + return( ceiling(nucPos/3) ) + } + + # Given a codon, returns all the nuc positions that make the codon + getCodonNucs <- function(codonNumb){ + getCodonPos(codonNumb*3) + } + + computeCodonTable <- function(testID=1){ + + if(testID<=4){ + # Pre-compute every codons + intCounter = 1 + for(pOne in NUCLEOTIDES){ + for(pTwo in NUCLEOTIDES){ + for(pThree in NUCLEOTIDES){ + codon = paste(pOne,pTwo,pThree,sep="") + colnames(CODON_TABLE)[intCounter] = codon + intCounter = intCounter + 1 + CODON_TABLE[,codon] = mutationTypeOptimized(cbind(permutateAllCodon(codon),rep(codon,12))) + } + } + } + chars = c("N","A","C","G","T", "-") + for(a in chars){ + for(b in chars){ + for(c in chars){ + if(a=="N" | b=="N" | c=="N"){ + #cat(paste(a,b,c),sep="","\n") + CODON_TABLE[,paste(a,b,c,sep="")] = rep(NA,12) + } + } + } + } + + chars = c("-","A","C","G","T") + for(a in chars){ + for(b in chars){ + for(c in chars){ + if(a=="-" | b=="-" | c=="-"){ + #cat(paste(a,b,c),sep="","\n") + CODON_TABLE[,paste(a,b,c,sep="")] = rep(NA,12) + } + } + } + } + CODON_TABLE <<- as.matrix(CODON_TABLE) + } + } + + collapseClone <- function(vecInputSeqs,glSeq,readEnd,nonTerminalOnly=0){ + #print(length(vecInputSeqs)) + vecInputSeqs = unique(vecInputSeqs) + if(length(vecInputSeqs)==1){ + return( list( c(vecInputSeqs,glSeq), F) ) + }else{ + charInputSeqs <- sapply(vecInputSeqs, function(x){ + s2c(x)[1:readEnd] + }) + charGLSeq <- s2c(glSeq) + matClone <- sapply(1:readEnd, function(i){ + posNucs = unique(charInputSeqs[i,]) + posGL = charGLSeq[i] + error = FALSE + if(posGL=="-" & sum(!(posNucs%in%c("-","N")))==0 ){ + return(c("-",error)) + } + if(length(posNucs)==1) + return(c(posNucs[1],error)) + else{ + if("N"%in%posNucs){ + error=TRUE + } + if(sum(!posNucs[posNucs!="N"]%in%posGL)==0){ + return( c(posGL,error) ) + }else{ + #return( c(sample(posNucs[posNucs!="N"],1),error) ) + if(nonTerminalOnly==0){ + return( c(sample(charInputSeqs[i,charInputSeqs[i,]!="N" & charInputSeqs[i,]!=posGL],1),error) ) + }else{ + posNucs = charInputSeqs[i,charInputSeqs[i,]!="N" & charInputSeqs[i,]!=posGL] + posNucsTable = table(posNucs) + if(sum(posNucsTable>1)==0){ + return( c(posGL,error) ) + }else{ + return( c(sample( posNucs[posNucs%in%names(posNucsTable)[posNucsTable>1]],1),error) ) + } + } + + } + } + }) + + + #print(length(vecInputSeqs)) + return(list(c(c2s(matClone[1,]),glSeq),"TRUE"%in%matClone[2,])) + } + } + + # Compute the expected for each sequence-germline pair + getExpectedIndividual <- function(matInput){ + if( any(grep("multicore",search())) ){ + facGL <- factor(matInput[,2]) + facLevels = levels(facGL) + LisGLs_MutabilityU = mclapply(1:length(facLevels), function(x){ + computeMutabilities(facLevels[x]) + }) + facIndex = match(facGL,facLevels) + + LisGLs_Mutability = mclapply(1:nrow(matInput), function(x){ + cInput = rep(NA,nchar(matInput[x,1])) + cInput[s2c(matInput[x,1])!="N"] = 1 + LisGLs_MutabilityU[[facIndex[x]]] * cInput + }) + + LisGLs_Targeting = mclapply(1:dim(matInput)[1], function(x){ + computeTargeting(matInput[x,2],LisGLs_Mutability[[x]]) + }) + + LisGLs_MutationTypes = mclapply(1:length(matInput[,2]),function(x){ + #print(x) + computeMutationTypes(matInput[x,2]) + }) + + LisGLs_Exp = mclapply(1:dim(matInput)[1], function(x){ + computeExpected(LisGLs_Targeting[[x]],LisGLs_MutationTypes[[x]]) + }) + + ul_LisGLs_Exp = unlist(LisGLs_Exp) + return(matrix(ul_LisGLs_Exp,ncol=4,nrow=(length(ul_LisGLs_Exp)/4),byrow=T)) + }else{ + facGL <- factor(matInput[,2]) + facLevels = levels(facGL) + LisGLs_MutabilityU = lapply(1:length(facLevels), function(x){ + computeMutabilities(facLevels[x]) + }) + facIndex = match(facGL,facLevels) + + LisGLs_Mutability = lapply(1:nrow(matInput), function(x){ + cInput = rep(NA,nchar(matInput[x,1])) + cInput[s2c(matInput[x,1])!="N"] = 1 + LisGLs_MutabilityU[[facIndex[x]]] * cInput + }) + + LisGLs_Targeting = lapply(1:dim(matInput)[1], function(x){ + computeTargeting(matInput[x,2],LisGLs_Mutability[[x]]) + }) + + LisGLs_MutationTypes = lapply(1:length(matInput[,2]),function(x){ + #print(x) + computeMutationTypes(matInput[x,2]) + }) + + LisGLs_Exp = lapply(1:dim(matInput)[1], function(x){ + computeExpected(LisGLs_Targeting[[x]],LisGLs_MutationTypes[[x]]) + }) + + ul_LisGLs_Exp = unlist(LisGLs_Exp) + return(matrix(ul_LisGLs_Exp,ncol=4,nrow=(length(ul_LisGLs_Exp)/4),byrow=T)) + + } + } + + # Compute mutabilities of sequence based on the tri-nucleotide model + computeMutabilities <- function(paramSeq){ + seqLen = nchar(paramSeq) + seqMutabilites = rep(NA,seqLen) + + gaplessSeq = gsub("-", "", paramSeq) + gaplessSeqLen = nchar(gaplessSeq) + gaplessSeqMutabilites = rep(NA,gaplessSeqLen) + + if(mutabilityModel!=5){ + pos<- 3:(gaplessSeqLen) + subSeq = substr(rep(gaplessSeq,gaplessSeqLen-2),(pos-2),(pos+2)) + gaplessSeqMutabilites[pos] = + tapply( c( + getMutability( substr(subSeq,1,3), 3) , + getMutability( substr(subSeq,2,4), 2), + getMutability( substr(subSeq,3,5), 1) + ),rep(1:(gaplessSeqLen-2),3),mean,na.rm=TRUE + ) + #Pos 1 + subSeq = substr(gaplessSeq,1,3) + gaplessSeqMutabilites[1] = getMutability(subSeq , 1) + #Pos 2 + subSeq = substr(gaplessSeq,1,4) + gaplessSeqMutabilites[2] = mean( c( + getMutability( substr(subSeq,1,3), 2) , + getMutability( substr(subSeq,2,4), 1) + ),na.rm=T + ) + seqMutabilites[which(s2c(paramSeq)!="-")]<- gaplessSeqMutabilites + return(seqMutabilites) + }else{ + + pos<- 3:(gaplessSeqLen) + subSeq = substr(rep(gaplessSeq,gaplessSeqLen-2),(pos-2),(pos+2)) + gaplessSeqMutabilites[pos] = sapply(subSeq,function(x){ getMutability5(x) }, simplify=T) + seqMutabilites[which(s2c(paramSeq)!="-")]<- gaplessSeqMutabilites + return(seqMutabilites) + } + + } + + # Returns the mutability of a triplet at a given position + getMutability <- function(codon, pos=1:3){ + triplets <- rownames(mutability) + mutability[ match(codon,triplets) ,pos] + } + + getMutability5 <- function(fivemer){ + return(mutability[fivemer]) + } + + # Returns the substitution probabilty + getTransistionProb <- function(nuc){ + substitution[nuc,] + } + + getTransistionProb5 <- function(fivemer){ + if(any(which(fivemer==colnames(substitution)))){ + return(substitution[,fivemer]) + }else{ + return(array(NA,4)) + } + } + + # Given a nuc, returns the other 3 nucs it can mutate to + canMutateTo <- function(nuc){ + NUCLEOTIDES[- which(NUCLEOTIDES==nuc)] + } + + # Given a nucleotide, returns the probabilty of other nucleotide it can mutate to + canMutateToProb <- function(nuc){ + substitution[nuc,canMutateTo(nuc)] + } + + # Compute targeting, based on precomputed mutatbility & substitution + computeTargeting <- function(param_strSeq,param_vecMutabilities){ + + if(substitutionModel!=5){ + vecSeq = s2c(param_strSeq) + matTargeting = sapply( 1:length(vecSeq), function(x) { param_vecMutabilities[x] * getTransistionProb(vecSeq[x]) } ) + #matTargeting = apply( rbind(vecSeq,param_vecMutabilities),2, function(x) { as.vector(as.numeric(x[2]) * getTransistionProb(x[1])) } ) + dimnames( matTargeting ) = list(NUCLEOTIDES,1:(length(vecSeq))) + return (matTargeting) + }else{ + + seqLen = nchar(param_strSeq) + seqsubstitution = matrix(NA,ncol=seqLen,nrow=4) + paramSeq <- param_strSeq + gaplessSeq = gsub("-", "", paramSeq) + gaplessSeqLen = nchar(gaplessSeq) + gaplessSeqSubstitution = matrix(NA,ncol=gaplessSeqLen,nrow=4) + + pos<- 3:(gaplessSeqLen) + subSeq = substr(rep(gaplessSeq,gaplessSeqLen-2),(pos-2),(pos+2)) + gaplessSeqSubstitution[,pos] = sapply(subSeq,function(x){ getTransistionProb5(x) }, simplify=T) + seqsubstitution[,which(s2c(paramSeq)!="-")]<- gaplessSeqSubstitution + #matTargeting <- param_vecMutabilities %*% seqsubstitution + matTargeting <- sweep(seqsubstitution,2,param_vecMutabilities,`*`) + dimnames( matTargeting ) = list(NUCLEOTIDES,1:(seqLen)) + return (matTargeting) + } + } + + # Compute the mutations types + computeMutationTypes <- function(param_strSeq){ + #cat(param_strSeq,"\n") + #vecSeq = trimToLastCodon(param_strSeq) + lenSeq = nchar(param_strSeq) + vecCodons = sapply({1:(lenSeq/3)}*3-2,function(x){substr(param_strSeq,x,x+2)}) + matMutationTypes = matrix( unlist(CODON_TABLE[,vecCodons]) ,ncol=lenSeq,nrow=4, byrow=F) + dimnames( matMutationTypes ) = list(NUCLEOTIDES,1:(ncol(matMutationTypes))) + return(matMutationTypes) + } + computeMutationTypesFast <- function(param_strSeq){ + matMutationTypes = matrix( CODON_TABLE[,param_strSeq] ,ncol=3,nrow=4, byrow=F) + #dimnames( matMutationTypes ) = list(NUCLEOTIDES,1:(length(vecSeq))) + return(matMutationTypes) + } + mutationTypeOptimized <- function( matOfCodons ){ + apply( matOfCodons,1,function(x){ mutationType(x[2],x[1]) } ) + } + + # Returns a vector of codons 1 mutation away from the given codon + permutateAllCodon <- function(codon){ + cCodon = s2c(codon) + matCodons = t(array(cCodon,dim=c(3,12))) + matCodons[1:4,1] = NUCLEOTIDES + matCodons[5:8,2] = NUCLEOTIDES + matCodons[9:12,3] = NUCLEOTIDES + apply(matCodons,1,c2s) + } + + # Given two codons, tells you if the mutation is R or S (based on your definition) + mutationType <- function(codonFrom,codonTo){ + if(testID==4){ + if( is.na(codonFrom) | is.na(codonTo) | is.na(translateCodonToAminoAcid(codonFrom)) | is.na(translateCodonToAminoAcid(codonTo)) ){ + return(NA) + }else{ + mutationType = "S" + if( translateAminoAcidToTraitChange(translateCodonToAminoAcid(codonFrom)) != translateAminoAcidToTraitChange(translateCodonToAminoAcid(codonTo)) ){ + mutationType = "R" + } + if(translateCodonToAminoAcid(codonTo)=="*" | translateCodonToAminoAcid(codonFrom)=="*"){ + mutationType = "Stop" + } + return(mutationType) + } + }else if(testID==5){ + if( is.na(codonFrom) | is.na(codonTo) | is.na(translateCodonToAminoAcid(codonFrom)) | is.na(translateCodonToAminoAcid(codonTo)) ){ + return(NA) + }else{ + if(codonFrom==codonTo){ + mutationType = "S" + }else{ + codonFrom = s2c(codonFrom) + codonTo = s2c(codonTo) + mutationType = "Stop" + nucOfI = codonFrom[which(codonTo!=codonFrom)] + if(nucOfI=="C"){ + mutationType = "R" + }else if(nucOfI=="G"){ + mutationType = "S" + } + } + return(mutationType) + } + }else{ + if( is.na(codonFrom) | is.na(codonTo) | is.na(translateCodonToAminoAcid(codonFrom)) | is.na(translateCodonToAminoAcid(codonTo)) ){ + return(NA) + }else{ + mutationType = "S" + if( translateCodonToAminoAcid(codonFrom) != translateCodonToAminoAcid(codonTo) ){ + mutationType = "R" + } + if(translateCodonToAminoAcid(codonTo)=="*" | translateCodonToAminoAcid(codonFrom)=="*"){ + mutationType = "Stop" + } + return(mutationType) + } + } + } + + + #given a mat of targeting & it's corresponding mutationtypes returns + #a vector of Exp_RCDR,Exp_SCDR,Exp_RFWR,Exp_RFWR + computeExpected <- function(paramTargeting,paramMutationTypes){ + # Replacements + RPos = which(paramMutationTypes=="R") + #FWR + Exp_R_FWR = sum(paramTargeting[ RPos[which(FWR_Nuc_Mat[RPos]==T)] ],na.rm=T) + #CDR + Exp_R_CDR = sum(paramTargeting[ RPos[which(CDR_Nuc_Mat[RPos]==T)] ],na.rm=T) + # Silents + SPos = which(paramMutationTypes=="S") + #FWR + Exp_S_FWR = sum(paramTargeting[ SPos[which(FWR_Nuc_Mat[SPos]==T)] ],na.rm=T) + #CDR + Exp_S_CDR = sum(paramTargeting[ SPos[which(CDR_Nuc_Mat[SPos]==T)] ],na.rm=T) + + return(c(Exp_R_CDR,Exp_S_CDR,Exp_R_FWR,Exp_S_FWR)) + } + + # Count the mutations in a sequence + # each mutation is treated independently + analyzeMutations2NucUri_website <- function( rev_in_matrix ){ + paramGL = rev_in_matrix[2,] + paramSeq = rev_in_matrix[1,] + + #Fill seq with GL seq if gapped + #if( any(paramSeq=="-") ){ + # gapPos_Seq = which(paramSeq=="-") + # gapPos_Seq_ToReplace = gapPos_Seq[paramGL[gapPos_Seq] != "-"] + # paramSeq[gapPos_Seq_ToReplace] = paramGL[gapPos_Seq_ToReplace] + #} + + + #if( any(paramSeq=="N") ){ + # gapPos_Seq = which(paramSeq=="N") + # gapPos_Seq_ToReplace = gapPos_Seq[paramGL[gapPos_Seq] != "N"] + # paramSeq[gapPos_Seq_ToReplace] = paramGL[gapPos_Seq_ToReplace] + #} + + analyzeMutations2NucUri( matrix(c( paramGL, paramSeq ),2,length(paramGL),byrow=T) ) + + } + + #1 = GL + #2 = Seq + analyzeMutations2NucUri <- function( in_matrix=matrix(c(c("A","A","A","C","C","C"),c("A","G","G","C","C","A")),2,6,byrow=T) ){ + paramGL = in_matrix[2,] + paramSeq = in_matrix[1,] + paramSeqUri = paramGL + #mutations = apply(rbind(paramGL,paramSeq), 2, function(x){!x[1]==x[2]}) + mutations_val = paramGL != paramSeq + if(any(mutations_val)){ + mutationPos = {1:length(mutations_val)}[mutations_val] + mutationPos = mutationPos[sapply(mutationPos, function(x){!any(paramSeq[getCodonPos(x)]=="N")})] + length_mutations =length(mutationPos) + mutationInfo = rep(NA,length_mutations) + if(any(mutationPos)){ + + pos<- mutationPos + pos_array<-array(sapply(pos,getCodonPos)) + codonGL = paramGL[pos_array] + + codonSeq = sapply(pos,function(x){ + seqP = paramGL[getCodonPos(x)] + muCodonPos = {x-1}%%3+1 + seqP[muCodonPos] = paramSeq[x] + return(seqP) + }) + GLcodons = apply(matrix(codonGL,length_mutations,3,byrow=TRUE),1,c2s) + Seqcodons = apply(codonSeq,2,c2s) + mutationInfo = apply(rbind(GLcodons , Seqcodons),2,function(x){mutationType(c2s(x[1]),c2s(x[2]))}) + names(mutationInfo) = mutationPos + } + if(any(!is.na(mutationInfo))){ + return(mutationInfo[!is.na(mutationInfo)]) + }else{ + return(NA) + } + + + }else{ + return (NA) + } + } + + processNucMutations2 <- function(mu){ + if(!is.na(mu)){ + #R + if(any(mu=="R")){ + Rs = mu[mu=="R"] + nucNumbs = as.numeric(names(Rs)) + R_CDR = sum(as.integer(CDR_Nuc[nucNumbs]),na.rm=T) + R_FWR = sum(as.integer(FWR_Nuc[nucNumbs]),na.rm=T) + }else{ + R_CDR = 0 + R_FWR = 0 + } + + #S + if(any(mu=="S")){ + Ss = mu[mu=="S"] + nucNumbs = as.numeric(names(Ss)) + S_CDR = sum(as.integer(CDR_Nuc[nucNumbs]),na.rm=T) + S_FWR = sum(as.integer(FWR_Nuc[nucNumbs]),na.rm=T) + }else{ + S_CDR = 0 + S_FWR = 0 + } + + + retVec = c(R_CDR,S_CDR,R_FWR,S_FWR) + retVec[is.na(retVec)]=0 + return(retVec) + }else{ + return(rep(0,4)) + } + } + + + ## Z-score Test + computeZScore <- function(mat, test="Focused"){ + matRes <- matrix(NA,ncol=2,nrow=(nrow(mat))) + if(test=="Focused"){ + #Z_Focused_CDR + #P_Denom = sum( mat[1,c(5,6,8)], na.rm=T ) + P = apply(mat[,c(5,6,8)],1,function(x){(x[1]/sum(x))}) + R_mean = apply(cbind(mat[,c(1,2,4)],P),1,function(x){x[4]*(sum(x[1:3]))}) + R_sd=sqrt(R_mean*(1-P)) + matRes[,1] = (mat[,1]-R_mean)/R_sd + + #Z_Focused_FWR + #P_Denom = sum( mat[1,c(7,6,8)], na.rm=T ) + P = apply(mat[,c(7,6,8)],1,function(x){(x[1]/sum(x))}) + R_mean = apply(cbind(mat[,c(3,2,4)],P),1,function(x){x[4]*(sum(x[1:3]))}) + R_sd=sqrt(R_mean*(1-P)) + matRes[,2] = (mat[,3]-R_mean)/R_sd + } + + if(test=="Local"){ + #Z_Focused_CDR + #P_Denom = sum( mat[1,c(5,6,8)], na.rm=T ) + P = apply(mat[,c(5,6)],1,function(x){(x[1]/sum(x))}) + R_mean = apply(cbind(mat[,c(1,2)],P),1,function(x){x[3]*(sum(x[1:2]))}) + R_sd=sqrt(R_mean*(1-P)) + matRes[,1] = (mat[,1]-R_mean)/R_sd + + #Z_Focused_FWR + #P_Denom = sum( mat[1,c(7,6,8)], na.rm=T ) + P = apply(mat[,c(7,8)],1,function(x){(x[1]/sum(x))}) + R_mean = apply(cbind(mat[,c(3,4)],P),1,function(x){x[3]*(sum(x[1:2]))}) + R_sd=sqrt(R_mean*(1-P)) + matRes[,2] = (mat[,3]-R_mean)/R_sd + } + + if(test=="Imbalanced"){ + #Z_Focused_CDR + #P_Denom = sum( mat[1,c(5,6,8)], na.rm=T ) + P = apply(mat[,5:8],1,function(x){((x[1]+x[2])/sum(x))}) + R_mean = apply(cbind(mat[,1:4],P),1,function(x){x[5]*(sum(x[1:4]))}) + R_sd=sqrt(R_mean*(1-P)) + matRes[,1] = (mat[,1]-R_mean)/R_sd + + #Z_Focused_FWR + #P_Denom = sum( mat[1,c(7,6,8)], na.rm=T ) + P = apply(mat[,5:8],1,function(x){((x[3]+x[4])/sum(x))}) + R_mean = apply(cbind(mat[,1:4],P),1,function(x){x[5]*(sum(x[1:4]))}) + R_sd=sqrt(R_mean*(1-P)) + matRes[,2] = (mat[,3]-R_mean)/R_sd + } + + matRes[is.nan(matRes)] = NA + return(matRes) + } + + # Return a p-value for a z-score + z2p <- function(z){ + p=NA + if( !is.nan(z) && !is.na(z)){ + if(z>0){ + p = (1 - pnorm(z,0,1)) + } else if(z<0){ + p = (-1 * pnorm(z,0,1)) + } else{ + p = 0.5 + } + }else{ + p = NA + } + return(p) + } + + + ## Bayesian Test + + # Fitted parameter for the bayesian framework +BAYESIAN_FITTED<-c(0.407277142798302, 0.554007336744485, 0.63777155771234, 0.693989162719009, 0.735450014674917, 0.767972534429806, 0.794557287143399, 0.816906816601605, 0.83606796225341, 0.852729446430296, 0.867370424541641, 0.880339760590323, 0.891900995024999, 0.902259181289864, 0.911577919359,0.919990301665853, 0.927606458124537, 0.934518806350661, 0.940805863754375, 0.946534836475715, 0.951763691199255, 0.95654428191308, 0.960920179487397, 0.964930893680829, 0.968611312149038, 0.971992459313836, 0.975102110004818, 0.977964943023096, 0.980603428208439, 0.983037660179428, 0.985285800977406, 0.987364285326685, 0.989288037855441, 0.991070478823525, 0.992723699729969, 0.994259575477392, 0.995687688867975, 0.997017365051493, 0.998257085153047, 0.999414558305388, 1.00049681357804, 1.00151036237481, 1.00246080204981, 1.00335370751909, 1.0041939329768, 1.0049859393417, 1.00573382091263, 1.00644127217376, 1.00711179729107, 1.00774845526417, 1.00835412715854, 1.00893143010366, 1.00948275846309, 1.01001030293661, 1.01051606798079, 1.01100188771288, 1.01146944044216, 1.01192026195449, 1.01235575766094, 1.01277721370986) + CONST_i <- sort(c(((2^(seq(-39,0,length.out=201)))/2)[1:200],(c(0:11,13:99)+0.5)/100,1-(2^(seq(-39,0,length.out=201)))/2)) + + # Given x, M & p, returns a pdf + calculate_bayes <- function ( x=3, N=10, p=0.33, + i=CONST_i, + max_sigma=20,length_sigma=4001 + ){ + if(!0%in%N){ + G <- max(length(x),length(N),length(p)) + x=array(x,dim=G) + N=array(N,dim=G) + p=array(p,dim=G) + sigma_s<-seq(-max_sigma,max_sigma,length.out=length_sigma) + sigma_1<-log({i/{1-i}}/{p/{1-p}}) + index<-min(N,60) + y<-dbeta(i,x+BAYESIAN_FITTED[index],N+BAYESIAN_FITTED[index]-x)*(1-p)*p*exp(sigma_1)/({1-p}^2+2*p*{1-p}*exp(sigma_1)+{p^2}*exp(2*sigma_1)) + if(!sum(is.na(y))){ + tmp<-approx(sigma_1,y,sigma_s)$y + tmp/sum(tmp)/{2*max_sigma/{length_sigma-1}} + }else{ + return(NA) + } + }else{ + return(NA) + } + } + # Given a mat of observed & expected, return a list of CDR & FWR pdf for selection + computeBayesianScore <- function(mat, test="Focused", max_sigma=20,length_sigma=4001){ + flagOneSeq = F + if(nrow(mat)==1){ + mat=rbind(mat,mat) + flagOneSeq = T + } + if(test=="Focused"){ + #CDR + P = c(apply(mat[,c(5,6,8)],1,function(x){(x[1]/sum(x))}),0.5) + N = c(apply(mat[,c(1,2,4)],1,function(x){(sum(x))}),0) + X = c(mat[,1],0) + bayesCDR = apply(cbind(X,N,P),1,function(x){calculate_bayes(x=x[1],N=x[2],p=x[3],max_sigma=max_sigma,length_sigma=length_sigma)}) + bayesCDR = bayesCDR[-length(bayesCDR)] + + #FWR + P = c(apply(mat[,c(7,6,8)],1,function(x){(x[1]/sum(x))}),0.5) + N = c(apply(mat[,c(3,2,4)],1,function(x){(sum(x))}),0) + X = c(mat[,3],0) + bayesFWR = apply(cbind(X,N,P),1,function(x){calculate_bayes(x=x[1],N=x[2],p=x[3],max_sigma=max_sigma,length_sigma=length_sigma)}) + bayesFWR = bayesFWR[-length(bayesFWR)] + } + + if(test=="Local"){ + #CDR + P = c(apply(mat[,c(5,6)],1,function(x){(x[1]/sum(x))}),0.5) + N = c(apply(mat[,c(1,2)],1,function(x){(sum(x))}),0) + X = c(mat[,1],0) + bayesCDR = apply(cbind(X,N,P),1,function(x){calculate_bayes(x=x[1],N=x[2],p=x[3],max_sigma=max_sigma,length_sigma=length_sigma)}) + bayesCDR = bayesCDR[-length(bayesCDR)] + + #FWR + P = c(apply(mat[,c(7,8)],1,function(x){(x[1]/sum(x))}),0.5) + N = c(apply(mat[,c(3,4)],1,function(x){(sum(x))}),0) + X = c(mat[,3],0) + bayesFWR = apply(cbind(X,N,P),1,function(x){calculate_bayes(x=x[1],N=x[2],p=x[3],max_sigma=max_sigma,length_sigma=length_sigma)}) + bayesFWR = bayesFWR[-length(bayesFWR)] + } + + if(test=="Imbalanced"){ + #CDR + P = c(apply(mat[,c(5:8)],1,function(x){((x[1]+x[2])/sum(x))}),0.5) + N = c(apply(mat[,c(1:4)],1,function(x){(sum(x))}),0) + X = c(apply(mat[,c(1:2)],1,function(x){(sum(x))}),0) + bayesCDR = apply(cbind(X,N,P),1,function(x){calculate_bayes(x=x[1],N=x[2],p=x[3],max_sigma=max_sigma,length_sigma=length_sigma)}) + bayesCDR = bayesCDR[-length(bayesCDR)] + + #FWR + P = c(apply(mat[,c(5:8)],1,function(x){((x[3]+x[4])/sum(x))}),0.5) + N = c(apply(mat[,c(1:4)],1,function(x){(sum(x))}),0) + X = c(apply(mat[,c(3:4)],1,function(x){(sum(x))}),0) + bayesFWR = apply(cbind(X,N,P),1,function(x){calculate_bayes(x=x[1],N=x[2],p=x[3],max_sigma=max_sigma,length_sigma=length_sigma)}) + bayesFWR = bayesFWR[-length(bayesFWR)] + } + + if(test=="ImbalancedSilent"){ + #CDR + P = c(apply(mat[,c(6,8)],1,function(x){((x[1])/sum(x))}),0.5) + N = c(apply(mat[,c(2,4)],1,function(x){(sum(x))}),0) + X = c(apply(mat[,c(2,4)],1,function(x){(x[1])}),0) + bayesCDR = apply(cbind(X,N,P),1,function(x){calculate_bayes(x=x[1],N=x[2],p=x[3],max_sigma=max_sigma,length_sigma=length_sigma)}) + bayesCDR = bayesCDR[-length(bayesCDR)] + + #FWR + P = c(apply(mat[,c(6,8)],1,function(x){((x[2])/sum(x))}),0.5) + N = c(apply(mat[,c(2,4)],1,function(x){(sum(x))}),0) + X = c(apply(mat[,c(2,4)],1,function(x){(x[2])}),0) + bayesFWR = apply(cbind(X,N,P),1,function(x){calculate_bayes(x=x[1],N=x[2],p=x[3],max_sigma=max_sigma,length_sigma=length_sigma)}) + bayesFWR = bayesFWR[-length(bayesFWR)] + } + + if(flagOneSeq==T){ + bayesCDR = bayesCDR[1] + bayesFWR = bayesFWR[1] + } + return( list("CDR"=bayesCDR, "FWR"=bayesFWR) ) + } + + ##Covolution + break2chunks<-function(G=1000){ + base<-2^round(log(sqrt(G),2),0) + return(c(rep(base,floor(G/base)-1),base+G-(floor(G/base)*base))) + } + + PowersOfTwo <- function(G=100){ + exponents <- array() + i = 0 + while(G > 0){ + i=i+1 + exponents[i] <- floor( log2(G) ) + G <- G-2^exponents[i] + } + return(exponents) + } + + convolutionPowersOfTwo <- function( cons, length_sigma=4001 ){ + G = ncol(cons) + if(G>1){ + for(gen in log(G,2):1){ + ll<-seq(from=2,to=2^gen,by=2) + sapply(ll,function(l){cons[,l/2]<<-weighted_conv(cons[,l],cons[,l-1],length_sigma=length_sigma)}) + } + } + return( cons[,1] ) + } + + convolutionPowersOfTwoByTwos <- function( cons, length_sigma=4001,G=1 ){ + if(length(ncol(cons))) G<-ncol(cons) + groups <- PowersOfTwo(G) + matG <- matrix(NA, ncol=length(groups), nrow=length(cons)/G ) + startIndex = 1 + for( i in 1:length(groups) ){ + stopIndex <- 2^groups[i] + startIndex - 1 + if(stopIndex!=startIndex){ + matG[,i] <- convolutionPowersOfTwo( cons[,startIndex:stopIndex], length_sigma=length_sigma ) + startIndex = stopIndex + 1 + } + else { + if(G>1) matG[,i] <- cons[,startIndex:stopIndex] + else matG[,i] <- cons + #startIndex = stopIndex + 1 + } + } + return( list( matG, groups ) ) + } + + weighted_conv<-function(x,y,w=1,m=100,length_sigma=4001){ + lx<-length(x) + ly<-length(y) + if({lx<m}| {{lx*w}<m}| {{ly}<m}| {{ly*w}<m}){ + if(w<1){ + y1<-approx(1:ly,y,seq(1,ly,length.out=m))$y + x1<-approx(1:lx,x,seq(1,lx,length.out=m/w))$y + lx<-length(x1) + ly<-length(y1) + } + else { + y1<-approx(1:ly,y,seq(1,ly,length.out=m*w))$y + x1<-approx(1:lx,x,seq(1,lx,length.out=m))$y + lx<-length(x1) + ly<-length(y1) + } + } + else{ + x1<-x + y1<-approx(1:ly,y,seq(1,ly,length.out=floor(lx*w)))$y + ly<-length(y1) + } + tmp<-approx(x=1:(lx+ly-1),y=convolve(x1,rev(y1),type="open"),xout=seq(1,lx+ly-1,length.out=length_sigma))$y + tmp[tmp<=0] = 0 + return(tmp/sum(tmp)) + } + + calculate_bayesGHelper <- function( listMatG,length_sigma=4001 ){ + matG <- listMatG[[1]] + groups <- listMatG[[2]] + i = 1 + resConv <- matG[,i] + denom <- 2^groups[i] + if(length(groups)>1){ + while( i<length(groups) ){ + i = i + 1 + resConv <- weighted_conv(resConv, matG[,i], w= {{2^groups[i]}/denom} ,length_sigma=length_sigma) + #cat({{2^groups[i]}/denom},"\n") + denom <- denom + 2^groups[i] + } + } + return(resConv) + } + + # Given a list of PDFs, returns a convoluted PDF + groupPosteriors <- function( listPosteriors, max_sigma=20, length_sigma=4001 ,Threshold=2 ){ + listPosteriors = listPosteriors[ !is.na(listPosteriors) ] + Length_Postrior<-length(listPosteriors) + if(Length_Postrior>1 & Length_Postrior<=Threshold){ + cons = matrix(unlist(listPosteriors),length(listPosteriors[[1]]),length(listPosteriors)) + listMatG <- convolutionPowersOfTwoByTwos(cons,length_sigma=length_sigma) + y<-calculate_bayesGHelper(listMatG,length_sigma=length_sigma) + return( y/sum(y)/(2*max_sigma/(length_sigma-1)) ) + }else if(Length_Postrior==1) return(listPosteriors[[1]]) + else if(Length_Postrior==0) return(NA) + else { + cons = matrix(unlist(listPosteriors),length(listPosteriors[[1]]),length(listPosteriors)) + y = fastConv(cons,max_sigma=max_sigma, length_sigma=length_sigma ) + return( y/sum(y)/(2*max_sigma/(length_sigma-1)) ) + } + } + + fastConv<-function(cons, max_sigma=20, length_sigma=4001){ + chunks<-break2chunks(G=ncol(cons)) + if(ncol(cons)==3) chunks<-2:1 + index_chunks_end <- cumsum(chunks) + index_chunks_start <- c(1,index_chunks_end[-length(index_chunks_end)]+1) + index_chunks <- cbind(index_chunks_start,index_chunks_end) + + case <- sum(chunks!=chunks[1]) + if(case==1) End <- max(1,((length(index_chunks)/2)-1)) + else End <- max(1,((length(index_chunks)/2))) + + firsts <- sapply(1:End,function(i){ + indexes<-index_chunks[i,1]:index_chunks[i,2] + convolutionPowersOfTwoByTwos(cons[ ,indexes])[[1]] + }) + if(case==0){ + result<-calculate_bayesGHelper( convolutionPowersOfTwoByTwos(firsts) ) + }else if(case==1){ + last<-list(calculate_bayesGHelper( + convolutionPowersOfTwoByTwos( cons[ ,index_chunks[length(index_chunks)/2,1]:index_chunks[length(index_chunks)/2,2]] ) + ),0) + result_first<-calculate_bayesGHelper(convolutionPowersOfTwoByTwos(firsts)) + result<-calculate_bayesGHelper( + list( + cbind( + result_first,last[[1]]), + c(log(index_chunks_end[length(index_chunks)/2-1],2),log(index_chunks[length(index_chunks)/2,2]-index_chunks[length(index_chunks)/2,1]+1,2)) + ) + ) + } + return(as.vector(result)) + } + + # Computes the 95% CI for a pdf + calcBayesCI <- function(Pdf,low=0.025,up=0.975,max_sigma=20, length_sigma=4001){ + if(length(Pdf)!=length_sigma) return(NA) + sigma_s=seq(-max_sigma,max_sigma,length.out=length_sigma) + cdf = cumsum(Pdf) + cdf = cdf/cdf[length(cdf)] + return( c(sigma_s[findInterval(low,cdf)-1] , sigma_s[findInterval(up,cdf)]) ) + } + + # Computes a mean for a pdf + calcBayesMean <- function(Pdf,max_sigma=20,length_sigma=4001){ + if(length(Pdf)!=length_sigma) return(NA) + sigma_s=seq(-max_sigma,max_sigma,length.out=length_sigma) + norm = {length_sigma-1}/2/max_sigma + return( (Pdf%*%sigma_s/norm) ) + } + + # Returns the mean, and the 95% CI for a pdf + calcBayesOutputInfo <- function(Pdf,low=0.025,up=0.975,max_sigma=20, length_sigma=4001){ + if(is.na(Pdf)) + return(rep(NA,3)) + bCI = calcBayesCI(Pdf=Pdf,low=low,up=up,max_sigma=max_sigma,length_sigma=length_sigma) + bMean = calcBayesMean(Pdf=Pdf,max_sigma=max_sigma,length_sigma=length_sigma) + return(c(bMean, bCI)) + } + + # Computes the p-value of a pdf + computeSigmaP <- function(Pdf, length_sigma=4001, max_sigma=20){ + if(length(Pdf)>1){ + norm = {length_sigma-1}/2/max_sigma + pVal = {sum(Pdf[1:{{length_sigma-1}/2}]) + Pdf[{{length_sigma+1}/2}]/2}/norm + if(pVal>0.5){ + pVal = pVal-1 + } + return(pVal) + }else{ + return(NA) + } + } + + # Compute p-value of two distributions + compareTwoDistsFaster <-function(sigma_S=seq(-20,20,length.out=4001), N=10000, dens1=runif(4001,0,1), dens2=runif(4001,0,1)){ + #print(c(length(dens1),length(dens2))) + if(length(dens1)>1 & length(dens2)>1 ){ + dens1<-dens1/sum(dens1) + dens2<-dens2/sum(dens2) + cum2 <- cumsum(dens2)-dens2/2 + tmp<- sum(sapply(1:length(dens1),function(i)return(dens1[i]*cum2[i]))) + #print(tmp) + if(tmp>0.5)tmp<-tmp-1 + return( tmp ) + } + else { + return(NA) + } + #return (sum(sapply(1:N,function(i)(sample(sigma_S,1,prob=dens1)>sample(sigma_S,1,prob=dens2))))/N) + } + + # get number of seqeunces contributing to the sigma (i.e. seqeunces with mutations) + numberOfSeqsWithMutations <- function(matMutations,test=1){ + if(test==4)test=2 + cdrSeqs <- 0 + fwrSeqs <- 0 + if(test==1){#focused + cdrMutations <- apply(matMutations, 1, function(x){ sum(x[c(1,2,4)]) }) + fwrMutations <- apply(matMutations, 1, function(x){ sum(x[c(3,4,2)]) }) + if( any(which(cdrMutations>0)) ) cdrSeqs <- sum(cdrMutations>0) + if( any(which(fwrMutations>0)) ) fwrSeqs <- sum(fwrMutations>0) + } + if(test==2){#local + cdrMutations <- apply(matMutations, 1, function(x){ sum(x[c(1,2)]) }) + fwrMutations <- apply(matMutations, 1, function(x){ sum(x[c(3,4)]) }) + if( any(which(cdrMutations>0)) ) cdrSeqs <- sum(cdrMutations>0) + if( any(which(fwrMutations>0)) ) fwrSeqs <- sum(fwrMutations>0) + } + return(c("CDR"=cdrSeqs, "FWR"=fwrSeqs)) +} + + + +shadeColor <- function(sigmaVal=NA,pVal=NA){ + if(is.na(sigmaVal) & is.na(pVal)) return(NA) + if(is.na(sigmaVal) & !is.na(pVal)) sigmaVal=sign(pVal) + if(is.na(pVal) || pVal==1 || pVal==0){ + returnColor = "#FFFFFF"; + }else{ + colVal=abs(pVal); + + if(sigmaVal<0){ + if(colVal>0.1) + returnColor = "#CCFFCC"; + if(colVal<=0.1) + returnColor = "#99FF99"; + if(colVal<=0.050) + returnColor = "#66FF66"; + if(colVal<=0.010) + returnColor = "#33FF33"; + if(colVal<=0.005) + returnColor = "#00FF00"; + + }else{ + if(colVal>0.1) + returnColor = "#FFCCCC"; + if(colVal<=0.1) + returnColor = "#FF9999"; + if(colVal<=0.05) + returnColor = "#FF6666"; + if(colVal<=0.01) + returnColor = "#FF3333"; + if(colVal<0.005) + returnColor = "#FF0000"; + } + } + + return(returnColor) +} + + + +plotHelp <- function(xfrac=0.05,yfrac=0.05,log=FALSE){ + if(!log){ + x = par()$usr[1]-(par()$usr[2]-par()$usr[1])*xfrac + y = par()$usr[4]+(par()$usr[4]-par()$usr[3])*yfrac + }else { + if(log==2){ + x = par()$usr[1]-(par()$usr[2]-par()$usr[1])*xfrac + y = 10^((par()$usr[4])+((par()$usr[4])-(par()$usr[3]))*yfrac) + } + if(log==1){ + x = 10^((par()$usr[1])-((par()$usr[2])-(par()$usr[1]))*xfrac) + y = par()$usr[4]+(par()$usr[4]-par()$usr[3])*yfrac + } + if(log==3){ + x = 10^((par()$usr[1])-((par()$usr[2])-(par()$usr[1]))*xfrac) + y = 10^((par()$usr[4])+((par()$usr[4])-(par()$usr[3]))*yfrac) + } + } + return(c("x"=x,"y"=y)) +} + +# SHMulation + + # Based on targeting, introduce a single mutation & then update the targeting + oneMutation <- function(){ + # Pick a postion + mutation + posMutation = sample(1:(seqGermlineLen*4),1,replace=F,prob=as.vector(seqTargeting)) + posNucNumb = ceiling(posMutation/4) # Nucleotide number + posNucKind = 4 - ( (posNucNumb*4) - posMutation ) # Nuc the position mutates to + + #mutate the simulation sequence + seqSimVec <- s2c(seqSim) + seqSimVec[posNucNumb] <- NUCLEOTIDES[posNucKind] + seqSim <<- c2s(seqSimVec) + + #update Mutability, Targeting & MutationsTypes + updateMutabilityNTargeting(posNucNumb) + + #return(c(posNucNumb,NUCLEOTIDES[posNucKind])) + return(posNucNumb) + } + + updateMutabilityNTargeting <- function(position){ + min_i<-max((position-2),1) + max_i<-min((position+2),nchar(seqSim)) + min_ii<-min(min_i,3) + + #mutability - update locally + seqMutability[(min_i):(max_i)] <<- computeMutabilities(substr(seqSim,position-4,position+4))[(min_ii):(max_i-min_i+min_ii)] + + + #targeting - compute locally + seqTargeting[,min_i:max_i] <<- computeTargeting(substr(seqSim,min_i,max_i),seqMutability[min_i:max_i]) + seqTargeting[is.na(seqTargeting)] <<- 0 + #mutCodonPos = getCodonPos(position) + mutCodonPos = seq(getCodonPos(min_i)[1],getCodonPos(max_i)[3]) + #cat(mutCodonPos,"\n") + mutTypeCodon = getCodonPos(position) + seqMutationTypes[,mutTypeCodon] <<- computeMutationTypesFast( substr(seqSim,mutTypeCodon[1],mutTypeCodon[3]) ) + # Stop = 0 + if(any(seqMutationTypes[,mutCodonPos]=="Stop",na.rm=T )){ + seqTargeting[,mutCodonPos][seqMutationTypes[,mutCodonPos]=="Stop"] <<- 0 + } + + + #Selection + selectedPos = (min_i*4-4)+(which(seqMutationTypes[,min_i:max_i]=="R")) + # CDR + selectedCDR = selectedPos[which(matCDR[selectedPos]==T)] + seqTargeting[selectedCDR] <<- seqTargeting[selectedCDR] * exp(selCDR) + seqTargeting[selectedCDR] <<- seqTargeting[selectedCDR]/baseLineCDR_K + + # FWR + selectedFWR = selectedPos[which(matFWR[selectedPos]==T)] + seqTargeting[selectedFWR] <<- seqTargeting[selectedFWR] * exp(selFWR) + seqTargeting[selectedFWR] <<- seqTargeting[selectedFWR]/baseLineFWR_K + + } + + + + # Validate the mutation: if the mutation has not been sampled before validate it, else discard it. + validateMutation <- function(){ + if( !(mutatedPos%in%mutatedPositions) ){ # if it's a new mutation + uniqueMutationsIntroduced <<- uniqueMutationsIntroduced + 1 + mutatedPositions[uniqueMutationsIntroduced] <<- mutatedPos + }else{ + if(substr(seqSim,mutatedPos,mutatedPos)==substr(seqGermline,mutatedPos,mutatedPos)){ # back to germline mutation + mutatedPositions <<- mutatedPositions[-which(mutatedPositions==mutatedPos)] + uniqueMutationsIntroduced <<- uniqueMutationsIntroduced - 1 + } + } + } + + + + # Places text (labels) at normalized coordinates + myaxis <- function(xfrac=0.05,yfrac=0.05,log=FALSE,w="text",cex=1,adj=1,thecol="black"){ + par(xpd=TRUE) + if(!log) + text(par()$usr[1]-(par()$usr[2]-par()$usr[1])*xfrac,par()$usr[4]+(par()$usr[4]-par()$usr[3])*yfrac,w,cex=cex,adj=adj,col=thecol) + else { + if(log==2) + text( + par()$usr[1]-(par()$usr[2]-par()$usr[1])*xfrac, + 10^((par()$usr[4])+((par()$usr[4])-(par()$usr[3]))*yfrac), + w,cex=cex,adj=adj,col=thecol) + if(log==1) + text( + 10^((par()$usr[1])-((par()$usr[2])-(par()$usr[1]))*xfrac), + par()$usr[4]+(par()$usr[4]-par()$usr[3])*yfrac, + w,cex=cex,adj=adj,col=thecol) + if(log==3) + text( + 10^((par()$usr[1])-((par()$usr[2])-(par()$usr[1]))*xfrac), + 10^((par()$usr[4])+((par()$usr[4])-(par()$usr[3]))*yfrac), + w,cex=cex,adj=adj,col=thecol) + } + par(xpd=FALSE) + } + + + + # Count the mutations in a sequence + analyzeMutations <- function( inputMatrixIndex, model = 0 , multipleMutation=0, seqWithStops=0){ + + paramGL = s2c(matInput[inputMatrixIndex,2]) + paramSeq = s2c(matInput[inputMatrixIndex,1]) + + #if( any(paramSeq=="N") ){ + # gapPos_Seq = which(paramSeq=="N") + # gapPos_Seq_ToReplace = gapPos_Seq[paramGL[gapPos_Seq] != "N"] + # paramSeq[gapPos_Seq_ToReplace] = paramGL[gapPos_Seq_ToReplace] + #} + mutations_val = paramGL != paramSeq + + if(any(mutations_val)){ + mutationPos = which(mutations_val)#{1:length(mutations_val)}[mutations_val] + length_mutations =length(mutationPos) + mutationInfo = rep(NA,length_mutations) + + pos<- mutationPos + pos_array<-array(sapply(pos,getCodonPos)) + codonGL = paramGL[pos_array] + codonSeqWhole = paramSeq[pos_array] + codonSeq = sapply(pos,function(x){ + seqP = paramGL[getCodonPos(x)] + muCodonPos = {x-1}%%3+1 + seqP[muCodonPos] = paramSeq[x] + return(seqP) + }) + GLcodons = apply(matrix(codonGL,length_mutations,3,byrow=TRUE),1,c2s) + SeqcodonsWhole = apply(matrix(codonSeqWhole,length_mutations,3,byrow=TRUE),1,c2s) + Seqcodons = apply(codonSeq,2,c2s) + + mutationInfo = apply(rbind(GLcodons , Seqcodons),2,function(x){mutationType(c2s(x[1]),c2s(x[2]))}) + names(mutationInfo) = mutationPos + + mutationInfoWhole = apply(rbind(GLcodons , SeqcodonsWhole),2,function(x){mutationType(c2s(x[1]),c2s(x[2]))}) + names(mutationInfoWhole) = mutationPos + + mutationInfo <- mutationInfo[!is.na(mutationInfo)] + mutationInfoWhole <- mutationInfoWhole[!is.na(mutationInfoWhole)] + + if(any(!is.na(mutationInfo))){ + + #Filter based on Stop (at the codon level) + if(seqWithStops==1){ + nucleotidesAtStopCodons = names(mutationInfoWhole[mutationInfoWhole!="Stop"]) + mutationInfo = mutationInfo[nucleotidesAtStopCodons] + mutationInfoWhole = mutationInfo[nucleotidesAtStopCodons] + }else{ + countStops = sum(mutationInfoWhole=="Stop") + if(seqWithStops==2 & countStops==0) mutationInfo = NA + if(seqWithStops==3 & countStops>0) mutationInfo = NA + } + + if(any(!is.na(mutationInfo))){ + #Filter mutations based on multipleMutation + if(multipleMutation==1 & !is.na(mutationInfo)){ + mutationCodons = getCodonNumb(as.numeric(names(mutationInfoWhole))) + tableMutationCodons <- table(mutationCodons) + codonsWithMultipleMutations <- as.numeric(names(tableMutationCodons[tableMutationCodons>1])) + if(any(codonsWithMultipleMutations)){ + #remove the nucleotide mutations in the codons with multiple mutations + mutationInfo <- mutationInfo[!(mutationCodons %in% codonsWithMultipleMutations)] + #replace those codons with Ns in the input sequence + paramSeq[unlist(lapply(codonsWithMultipleMutations, getCodonNucs))] = "N" + matInput[inputMatrixIndex,1] <<- c2s(paramSeq) + } + } + + #Filter mutations based on the model + if(any(mutationInfo)==T | is.na(any(mutationInfo))){ + + if(model==1 & !is.na(mutationInfo)){ + mutationInfo <- mutationInfo[mutationInfo=="S"] + } + if(any(mutationInfo)==T | is.na(any(mutationInfo))) return(mutationInfo) + else return(NA) + }else{ + return(NA) + } + }else{ + return(NA) + } + + + }else{ + return(NA) + } + + + }else{ + return (NA) + } + } + + analyzeMutationsFixed <- function( inputArray, model = 0 , multipleMutation=0, seqWithStops=0){ + + paramGL = s2c(inputArray[2]) + paramSeq = s2c(inputArray[1]) + inputSeq <- inputArray[1] + #if( any(paramSeq=="N") ){ + # gapPos_Seq = which(paramSeq=="N") + # gapPos_Seq_ToReplace = gapPos_Seq[paramGL[gapPos_Seq] != "N"] + # paramSeq[gapPos_Seq_ToReplace] = paramGL[gapPos_Seq_ToReplace] + #} + mutations_val = paramGL != paramSeq + + if(any(mutations_val)){ + mutationPos = which(mutations_val)#{1:length(mutations_val)}[mutations_val] + length_mutations =length(mutationPos) + mutationInfo = rep(NA,length_mutations) + + pos<- mutationPos + pos_array<-array(sapply(pos,getCodonPos)) + codonGL = paramGL[pos_array] + codonSeqWhole = paramSeq[pos_array] + codonSeq = sapply(pos,function(x){ + seqP = paramGL[getCodonPos(x)] + muCodonPos = {x-1}%%3+1 + seqP[muCodonPos] = paramSeq[x] + return(seqP) + }) + GLcodons = apply(matrix(codonGL,length_mutations,3,byrow=TRUE),1,c2s) + SeqcodonsWhole = apply(matrix(codonSeqWhole,length_mutations,3,byrow=TRUE),1,c2s) + Seqcodons = apply(codonSeq,2,c2s) + + mutationInfo = apply(rbind(GLcodons , Seqcodons),2,function(x){mutationType(c2s(x[1]),c2s(x[2]))}) + names(mutationInfo) = mutationPos + + mutationInfoWhole = apply(rbind(GLcodons , SeqcodonsWhole),2,function(x){mutationType(c2s(x[1]),c2s(x[2]))}) + names(mutationInfoWhole) = mutationPos + + mutationInfo <- mutationInfo[!is.na(mutationInfo)] + mutationInfoWhole <- mutationInfoWhole[!is.na(mutationInfoWhole)] + + if(any(!is.na(mutationInfo))){ + + #Filter based on Stop (at the codon level) + if(seqWithStops==1){ + nucleotidesAtStopCodons = names(mutationInfoWhole[mutationInfoWhole!="Stop"]) + mutationInfo = mutationInfo[nucleotidesAtStopCodons] + mutationInfoWhole = mutationInfo[nucleotidesAtStopCodons] + }else{ + countStops = sum(mutationInfoWhole=="Stop") + if(seqWithStops==2 & countStops==0) mutationInfo = NA + if(seqWithStops==3 & countStops>0) mutationInfo = NA + } + + if(any(!is.na(mutationInfo))){ + #Filter mutations based on multipleMutation + if(multipleMutation==1 & !is.na(mutationInfo)){ + mutationCodons = getCodonNumb(as.numeric(names(mutationInfoWhole))) + tableMutationCodons <- table(mutationCodons) + codonsWithMultipleMutations <- as.numeric(names(tableMutationCodons[tableMutationCodons>1])) + if(any(codonsWithMultipleMutations)){ + #remove the nucleotide mutations in the codons with multiple mutations + mutationInfo <- mutationInfo[!(mutationCodons %in% codonsWithMultipleMutations)] + #replace those codons with Ns in the input sequence + paramSeq[unlist(lapply(codonsWithMultipleMutations, getCodonNucs))] = "N" + #matInput[inputMatrixIndex,1] <<- c2s(paramSeq) + inputSeq <- c2s(paramSeq) + } + } + + #Filter mutations based on the model + if(any(mutationInfo)==T | is.na(any(mutationInfo))){ + + if(model==1 & !is.na(mutationInfo)){ + mutationInfo <- mutationInfo[mutationInfo=="S"] + } + if(any(mutationInfo)==T | is.na(any(mutationInfo))) return(list(mutationInfo,inputSeq)) + else return(list(NA,inputSeq)) + }else{ + return(list(NA,inputSeq)) + } + }else{ + return(list(NA,inputSeq)) + } + + + }else{ + return(list(NA,inputSeq)) + } + + + }else{ + return (list(NA,inputSeq)) + } + } + + # triMutability Background Count + buildMutabilityModel <- function( inputMatrixIndex, model=0 , multipleMutation=0, seqWithStops=0, stopMutations=0){ + + #rowOrigMatInput = matInput[inputMatrixIndex,] + seqGL = gsub("-", "", matInput[inputMatrixIndex,2]) + seqInput = gsub("-", "", matInput[inputMatrixIndex,1]) + #matInput[inputMatrixIndex,] <<- cbind(seqInput,seqGL) + tempInput <- cbind(seqInput,seqGL) + seqLength = nchar(seqGL) + list_analyzeMutationsFixed<- analyzeMutationsFixed(tempInput, model, multipleMutation, seqWithStops) + mutationCount <- list_analyzeMutationsFixed[[1]] + seqInput <- list_analyzeMutationsFixed[[2]] + BackgroundMatrix = mutabilityMatrix + MutationMatrix = mutabilityMatrix + MutationCountMatrix = mutabilityMatrix + if(!is.na(mutationCount)){ + if((stopMutations==0 & model==0) | (stopMutations==1 & (sum(mutationCount=="Stop")<length(mutationCount))) | (model==1 & (sum(mutationCount=="S")>0)) ){ + + fivermerStartPos = 1:(seqLength-4) + fivemerLength <- length(fivermerStartPos) + fivemerGL <- substr(rep(seqGL,length(fivermerStartPos)),(fivermerStartPos),(fivermerStartPos+4)) + fivemerSeq <- substr(rep(seqInput,length(fivermerStartPos)),(fivermerStartPos),(fivermerStartPos+4)) + + #Background + for(fivemerIndex in 1:fivemerLength){ + fivemer = fivemerGL[fivemerIndex] + if(!any(grep("N",fivemer))){ + fivemerCodonPos = fivemerCodon(fivemerIndex) + fivemerReadingFrameCodon = substr(fivemer,fivemerCodonPos[1],fivemerCodonPos[3]) + fivemerReadingFrameCodonInputSeq = substr(fivemerSeq[fivemerIndex],fivemerCodonPos[1],fivemerCodonPos[3]) + + # All mutations model + #if(!any(grep("N",fivemerReadingFrameCodon))){ + if(model==0){ + if(stopMutations==0){ + if(!any(grep("N",fivemerReadingFrameCodonInputSeq))) + BackgroundMatrix[fivemer] <- (BackgroundMatrix[fivemer] + 1) + }else{ + if( !any(grep("N",fivemerReadingFrameCodonInputSeq)) & translateCodonToAminoAcid(fivemerReadingFrameCodon)!="*" ){ + positionWithinCodon = which(fivemerCodonPos==3)#positionsWithinCodon[(fivemerCodonPos[1]%%3)+1] + BackgroundMatrix[fivemer] <- (BackgroundMatrix[fivemer] + probNonStopMutations[fivemerReadingFrameCodon,positionWithinCodon]) + } + } + }else{ # Only silent mutations + if( !any(grep("N",fivemerReadingFrameCodonInputSeq)) & translateCodonToAminoAcid(fivemerReadingFrameCodon)!="*" & translateCodonToAminoAcid(fivemerReadingFrameCodonInputSeq)==translateCodonToAminoAcid(fivemerReadingFrameCodon)){ + positionWithinCodon = which(fivemerCodonPos==3) + BackgroundMatrix[fivemer] <- (BackgroundMatrix[fivemer] + probSMutations[fivemerReadingFrameCodon,positionWithinCodon]) + } + } + #} + } + } + + #Mutations + if(stopMutations==1) mutationCount = mutationCount[mutationCount!="Stop"] + if(model==1) mutationCount = mutationCount[mutationCount=="S"] + mutationPositions = as.numeric(names(mutationCount)) + mutationCount = mutationCount[mutationPositions>2 & mutationPositions<(seqLength-1)] + mutationPositions = mutationPositions[mutationPositions>2 & mutationPositions<(seqLength-1)] + countMutations = 0 + for(mutationPosition in mutationPositions){ + fivemerIndex = mutationPosition-2 + fivemer = fivemerSeq[fivemerIndex] + GLfivemer = fivemerGL[fivemerIndex] + fivemerCodonPos = fivemerCodon(fivemerIndex) + fivemerReadingFrameCodon = substr(fivemer,fivemerCodonPos[1],fivemerCodonPos[3]) + fivemerReadingFrameCodonGL = substr(GLfivemer,fivemerCodonPos[1],fivemerCodonPos[3]) + if(!any(grep("N",fivemer)) & !any(grep("N",GLfivemer))){ + if(model==0){ + countMutations = countMutations + 1 + MutationMatrix[GLfivemer] <- (MutationMatrix[GLfivemer] + 1) + MutationCountMatrix[GLfivemer] <- (MutationCountMatrix[GLfivemer] + 1) + }else{ + if( translateCodonToAminoAcid(fivemerReadingFrameCodonGL)!="*" ){ + countMutations = countMutations + 1 + positionWithinCodon = which(fivemerCodonPos==3) + glNuc = substr(fivemerReadingFrameCodonGL,positionWithinCodon,positionWithinCodon) + inputNuc = substr(fivemerReadingFrameCodon,positionWithinCodon,positionWithinCodon) + MutationMatrix[GLfivemer] <- (MutationMatrix[GLfivemer] + substitution[glNuc,inputNuc]) + MutationCountMatrix[GLfivemer] <- (MutationCountMatrix[GLfivemer] + 1) + } + } + } + } + + seqMutability = MutationMatrix/BackgroundMatrix + seqMutability = seqMutability/sum(seqMutability,na.rm=TRUE) + #cat(inputMatrixIndex,"\t",countMutations,"\n") + return(list("seqMutability" = seqMutability,"numbMutations" = countMutations,"seqMutabilityCount" = MutationCountMatrix, "BackgroundMatrix"=BackgroundMatrix)) + + } + } + + } + + #Returns the codon position containing the middle nucleotide + fivemerCodon <- function(fivemerIndex){ + codonPos = list(2:4,1:3,3:5) + fivemerType = fivemerIndex%%3 + return(codonPos[[fivemerType+1]]) + } + + #returns probability values for one mutation in codons resulting in R, S or Stop + probMutations <- function(typeOfMutation){ + matMutationProb <- matrix(0,ncol=3,nrow=125,dimnames=list(words(alphabet = c(NUCLEOTIDES,"N"), length=3),c(1:3))) + for(codon in rownames(matMutationProb)){ + if( !any(grep("N",codon)) ){ + for(muPos in 1:3){ + matCodon = matrix(rep(s2c(codon),3),nrow=3,ncol=3,byrow=T) + glNuc = matCodon[1,muPos] + matCodon[,muPos] = canMutateTo(glNuc) + substitutionRate = substitution[glNuc,matCodon[,muPos]] + typeOfMutations = apply(rbind(rep(codon,3),apply(matCodon,1,c2s)),2,function(x){mutationType(c2s(x[1]),c2s(x[2]))}) + matMutationProb[codon,muPos] <- sum(substitutionRate[typeOfMutations==typeOfMutation]) + } + } + } + + return(matMutationProb) + } + + + + +#Mapping Trinucleotides to fivemers +mapTriToFivemer <- function(triMutability=triMutability_Literature_Human){ + rownames(triMutability) <- triMutability_Names + Fivemer<-rep(NA,1024) + names(Fivemer)<-words(alphabet=NUCLEOTIDES,length=5) + Fivemer<-sapply(names(Fivemer),function(Word)return(sum( c(triMutability[substring(Word,3,5),1],triMutability[substring(Word,2,4),2],triMutability[substring(Word,1,3),3]),na.rm=TRUE))) + Fivemer<-Fivemer/sum(Fivemer) + return(Fivemer) +} + +collapseFivemerToTri<-function(Fivemer,Weights=MutabilityWeights,position=1,NUC="A"){ + Indices<-substring(names(Fivemer),3,3)==NUC + Factors<-substring(names(Fivemer[Indices]),(4-position),(6-position)) + tapply(which(Indices),Factors,function(i)weighted.mean(Fivemer[i],Weights[i],na.rm=TRUE)) +} + + + +CountFivemerToTri<-function(Fivemer,Weights=MutabilityWeights,position=1,NUC="A"){ + Indices<-substring(names(Fivemer),3,3)==NUC + Factors<-substring(names(Fivemer[Indices]),(4-position),(6-position)) + tapply(which(Indices),Factors,function(i)sum(Weights[i],na.rm=TRUE)) +} + +#Uses the real counts of the mutated fivemers +CountFivemerToTri2<-function(Fivemer,Counts=MutabilityCounts,position=1,NUC="A"){ + Indices<-substring(names(Fivemer),3,3)==NUC + Factors<-substring(names(Fivemer[Indices]),(4-position),(6-position)) + tapply(which(Indices),Factors,function(i)sum(Counts[i],na.rm=TRUE)) +} + +bootstrap<-function(x=c(33,12,21),M=10000,alpha=0.05){ +N<-sum(x) +if(N){ +p<-x/N +k<-length(x)-1 +tmp<-rmultinom(M, size = N, prob=p) +tmp_p<-apply(tmp,2,function(y)y/N) +(apply(tmp_p,1,function(y)quantile(y,c(alpha/2/k,1-alpha/2/k)))) +} +else return(matrix(0,2,length(x))) +} + + + + +bootstrap2<-function(x=c(33,12,21),n=10,M=10000,alpha=0.05){ + +N<-sum(x) +k<-length(x) +y<-rep(1:k,x) +tmp<-sapply(1:M,function(i)sample(y,n)) +if(n>1)tmp_p<-sapply(1:M,function(j)sapply(1:k,function(i)sum(tmp[,j]==i)))/n +if(n==1)tmp_p<-sapply(1:M,function(j)sapply(1:k,function(i)sum(tmp[j]==i)))/n +(apply(tmp_p,1,function(z)quantile(z,c(alpha/2/(k-1),1-alpha/2/(k-1))))) +} + + + +p_value<-function(x=c(33,12,21),M=100000,x_obs=c(2,5,3)){ +n=sum(x_obs) +N<-sum(x) +k<-length(x) +y<-rep(1:k,x) +tmp<-sapply(1:M,function(i)sample(y,n)) +if(n>1)tmp_p<-sapply(1:M,function(j)sapply(1:k,function(i)sum(tmp[,j]==i))) +if(n==1)tmp_p<-sapply(1:M,function(j)sapply(1:k,function(i)sum(tmp[j]==i))) +tmp<-rbind(sapply(1:3,function(i)sum(tmp_p[i,]>=x_obs[i])/M), +sapply(1:3,function(i)sum(tmp_p[i,]<=x_obs[i])/M)) +sapply(1:3,function(i){if(tmp[1,i]>=tmp[2,i])return(-tmp[2,i])else return(tmp[1,i])}) +} + +#"D:\\Sequences\\IMGT Germlines\\Human_SNPless_IGHJ.FASTA" +# Remove SNPs from IMGT germline segment alleles +generateUnambiguousRepertoire <- function(repertoireInFile,repertoireOutFile){ + repertoireIn <- read.fasta(repertoireInFile, seqtype="DNA",as.string=T,set.attributes=F,forceDNAtolower=F) + alleleNames <- sapply(names(repertoireIn),function(x)strsplit(x,"|",fixed=TRUE)[[1]][2]) + SNPs <- tapply(repertoireIn,sapply(alleleNames,function(x)strsplit(x,"*",fixed=TRUE)[[1]][1]),function(x){ + Indices<-NULL + for(i in 1:length(x)){ + firstSeq = s2c(x[[1]]) + iSeq = s2c(x[[i]]) + Indices<-c(Indices,which(firstSeq[1:320]!=iSeq[1:320] & firstSeq[1:320]!="." & iSeq[1:320]!="." )) + } + return(sort(unique(Indices))) + }) + repertoireOut <- repertoireIn + repertoireOut <- lapply(names(repertoireOut), function(repertoireName){ + alleleName <- strsplit(repertoireName,"|",fixed=TRUE)[[1]][2] + geneSegmentName <- strsplit(alleleName,"*",fixed=TRUE)[[1]][1] + alleleSeq <- s2c(repertoireOut[[repertoireName]]) + alleleSeq[as.numeric(unlist(SNPs[geneSegmentName]))] <- "N" + alleleSeq <- c2s(alleleSeq) + repertoireOut[[repertoireName]] <- alleleSeq + }) + names(repertoireOut) <- names(repertoireIn) + write.fasta(repertoireOut,names(repertoireOut),file.out=repertoireOutFile) + +} + + + + + + +############ +groupBayes2 = function(indexes, param_resultMat){ + + BayesGDist_Focused_CDR = calculate_bayesG( x=param_resultMat[indexes,1], N=apply(param_resultMat[indexes,c(1,2,4)],1,sum,na.rm=T), p=apply(param_resultMat[indexes,5:8],1,function(x){x[1]/(x[1]+x[2]+x[4])})) + BayesGDist_Focused_FWR = calculate_bayesG( x=param_resultMat[indexes,3], N=apply(param_resultMat[indexes,c(3,2,4)],1,sum,na.rm=T), p=apply(param_resultMat[indexes,5:8],1,function(x){x[3]/(x[3]+x[2]+x[4])})) + #BayesGDist_Local_CDR = calculate_bayesG( x=param_resultMat[indexes,1], N=apply(param_resultMat[indexes,c(1,2)],1,sum,na.rm=T), p=apply(param_resultMat[indexes,5:8],1,function(x){x[1]/(x[1]+x[2])})) + #BayesGDist_Local_FWR = calculate_bayesG( x=param_resultMat[indexes,3], N=apply(param_resultMat[indexes,c(3,4)],1,sum,na.rm=T), p=apply(param_resultMat[indexes,5:8],1,function(x){x[3]/(x[3]+x[4])})) + #BayesGDist_Global_CDR = calculate_bayesG( x=param_resultMat[indexes,1], N=apply(param_resultMat[indexes,c(1,2,3,4)],1,sum,na.rm=T), p=apply(param_resultMat[indexes,5:8],1,function(x){x[1]/(x[1]+x[2]+x[3]+x[4])})) + #BayesGDist_Global_FWR = calculate_bayesG( x=param_resultMat[indexes,3], N=apply(param_resultMat[indexes,c(1,2,3,4)],1,sum,na.rm=T), p=apply(param_resultMat[indexes,5:8],1,function(x){x[3]/(x[1]+x[2]+x[3]+x[4])})) + return ( list("BayesGDist_Focused_CDR"=BayesGDist_Focused_CDR, + "BayesGDist_Focused_FWR"=BayesGDist_Focused_FWR) ) + #"BayesGDist_Local_CDR"=BayesGDist_Local_CDR, + #"BayesGDist_Local_FWR" = BayesGDist_Local_FWR)) +# "BayesGDist_Global_CDR" = BayesGDist_Global_CDR, +# "BayesGDist_Global_FWR" = BayesGDist_Global_FWR) ) + + +} + + +calculate_bayesG <- function( x=array(), N=array(), p=array(), max_sigma=20, length_sigma=4001){ + G <- max(length(x),length(N),length(p)) + x=array(x,dim=G) + N=array(N,dim=G) + p=array(p,dim=G) + + indexOfZero = N>0 & p>0 + N = N[indexOfZero] + x = x[indexOfZero] + p = p[indexOfZero] + G <- length(x) + + if(G){ + + cons<-array( dim=c(length_sigma,G) ) + if(G==1) { + return(calculate_bayes(x=x[G],N=N[G],p=p[G],max_sigma=max_sigma,length_sigma=length_sigma)) + } + else { + for(g in 1:G) cons[,g] <- calculate_bayes(x=x[g],N=N[g],p=p[g],max_sigma=max_sigma,length_sigma=length_sigma) + listMatG <- convolutionPowersOfTwoByTwos(cons,length_sigma=length_sigma) + y<-calculate_bayesGHelper(listMatG,length_sigma=length_sigma) + return( y/sum(y)/(2*max_sigma/(length_sigma-1)) ) + } + }else{ + return(NA) + } +} + + +calculate_bayesGHelper <- function( listMatG,length_sigma=4001 ){ + matG <- listMatG[[1]] + groups <- listMatG[[2]] + i = 1 + resConv <- matG[,i] + denom <- 2^groups[i] + if(length(groups)>1){ + while( i<length(groups) ){ + i = i + 1 + resConv <- weighted_conv(resConv, matG[,i], w= {{2^groups[i]}/denom} ,length_sigma=length_sigma) + #cat({{2^groups[i]}/denom},"\n") + denom <- denom + 2^groups[i] + } + } + return(resConv) +} + +weighted_conv<-function(x,y,w=1,m=100,length_sigma=4001){ +lx<-length(x) +ly<-length(y) +if({lx<m}| {{lx*w}<m}| {{ly}<m}| {{ly*w}<m}){ +if(w<1){ +y1<-approx(1:ly,y,seq(1,ly,length.out=m))$y +x1<-approx(1:lx,x,seq(1,lx,length.out=m/w))$y +lx<-length(x1) +ly<-length(y1) +} +else { +y1<-approx(1:ly,y,seq(1,ly,length.out=m*w))$y +x1<-approx(1:lx,x,seq(1,lx,length.out=m))$y +lx<-length(x1) +ly<-length(y1) +} +} +else{ +x1<-x +y1<-approx(1:ly,y,seq(1,ly,length.out=floor(lx*w)))$y +ly<-length(y1) +} +tmp<-approx(x=1:(lx+ly-1),y=convolve(x1,rev(y1),type="open"),xout=seq(1,lx+ly-1,length.out=length_sigma))$y +tmp[tmp<=0] = 0 +return(tmp/sum(tmp)) +} + +######################## + + + + +mutabilityMatrixONE<-rep(0,4) +names(mutabilityMatrixONE)<-NUCLEOTIDES + + # triMutability Background Count + buildMutabilityModelONE <- function( inputMatrixIndex, model=0 , multipleMutation=0, seqWithStops=0, stopMutations=0){ + + #rowOrigMatInput = matInput[inputMatrixIndex,] + seqGL = gsub("-", "", matInput[inputMatrixIndex,2]) + seqInput = gsub("-", "", matInput[inputMatrixIndex,1]) + matInput[inputMatrixIndex,] <<- c(seqInput,seqGL) + seqLength = nchar(seqGL) + mutationCount <- analyzeMutations(inputMatrixIndex, model, multipleMutation, seqWithStops) + BackgroundMatrix = mutabilityMatrixONE + MutationMatrix = mutabilityMatrixONE + MutationCountMatrix = mutabilityMatrixONE + if(!is.na(mutationCount)){ + if((stopMutations==0 & model==0) | (stopMutations==1 & (sum(mutationCount=="Stop")<length(mutationCount))) | (model==1 & (sum(mutationCount=="S")>0)) ){ + +# ONEmerStartPos = 1:(seqLength) +# ONEmerLength <- length(ONEmerStartPos) + ONEmerGL <- s2c(seqGL) + ONEmerSeq <- s2c(seqInput) + + #Background + for(ONEmerIndex in 1:seqLength){ + ONEmer = ONEmerGL[ONEmerIndex] + if(ONEmer!="N"){ + ONEmerCodonPos = getCodonPos(ONEmerIndex) + ONEmerReadingFrameCodon = c2s(ONEmerGL[ONEmerCodonPos]) + ONEmerReadingFrameCodonInputSeq = c2s(ONEmerSeq[ONEmerCodonPos] ) + + # All mutations model + #if(!any(grep("N",ONEmerReadingFrameCodon))){ + if(model==0){ + if(stopMutations==0){ + if(!any(grep("N",ONEmerReadingFrameCodonInputSeq))) + BackgroundMatrix[ONEmer] <- (BackgroundMatrix[ONEmer] + 1) + }else{ + if( !any(grep("N",ONEmerReadingFrameCodonInputSeq)) & translateCodonToAminoAcid(ONEmerReadingFrameCodonInputSeq)!="*"){ + positionWithinCodon = which(ONEmerCodonPos==ONEmerIndex)#positionsWithinCodon[(ONEmerCodonPos[1]%%3)+1] + BackgroundMatrix[ONEmer] <- (BackgroundMatrix[ONEmer] + probNonStopMutations[ONEmerReadingFrameCodon,positionWithinCodon]) + } + } + }else{ # Only silent mutations + if( !any(grep("N",ONEmerReadingFrameCodonInputSeq)) & translateCodonToAminoAcid(ONEmerReadingFrameCodonInputSeq)!="*" & translateCodonToAminoAcid(ONEmerReadingFrameCodonInputSeq)==translateCodonToAminoAcid(ONEmerReadingFrameCodon) ){ + positionWithinCodon = which(ONEmerCodonPos==ONEmerIndex) + BackgroundMatrix[ONEmer] <- (BackgroundMatrix[ONEmer] + probSMutations[ONEmerReadingFrameCodon,positionWithinCodon]) + } + } + } + } + } + + #Mutations + if(stopMutations==1) mutationCount = mutationCount[mutationCount!="Stop"] + if(model==1) mutationCount = mutationCount[mutationCount=="S"] + mutationPositions = as.numeric(names(mutationCount)) + mutationCount = mutationCount[mutationPositions>2 & mutationPositions<(seqLength-1)] + mutationPositions = mutationPositions[mutationPositions>2 & mutationPositions<(seqLength-1)] + countMutations = 0 + for(mutationPosition in mutationPositions){ + ONEmerIndex = mutationPosition + ONEmer = ONEmerSeq[ONEmerIndex] + GLONEmer = ONEmerGL[ONEmerIndex] + ONEmerCodonPos = getCodonPos(ONEmerIndex) + ONEmerReadingFrameCodon = c2s(ONEmerSeq[ONEmerCodonPos]) + ONEmerReadingFrameCodonGL =c2s(ONEmerGL[ONEmerCodonPos]) + if(!any(grep("N",ONEmer)) & !any(grep("N",GLONEmer))){ + if(model==0){ + countMutations = countMutations + 1 + MutationMatrix[GLONEmer] <- (MutationMatrix[GLONEmer] + 1) + MutationCountMatrix[GLONEmer] <- (MutationCountMatrix[GLONEmer] + 1) + }else{ + if( translateCodonToAminoAcid(ONEmerReadingFrameCodonGL)!="*" ){ + countMutations = countMutations + 1 + positionWithinCodon = which(ONEmerCodonPos==ONEmerIndex) + glNuc = substr(ONEmerReadingFrameCodonGL,positionWithinCodon,positionWithinCodon) + inputNuc = substr(ONEmerReadingFrameCodon,positionWithinCodon,positionWithinCodon) + MutationMatrix[GLONEmer] <- (MutationMatrix[GLONEmer] + substitution[glNuc,inputNuc]) + MutationCountMatrix[GLONEmer] <- (MutationCountMatrix[GLONEmer] + 1) + } + } + } + } + + seqMutability = MutationMatrix/BackgroundMatrix + seqMutability = seqMutability/sum(seqMutability,na.rm=TRUE) + #cat(inputMatrixIndex,"\t",countMutations,"\n") + return(list("seqMutability" = seqMutability,"numbMutations" = countMutations,"seqMutabilityCount" = MutationCountMatrix, "BackgroundMatrix"=BackgroundMatrix)) +# tmp<-list("seqMutability" = seqMutability,"numbMutations" = countMutations,"seqMutabilityCount" = MutationCountMatrix) + } + } + +################ +# $Id: trim.R 989 2006-10-29 15:28:26Z ggorjan $ + +trim <- function(s, recode.factor=TRUE, ...) + UseMethod("trim", s) + +trim.default <- function(s, recode.factor=TRUE, ...) + s + +trim.character <- function(s, recode.factor=TRUE, ...) +{ + s <- sub(pattern="^ +", replacement="", x=s) + s <- sub(pattern=" +$", replacement="", x=s) + s +} + +trim.factor <- function(s, recode.factor=TRUE, ...) +{ + levels(s) <- trim(levels(s)) + if(recode.factor) { + dots <- list(x=s, ...) + if(is.null(dots$sort)) dots$sort <- sort + s <- do.call(what=reorder.factor, args=dots) + } + s +} + +trim.list <- function(s, recode.factor=TRUE, ...) + lapply(s, trim, recode.factor=recode.factor, ...) + +trim.data.frame <- function(s, recode.factor=TRUE, ...) +{ + s[] <- trim.list(s, recode.factor=recode.factor, ...) + s +} +####################################### +# Compute the expected for each sequence-germline pair by codon +getExpectedIndividualByCodon <- function(matInput){ +if( any(grep("multicore",search())) ){ + facGL <- factor(matInput[,2]) + facLevels = levels(facGL) + LisGLs_MutabilityU = mclapply(1:length(facLevels), function(x){ + computeMutabilities(facLevels[x]) + }) + facIndex = match(facGL,facLevels) + + LisGLs_Mutability = mclapply(1:nrow(matInput), function(x){ + cInput = rep(NA,nchar(matInput[x,1])) + cInput[s2c(matInput[x,1])!="N"] = 1 + LisGLs_MutabilityU[[facIndex[x]]] * cInput + }) + + LisGLs_Targeting = mclapply(1:dim(matInput)[1], function(x){ + computeTargeting(matInput[x,2],LisGLs_Mutability[[x]]) + }) + + LisGLs_MutationTypes = mclapply(1:length(matInput[,2]),function(x){ + #print(x) + computeMutationTypes(matInput[x,2]) + }) + + LisGLs_R_Exp = mclapply(1:nrow(matInput), function(x){ + Exp_R <- rollapply(as.zoo(1:readEnd),width=3,by=3, + function(codonNucs){ + RPos = which(LisGLs_MutationTypes[[x]][,codonNucs]=="R") + sum( LisGLs_Targeting[[x]][,codonNucs][RPos], na.rm=T ) + } + ) + }) + + LisGLs_S_Exp = mclapply(1:nrow(matInput), function(x){ + Exp_S <- rollapply(as.zoo(1:readEnd),width=3,by=3, + function(codonNucs){ + SPos = which(LisGLs_MutationTypes[[x]][,codonNucs]=="S") + sum( LisGLs_Targeting[[x]][,codonNucs][SPos], na.rm=T ) + } + ) + }) + + Exp_R = matrix(unlist(LisGLs_R_Exp),nrow=nrow(matInput),ncol=readEnd/3,T) + Exp_S = matrix(unlist(LisGLs_S_Exp),nrow=nrow(matInput),ncol=readEnd/3,T) + return( list( "Expected_R"=Exp_R, "Expected_S"=Exp_S) ) + }else{ + facGL <- factor(matInput[,2]) + facLevels = levels(facGL) + LisGLs_MutabilityU = lapply(1:length(facLevels), function(x){ + computeMutabilities(facLevels[x]) + }) + facIndex = match(facGL,facLevels) + + LisGLs_Mutability = lapply(1:nrow(matInput), function(x){ + cInput = rep(NA,nchar(matInput[x,1])) + cInput[s2c(matInput[x,1])!="N"] = 1 + LisGLs_MutabilityU[[facIndex[x]]] * cInput + }) + + LisGLs_Targeting = lapply(1:dim(matInput)[1], function(x){ + computeTargeting(matInput[x,2],LisGLs_Mutability[[x]]) + }) + + LisGLs_MutationTypes = lapply(1:length(matInput[,2]),function(x){ + #print(x) + computeMutationTypes(matInput[x,2]) + }) + + LisGLs_R_Exp = lapply(1:nrow(matInput), function(x){ + Exp_R <- rollapply(as.zoo(1:readEnd),width=3,by=3, + function(codonNucs){ + RPos = which(LisGLs_MutationTypes[[x]][,codonNucs]=="R") + sum( LisGLs_Targeting[[x]][,codonNucs][RPos], na.rm=T ) + } + ) + }) + + LisGLs_S_Exp = lapply(1:nrow(matInput), function(x){ + Exp_S <- rollapply(as.zoo(1:readEnd),width=3,by=3, + function(codonNucs){ + SPos = which(LisGLs_MutationTypes[[x]][,codonNucs]=="S") + sum( LisGLs_Targeting[[x]][,codonNucs][SPos], na.rm=T ) + } + ) + }) + + Exp_R = matrix(unlist(LisGLs_R_Exp),nrow=nrow(matInput),ncol=readEnd/3,T) + Exp_S = matrix(unlist(LisGLs_S_Exp),nrow=nrow(matInput),ncol=readEnd/3,T) + return( list( "Expected_R"=Exp_R, "Expected_S"=Exp_S) ) + } +} + +# getObservedMutationsByCodon <- function(listMutations){ +# numbSeqs <- length(listMutations) +# obsMu_R <- matrix(0,nrow=numbSeqs,ncol=readEnd/3,dimnames=list(c(1:numbSeqs),c(1:(readEnd/3)))) +# obsMu_S <- obsMu_R +# temp <- mclapply(1:length(listMutations), function(i){ +# arrMutations = listMutations[[i]] +# RPos = as.numeric(names(arrMutations)[arrMutations=="R"]) +# RPos <- sapply(RPos,getCodonNumb) +# if(any(RPos)){ +# tabR <- table(RPos) +# obsMu_R[i,as.numeric(names(tabR))] <<- tabR +# } +# +# SPos = as.numeric(names(arrMutations)[arrMutations=="S"]) +# SPos <- sapply(SPos,getCodonNumb) +# if(any(SPos)){ +# tabS <- table(SPos) +# obsMu_S[i,names(tabS)] <<- tabS +# } +# } +# ) +# return( list( "Observed_R"=obsMu_R, "Observed_S"=obsMu_S) ) +# } + +getObservedMutationsByCodon <- function(listMutations){ + numbSeqs <- length(listMutations) + obsMu_R <- matrix(0,nrow=numbSeqs,ncol=readEnd/3,dimnames=list(c(1:numbSeqs),c(1:(readEnd/3)))) + obsMu_S <- obsMu_R + temp <- lapply(1:length(listMutations), function(i){ + arrMutations = listMutations[[i]] + RPos = as.numeric(names(arrMutations)[arrMutations=="R"]) + RPos <- sapply(RPos,getCodonNumb) + if(any(RPos)){ + tabR <- table(RPos) + obsMu_R[i,as.numeric(names(tabR))] <<- tabR + } + + SPos = as.numeric(names(arrMutations)[arrMutations=="S"]) + SPos <- sapply(SPos,getCodonNumb) + if(any(SPos)){ + tabS <- table(SPos) + obsMu_S[i,names(tabS)] <<- tabS + } + } + ) + return( list( "Observed_R"=obsMu_R, "Observed_S"=obsMu_S) ) +} +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/shm_csr/baseline/Baseline_Main.r Fri Feb 19 15:08:51 2021 +0000 @@ -0,0 +1,388 @@ +######################################################################################### +# License Agreement +# +# THIS WORK IS PROVIDED UNDER THE TERMS OF THIS CREATIVE COMMONS PUBLIC LICENSE +# ("CCPL" OR "LICENSE"). THE WORK IS PROTECTED BY COPYRIGHT AND/OR OTHER +# APPLICABLE LAW. ANY USE OF THE WORK OTHER THAN AS AUTHORIZED UNDER THIS LICENSE +# OR COPYRIGHT LAW IS PROHIBITED. +# +# BY EXERCISING ANY RIGHTS TO THE WORK PROVIDED HERE, YOU ACCEPT AND AGREE TO BE +# BOUND BY THE TERMS OF THIS LICENSE. TO THE EXTENT THIS LICENSE MAY BE CONSIDERED +# TO BE A CONTRACT, THE LICENSOR GRANTS YOU THE RIGHTS CONTAINED HERE IN +# CONSIDERATION OF YOUR ACCEPTANCE OF SUCH TERMS AND CONDITIONS. +# +# BASELIne: Bayesian Estimation of Antigen-Driven Selection in Immunoglobulin Sequences +# Coded by: Mohamed Uduman & Gur Yaari +# Copyright 2012 Kleinstein Lab +# Version: 1.3 (01/23/2014) +######################################################################################### + +op <- options(); +options(showWarnCalls=FALSE, showErrorCalls=FALSE, warn=-1) +library('seqinr') +if( F & Sys.info()[1]=="Linux"){ + library("multicore") +} + +# Load functions and initialize global variables +source("Baseline_Functions.r") + +# Initialize parameters with user provided arguments + arg <- commandArgs(TRUE) + #arg = c(2,1,5,5,0,1,"1:26:38:55:65:104:116", "test.fasta","","sample") + #arg = c(1,1,5,5,0,1,"1:38:55:65:104:116:200", "test.fasta","","sample") + #arg = c(1,1,5,5,1,1,"1:26:38:55:65:104:116", "/home/mu37/Wu/Wu_Cloned_gapped_sequences_D-masked.fasta","/home/mu37/Wu/","Wu") + testID <- as.numeric(arg[1]) # 1 = Focused, 2 = Local + species <- as.numeric(arg[2]) # 1 = Human. 2 = Mouse + substitutionModel <- as.numeric(arg[3]) # 0 = Uniform substitution, 1 = Smith DS et al. 1996, 5 = FiveS + mutabilityModel <- as.numeric(arg[4]) # 0 = Uniform mutablity, 1 = Tri-nucleotide (Shapiro GS et al. 2002) , 5 = FiveS + clonal <- as.numeric(arg[5]) # 0 = Independent sequences, 1 = Clonally related, 2 = Clonally related & only non-terminal mutations + fixIndels <- as.numeric(arg[6]) # 0 = Do nothing, 1 = Try and fix Indels + region <- as.numeric(strsplit(arg[7],":")[[1]]) # StartPos:LastNucleotideF1:C1:F2:C2:F3:C3 + inputFilePath <- arg[8] # Full path to input file + outputPath <- arg[9] # Full path to location of output files + outputID <- arg[10] # ID for session output + + + if(testID==5){ + traitChangeModel <- 1 + if( !is.na(any(arg[11])) ) traitChangeModel <- as.numeric(arg[11]) # 1 <- Chothia 1998 + initializeTraitChange(traitChangeModel) + } + +# Initialize other parameters/variables + + # Initialzie the codon table ( definitions of R/S ) + computeCodonTable(testID) + + # Initialize + # Test Name + testName<-"Focused" + if(testID==2) testName<-"Local" + if(testID==3) testName<-"Imbalanced" + if(testID==4) testName<-"ImbalancedSilent" + + # Indel placeholders initialization + indelPos <- NULL + delPos <- NULL + insPos <- NULL + + # Initialize in Tranistion & Mutability matrixes + substitution <- initializeSubstitutionMatrix(substitutionModel,species) + mutability <- initializeMutabilityMatrix(mutabilityModel,species) + + # FWR/CDR boundaries + flagTrim <- F + if( is.na(region[7])){ + flagTrim <- T + region[7]<-region[6] + } + readStart = min(region,na.rm=T) + readEnd = max(region,na.rm=T) + if(readStart>1){ + region = region - (readStart - 1) + } + region_Nuc = c( (region[1]*3-2) , (region[2:7]*3) ) + region_Cod = region + + readStart = (readStart*3)-2 + readEnd = (readEnd*3) + + FWR_Nuc <- c( rep(TRUE,(region_Nuc[2])), + rep(FALSE,(region_Nuc[3]-region_Nuc[2])), + rep(TRUE,(region_Nuc[4]-region_Nuc[3])), + rep(FALSE,(region_Nuc[5]-region_Nuc[4])), + rep(TRUE,(region_Nuc[6]-region_Nuc[5])), + rep(FALSE,(region_Nuc[7]-region_Nuc[6])) + ) + CDR_Nuc <- (1-FWR_Nuc) + CDR_Nuc <- as.logical(CDR_Nuc) + FWR_Nuc_Mat <- matrix( rep(FWR_Nuc,4), ncol=length(FWR_Nuc), nrow=4, byrow=T) + CDR_Nuc_Mat <- matrix( rep(CDR_Nuc,4), ncol=length(CDR_Nuc), nrow=4, byrow=T) + + FWR_Codon <- c( rep(TRUE,(region[2])), + rep(FALSE,(region[3]-region[2])), + rep(TRUE,(region[4]-region[3])), + rep(FALSE,(region[5]-region[4])), + rep(TRUE,(region[6]-region[5])), + rep(FALSE,(region[7]-region[6])) + ) + CDR_Codon <- (1-FWR_Codon) + CDR_Codon <- as.logical(CDR_Codon) + + +# Read input FASTA file + tryCatch( + inputFASTA <- baseline.read.fasta(inputFilePath, seqtype="DNA",as.string=T,set.attributes=F,forceDNAtolower=F) + , error = function(ex){ + cat("Error|Error reading input. Please enter or upload a valid FASTA file.\n") + q() + } + ) + + if (length(inputFASTA)==1) { + cat("Error|Error reading input. Please enter or upload a valid FASTA file.\n") + q() + } + + # Process sequence IDs/names + names(inputFASTA) <- sapply(names(inputFASTA),function(x){trim(x)}) + + # Convert non nucleotide characters to N + inputFASTA[length(inputFASTA)] = gsub("\t","",inputFASTA[length(inputFASTA)]) + inputFASTA <- lapply(inputFASTA,replaceNonFASTAChars) + + # Process the FASTA file and conver to Matrix[inputSequence, germlineSequence] + processedInput <- processInputAdvanced(inputFASTA) + matInput <- processedInput[[1]] + germlines <- processedInput[[2]] + lenGermlines = length(unique(germlines)) + groups <- processedInput[[3]] + lenGroups = length(unique(groups)) + rm(processedInput) + rm(inputFASTA) + +# # remove clones with less than 2 seqeunces +# tableGL <- table(germlines) +# singletons <- which(tableGL<8) +# rowsToRemove <- match(singletons,germlines) +# if(any(rowsToRemove)){ +# matInput <- matInput[-rowsToRemove,] +# germlines <- germlines[-rowsToRemove] +# groups <- groups[-rowsToRemove] +# } +# +# # remove unproductive seqs +# nonFuctionalSeqs <- sapply(rownames(matInput),function(x){any(grep("unproductive",x))}) +# if(any(nonFuctionalSeqs)){ +# if(sum(nonFuctionalSeqs)==length(germlines)){ +# write.table("Unproductive",file=paste(outputPath,outputID,".txt",sep=""),quote=F,sep="\t",row.names=F,col.names=T) +# q() +# } +# matInput <- matInput[-which(nonFuctionalSeqs),] +# germlines <- germlines[-which(nonFuctionalSeqs)] +# germlines[1:length(germlines)] <- 1:length(germlines) +# groups <- groups[-which(nonFuctionalSeqs)] +# } +# +# if(class(matInput)=="character"){ +# write.table("All unproductive seqs",file=paste(outputPath,outputID,".txt",sep=""),quote=F,sep="\t",row.names=F,col.names=T) +# q() +# } +# +# if(nrow(matInput)<10 | is.null(nrow(matInput))){ +# write.table(paste(nrow(matInput), "seqs only",sep=""),file=paste(outputPath,outputID,".txt",sep=""),quote=F,sep="\t",row.names=F,col.names=T) +# q() +# } + +# replace leading & trailing "-" with "N: + matInput <- t(apply(matInput,1,replaceLeadingTrailingDashes,readEnd)) + + # Trim (nucleotide) input sequences to the last codon + #matInput[,1] <- apply(matrix(matInput[,1]),1,trimToLastCodon) + +# # Check for Indels +# if(fixIndels){ +# delPos <- fixDeletions(matInput) +# insPos <- fixInsertions(matInput) +# }else{ +# # Check for indels +# indelPos <- checkForInDels(matInput) +# indelPos <- apply(cbind(indelPos[[1]],indelPos[[2]]),1,function(x){(x[1]==T & x[2]==T)}) +# } + + # If indels are present, remove mutations in the seqeunce & throw warning at end + #matInput[indelPos,] <- apply(matrix(matInput[indelPos,],nrow=sum(indelPos),ncol=2),1,function(x){x[1]=x[2]; return(x) }) + + colnames(matInput)=c("Input","Germline") + + # If seqeunces are clonal, create effective sequence for each clone & modify germline/group definitions + germlinesOriginal = NULL + if(clonal){ + germlinesOriginal <- germlines + collapseCloneResults <- tapply(1:nrow(matInput),germlines,function(i){ + collapseClone(matInput[i,1],matInput[i[1],2],readEnd,nonTerminalOnly=(clonal-1)) + }) + matInput = t(sapply(collapseCloneResults,function(x){return(x[[1]])})) + names_groups = tapply(groups,germlines,function(x){names(x[1])}) + groups = tapply(groups,germlines,function(x){array(x[1],dimnames=names(x[1]))}) + names(groups) = names_groups + + names_germlines = tapply(germlines,germlines,function(x){names(x[1])}) + germlines = tapply( germlines,germlines,function(x){array(x[1],dimnames=names(x[1]))} ) + names(germlines) = names_germlines + matInputErrors = sapply(collapseCloneResults,function(x){return(x[[2]])}) + } + + +# Selection Analysis + + +# if (length(germlines)>sequenceLimit) { +# # Code to parallelize processing goes here +# stop( paste("Error: Cannot process more than ", Upper_limit," sequences",sep="") ) +# } + +# if (length(germlines)<sequenceLimit) {} + + # Compute expected mutation frequencies + matExpected <- getExpectedIndividual(matInput) + + # Count observed number of mutations in the different regions + mutations <- lapply( 1:nrow(matInput), function(i){ + #cat(i,"\n") + seqI = s2c(matInput[i,1]) + seqG = s2c(matInput[i,2]) + matIGL = matrix(c(seqI,seqG),ncol=length(seqI),nrow=2,byrow=T) + retVal <- NA + tryCatch( + retVal <- analyzeMutations2NucUri(matIGL) + , error = function(ex){ + retVal <- NA + } + ) + + + return( retVal ) + }) + + matObserved <- t(sapply( mutations, processNucMutations2 )) + numberOfSeqsWithMutations <- numberOfSeqsWithMutations(matObserved, testID) + + #if(sum(numberOfSeqsWithMutations)==0){ + # write.table("No mutated sequences",file=paste(outputPath,outputID,".txt",sep=""),quote=F,sep="\t",row.names=F,col.names=T) + # q() + #} + + matMutationInfo <- cbind(matObserved,matExpected) + rm(matObserved,matExpected) + + + #Bayesian PDFs + bayes_pdf = computeBayesianScore(matMutationInfo, test=testName, max_sigma=20,length_sigma=4001) + bayesPDF_cdr = bayes_pdf[[1]] + bayesPDF_fwr = bayes_pdf[[2]] + rm(bayes_pdf) + + bayesPDF_germlines_cdr = tapply(bayesPDF_cdr,germlines,function(x) groupPosteriors(x,length_sigma=4001)) + bayesPDF_germlines_fwr = tapply(bayesPDF_fwr,germlines,function(x) groupPosteriors(x,length_sigma=4001)) + + bayesPDF_groups_cdr = tapply(bayesPDF_cdr,groups,function(x) groupPosteriors(x,length_sigma=4001)) + bayesPDF_groups_fwr = tapply(bayesPDF_fwr,groups,function(x) groupPosteriors(x,length_sigma=4001)) + + if(lenGroups>1){ + groups <- c(groups,lenGroups+1) + names(groups)[length(groups)] = "All sequences combined" + bayesPDF_groups_cdr[[lenGroups+1]] = groupPosteriors(bayesPDF_groups_cdr,length_sigma=4001) + bayesPDF_groups_fwr[[lenGroups+1]] = groupPosteriors(bayesPDF_groups_fwr,length_sigma=4001) + } + + #Bayesian Outputs + bayes_cdr = t(sapply(bayesPDF_cdr,calcBayesOutputInfo)) + bayes_fwr = t(sapply(bayesPDF_fwr,calcBayesOutputInfo)) + bayes_germlines_cdr = t(sapply(bayesPDF_germlines_cdr,calcBayesOutputInfo)) + bayes_germlines_fwr = t(sapply(bayesPDF_germlines_fwr,calcBayesOutputInfo)) + bayes_groups_cdr = t(sapply(bayesPDF_groups_cdr,calcBayesOutputInfo)) + bayes_groups_fwr = t(sapply(bayesPDF_groups_fwr,calcBayesOutputInfo)) + + #P-values + simgaP_cdr = sapply(bayesPDF_cdr,computeSigmaP) + simgaP_fwr = sapply(bayesPDF_fwr,computeSigmaP) + + simgaP_germlines_cdr = sapply(bayesPDF_germlines_cdr,computeSigmaP) + simgaP_germlines_fwr = sapply(bayesPDF_germlines_fwr,computeSigmaP) + + simgaP_groups_cdr = sapply(bayesPDF_groups_cdr,computeSigmaP) + simgaP_groups_fwr = sapply(bayesPDF_groups_fwr,computeSigmaP) + + + #Format output + + # Round expected mutation frequencies to 3 decimal places + matMutationInfo[germlinesOriginal[indelPos],] = NA + if(nrow(matMutationInfo)==1){ + matMutationInfo[5:8] = round(matMutationInfo[,5:8]/sum(matMutationInfo[,5:8],na.rm=T),3) + }else{ + matMutationInfo[,5:8] = t(round(apply(matMutationInfo[,5:8],1,function(x){ return(x/sum(x,na.rm=T)) }),3)) + } + + listPDFs = list() + nRows = length(unique(groups)) + length(unique(germlines)) + length(groups) + + matOutput = matrix(NA,ncol=18,nrow=nRows) + rowNumb = 1 + for(G in unique(groups)){ + #print(G) + matOutput[rowNumb,c(1,2,11:18)] = c("Group",names(groups)[groups==G][1],bayes_groups_cdr[G,],bayes_groups_fwr[G,],simgaP_groups_cdr[G],simgaP_groups_fwr[G]) + listPDFs[[rowNumb]] = list("CDR"=bayesPDF_groups_cdr[[G]],"FWR"=bayesPDF_groups_fwr[[G]]) + names(listPDFs)[rowNumb] = names(groups[groups==paste(G)])[1] + #if(names(groups)[which(groups==G)[1]]!="All sequences combined"){ + gs = unique(germlines[groups==G]) + rowNumb = rowNumb+1 + if( !is.na(gs) ){ + for( g in gs ){ + matOutput[rowNumb,c(1,2,11:18)] = c("Germline",names(germlines)[germlines==g][1],bayes_germlines_cdr[g,],bayes_germlines_fwr[g,],simgaP_germlines_cdr[g],simgaP_germlines_fwr[g]) + listPDFs[[rowNumb]] = list("CDR"=bayesPDF_germlines_cdr[[g]],"FWR"=bayesPDF_germlines_fwr[[g]]) + names(listPDFs)[rowNumb] = names(germlines[germlines==paste(g)])[1] + rowNumb = rowNumb+1 + indexesOfInterest = which(germlines==g) + numbSeqsOfInterest = length(indexesOfInterest) + rowNumb = seq(rowNumb,rowNumb+(numbSeqsOfInterest-1)) + matOutput[rowNumb,] = matrix( c( rep("Sequence",numbSeqsOfInterest), + rownames(matInput)[indexesOfInterest], + c(matMutationInfo[indexesOfInterest,1:4]), + c(matMutationInfo[indexesOfInterest,5:8]), + c(bayes_cdr[indexesOfInterest,]), + c(bayes_fwr[indexesOfInterest,]), + c(simgaP_cdr[indexesOfInterest]), + c(simgaP_fwr[indexesOfInterest]) + ), ncol=18, nrow=numbSeqsOfInterest,byrow=F) + increment=0 + for( ioi in indexesOfInterest){ + listPDFs[[min(rowNumb)+increment]] = list("CDR"=bayesPDF_cdr[[ioi]] , "FWR"=bayesPDF_fwr[[ioi]]) + names(listPDFs)[min(rowNumb)+increment] = rownames(matInput)[ioi] + increment = increment + 1 + } + rowNumb=max(rowNumb)+1 + + } + } + } + colsToFormat = 11:18 + matOutput[,colsToFormat] = formatC( matrix(as.numeric(matOutput[,colsToFormat]), nrow=nrow(matOutput), ncol=length(colsToFormat)) , digits=3) + matOutput[matOutput== " NaN"] = NA + + + + colnames(matOutput) = c("Type", "ID", "Observed_CDR_R", "Observed_CDR_S", "Observed_FWR_R", "Observed_FWR_S", + "Expected_CDR_R", "Expected_CDR_S", "Expected_FWR_R", "Expected_FWR_S", + paste( rep(testName,6), rep(c("Sigma","CIlower","CIupper"),2),rep(c("CDR","FWR"),each=3), sep="_"), + paste( rep(testName,2), rep("P",2),c("CDR","FWR"), sep="_") + ) + fileName = paste(outputPath,outputID,".txt",sep="") + write.table(matOutput,file=fileName,quote=F,sep="\t",row.names=T,col.names=NA) + fileName = paste(outputPath,outputID,".RData",sep="") + save(listPDFs,file=fileName) + +indelWarning = FALSE +if(sum(indelPos)>0){ + indelWarning = "<P>Warning: The following sequences have either gaps and/or deletions, and have been ommited from the analysis."; + indelWarning = paste( indelWarning , "<UL>", sep="" ) + for(indels in names(indelPos)[indelPos]){ + indelWarning = paste( indelWarning , "<LI>", indels, "</LI>", sep="" ) + } + indelWarning = paste( indelWarning , "</UL></P>", sep="" ) +} + +cloneWarning = FALSE +if(clonal==1){ + if(sum(matInputErrors)>0){ + cloneWarning = "<P>Warning: The following clones have sequences of unequal length."; + cloneWarning = paste( cloneWarning , "<UL>", sep="" ) + for(clone in names(matInputErrors)[matInputErrors]){ + cloneWarning = paste( cloneWarning , "<LI>", names(germlines)[as.numeric(clone)], "</LI>", sep="" ) + } + cloneWarning = paste( cloneWarning , "</UL></P>", sep="" ) + } +} +cat(paste("Success",outputID,indelWarning,cloneWarning,sep="|"))
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/shm_csr/baseline/IMGT-reference-seqs-IGHV-2015-11-05.fa Fri Feb 19 15:08:51 2021 +0000 @@ -0,0 +1,703 @@ +>IGHV1-18*01 +caggttcagctggtgcagtctggagct...gaggtgaagaagcctggggcctcagtgaaggtctcctgcaaggcttctggttacaccttt............accagctatggtatcagctgggtgcgacaggcccctggacaagggcttgagtggatgggatggatcagcgcttac......aatggtaacacaaactatgcacagaagctccag...ggcagagtcaccatgaccacagacacatccacgagcacagcctacatggagctgaggagcctgagatctgacgacacggccgtgtattactgtgcgagaga +>IGHV1-18*02 +caggttcagctggtgcagtctggagct...gaggtgaagaagcctggggcctcagtgaaggtctcctgcaaggcttctggttacaccttt............accagctatggtatcagctgggtgcgacaggcccctggacaagggcttgagtggatgggatggatcagcgcttac......aatggtaacacaaactatgcacagaagctccag...ggcagagtcaccatgaccacagacacatccacgagcacagcctacatggagctgaggagcctaagatctgacgacacggcc +>IGHV1-18*03 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+>IGHV3-72*02 +....................................................................................accttc............agtgaccactacatggactgggtccgccaggctccagggaaggggctggagtgggttggccgtactagaaacaaagctaacagctacaccacagaatacgccgcgtctgtgaaa...ggcagattcaccatctcaagagatgattcaaagaactcactgtat +>IGHV3-73*01 +gaggtgcagctggtggagtctggggga...ggcttggtccagcctggggggtccctgaaactctcctgtgcagcctctgggttcaccttc............agtggctctgctatgcactgggtccgccaggcttccgggaaagggctggagtgggttggccgtattagaagcaaagctaacagttacgcgacagcatatgctgcgtcggtgaaa...ggcaggttcaccatctccagagatgattcaaagaacacggcgtatctgcaaatgaacagcctgaaaaccgaggacacggccgtgtattactgtactagaca +>IGHV3-73*02 +gaggtgcagctggtggagtccggggga...ggcttggtccagcctggggggtccctgaaactctcctgtgcagcctctgggttcaccttc............agtggctctgctatgcactgggtccgccaggcttccgggaaagggctggagtgggttggccgtattagaagcaaagctaacagttacgcgacagcatatgctgcgtcggtgaaa...ggcaggttcaccatctccagagatgattcaaagaacacggcgtatctgcaaatgaacagcctgaaaaccgaggacacggccgtgtattactgtactagaca +>IGHV3-74*01 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+gaagtgcagctggtgcagtctggagca...gaggtgaaaaagcccggggagtctctgaggatctcctgtaagggttctggatacagcttt............accagctactggatcagctgggtgcgccagatgcccgggaaaggcctggagtggatggggaggattgatcctagt......gactcttataccaactacagcccgtccttccaa...ggccacgtcaccatctcagctgacaagtccatcagcactgcctacctgcagtggagcagcctgaaggcctcggacaccgccatgtattactgtgcgaga +>IGHV5-10-1*02 +gaagtgcagctggtgcagtctggagca...gaggtgaaaaagcccggggagtctctgaggatctcctgtaagggttctggatacagcttt............accagctactggatcagctgggtgcgccagatgcccgggaaaggcttggagtggatggggaggattgatcctagt......gactcttataccaactacagcccgtccttccaa...ggccacgtcaccatctcagctgacaagtccatcagcactgcctacctgcagtggagcagcctgaaggc.tcggacaccgccatgtattactgtgcgagaca +>IGHV5-10-1*03 +gaagtgcagctggtgcagtccggagca...gaggtgaaaaagcccggggagtctctgaggatctcctgtaagggttctggatacagcttt............accagctactggatcagctgggtgcgccagatgcccgggaaaggcctggagtggatggggaggattgatcctagt......gactcttataccaactacagcccgtccttccaa...ggccacgtcaccatctcagctgacaagtccatcagcactgcctacctgcagtggagcagcctgaaggcctcggacaccgccatgtattactgtgcgaga +>IGHV5-10-1*04 +gaagtgcagctggtgcagtctggagca...gaggtgaaaaagcccggggagtctctgaggatctcctgtaagggttctggatacagcttt............accagctactggatcagctgggtgcgccagatgcccgggaaaggcctggagtggatggggaggattgatcctagt......gactcttataccaactacagcccgtccttccaa...ggccaggtcaccatctcagctgacaagtccatcagcactgcctacctgcagtggagcagcctgaaggcctcggacaccgccatgtattactgtgcgaga +>IGHV5-51*01 +gaggtgcagctggtgcagtctggagca...gaggtgaaaaagcccggggagtctctgaagatctcctgtaagggttctggatacagcttt............accagctactggatcggctgggtgcgccagatgcccgggaaaggcctggagtggatggggatcatctatcctggt......gactctgataccagatacagcccgtccttccaa...ggccaggtcaccatctcagccgacaagtccatcagcaccgcctacctgcagtggagcagcctgaaggcctcggacaccgccatgtattactgtgcgagaca +>IGHV5-51*02 +gaggtgcagctggtgcagtctggagca...gaggtgaaaaagcccggggagtctctgaagatctcctgtaagggttctggatacagcttt............accagctactggaccggctgggtgcgccagatgcccgggaaaggcttggagtggatggggatcatctatcctggt......gactctgataccagatacagcccgtccttccaa...ggccaggtcaccatctcagccgacaagtccatcagcaccgcctacctgcagtggagcagcctgaaggcctcggacaccgccatgtattactgtgcgagaca +>IGHV5-51*03 +gaggtgcagctggtgcagtctggagca...gaggtgaaaaagccgggggagtctctgaagatctcctgtaagggttctggatacagcttt............accagctactggatcggctgggtgcgccagatgcccgggaaaggcctggagtggatggggatcatctatcctggt......gactctgataccagatacagcccgtccttccaa...ggccaggtcaccatctcagccgacaagtccatcagcaccgcctacctgcagtggagcagcctgaaggcctcggacaccgccatgtattactgtgcgaga +>IGHV5-51*04 +gaggtgcagctggtgcagtctggagca...gaggtgaaaaagccgggggagtctctgaagatctcctgtaagggttctggatacagcttt............accagctactggatcggctgggtgcgccagatgcccgggaaaggcctggagtggatggggatcatctatcctggt......gactctgataccagatacagcccgtccttccaa...ggccaggtcaccatctcagccgacaagcccatcagcaccgcctacctgcagtggagcagcctgaaggcctcggacaccgccatgtattactgtgcgaga +>IGHV5-51*05 +.....................................aaaagcccggggagtctctgaagatctcctgtaagggttctggatacagcttt............accagctactggatcggctgggtgcgccagatgcccaggaaaggcctggagtggatggggatcatctatcctggt......gactctgataccagatacagcccgtccttccaa...ggccaggtcaccatctcagccgacaagtccatcagcaccgcctacctgcagtggagcagcctgaaggcctcggacaccgccatg +>IGHV5-78*01 +gaggtgcagctgttgcagtctgcagca...gaggtgaaaagacccggggagtctctgaggatctcctgtaagacttctggatacagcttt............accagctactggatccactgggtgcgccagatgcccgggaaagaactggagtggatggggagcatctatcctggg......aactctgataccagatacagcccatccttccaa...ggccacgtcaccatctcagccgacagctccagcagcaccgcctacctgcagtggagcagcctgaaggcctcggacgccgccatgtattattgtgtgaga +>IGHV6-1*01 +caggtacagctgcagcagtcaggtcca...ggactggtgaagccctcgcagaccctctcactcacctgtgccatctccggggacagtgtctct......agcaacagtgctgcttggaactggatcaggcagtccccatcgagaggccttgagtggctgggaaggacatactacaggtcc...aagtggtataatgattatgcagtatctgtgaaa...agtcgaataaccatcaacccagacacatccaagaaccagttctccctgcagctgaactctgtgactcccgaggacacggctgtgtattactgtgcaagaga +>IGHV6-1*02 +caggtacagctgcagcagtcaggtccg...ggactggtgaagccctcgcagaccctctcactcacctgtgccatctccggggacagtgtctct......agcaacagtgctgcttggaactggatcaggcagtccccatcgagaggccttgagtggctgggaaggacatactacaggtcc...aagtggtataatgattatgcagtatctgtgaaa...agtcgaataaccatcaacccagacacatccaagaaccagttctccctgcagctgaactctgtgactcccgaggacacggctgtgtattactgtgcaagaga +>IGHV7-34-1*01 +...ctgcagctggtgcagtctgggcct...gaggtgaagaagcctggggcctcagtgaaggtctcctataagtcttctggttacaccttc............accatctatggtatgaattgggtatgatagacccctggacagggctttgagtggatgtgatggatcatcacctac......actgggaacccaacgtatacccacggcttcaca...ggatggtttgtcttctccatggacacgtctgtcagcacggcgtgtcttcagatcagcagcctaaaggctgaggacacggccgagtattactgtgcgaagta +>IGHV7-34-1*02 +...ctgcagctggtgcagtctgggcct...gaggtgaagaagcctggggcctcagtgaaggtctcctataagtcttctggttacaccttc............accatctatggtatgaattgggtatgatagacccctggacagggctttgagtggatgtgatggatcatcacctac......aatgggaacccaacgtatacccacggcttcaca...ggatggtttgtcttctccatggacacgtctgtcagcacggcgtgtcttcagatcagcagcctaaaggctgaggacacggccgagtattactgtgcgaagta +>IGHV7-4-1*01 +caggtgcagctggtgcaatctgggtct...gagttgaagaagcctggggcctcagtgaaggtttcctgcaaggcttctggatacaccttc............actagctatgctatgaattgggtgcgacaggcccctggacaagggcttgagtggatgggatggatcaacaccaac......actgggaacccaacgtatgcccagggcttcaca...ggacggtttgtcttctccttggacacctctgtcagcacggcatatctgcagatctgcagcctaaaggctgaggacactgccgtgtattactgtgcgaga +>IGHV7-4-1*02 +caggtgcagctggtgcaatctgggtct...gagttgaagaagcctggggcctcagtgaaggtttcctgcaaggcttctggatacaccttc............actagctatgctatgaattgggtgcgacaggcccctggacaagggcttgagtggatgggatggatcaacaccaac......actgggaacccaacgtatgcccagggcttcaca...ggacggtttgtcttctccttggacacctctgtcagcacggcatatctgcagatcagcagcctaaaggctgaggacactgccgtgtattactgtgcgagaga +>IGHV7-4-1*03 +caggtgcagctggtgcaatctgggtct...gagttgaagaagcctggggcctcagtgaaggtttcctgcaaggcttctggatacaccttc............actagctatgctatgaattgggtgcgacaggcccctggacaagggcttgagtggatgggatggatcaacaccaac......actgggaacccaacgtatgcccagggcttcaca...ggacggtttgtcttctccttggacacctctgtcagcacggcatatctgcagatcagcacgctaaaggctgaggacactg +>IGHV7-4-1*04 +caggtgcagctggtgcaatctgggtct...gagttgaagaagcctggggcctcagtgaaggtttcctgcaaggcttctggatacaccttc............actagctatgctatgaattgggtgcgacaggcccctggacaagggcttgagtggatgggatggatcaacaccaac......actgggaacccaacgtatgcccagggcttcaca...ggacggtttgtcttctccttggacacctctgtcagcatggcatatctgcagatcagcagcctaaaggctgaggacactgccgtgtattactgtgcgagaga +>IGHV7-4-1*05 +caggtgcagctggtgcaatctgggtct...gagttgaagaagcctggggcctcagtgaaggtttcctgcaaggcttctggatacaccttc............actagctatgctatgaattgggtgcgacaggcccctggacaagggcttgagtggatgggatggatcaacaccaac......actgggaacccaacgtatgcccagggcttcaca...ggacggtttgtcttctccttggacacctctgtcagcatggcatatctgcagatcagcagcctaaaggctgaggacactgccgtgtgttactgtgcgagaga +>AIGHV7-40*03| +ttttcaatagaaaagtcaaataatcta...agtgtcaatcagtggatgattagataaaatatgatatatgtaaatcatggaatactatgc............agccagtatggtatgaattcagtgtgaccagcccctggacaagggcttgagtggatgggatggatcatcacctac......actgggaacccaacatataccaacggcttcaca...ggacggtttctattctccatggacacctctgtcagcatggcgtatctgcagatcagcagcctaaaggctgaggacacggccgtgtatgactgtatgagaga +>IGHV7-81*01 +caggtgcagctggtgcagtctggccat...gaggtgaagcagcctggggcctcagtgaaggtctcctgcaaggcttctggttacagtttc............accacctatggtatgaattgggtgccacaggcccctggacaagggcttgagtggatgggatggttcaacacctac......actgggaacccaacatatgcccagggcttcaca...ggacggtttgtcttctccatggacacctctgccagcacagcatacctgcagatcagcagcctaaaggctgaggacatggccatgtattactgtgcgagata
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/shm_csr/baseline/IMGTVHreferencedataset20161215.fa Fri Feb 19 15:08:51 2021 +0000 @@ -0,0 +1,1 @@ +>IGHV1-18*01 caggttcagctggtgcagtctggagct...gaggtgaagaagcctggggcctcagtgaaggtctcctgcaaggcttctggttacaccttt............accagctatggtatcagctgggtgcgacaggcccctggacaagggcttgagtggatgggatggatcagcgcttac......aatggtaacacaaactatgcacagaagctccag...ggcagagtcaccatgaccacagacacatccacgagcacagcctacatggagctgaggagcctgagatctgacgacacggccgtgtattactgtgcgagaga >IGHV1-18*02 caggttcagctggtgcagtctggagct...gaggtgaagaagcctggggcctcagtgaaggtctcctgcaaggcttctggttacaccttt............accagctatggtatcagctgggtgcgacaggcccctggacaagggcttgagtggatgggatggatcagcgcttac......aatggtaacacaaactatgcacagaagctccag...ggcagagtcaccatgaccacagacacatccacgagcacagcctacatggagctgaggagcctaagatctgacgacacggcc >IGHV1-18*03 caggttcagctggtgcagtctggagct...gaggtgaagaagcctggggcctcagtgaaggtctcctgcaaggcttctggttacaccttt............accagctatggtatcagctgggtgcgacaggcccctggacaagggcttgagtggatgggatggatcagcgcttac......aatggtaacacaaactatgcacagaagctccag...ggcagagtcaccatgaccacagacacatccacgagcacagcctacatggagctgaggagcctgagatctgacgacatggccgtgtattactgtgcgagaga >IGHV1-18*04 caggttcagctggtgcagtctggagct...gaggtgaagaagcctggggcctcagtgaaggtctcctgcaaggcttctggttacaccttt............accagctacggtatcagctgggtgcgacaggcccctggacaagggcttgagtggatgggatggatcagcgcttac......aatggtaacacaaactatgcacagaagctccag...ggcagagtcaccatgaccacagacacatccacgagcacagcctacatggagctgaggagcctgagatctgacgacacggccgtgtattactgtgcgagaga >IGHV1-2*01 caggtgcagctggtgcagtctggggct...gaggtgaagaagcctggggcctcagtgaaggtctcctgcaaggcttctggatacaccttc............accggctactatatgcactgggtgcgacaggcccctggacaagggcttgagtggatgggacggatcaaccctaac......agtggtggcacaaactatgcacagaagtttcag...ggcagggtcaccagtaccagggacacgtccatcagcacagcctacatggagctgagcaggctgagatctgacgacacggtcgtgtattactgtgcgagaga >IGHV1-2*02 caggtgcagctggtgcagtctggggct...gaggtgaagaagcctggggcctcagtgaaggtctcctgcaaggcttctggatacaccttc............accggctactatatgcactgggtgcgacaggcccctggacaagggcttgagtggatgggatggatcaaccctaac......agtggtggcacaaactatgcacagaagtttcag...ggcagggtcaccatgaccagggacacgtccatcagcacagcctacatggagctgagcaggctgagatctgacgacacggccgtgtattactgtgcgagaga >IGHV1-2*03 caggtgcagctggtgcagtctggggct...gaggtgaagaagcttggggcctcagtgaaggtctcctgcaaggcttctggatacaccttc............accggctactatatgcactgggtgcnacaggcccctggacaagggcttgagtggatgggatggatcaaccctaac......agtggtggcacaaactatgcacagaagtttcag...ggcagggtcaccatgaccagggacacgtccatcagcacagcctacatggagctgagcaggctgagatctgacgacacggccgtgtattactgtgcgagaga >IGHV1-2*04 caggtgcagctggtgcagtctggggct...gaggtgaagaagcctggggcctcagtgaaggtctcctgcaaggcttctggatacaccttc............accggctactatatgcactgggtgcgacaggcccctggacaagggcttgagtggatgggatggatcaaccctaac......agtggtggcacaaactatgcacagaagtttcag...ggctgggtcaccatgaccagggacacgtccatcagcacagcctacatggagctgagcaggctgagatctgacgacacggccgtgtattactgtgcgagaga >IGHV1-2*05 caggtgcagctggtgcagtctggggct...gaggtgaagaagcctggggcctcagtgaaggtctcctgcaaggcttctggatacaccttc............accggctactatatgcactgggtgcgacaggcccctggacaagggcttgagtggatgggacggatcaaccctaac......agtggtggcacaaactatgcacagaagtttcag...ggcagggtcaccatgaccagggacacgtccatcagcacagcctacatggagctgagcaggctgagatctgacgacacggtcgtgtattactgtgcgagaga >IGHV1-24*01 caggtccagctggtacagtctggggct...gaggtgaagaagcctggggcctcagtgaaggtctcctgcaaggtttccggatacaccctc............actgaattatccatgcactgggtgcgacaggctcctggaaaagggcttgagtggatgggaggttttgatcctgaa......gatggtgaaacaatctacgcacagaagttccag...ggcagagtcaccatgaccgaggacacatctacagacacagcctacatggagctgagcagcctgagatctgaggacacggccgtgtattactgtgcaacaga >IGHV1-3*01 caggtccagcttgtgcagtctggggct...gaggtgaagaagcctggggcctcagtgaaggtttcctgcaaggcttctggatacaccttc............actagctatgctatgcattgggtgcgccaggcccccggacaaaggcttgagtggatgggatggatcaacgctggc......aatggtaacacaaaatattcacagaagttccag...ggcagagtcaccattaccagggacacatccgcgagcacagcctacatggagctgagcagcctgagatctgaagacacggctgtgtattactgtgcgagaga >IGHV1-3*02 caggttcagctggtgcagtctggggct...gaggtgaagaagcctggggcctcagtgaaggtttcctgcaaggcttctggatacaccttc............actagctatgctatgcattgggtgcgccaggcccccggacaaaggcttgagtggatgggatggagcaacgctggc......aatggtaacacaaaatattcacaggagttccag...ggcagagtcaccattaccagggacacatccgcgagcacagcctacatggagctgagcagcctgagatctgaggacatggctgtgtattactgtgcgagaga >IGHV1-38-4*01 caggtccagctggtgcagtcttgggct...gaggtgaggaagtctggggcctcagtgaaagtctcctgtagtttttctgggtttaccatc............accagctacggtatacattgggtgcaacagtcccctggacaagggcttgagtggatgggatggatcaaccctggc......aatggtagcccaagctatgccaagaagtttcag...ggcagattcaccatgaccagggacatgtccacaaccacagcctacacagacctgagcagcctgacatctgaggacatggctgtgtattactatgcaagaca >IGHV1-45*01 cagatgcagctggtgcagtctggggct...gaggtgaagaagactgggtcctcagtgaaggtttcctgcaaggcttccggatacaccttc............acctaccgctacctgcactgggtgcgacaggcccccggacaagcgcttgagtggatgggatggatcacacctttc......aatggtaacaccaactacgcacagaaattccag...gacagagtcaccattactagggacaggtctatgagcacagcctacatggagctgagcagcctgagatctgaggacacagccatgtattactgtgcaagana >IGHV1-45*02 cagatgcagctggtgcagtctggggct...gaggtgaagaagactgggtcctcagtgaaggtttcctgcaaggcttccggatacaccttc............acctaccgctacctgcactgggtgcgacaggcccccggacaagcgcttgagtggatgggatggatcacacctttc......aatggtaacaccaactacgcacagaaattccag...gacagagtcaccattaccagggacaggtctatgagcacagcctacatggagctgagcagcctgagatctgaggacacagccatgtattactgtgcaagata >IGHV1-45*03 .....................................agaagactgggtcctcagtgaaggtttcctgcaaggcttccggatacaccttc............acctaccgctacctgcactgggtgcgacaggcccccagacaagcgcttgagtggatgggatggatcacacctttc......aatggtaacaccaactacgcacagaaattccag...gacagagtcaccattaccagggacaggtctatgagcacagcctacatggagctgagcagcctgagatctgaggacacagccatgtattactgtgcaaga >IGHV1-46*01 caggtgcagctggtgcagtctggggct...gaggtgaagaagcctggggcctcagtgaaggtttcctgcaaggcatctggatacaccttc............accagctactatatgcactgggtgcgacaggcccctggacaagggcttgagtggatgggaataatcaaccctagt......ggtggtagcacaagctacgcacagaagttccag...ggcagagtcaccatgaccagggacacgtccacgagcacagtctacatggagctgagcagcctgagatctgaggacacggccgtgtattactgtgcgagaga >IGHV1-46*02 caggtgcagctggtgcagtctggggct...gaggtgaagaagcctggggcctcagtgaaggtttcctgcaaggcatctggatacaccttc............aacagctactatatgcactgggtgcgacaggcccctggacaagggcttgagtggatgggaataatcaaccctagt......ggtggtagcacaagctacgcacagaagttccag...ggcagagtcaccatgaccagggacacgtccacgagcacagtctacatggagctgagcagcctgagatctgaggacacggccgtgtattactgtgcgagaga >IGHV1-46*03 caggtgcagctggtgcagtctggggct...gaggtgaagaagcctggggcctcagtgaaggtttcctgcaaggcatctggatacaccttc............accagctactatatgcactgggtgcgacaggcccctggacaagggcttgagtggatgggaataatcaaccctagt......ggtggtagcacaagctacgcacagaagttccag...ggcagagtcaccatgaccagggacacgtccacgagcacagtctacatggagctgagcagcctgagatctgaggacacggccgtgtattactgtgctagaga >IGHV1-58*01 caaatgcagctggtgcagtctgggcct...gaggtgaagaagcctgggacctcagtgaaggtctcctgcaaggcttctggattcaccttt............actagctctgctgtgcagtgggtgcgacaggctcgtggacaacgccttgagtggataggatggatcgtcgttggc......agtggtaacacaaactacgcacagaagttccag...gaaagagtcaccattaccagggacatgtccacaagcacagcctacatggagctgagcagcctgagatccgaggacacggccgtgtattactgtgcggcaga >IGHV1-58*02 caaatgcagctggtgcagtctgggcct...gaggtgaagaagcctgggacctcagtgaaggtctcctgcaaggcttctggattcaccttt............actagctctgctatgcagtgggtgcgacaggctcgtggacaacgccttgagtggataggatggatcgtcgttggc......agtggtaacacaaactacgcacagaagttccag...gaaagagtcaccattaccagggacatgtccacaagcacagcctacatggagctgagcagcctgagatccgaggacacggccgtgtattactgtgcggcaga >IGHV1-68*01 caggtgcagctggggcagtctgaggct...gaggtaaagaagcctggggcctcagtgaaggtctcctgcaaggcttccggatacaccttc............acttgctgctccttgcactggttgcaacaggcccctggacaagggcttgaaaggatgagatggatcacactttac......aatggtaacaccaactatgcaaagaagttccag...ggcagagtcaccattaccagggacatgtccctgaggacagcctacatagagctgagcagcctgagatctgaggactcggctgtgtattactgggcaagata >IGHV1-69*01 caggtgcagctggtgcagtctggggct...gaggtgaagaagcctgggtcctcggtgaaggtctcctgcaaggcttctggaggcaccttc............agcagctatgctatcagctgggtgcgacaggcccctggacaagggcttgagtggatgggagggatcatccctatc......tttggtacagcaaactacgcacagaagttccag...ggcagagtcacgattaccgcggacgaatccacgagcacagcctacatggagctgagcagcctgagatctgaggacacggccgtgtattactgtgcgagaga >IGHV1-69*02 caggtccagctggtgcaatctggggct...gaggtgaagaagcctgggtcctcggtgaaggtctcctgcaaggcttctggaggcaccttc............agcagctatactatcagctgggtgcgacaggcccctggacaagggcttgagtggatgggaaggatcatccctatc......cttggtatagcaaactacgcacagaagttccag...ggcagagtcacgattaccgcggacaaatccacgagcacagcctacatggagctgagcagcctgagatctgaggacacggccgtgtattactgtgcgaga >IGHV1-69*03 caggtgcagctggtgcagtctggggct...gaggtgaagaagcctgggtcctcggtgaaggtctcctgcaaggcttctggaggcaccttc............agcagctatgctatcagctgggtgcgacaggcccctggacaagggcttgagtggatgggagggatcatccctatc......tttggtacagcaaactacgcacagaagttccag...ggcagagtcacgattaccgcggacgaatccacgagcacagcctacatggagctgagcagcctgagatctgatgacacggc >IGHV1-69*04 caggtccagctggtgcagtctggggct...gaggtgaagaagcctgggtcctcggtgaaggtctcctgcaaggcttctggaggcaccttc............agcagctatgctatcagctgggtgcgacaggcccctggacaagggcttgagtggatgggaaggatcatccctatc......cttggtatagcaaactacgcacagaagttccag...ggcagagtcacgattaccgcggacaaatccacgagcacagcctacatggagctgagcagcctgagatctgaggacacggccgtgtattactgtgcgagaga >IGHV1-69*05 caggtccagctggtgcagtctggggct...gaggtgaagaagcctgggtcctcggtgaaggtctcctgcaaggcttctggaggcaccttc............agcagctatgctatcagctgggtgcgacaggcccctggacaagggcttgagtggatgggagggatcatccctatc......tttggtacagcaaactacgcacagaagttccag...ggcagagtcacgattaccacggacgaatccacgagcacagcctacatggagctgagcagcctgagatctgaggacacggccgtgtattactgtgcgaga >IGHV1-69*06 caggtgcagctggtgcagtctggggct...gaggtgaagaagcctgggtcctcggtgaaggtctcctgcaaggcttctggaggcaccttc............agcagctatgctatcagctgggtgcgacaggcccctggacaagggcttgagtggatgggagggatcatccctatc......tttggtacagcaaactacgcacagaagttccag...ggcagagtcacgattaccgcggacaaatccacgagcacagcctacatggagctgagcagcctgagatctgaggacacggccgtgtattactgtgcgagaga >IGHV1-69*07 .....................................agaagcctgggtcctcggtgaaggtctcctgcaaggcttctggaggcaccttc............agcagctatgctatcagctgggtgcgacaggcccctggacaagggcttgagtggatgggaaggatcatccctatc......tttggtacagcaaactacgcacagaagttccag...ggcagagtcacgattaccgcggacgaatccacgagcacagcctacatggagctgagcagcctgagatctgag >IGHV1-69*08 caggtccagctggtgcaatctggggct...gaggtgaagaagcctgggtcctcggtgaaggtctcctgcaaggcttctggaggcaccttc............agcagctatactatcagctgggtgcgacaggcccctggacaagggcttgagtggatgggaaggatcatccctatc......cttggtacagcaaactacgcacagaagttccag...ggcagagtcacgattaccgcggacaaatccacgagcacagcctacatggagctgagcagcctgagatctgaggacacggccgtgtattactgtgcgagaga >IGHV1-69*09 caggtgcagctggtgcagtctggggct...gaggtgaagaagcctgggtcctcggtgaaggtctcctgcaaggcttctggaggcaccttc............agcagctatgctatcagctgggtgcgacaggcccctggacaagggcttgagtggatgggaaggatcatccctatc......cttggtatagcaaactacgcacagaagttccag...ggcagagtcacgattaccgcggacaaatccacgagcacagcctacatggagctgagcagcctgagatctgaggacacggccgtgtattactgtgcgagaga >IGHV1-69*10 caggtccagctggtgcagtctggggct...gaggtgaagaagcctgggtcctcagtgaaggtctcctgcaaggcttctggaggcaccttc............agcagctatgctatcagctgggtgcgacaggcccctggacaagggcttgagtggatgggagggatcatccctatc......cttggtatagcaaactacgcacagaagttccag...ggcagagtcacgattaccgcggacaaatccacgagcacagcctacatggagctgagcagcctgagatctgaggacacggccgtgtattactgtgcgagaga >IGHV1-69*11 caggtccagctggtgcagtctggggct...gaggtgaagaagcctgggtcctcggtgaaggtctcctgcaaggcttctggaggcaccttc............agcagctatgctatcagctgggtgcgacaggcccctggacaagggcttgagtggatgggaaggatcatccctatc......cttggtacagcaaactacgcacagaagttccag...ggcagagtcacgattaccgcggacgaatccacgagcacagcctacatggagctgagcagcctgagatctgaggacacggccgtgtattactgtgcgagaga >IGHV1-69*12 caggtccagctggtgcagtctggggct...gaggtgaagaagcctgggtcctcggtgaaggtctcctgcaaggcttctggaggcaccttc............agcagctatgctatcagctgggtgcgacaggcccctggacaagggcttgagtggatgggagggatcatccctatc......tttggtacagcaaactacgcacagaagttccag...ggcagagtcacgattaccgcggacgaatccacgagcacagcctacatggagctgagcagcctgagatctgaggacacggccgtgtattactgtgcgagaga >IGHV1-69*13 caggtccagctggtgcagtctggggct...gaggtgaagaagcctgggtcctcagtgaaggtctcctgcaaggcttctggaggcaccttc............agcagctatgctatcagctgggtgcgacaggcccctggacaagggcttgagtggatgggagggatcatccctatc......tttggtacagcaaactacgcacagaagttccag...ggcagagtcacgattaccgcggacgaatccacgagcacagcctacatggagctgagcagcctgagatctgaggacacggccgtgtattactgtgcgagaga >IGHV1-69*14 caggtccagctggtgcagtctggggct...gaggtgaagaagcctgggtcctcggtgaaggtctcctgcaaggcttctggaggcaccttc............agcagctatgctatcagctgggtgcgacaggcccctggacaagggcttgagtggatgggagggatcatccctatc......tttggtacagcaaactacgcacagaagttccag...ggcagagtcacgattaccgcggacaaatccacgagcacagcctacatggagctgagcagcctgagatctgaggacacggccgtgtattactgtgcgagaga >IGHV1-69-2*01 gaggtccagctggtacagtctggggct...gaggtgaagaagcctggggctacagtgaaaatctcctgcaaggtttctggatacaccttc............accgactactacatgcactgggtgcaacaggcccctggaaaagggcttgagtggatgggacttgttgatcctgaa......gatggtgaaacaatatacgcagagaagttccag...ggcagagtcaccataaccgcggacacgtctacagacacagcctacatggagctgagcagcctgagatctgaggacacggccgtgtattactgtgcaacaga >IGHV1-69-2*02 .....................................agaagcctggggctacagtgaaaatctcctgcaaggtttctggatacaccttc............accgactactacatgcactgggtgcaacaggcccctggaaaagggcttgagtggatgggacttgttgatcctgaa......gatggtgaaacaatatatgcagagaagttccag...ggcagagtcaccataaccgcggacacgtctacagacacagcctacatggagctgagcagcctgagatctgag >IGHV1-69D*01 caggtgcagctggtgcagtctggggct...gaggtgaagaagcctgggtcctcggtgaaggtctcctgcaaggcttctggaggcaccttc............agcagctatgctatcagctgggtgcgacaggcccctggacaagggcttgagtggatgggagggatcatccctatc......tttggtacagcaaactacgcacagaagttccag...ggcagagtcacgattaccgcggacgaatccacgagcacagcctacatggagctgagcagcctgagatctgaggacacggccgtgtattactgtgcgagaga >IGHV1-8*01 caggtgcagctggtgcagtctggggct...gaggtgaagaagcctggggcctcagtgaaggtctcctgcaaggcttctggatacaccttc............accagttatgatatcaactgggtgcgacaggccactggacaagggcttgagtggatgggatggatgaaccctaac......agtggtaacacaggctatgcacagaagttccag...ggcagagtcaccatgaccaggaacacctccataagcacagcctacatggagctgagcagcctgagatctgaggacacggccgtgtattactgtgcgagagg >IGHV1-8*02 caggtgcagctggtgcagtctggggct...gaggtgaagaagcctggggcctcagtgaaggtctcctgcaaggcttctggatacaccttc............accagctatgatatcaactgggtgcgacaggccactggacaagggcttgagtggatgggatggatgaaccctaac......agtggtaacacaggctatgcacagaagttccag...ggcagagtcaccatgaccaggaacacctccataagcacagcctacatggagctgagcagcctgagatctgaggacacggccgtgtattactgtgcgagagg >IGHV1-NL1*01 caggttcagctgttgcagcctggggtc...caggtgaagaagcctgggtcctcagtgaaggtctcctgctaggcttccagatacaccttc............accaaatactttacacggtgggtgtgacaaagccctggacaagggcatnagtggatgggatgaatcaacccttac......aacgataacacacactacgcacagacgttctgg...ggcagagtcaccattaccagtgacaggtccatgagcacagcctacatggagctgagcngcctgagatccgaagacatggtcgtgtattactgtgtgagaga >IGHV1/OR15-1*01 caggtgcagctggtgcagtctggggct...gaggtgaagaagcctggggcctcagtgaaggtctcctgcaaggcttctggatacatcttc............accgactactatatgcactgggtgcgacaggcccctggacaagagcttgggtggatgggacggatcaaccctaac......agtggtggcacaaactatgcacagaagtttcag...ggcagagtcaccatgaccagggacacgtccatcagcacagcctacacggagctgagcagcctgagatctgaggacacggccacgtattactgtgcgaga >IGHV1/OR15-1*02 caggtgcagctggtgcagtctggggct...gaggtgaagaagcctggggcctcagtgaaggtctcctgcaaggcttctggatacatcttc............accgactactatatgcactgggtgcgacaggcccctggacaagagcttgggtggatgggacggatcaaccctaac......agtggtggcacaaactatgcacagaagtttcag...ggcagagtcaccatgaccagggacacgtccatcagcacagcctgcacggagctgagcagcctgagatctgaggacacggccacgtattactgtgcgagaga >IGHV1/OR15-1*03 caggtgcagctggtgcagtctggggct...gaggtgaagaagcctggggcctcagtgaaggtctcctgcaaggcttctggatacatcttc............accgactactatatgcactgggtgcgacaggcccctggacaagagcttgggtggatgggacggatcaaccctaac......agtggtggcacaaactatgcacagaagtttcag...ggcagagtcaccatgaccagggacacgtccatcagcacagcctacacggagctgagcagcctgagatctgaggacacagccacgtattactgtgcgagaga >IGHV1/OR15-1*04 caggtgcagctggtgcagtctggggct...gaggtgaagaagcctggggcctcagtgaaggtctcctgcaaggcttctggatacatcttc............accgactactatatgcactgggtgcgacaggcccctggacaagagcttgggtggatgggacggatcaaccctaac......agtggtggcacaaactatgcacagaagtttcag...ggcagagtcaccatgaccagggacacgtccatcagcacagcctacatggagctgagcagcctgagatctgaggacacggccacgtattactgtgcgagaga >IGHV1/OR15-2*01 caggtgcagctggtgcagtctggagct...gaggtgaagaagcctagagcctcagtgaaggtctcctgcaaggcttctggttacaccttt............accagctactatatgcactgggtgtgacaggcccctgaacaagggcttgagtggatgggatggatcaacacttac......aatggtaacacaaactacccacagaagctccag...ggcagagtcaccatgaccagagacacatccacgagcacagcctacatggagctgagcaggctgagatctgacgacatggccgtgtattactgtgcgagaga >IGHV1/OR15-2*02 caggtgcagctggtgcagtctggagct...gaggtgaagaagcctggagcctcagtgaaggtctcctgcaaggcttctggttacaccttt............accagctactatatgcactgggtgtgacaggcccctgaacaagggcttgagtggatgggatggatcaacacttac......aatggtaacacaaactacccacagaagctccag...ggcagagtcaccatgaccagagacacatccacgagcacagcctacatggagctgagcagcctgagatctgacgacatggccgtgtattactgtgcgagaga >IGHV1/OR15-2*03 caggtgcagctggtgcagtctggagct...gaggtgaagaagcctagagcctcagtgaaggtctcctgcaaggcttctggttacaccttt............accagctactatatgcactgggtgtgacaggcccctgaacaagggcttgagtggatgggatggatcaacacttac......aatggtaacacaaactacccacagaagctccag...ggcagagtcaccatgaccagagacacatccacgagcacagcctacatggagctgagcagcctgagatctgacgacatggccgtgtattactgtgcgagaga >IGHV1/OR15-3*01 caggtccaactggtgtagtctggagct...gaggtgaagaagcctggggcctcagtgaaggtctcctgcaaggcttctggatacaccttc............accgactactttatgaactggatgcgccaggcccctggacaaaggcttgagtggatgggatggatcaacgctggc......aatggtaacacaaaatattcacagaagctccag...ggcagagtcaccattaccagggacacatcttcgagcacagcctacatgcagctgagcagcctgagatctgaggacacggccgtgtattactgtgcgaga >IGHV1/OR15-3*02 caggtccaactggtgtagtctggagct...gaggtgaagaagcctggggcctcagtgaaggtctcctgcaaggcttctggatacaccttc............accgactactttatgaactggatgcgccaggcccctggacaaaggcttgagtggatgggatggatcaacgctggc......aatggtaacacaaaatattcacagaagctccag...ggcagagtcaccattaccagggacacatctgcgagcacagcctacatgcagctgagcagcctgagatctgaggacacggccgtgtattactgtgcgagaga >IGHV1/OR15-3*03 caggtccaactggtgtagtctggagct...gaggtgaagaagcctggggcctcagtgaaggtctcctgcaaggcttctggatacaccttc............accagctactatatgaactggatgcgccaggcccctggacaaggcttcgagtggatgggatggatcaacgctggc......aatggtaacacaaagtattcacagaagctccag...ggcagagtcaccattaccagggacacatctgcgagcacagcctacatgcagctgagcagcctgagatctgaggacacggccgtgtattactgtgcgaga >IGHV1/OR15-4*01 caggaccagttggtgcagtctggggct...gaggtgaagaagcctctgtcctcagtgaaggtctccttcaaggcttctggatacaccttc............accaacaactttatgcactgggtgtgacaggcccctggacaaggacttgagtggatgggatggatcaatgctggc......aatggtaacacaacatatgcacagaagttccag...ggcagagtcaccataaccagggacacgtccatgagcacagcctacacggagctgagcagcctgagatctgaggacacggccgtgtattactgtgcgaga >IGHV1/OR15-5*01 .....................................agaagcctggggcctcagtgaaggtctcctgcaaggcttctggatacaccttc............accagctactgtatgcactgggtgcaccaggtccatgcacaagggcttgagtggatgggattggtgtgccctagt......gatggcagcacaagctatgcacagaagttccag...gccagagtcaccataaccagggacacatccatgagcacagcctacatggagctaagcagtctgagatctgaggacacggccatgtattactgtgtgaga >IGHV1/OR15-5*02 caggtacagctggtgcagtctggggct...gaggtgaagaagcctggggcctcagtgaaggtctcctgcaaggcttctggatacaccttc............accaactactgtatgcactgggtgcgccaggtccatgcacaagggcttgagtggatgggattggtgtgccctagt......gatggcagcacaagctatgcacaaaagttccag...gccagagtcaccataaccagggacacatccatgagcacagcctacatggagctaagcagtctgagatctgaggacacggccatgtattactgtgtgaga >IGHV1/OR15-9*01 caggtacagctgatgcagtctggggct...gaggtgaagaagcctggggcctcagtgaggatctcctgcaaggcttctggatacaccttc............accagctactgtatgcactgggtgtgccaggcccatgcacaagggcttgagtggatgggattggtgtgccctagt......gatggcagcacaagctatgcacagaagttccag...ggcagagtcaccataaccagggacacatccatgggcacagcctacatggagctaagcagcctgagatctgaggacacggccatgtattactgtgtgagaga >IGHV1/OR21-1*01 caggtacagctggtgcagtctggggct...gaggtgaagaagcctggggcctcagtgaaggtctcctgcaaggcttctggatacaccatc............accagctactgtatgcactgggtgcaccaggtccatgcacaagggcttgagtggatgggattggtgtgccctagt......gatggcagcacaagctatgcacagaagttccag...gccagagtcaccataaccagggacacatccatgagcacagcctacatggagctaagcagtctgagatctgaggacacggccatgtattactgtgtgagaga >IGHV2-10*01 caggtcaccttgaaggagtctggtcct...gcactggtgaaacccacacagaccctcatgctgacctgcaccttctctgggttctcactcagc......acttctggaatgggtgtgggttagatctgtcagccctcagcaaaggccctggagtggcttgcacacatttattagaat.........gataataaatactacagcccatctctgaag...agtaggctcattatctccaaggacacctccaagaatgaagtggttctaacagtgatcaacatggacattgtggacacagccacacattactgtgcaaggagac >IGHV2-26*01 caggtcaccttgaaggagtctggtcct...gtgctggtgaaacccacagagaccctcacgctgacctgcaccgtctctgggttctcactcagc......aatgctagaatgggtgtgagctggatccgtcagcccccagggaaggccctggagtggcttgcacacattttttcgaat.........gacgaaaaatcctacagcacatctctgaag...agcaggctcaccatctccaaggacacctccaaaagccaggtggtccttaccatgaccaacatggaccctgtggacacagccacatattactgtgcacggatac >IGHV2-5*01 cagatcaccttgaaggagtctggtcct...acgctggtgaaacccacacagaccctcacgctgacctgcaccttctctgggttctcactcagc......actagtggagtgggtgtgggctggatccgtcagcccccaggaaaggccctggagtggcttgcactcatttattggaat.........gatgataagcgctacagcccatctctgaag...agcaggctcaccatcaccaaggacacctccaaaaaccaggtggtccttacaatgaccaacatggaccctgtggacacagccacatattactgtgcacacagac >IGHV2-5*02 cagatcaccttgaaggagtctggtcct...acgctggtgaaacccacacagaccctcacgctgacctgcaccttctctgggttctcactcagc......actagtggagtgggtgtgggctggatccgtcagcccccaggaaaggccctggagtggcttgcactcatttattgggat.........gatgataagcgctacagcccatctctgaag...agcaggctcaccatcaccaaggacacctccaaaaaccaggtggtccttacaatgaccaacatggaccctgtggacacagccacatattactgtgcacacagac >IGHV2-5*03 ................................gctggtgaaacccacacagaccctcacgctgacctgcaccttctctgggttctcactcagc......actagtggagtgggtgtgggctggatccgtcagcccccaggaaaggccctggagtggcttgcactcatttattgggat.........gatgataagcgctacagcccatctctgaag...agcaggctcaccattaccaaggacacctccaaaaaccaggt >IGHV2-5*04 cagatcaccttgaaggagtctggtcct...acgctggtgaaacccacacagaccctcacgctgacctgcaccttctctgggttctcactcagc......actagtggagtgggtgtgggctggatccgtcagcccccaggaaaggccctggagtggcttgcactcatttattggaat.........gatgataagcgctacagcccatctctgaag...agcaggctcaccatcaccaaggacacctccaaaaaccaggtggtccttacaatgaccaacatggaccctgtggacacaggcacatattactgtgtac >IGHV2-5*05 cagatcaccttgaaggagtctggtcct...acgctggtgaaacccacacagaccctcacgctgacctgcaccttctctgggttctcactcagc......actagtggagtgggtgtgggctggatccgtcagcccccaggaaaggccctggagtggcttgcactcatttattgggat.........gatgataagcgctacggcccatctctgaag...agcaggctcaccatcaccaaggacacctccaaaaaccaggtggtccttacaatgaccaacatggaccctgtggacacagccacatattactgtgcacacagac >IGHV2-5*06 cagatcaccttgaaggagtctggtcct...acgctggtaaaacccacacagaccctcacgctgacctgcaccttctctgggttctcactcagc......actagtggagtgggtgtgggctggatccgtcagcccccaggaaaggccctggagtggcttgcactcatttattgggat.........gatgataagcgctacggcccatctctgaag...agcaggctcaccatcaccaaggacacctccaaaaaccaggtggtccttacaatgaccaacatggaccctgtggacacagccacatattactgtgcacacaga >IGHV2-5*08 caggtcaccttgaaggagtctggtcct...gcgctggtgaaacccacacagaccctcacactgacctgcaccttctctgggttctcactcagc......actagtggaatgcgtgtgagctggatccgtcagcccccaggaaaggccctggagtggcttgcactcatttattgggat.........gatgataagcgctacagcccatctctgaag...agcaggctcaccatcaccaaggacacctccaaaaaccaggtggtccttacaatgaccaacatggaccctgtggacacagccacatattactgtgcacacagac >IGHV2-5*09 caggtcaccttgaaggagtctggtcct...acgctggtgaaacccacacagaccctcacgctgacctgcaccttctctgggttctcactcagc......actagtggagtgggtgtgggctggatccgtcagcccccaggaaaggccctggagtggcttgcactcatttattgggat.........gatgataagcgctacggcccatctctgaag...agcaggctcaccatcaccaaggacacctccaaaaaccaggtggtccttacaatgaccaacatggaccctgtggacacagccacatattactgtgcacacagac >IGHV2-70*01 caggtcaccttgagggagtctggtcct...gcgctggtgaaacccacacagaccctcacactgacctgcaccttctctgggttctcactcagc......actagtggaatgtgtgtgagctggatccgtcagcccccagggaaggccctggagtggcttgcactcattgattgggat.........gatgataaatactacagcacatctctgaag...accaggctcaccatctccaaggacacctccaaaaaccaggtggtccttacaatgaccaacatggaccctgtggacacagccacgtattactgtgcacggatac >IGHV2-70*02 caggtcaccttgagggagtctggtcct...gcgctggtgaaacccacacagaccctcacactgacctgcaccttctctgggttctcactcagc......actagtggaatgtgtgtgagctggatccgtcagcccccagggaaggccctggagtggcttgcactcattgattgggat.........gatgataaatactacagcacatctctgaag...accaggctcaccatctccaaggacacctccaaaaaccaggtggtccttacaatgaccaacatggaccctgtggacacggccgtgtattactg >IGHV2-70*03 caggtcaccttgaaggagtctggtcct...gcgctggtgaaacccacacagaccctcacactgacctgcaccttctctgggttctcactcagc......actagtggaatgcgtgtgagctggatccgtcagcccccagggaaggccctggagtggcttgcacgcattgattgggat.........gatgataaattctacagcacatctctgaag...accaggctcaccatctccaaggacacctccaaaaaccaggtggtccttacaatgaccaacatggaccctgtggacacggccgtgtattactg >IGHV2-70*04 caggtcaccttgaaggagtctggtcct...gcgctggtgaaacccacacagaccctcacactgacctgcaccttctctgggttctcactcagc......actagtggaatgcgtgtgagctggatccgtcagcccccagggaaggccctggagtggcttgcacgcattgattgggat.........gatgataaattctacagcacatctctgaag...accaggctcaccatctccaaggacacctccaaaaaccaggtggtccttacaatgaccaacatggaccctgtggacacagccacgtattac >IGHV2-70*05 ..........................t...gcgctggtgaaacccacacagaccctcacactgacctgcaccttctctgggttctcactcagc......actagtggaatgcgtgcgagctggatccgtcagcccccagggaaggccctggagtggcttgcacgcattgattgggat.........gatgataaattctacagcacatctctgaag...accaggctcaccatctccaaggacacctccaaaaaccaggtggtccttacaatgaccaacatgga >IGHV2-70*06 caggtcaccttgaaggagtctggtcct...gcgctggtgaaacccacacagaccctcacactgacctgcaccttctctgggttctcactcagc......actagtggaatgcgtgtgagctggatccgtcagcccccagggaaggccctggagtggcttgcacgcattgattgggat.........gatgataaattctacagcacatccctgaag...accaggctcaccatctccaaggacacctccaaaaaccaggtggtccttacaatgaccaacatggaccctgtggacacggccgtgtattactg >IGHV2-70*07 caggtcaccttgagggagtctggtcct...gcgctggtgaaacccacacagaccctcacactgacctgcaccttctctgggttctcactcagc......actagtggaatgtgtgtgagctggatccgtcagcccccggggaaggccctggagtggcttgcactcattgattgggat.........gatgataaatactacagcacatctctgaag...accaggctcaccatctccaaggacacctccaaaaaccaggtggtccttacaatgaccaacatggaccctgtggacacggccgtgtattactg >IGHV2-70*08 caggtcaccttgagggagtctggtcct...gcgctggtgaaacccacacagaccctcacactgacctgcgccttctctgggttctcactcagc......actagtggaatgtgtgtgagctggatccgtcagcccccagggaaggccctggagtggcttgcacgcattgattgggat.........gatgataaatactacagcacatctctgaag...accaggctcaccatctccaaggacacctccaaaaaccaggtggtccttacaatgaccaacatggaccctgtggacacggccgtgtattactg >IGHV2-70*09 cagatcaccttgaaggagtctggtcct...acgctggtgaaacccacacagaccctcacgctgacccgcaccttctctgggttctcactcagc......actagtggaatgtgtgtgagctggatccgtcagcccccagggaaggccctggagtggcttgcactcattgattgggat.........gatgataaatactacagcacatctctgaac...accaggctcaccatctccaaggacacctccaaaaaccaggtggtccttacaatgaccaacatggaccctgtggacacaggcacatattactgtgtacgg >IGHV2-70*10 caggtcaccttgaaggagtctggtcct...gcgctggtgaaacccacacagaccctcacactgacctgcaccttctctgggttctcactcagc......actagtggaatgcgtgtgagctggatccgtcagcccccagggaaggccctggagtggattgcacgcattgattgggat.........gatgataaatactacagcacatctctgaag...accaggctcaccatctccaaggacacctccaaaaaccaggtggtccttacaatgaccaacatggaccctgtggacacagccacgtattactgtgcacggatac >IGHV2-70*11 cgggtcaccttgagggagtctggtcct...gcgctggtgaaacccacacagaccctcacactgacctgcaccttctctgggttctcactcagc......actagtggaatgtgtgtgagctggatccgtcagcccccagggaaggccctggagtggcttgcacgcattgattgggat.........gatgataaatactacagcacatctctgaag...accaggctcaccatctccaaggacacctccaaaaaccaggtggtccttacaatgaccaacatggaccctgtggacacagccacgtattactgtgcacggatac >IGHV2-70*12 cagatcaccttgaaggagtctggtcct...acgctggtgaaacccacacagaccctcacgctgacctgcaccttctctgggttctcactcagc......actagtggaatgtgtgtgagctggatccgtcagcccccagggaaggccctggagtggcttgcactcattgattgggat.........gatgataaatactacagcacatctctgaag...accaggctcaccatctccaaggacacctccaaaaaccaggtggtccttacaatgaccaacatggaccctgtggacacagccacatattactgtgcacacagac >IGHV2-70*13 caggtcaccttgagggagtctggtcct...gcgctggtgaaacccacacagaccctcacactgacctgcaccttctctgggttctcactcagc......actagtggaatgtgtgtgagctggatccgtcagcccccagggaaggccctggagtggcttgcactcattgattgggat.........gatgataaatactacagcacatctctgaag...accaggctcaccatctccaaggacacctccaaaaaccaggtggtccttacaatgaccaacatggaccctgtggacacagccacgtattattgtgcacggatac >IGHV2-70D*04 caggtcaccttgaaggagtctggtcct...gcgctggtgaaacccacacagaccctcacactgacctgcaccttctctgggttctcactcagc......actagtggaatgcgtgtgagctggatccgtcagcccccagggaaggccctggagtggcttgcacgcattgattgggat.........gatgataaattctacagcacatctctgaag...accaggctcaccatctccaaggacacctccaaaaaccaggtggtccttacaatgaccaacatggaccctgtggacacagccacgtattactgtgcacggatac >IGHV2-70D*14 caggtcaccttgaaggagtctggtcct...gcgctggtgaaacccacacagaccctcacactgacctgcaccttctctgggttctcactcagc......actagtggaatgcgtgtgagctggatccgtcagcccccaggtaaggccctggagtggcttgcacgcattgattgggat.........gatgataaattctacagcacatctctgaag...accaggctcaccatctccaaggacacctccaaaaaccaggtggtccttacaatgaccaacatggaccctgtggacacagccacgtattactgtgcacggatac >IGHV2/OR16-5*01 caggtcaccttgaaggagtctggtcct...gcgctggtgaaacccacagagaccctcacgctgacctgcactctctctgggttctcactcagc......acttctggaatgggtatgagctggatccgtcagcccccagggaaggccctggagtggcttgctcacatttttttgaat.........gacaaaaaatcctacagcacgtctctgaag...aacaggctcatcatctccaaggacacctccaaaagccaggtggtccttaccatgaccaacatggaccctgtggacacagccacgtattactgtgcatggagag >IGHV3-11*01 caggtgcagctggtggagtctggggga...ggcttggtcaagcctggagggtccctgagactctcctgtgcagcctctggattcaccttc............agtgactactacatgagctggatccgccaggctccagggaaggggctggagtgggtttcatacattagtagtagt......ggtagtaccatatactacgcagactctgtgaag...ggccgattcaccatctccagggacaacgccaagaactcactgtatctgcaaatgaacagcctgagagccgaggacacggccgtgtattactgtgcgagaga >IGHV3-11*03 caggtgcagctgttggagtctggggga...ggcttggtcaagcctggagggtccctgagactctcctgtgcagcctctggattcaccttc............agtgactactacatgagctggatccgccaggctccagggaaggggctggagtgggtttcatacattagtagtagt......agtagttacacaaactacgcagactctgtgaag...ggccgattcaccatctccagagacaacgccaagaactcactgtatctgcaaatgaacagcctgagagccgaggacacggccgtgtattactgtgcgaga >IGHV3-11*04 caggtgcagctggtggagtctggggga...ggcttggtcaagcctggagggtccctgagactctcctgtgcagcctctggattcaccttc............agtgactactacatgagctggatccgccaggctccagggaaggggctggagtgggtttcatacattagtagtagt......ggtagtaccatatactacgcagactctgtgaag...ggccgattcaccatctccagggacaacgccaagaactcactgtatctgcaaatgaacagcctgagagccgaggacacggctgtgtattactgtgcgagaga >IGHV3-11*05 caggtgcagctggtggagtctggggga...ggcttggtcaagcctggagggtccctgagactctcctgtgcagcctctggattcaccttc............agtgactactacatgagctggatccgccaggctccagggaaggggctggagtgggtttcatacattagtagtagt......agtagttacacaaactacgcagactctgtgaag...ggccgattcaccatctccagagacaacgccaagaactcactgtatctgcaaatgaacagcctgagagccgaggacacggccgtgtattactgtgcgagaga >IGHV3-11*06 caggtgcagctggtggagtctggggga...ggcttggtcaagcctggagggtccctgagactctcctgtgcagcctctggattcaccttc............agtgactactacatgagctggatccgccaggctccagggaaggggctggagtgggtttcatacattagtagtagt......agtagttacacaaactacgcagactctgtgaag...ggccgattcaccatctccagagacaacgccaagaactcactgtatctgcaaatgaacagcctgagagccgaggacacggctgtgtattactgtgcgagaga >IGHV3-13*01 gaggtgcagctggtggagtctggggga...ggcttggtacagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctacgacatgcactgggtccgccaagctacaggaaaaggtctggagtgggtctcagctattggtactgct.........ggtgacacatactatccaggctccgtgaag...ggccgattcaccatctccagagaaaatgccaagaactccttgtatcttcaaatgaacagcctgagagccggggacacggctgtgtattactgtgcaagaga >IGHV3-13*02 gaggtgcatctggtggagtctggggga...ggcttggtacagcctgggggggccctgagactctcctgtgcagcctctggattcaccttc............agtaactacgacatgcactgggtccgccaagctacaggaaaaggtctggagtgggtctcagccaatggtactgct.........ggtgacacatactatccaggctccgtgaag...gggcgattcaccatctccagagaaaatgccaagaactccttgtatcttcaaatgaacagcctgagagccggggacacggctgtgtattactgtgcaagaga >IGHV3-13*03 gaggtgcagctggtggagtctggggga...ggcttggtacagcctggggggtccctgagactctcctgtgcagcctgtggattcaccttc............agtagctacgacatgcactgggtccgccaagctacaggaaaaggtctggagtgggtctcagctattggtactgct.........ggtgacacatactatccaggctccgtgaag...ggccaattcaccatctccagagaaaatgccaagaactccttgtatcttcaaatgaacagcctgagagccggggacacggctgtgtattactgtgcaaga >IGHV3-13*04 gaggtgcagctggtggagtctggggga...ggcttggtacagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctacgacatgcactgggtccgccaagctacaggaaaaggtctggaatgggtctcagctattggtactgct.........ggtgacacatactatccaggctccgtgaag...ggccgattcaccatctccagagaaaatgccaagaactccttgtatcttcaaatgaacagcctgagagccggggacacggctgtgtattactgtgcaagaga >IGHV3-13*05 gaggtgcagctggtggagtctggggga...ggcttggtacagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctacgacatgcactgggtccgccaagctacaggaaaaggtctggagtgggtctcagctattggtactgct.........ggtgacccatactatccaggctccgtgaag...ggccgattcaccatctccagagaaaatgccaagaactccttgtatcttcaaatgaacagcctgagagccggggacacggctgtgtattactgtgcaagaga >IGHV3-15*01 gaggtgcagctggtggagtctggggga...ggcttggtaaagcctggggggtcccttagactctcctgtgcagcctctggattcactttc............agtaacgcctggatgagctgggtccgccaggctccagggaaggggctggagtgggttggccgtattaaaagcaaaactgatggtgggacaacagactacgctgcacccgtgaaa...ggcagattcaccatctcaagagatgattcaaaaaacacgctgtatctgcaaatgaacagcctgaaaaccgaggacacagccgtgtattactgtaccacaga >IGHV3-15*02 gaggtgcagctggtggagtctggggga...gccttggtaaagcctggggggtcccttagactctcctgtgcagcctctggattcactttc............agtaacgcctggatgagctgggtccgccaggctccagggaaggggctggagtgggttggccgtattaaaagcaaaactgatggtgggacaacagactacgctgcacccgtgaaa...ggcagattcaccatctcaagagatgattcaaaaaacacgctgtatctgcaaatgaacagcctgaaaaccgaggacacagccgtgtattactgtaccacaga >IGHV3-15*03 gaggtgcagctggtggagtctgccgga...gccttggtacagcctggggggtcccttagactctcctgtgcagcctctggattcacttgc............agtaacgcctggatgagctgggtccgccaggctccagggaaggggctggagtgggttggccgtattaaaagcaaagctaatggtgggacaacagactacgctgcacctgtgaaa...ggcagattcaccatctcaagagttgattcaaaaaacacgctgtatctgcaaatgaacagcctgaaaaccgaggacacagccgtgtattactgtaccacaga >IGHV3-15*04 gaggtgcagctggtggagtctggggga...ggcttggtaaagcctggggggtcccttagactctcctgtgcagcctctggattcactttc............agtaacgcctggatgagctgggtccgccaggctccagggaaggggctggagtgggttggccgtattgaaagcaaaactgatggtgggacaacagactacgctgcacccgtgaaa...ggcagattcaccatctcaagagatgattcaaaaaacacgctgtatctgcaaatgaacagcctgaaaaccgaggacacagccgtgtattactgtaccacaga >IGHV3-15*05 gaggtgcagctggtggagtctggggga...ggcttggtaaagcctggggggtcccttagactctcctgtgcagcctctggattcactttc............agtaacgcctggatgagctgggtccgccaggctccagggaaggggctggagtgggttggccgtattaaaagcaaaactgatggtgggacaacagactacgctgcacccgtgaaa...ggcagattcaccatctcaagagatgattcaaaaaacacgctgtatctgcaaatgaacagtctgaaaaccgaggacacagccgtgtattactgtaccacaga >IGHV3-15*06 gaggtgcagctggtggagtctggggga...ggcttggtaaagcctggggggtcccttagactctcctgtgcagcctctggattcactttc............agtaacgcctggatgagctgggtccgccaggctccagggaaggggctggagtgggtcggccgtattaaaagcaaaactgatggtgggacaacaaactacgctgcacccgtgaaa...ggcagattcaccatctcaagagatgattcaaaaaacacgctgtatctgcaaatgaacagcctgaaaaccgaggacacagccgtgtattactgtaccacaga >IGHV3-15*07 gaggtgcagctggtggagtctggggga...ggcttggtaaagcctggggggtcccttagactctcctgtgcagcctctggtttcactttc............agtaacgcctggatgaactgggtccgccaggctccagggaaggggctggagtgggtcggccgtattaaaagcaaaactgatggtgggacaacagactacgctgcacccgtgaaa...ggcagattcaccatctcaagagatgattcaaaaaacacgctgtatctgcaaatgaacagcctgaaaaccgaggacacagccgtgtattactgtaccacaga >IGHV3-15*08 gaggtgcagctggtggagtctgcggga...ggcttggtacagcctggggggtcccttagactctcctgtgcagcctctggattcacttgc............agtaacgcctggatgagctgggtccgccaggctccagggaaggggctggagtgggttggctgtattaaaagcaaagctaatggtgggacaacagactacgctgcacctgtgaaa...ggcagattcaccatctcaagagatgattcaaaaaacacgctgtatctgcaaatgatcagcctgaaaaccgaggacacggccgtgtattactgtaccacagg >IGHV3-16*01 gaggtacaactggtggagtctggggga...ggcttggtacagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agtaacagtgacatgaactgggcccgcaaggctccaggaaaggggctggagtgggtatcgggtgttagttggaat......ggcagtaggacgcactatgtggactccgtgaag...cgccgattcatcatctccagagacaattccaggaactccctgtatctgcaaaagaacagacggagagccgaggacatggctgtgtattactgtgtgagaaa >IGHV3-16*02 gaggtgcagctggtggagtctggggga...ggcttggtacagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agtaacagtgacatgaactgggcccgcaaggctccaggaaaggggctggagtgggtatcgggtgttagttggaat......ggcagtaggacgcactatgtggactccgtgaag...cgccgattcatcatctccagagacaattccaggaactccctgtatctgcaaaagaacagacggagagccgaggacatggctgtgtattactgtgtgagaaa >IGHV3-19*01 acagtgcagctggtggagtctggggga...ggcttggtagagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agtaacagtgacatgaactgggtccgccaggctccaggaaaggggctggagtgggtatcgggtgttagttggaat......ggcagtaggacgcactatgcagactctgtgaag...ggccgattcatcatctccagagacaattccaggaacttcctgtatcagcaaatgaacagcctgaggcccgaggacatggctgtgtattactgtgtgagaaa >IGHV3-20*01 gaggtgcagctggtggagtctggggga...ggtgtggtacggcctggggggtccctgagactctcctgtgcagcctctggattcaccttt............gatgattatggcatgagctgggtccgccaagctccagggaaggggctggagtgggtctctggtattaattggaat......ggtggtagcacaggttatgcagactctgtgaag...ggccgattcaccatctccagagacaacgccaagaactccctgtatctgcaaatgaacagtctgagagccgaggacacggccttgtatcactgtgcgagaga >IGHV3-20*02 gaggtgcagctggtggagtctggggga...ggtgtggtacggcctggggggtccctgagactctcctttgcagcctctggattcaccttt............gatgattatggcatgagctgggtccgccaagctccagggaaggggctggagtgggtctctggtattaattggaat......ggtggtagcacaggttatgcagactctgtgaag...ggccgattcaccatctccagagacaacgccaagaactccctgtatctgcaaatgaacagtctgagagccgaggacacggccttgtatcactgtgcgagaga >IGHV3-21*01 gaggtgcagctggtggagtctggggga...ggcctggtcaagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatagcatgaactgggtccgccaggctccagggaaggggctggagtgggtctcatccattagtagtagt......agtagttacatatactacgcagactcagtgaag...ggccgattcaccatctccagagacaacgccaagaactcactgtatctgcaaatgaacagcctgagagccgaggacacggctgtgtattactgtgcgagaga >IGHV3-21*02 gaggtgcaactggtggagtctggggga...ggcctggtcaagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatagcatgaactgggtccgccaggctccagggaaggggctggagtgggtctcatccattagtagtagt......agtagttacatatactacgcagactcagtgaag...ggccgattcaccatctccagagacaacgccaagaactcactgtatctgcaaatgaacagcctgagagccgaggacacggctgtgtattactgtgcgagaga >IGHV3-21*03 gaggtgcagctggtggagtctggggga...ggcctggtcaagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatagcatgaactgggtccgccaggctccagggaaggggctggagtgggtctcatccattagtagtagt......agtagttacatatactacgcagactcagtgaag...ggccgattcaccatctccagagacaacgccaagaactcactgtatctgcaaatgaacagcctgagagccgaggacacagctgtgtattactgtgcgagaga >IGHV3-21*04 gaggtgcagctggtggagtctggggga...ggcctggtcaagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatagcatgaactgggtccgccaggctccagggaaggggctggagtgggtctcatccattagtagtagt......agtagttacatatactacgcagactcagtgaag...ggccgattcaccatctccagagacaacgccaagaactcactgtatctgcaaatgaacagcctgagagccgaggacacggccgtgtattactgtgcgagaga >IGHV3-22*01 gaggtgcatctggtggagtctggggga...gccttggtacagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agttactactacatgagcggggtccgccaggctcccgggaaggggctggaatgggtaggtttcattagaaacaaagctaatggtgggacaacagaatagaccacgtctgtgaaa...ggcagattcacaatctcaagagatgattccaaaagcatcacctatctgcaaatgaagagcctgaaaaccgaggacacggccgtgtattactgttccagaga >IGHV3-22*02 gaggtgcagctggtggagtctggggga...ggcttggtacagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agttactactacatgagcggggtccgccaggctcccgggaaggggctggaatgggtaggtttcattagaaacaaagctaatggtgggacaacagaatagaccacgtctgtgaaa...ggcagattcacaatctcaagagatgattccaaaagcatcacctatctgcaaatgaagagcctgaaaaccgaggacacggccgtgtattactgttccagaga >IGHV3-23*01 gaggtgcagctgttggagtctggggga...ggcttggtacagcctggggggtccctgagactctcctgtgcagcctctggattcaccttt............agcagctatgccatgagctgggtccgccaggctccagggaaggggctggagtgggtctcagctattagtggtagt......ggtggtagcacatactacgcagactccgtgaag...ggccggttcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagccgaggacacggccgtatattactgtgcgaaaga >IGHV3-23*02 gaggtgcagctgttggagtctggggga...ggcttggtacagcctggggggtccctgagactctcctgtgcagcctctggattcaccttt............agcagctatgccatgagctgggtccgccaggctccagggaaggggctggagtgggtctcagctattagtggtagt......ggtggtagcacatactacggagactccgtgaag...ggccggttcaccatctcaagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagccgaggacacggccgtatattactgtgcgaaaga >IGHV3-23*03 gaggtgcagctgttggagtctggggga...ggcttggtacagcctggggggtccctgagactctcctgtgcagcctctggattcaccttt............agcagctatgccatgagctgggtccgccaggctccagggaaggggctggagtgggtctcagttatttatagcggt......ggtagtagcacatactatgcagactccgtgaag...ggccggttcaccatctccagagataattccaagaacacgctgtatctgcaaatgaacagcctgagagccgaggacacggccgtatattactgtgcgaaaga >IGHV3-23*04 gaggtgcagctggtggagtctggggga...ggcttggtacagcctggggggtccctgagactctcctgtgcagcctctggattcaccttt............agcagctatgccatgagctgggtccgccaggctccagggaaggggctggagtgggtctcagctattagtggtagt......ggtggtagcacatactacgcagactccgtgaag...ggccggttcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagccgaggacacggccgtatattactgtgcgaaaga >IGHV3-23*05 gaggtgcagctgttggagtctggggga...ggcttggtacagcctggggggtccctgagactctcctgtgcagcctctggattcaccttt............agcagctatgccatgagctgggtccgccaggctccagggaaggggctggagtgggtctcagctatttatagcagt......ggtagtagcacatactatgcagactccgtgaag...ggccggttcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagccgaggacacggccgtatattactgtgcgaaa >IGHV3-23D*01 gaggtgcagctgttggagtctggggga...ggcttggtacagcctggggggtccctgagactctcctgtgcagcctctggattcaccttt............agcagctatgccatgagctgggtccgccaggctccagggaaggggctggagtgggtctcagctattagtggtagt......ggtggtagcacatactacgcagactccgtgaag...ggccggttcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagccgaggacacggccgtatattactgtgcgaaaga >IGHV3-25*01 gagatgcagctggtggagtctggggga...ggcttgcaaaagcctgcgtggtccccgagactctcctgtgcagcctctcaattcaccttc............agtagctactacatgaactgtgtccgccaggctccagggaatgggctggagttggtttgacaagttaatcctaat......gggggtagcacatacctcatagactccggtaag...gaccgattcaatacctccagagataacgccaagaacacacttcatctgcaaatgaacagcctgaaaaccgaggacacggccctctattagtgtaccagaga >IGHV3-25*02 gagatgcagctggtggagtctggggga...ggcttggcaaagcctgcgtggtccccgagactctcctgtgcagcctctcaattcaccttc............agtagctactacatgaactgtgtccgccaggctccagggaatgggctggagttggtttgacaagttaatcctaat......gggggtagcacatacctcatagactccggtaag...gaccgattcaatacctccagagataacgccaagaacacacttcatctgcaaatgaacagcctgaaaaccgaggacacggccctctattagtgtaccagaga >IGHV3-25*03 gagatgcagctggtggagtctggggga...ggcttggcaaagcctgcgtggtccccgagactctcctgtgcagcctctcaattcaccttc............agtagctactacatgaactgtgtccgccaggctccagggaatgggctggagttggttggacaagttaatcctaat......gggggtagcacatacctcatagactccggtaag...gaccgattcaatacctccagagataacgccaagaacacacttcatctgcaaatgaacagcctgaaaaccgaggacacggccctgtattagtgtaccaga >IGHV3-25*04 gagacgcagctggtggagtctggggga...ggcttggcaaagcctgggcggtccccgagactctcctgtgcagcctctcaattcaccttc............agtagctactacatgaactgtgtccgccaggctccagggaatgggctggagttggttggacaagttaatcctaat......gggggtagcacatacctcatagactccggtaag...gaccgattcaatacctccagagataacgccaagaacacacttcatctgcaaatgaacagcctgaaaaccgaggacacggccctgtattactgtaccagaga >IGHV3-25*05 gagatgcagctggtggagtctggggga...ggcttggcaaagcctgcgtggtccccgagactctcctgtgcagcctctcaattcaccttc............agtagctactacatgaactgtgtccgccaggctccagggaatgggctggagttggttggacaagttaatcctaat......gggggtagcacatacctcatagactccggtaag...gaccgattcaatacctccagagataacgccaagaacacacttcatctgcaaatgaacagcctgaaaaccgaggacacggccctctattagtgtaccagaga >IGHV3-29*01 gaggtggagctgatagagcccacagag...gacctgagacaacctgggaagttcctgagactctcctgtgtagcctctagattcgccttc............agtagcttctgaatgagcccagttcaccagtctgcaggcaaggggctggagtgagtaatagatataaaagatgat......ggaagtcagatacaccatgcagactctgtgaag...ggcagattctccatctccaaagacaatgctaagaactctctgtatctgcaaatgaacagtcagagaactgaggacatggctgtgtatggctgtacataaggtt >IGHV3-30*01 caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatgctatgcactgggtccgccaggctccaggcaaggggctagagtgggtggcagttatatcatatgat......ggaagtaataaatactacgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgcgagaga >IGHV3-30*02 caggtgcagctggtggagtctggggga...ggcgtggtccagcctggggggtccctgagactctcctgtgcagcgtctggattcaccttc............agtagctatggcatgcactgggtccgccaggctccaggcaaggggctggagtgggtggcatttatacggtatgat......ggaagtaataaatactatgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgcgaaaga >IGHV3-30*03 caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatggcatgcactgggtccgccaggctccaggcaaggggctggagtgggtggcagttatatcatatgat......ggaagtaataaatactatgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgcgagaga >IGHV3-30*04 caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatgctatgcactgggtccgccaggctccaggcaaggggctggagtgggtggcagttatatcatatgat......ggaagtaataaatactacgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgcgagaga >IGHV3-30*05 caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatggcatgcactgggtccgccaggctccaggcaaggggctagagtgggtggcagttatatcatatgat......ggaagtaataaatactacgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagctgagggcacggctgtgtattactgtgcgagaga >IGHV3-30*06 caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcgtctggattcaccttc............agtagctatggcatgcactgggtccgccaggctccaggcaaggggctagagtgggtggcagttatatcatatgat......ggaagtaataaatactacgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgcgagaga >IGHV3-30*07 caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatgctatgcactgggtccgccaggctccaggcaaggggctagagtgggtggcagttatatcatatgat......ggaagtaataaatactacgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagccgaggacacggctgtgtattactgtgcgagaga >IGHV3-30*08 caggtgcagctggtggactctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcctctgcattcaccttc............agtagctatgctatgcactgggtccgccaggctccaggcaaggggctagagtgggtggcagttatatcatatgat......ggaagtaataaatactacgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgcgaga >IGHV3-30*09 caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatgctatgcactgggtccgccaggctccaggcaaggggctggagtgggtggcagttatatcatatgat......ggaagtaataaatactacgcagactccgtgaag...ggccgattcgccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgcgagaga >IGHV3-30*10 caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatgctatgcactgggtccgccaggctccaggcaaggggctagagtgggtggcagttatatcatatgat......ggaagtaataaatactacacagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgcgagaga >IGHV3-30*11 caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcgtctggattcaccttc............agtagctatgctatgcactgggtccgccaggctccaggcaaggggctagagtgggtggcagttatatcatatgat......ggaagtaataaatactacgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgcgagaga >IGHV3-30*12 caggtgcagctggtggagtctgggggg...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcgtctggattcaccttc............agtagctatggcatgcactgggtccgccaggctccaggcaaggggctagagtgggtggcagttatatcatatgat......ggaagtaataaatactacgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagccgaggacacggctgtgtattactgtgcgagaga >IGHV3-30*13 caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatggcatgcactgggtccgccaggctccaggcaaggggctagagtgggtggcagttatatcatatgat......ggaagtaataaatactacgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacaggctgtatctgcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgcgagaga >IGHV3-30*14 caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatgctatgcactgggtccgccaggctccaggcaaggggctggagtgggtggcagttatatcatatgat......ggaagtaataaatactacgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatcttcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgcgagaga >IGHV3-30*15 caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatgctatgcactgggtccgccaggctccaggcaaggggctagagtgggtggcagttatatcatatgat......ggaagtaataaatactacgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgagcagcctgagagctgaggacacggctgtgtattactgtgcgagaga >IGHV3-30*16 caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatgctatgcactgggtccgccaggccccaggcaaggggctagagtgggtggcagttatatcatatgat......ggaagtaataaatactacgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgcgagaga >IGHV3-30*17 caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatgctatgcactgggtccgccaggctccgggcaaggggctagagtgggtggcagttatatcatatgat......ggaagtaataaatactacgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgcgagaga >IGHV3-30*18 caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatggcatgcactgggtccgccaggctccaggcaaggggctggagtgggtggcagttatatcatatgat......ggaagtaataaatactatgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgcgaaaga >IGHV3-30*19 caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcgtctggattcaccttc............agtagctatggcatgcactgggtccgccaggctccaggcaaggggctggagtgggtggcagttatatcatatgat......ggaagtaataaatactacgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgcgagaga >IGHV3-30-2*01 gaggtacagctcgtggagtccggagag...gacccaagacaacctgggggatccctgagactctcctgtgcagactctggattaaccttc............agtagctactgaaggaactcggtttcccaggctccagggaaggggctggagtgagtagtagatatacagtgtgat......ggaagtcagatatgttatgcataatctttgaag...agcaaattcaccatctccaaagaaaatgccaagaactcactgtatttgctaatgaacagtctgagagcagcgggcacagctgtgtgttactgtatgtgaggca >IGHV3-30-22*01 gaggtggagctgatagagtccatagag...gacctgagacaacctgggaagttcctgagactctcctgtgtagcctctagattcgccttc............agtagcttctgaatgagccgagttcaccagtctccaggcaaggggctggagtgagtaatagatataaaagatgat......ggaagtcagatacaccatgcagactctgtgaag...ggcagattctccatctccaaagacaatgctaagaactctctgtatctgcaaatgaacagtcagagagctgaggacatggacgtgtatggctgtacataaggtc >IGHV3-30-3*01 caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatgctatgcactgggtccgccaggctccaggcaaggggctggagtgggtggcagttatatcatatgat......ggaagcaataaatactacgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgcgagaga >IGHV3-30-3*02 caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcgtctggattcaccttc............agtagctatgctatgcactgggtccgccaggctccaggcaaggggctggagtgggtggcagttatatcatatgat......ggaagcaataaatactacgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgcgaaaga >IGHV3-30-3*03 caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatgctatgcactgggtccgccaggctccaggcaaggggctggagtgggtggcagttatatcatatgat......ggaagtaataaatactacgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgcgagaga >IGHV3-30-33*01 gaggtacagctcgtggagtccggagag...gacccaagacaacctgggggatccctgagactctcctgtgcagactctggattaaccttc............agtagctactgaaggagctcggtttcccaggctccagggaaggggctggagtgagtagtagatatacagtgtgat......ggaagtcagatatgttatgcataatctttgaag...agcaaattcaccatctccaaagaaaatgccaagaactcactgtatttgctaatgaacagtctgagagcagagggcacagctgtgtgttactgtatgtgagg >IGHV3-30-42*01 gaggtggagctgatagagcccacagag...gacctgagacaacctgggaagttcctgagactctcctgtgtagcctctagattcgccttc............agtagcttctgaatgagcccagttcaccagtctgcaggcaaggggctggagtgagtaatagatataaaagatgat......ggaagtcagatacaccatgcagactctgtgaag...ggcagattctccatctccaaagacaatgctaagaactctctgtatctgcaaatgaacagtcagagaactgaggacatggctgtgtatggctgtacataaggtt >IGHV3-30-5*01 caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatggcatgcactgggtccgccaggctccaggcaaggggctggagtgggtggcagttatatcatatgat......ggaagtaataaatactatgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgcgaaaga >IGHV3-30-5*02 caggtgcagctggtggagtctggggga...ggcgtggtccagcctggggggtccctgagactctcctgtgcagcgtctggattcaccttc............agtagctatggcatgcactgggtccgccaggctccaggcaaggggctggagtgggtggcatttatacggtatgat......ggaagtaataaatactatgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgcgaaaga >IGHV3-30-52*01 gaggtacagctcgtggagtccggagag...gacccaagacaacctgggggatccctgagactctcctgtgcagactctggattaaccttc............agtagctactgaaggaactcggtttcccaggctccagggaaggggctggagtgagtagtagatatacagtgtgat......ggaagtcagatatgttatgcataatctttgaag...agcaaattcaccatctccaaagaaaatgccaagaactcactgtatttgctaatgaacagtctgagagcagcgggcacagctgtgtgttactgtatgtgagg >IGHV3-32*01 gaggtggagctgatagagtccatagag...gacctgagacaacctgggaagttcctgagactctcctgtgtagcctctagattcgccttc............agtagcttctgaatgagccgagttcaccagtctccaggcaaggggctggagtgagtaatagatataaaagatgat......ggaagtcagatacaccatgcagactctgtgaag...ggcagattctccatctccaaagacaatgctaagaactctctgtatctgcaaatgaacactcagagagctgaggacgtggccgtgtatggctatacataaggtc >IGHV3-33*01 caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcgtctggattcaccttc............agtagctatggcatgcactgggtccgccaggctccaggcaaggggctggagtgggtggcagttatatggtatgat......ggaagtaataaatactatgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagccgaggacacggctgtgtattactgtgcgagaga >IGHV3-33*02 caggtacagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcgtctggattcaccttc............agtagctatggcatgcactgggtccgccaggctccaggcaaggggctggagtgggtggcagttatatggtatgat......ggaagtaataaatactatgcagactccgcgaag...ggccgattcaccatctccagagacaattccacgaacacgctgtttctgcaaatgaacagcctgagagccgaggacacggctgtgtattactgtgcgagaga >IGHV3-33*03 caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcgtctggattcaccttc............agtagctatggcatgcactgggtccgccaggctccaggcaaggggctggagtgggtggcagttatatggtatgat......ggaagtaataaatactatgcagactccgtgaag...ggccgattcaccatctccagagacaactccaagaacacgctgtatctgcaaatgaacagcctgagagccgaggacacggctgtgtattactgtgcgaaaga >IGHV3-33*04 caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcgtctggattcaccttc............agtagctatggcatgcactgggtccgccaggctccaggcaaggggctagagtgggtggcagttatatggtatgac......ggaagtaataaatactatgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagccgaggacacggctgtgtattactgtgcgagaga >IGHV3-33*05 caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcgtctggattcaccttc............agtagctatggcatgcactgggtccgccaggctccaggcaaggggctggagtgggtggcagttatatcatatgat......ggaagtaataaatactatgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagccgaggacacggctgtgtattactgtgcgagaga >IGHV3-33*06 caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcgtctggattcaccttc............agtagctatggcatgcactgggtccgccaggctccaggcaaggggctggagtgggtggcagttatatggtatgat......ggaagtaataaatactatgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagccgaggacacggctgtgtattactgtgcgaaaga >IGHV3-33-2*01 gaggtacagctcgtggagtccggagag...gacccaagacaacctgggggatccttgagactctcctgtgcagactctggattaaccttc............agtagctactgaatgagctcggtttcccaggctccagggaaggggctggagtgagtagtagatatacagtgtgat......ggaagtcagatatgttatgcccaatctgtgaag...agcaaattcaccatctccaaagaaaatgccaagaactcactgtatttgcaaatgaacagtctgagagcagagggcacagctgtgtgttactgtatgtgaggca >IGHV3-35*01 gaggtgcagctggtggagtctggggga...ggcttggtacagcctgggggatccctgagactctcctgtgcagcctctggattcaccttc............agtaacagtgacatgaactgggtccatcaggctccaggaaaggggctggagtgggtatcgggtgttagttggaat......ggcagtaggacgcactatgcagactctgtgaag...ggccgattcatcatctccagagacaattccaggaacaccctgtatctgcaaacgaatagcctgagggccgaggacacggctgtgtattactgtgtgagaaa >IGHV3-38*01 gaggtgcagctggtggagtctggggga...ggcttggtacagcctagggggtccctgagactctcctgtgcagcctctggattcaccgtc............agtagcaatgagatgagctggatccgccaggctccagggaaggggctggagtgggtctcatccattagtggt............ggtagcacatactacgcagactccaggaag...ggcagattcaccatctccagagacaattccaagaacacgctgtatcttcaaatgaacaacctgagagctgagggcacggccgcgtattactgtgccagatata >IGHV3-38*02 gaggtgcagctggtggagtctggggga...ggcttggtacagcctagggggtccctgagactctcctgtgcagcctctggattcaccgtc............agtagcaatgagatgagctggatccgccaggctccagggaaggggctggagtgggtctcatccattagtggt............ggtagcacatactacgcagactccaggaag...ggcagattcaccatctccagagacaattccaagaacacgctgtatcttcaaatgaacaacctgagagctgagggcacggccgtgtattactgtgccagatata >IGHV3-38*03 gaggtgcagctggtggagtctggggga...ggcttggtacagcctagggggtccctgagactctcctgtgcagcctctggattcaccgtc............agtagcaatgagatgagctggatccgccaggctccagggaagggtctggagtgggtctcatccattagtggt............ggtagcacatactacgcagactccaggaag...ggcagattcaccatctccagagacaattccaagaacacgctgtatcttcaaatgaacaacctgagagctgagggcacggccgtgtattactgtgccagatata >IGHV3-38-3*01 gaggtgcagctggtggagtctcgggga...gtcttggtacagcctggggggtccctgagactctcctgtgcagcctctggattcaccgtc............agtagcaatgagatgagctgggtccgccaggctccagggaagggtctggagtgggtctcatccattagtggt............ggtagcacatactacgcagactccaggaag...ggcagattcaccatctccagagacaattccaagaacacgctgcatcttcaaatgaacagcctgagagctgaggacacggctgtgtattactgtaagaaaga >IGHV3-43*01 gaagtgcagctggtggagtctggggga...gtcgtggtacagcctggggggtccctgagactctcctgtgcagcctctggattcaccttt............gatgattataccatgcactgggtccgtcaagctccggggaagggtctggagtgggtctctcttattagttgggat......ggtggtagcacatactatgcagactctgtgaag...ggccgattcaccatctccagagacaacagcaaaaactccctgtatctgcaaatgaacagtctgagaactgaggacaccgccttgtattactgtgcaaaagata >IGHV3-43*02 gaagtgcagctggtggagtctggggga...ggcgtggtacagcctggggggtccctgagactctcctgtgcagcctctggattcaccttt............gatgattatgccatgcactgggtccgtcaagctccagggaagggtctggagtgggtctctcttattagtggggat......ggtggtagcacatactatgcagactctgtgaag...ggccgattcaccatctccagagacaacagcaaaaactccctgtatctgcaaatgaacagtctgagaactgaggacaccgccttgtattactgtgcaaaagata >IGHV3-43D*01 gaagtgcagctggtggagtctggggga...gtcgtggtacagcctggggggtccctgagactctcctgtgcagcctctggattcaccttt............gatgattatgccatgcactgggtccgtcaagctccggggaagggtctggagtgggtctctcttattagttgggat......ggtggtagcacctactatgcagactctgtgaag...ggtcgattcaccatctccagagacaacagcaaaaactccctgtatctgcaaatgaacagtctgagagctgaggacaccgccttgtattactgtgcaaaagata >IGHV3-47*01 gaggatcagctggtggagtctggggga...ggcttggtacagcctggggggtccctgcgaccctcctgtgcagcctctggattcgccttc............agtagctatgctctgcactgggttcgccgggctccagggaagggtctggagtgggtatcagctattggtactggt.........ggtgatacatactatgcagactccgtgatg...ggccgattcaccatctccagagacaacgccaagaagtccttgtatcttcatatgaacagcctgatagctgaggacatggctgtgtattattgtgcaaga >IGHV3-47*02 gaggatcagctggtggagtctggggga...ggcttggtacagcctggggggtccctgagaccctcctgtgcagcctctggattcgccttc............agtagctatgttctgcactgggttcgccgggctccagggaagggtccggagtgggtatcagctattggtactggt.........ggtgatacatactatgcagactccgtgatg...ggccgattcaccatctccagagacaacgccaagaagtccttgtatcttcaaatgaacagcctgatagctgaggacatggctgtgtattattgtgcaagaga >IGHV3-48*01 gaggtgcagctggtggagtctggggga...ggcttggtacagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatagcatgaactgggtccgccaggctccagggaaggggctggagtgggtttcatacattagtagtagt......agtagtaccatatactacgcagactctgtgaag...ggccgattcaccatctccagagacaatgccaagaactcactgtatctgcaaatgaacagcctgagagccgaggacacggctgtgtattactgtgcgagaga >IGHV3-48*02 gaggtgcagctggtggagtctggggga...ggcttggtacagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatagcatgaactgggtccgccaggctccagggaaggggctggagtgggtttcatacattagtagtagt......agtagtaccatatactacgcagactctgtgaag...ggccgattcaccatctccagagacaatgccaagaactcactgtatctgcaaatgaacagcctgagagacgaggacacggctgtgtattactgtgcgagaga >IGHV3-48*03 gaggtgcagctggtggagtctggggga...ggcttggtacagcctggagggtccctgagactctcctgtgcagcctctggattcaccttc............agtagttatgaaatgaactgggtccgccaggctccagggaaggggctggagtgggtttcatacattagtagtagt......ggtagtaccatatactacgcagactctgtgaag...ggccgattcaccatctccagagacaacgccaagaactcactgtatctgcaaatgaacagcctgagagccgaggacacggctgtttattactgtgcgagaga >IGHV3-48*04 gaggtgcagctggtggagtctggggga...ggcttggtacagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatagcatgaactgggtccgccaggctccagggaaggggctggagtgggtttcatacattagtagtagt......agtagtaccatatactacgcagactctgtgaag...ggccgattcaccatctccagagacaacgccaagaactcactgtatctgcaaatgaacagcctgagagccgaggacacggctgtgtattactgtgcgagaga >IGHV3-49*01 gaggtgcagctggtggagtctggggga...ggcttggtacagccagggcggtccctgagactctcctgtacagcttctggattcaccttt............ggtgattatgctatgagctggttccgccaggctccagggaaggggctggagtgggtaggtttcattagaagcaaagcttatggtgggacaacagaatacaccgcgtctgtgaaa...ggcagattcaccatctcaagagatggttccaaaagcatcgcctatctgcaaatgaacagcctgaaaaccgaggacacagccgtgtattactgtactagaga >IGHV3-49*02 gaggtgcagctggtggagtctggggga...ggcttggtacagccagggccgtccctgagactctcctgtacagcttctggattcaccttt............gggtattatcctatgagctgggtccgccaggctccagggaaggggctggagtgggtaggtttcattagaagcaaagcttatggtgggacaacagaatacgccgcgtctgtgaaa...ggcagattcaccatctcaagagatgattccaaaagcatcgcctatctgcaaatgaacagcctgaaaaccgaggacacagccgtgtattactgtactagaga >IGHV3-49*03 gaggtgcagctggtggagtctggggga...ggcttggtacagccagggcggtccctgagactctcctgtacagcttctggattcaccttt............ggtgattatgctatgagctggttccgccaggctccagggaaggggctggagtgggtaggtttcattagaagcaaagcttatggtgggacaacagaatacgccgcgtctgtgaaa...ggcagattcaccatctcaagagatgattccaaaagcatcgcctatctgcaaatgaacagcctgaaaaccgaggacacagccgtgtattactgtactagaga >IGHV3-49*04 gaggtgcagctggtggagtctggggga...ggcttggtacagccagggcggtccctgagactctcctgtacagcttctggattcaccttt............ggtgattatgctatgagctgggtccgccaggctccagggaaggggctggagtgggtaggtttcattagaagcaaagcttatggtgggacaacagaatacgccgcgtctgtgaaa...ggcagattcaccatctcaagagatgattccaaaagcatcgcctatctgcaaatgaacagcctgaaaaccgaggacacagccgtgtattactgtactagaga >IGHV3-49*05 gaggtgcagctggtggagtctggggga...ggcttggtaaagccagggcggtccctgagactctcctgtacagcttctggattcaccttt............ggtgattatgctatgagctggttccgccaggctccagggaaggggctggagtgggtaggtttcattagaagcaaagcttatggtgggacaacagaatacgccgcgtctgtgaaa...ggcagattcaccatctcaagagatgattccaaaagcatcgcctatctgcaaatgaacagcctgaaaaccgaggacacagccgtgtattactgtactagaga >IGHV3-52*01 gaggtgcagctggtggagtctgggtga...ggcttggtacagcctggagggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctcctggatgcactgggtctgccaggctccggagaaggggctggagtgggtggccgacataaagtgtgac......ggaagtgagaaatactatgtagactctgtgaag...ggccgattgaccatctccagagacaatgccaagaactccctctatctgcaagtgaacagcctgagagctgaggacatgaccgtgtattactgtgtgagagg >IGHV3-52*02 gaggtgcagctggtggagtctgggtga...ggcttggtacagcctggagggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctcctggatgcactgggtctgccaggctccggagaaggggcaggagtgggtggccgacataaagtgtgac......ggaagtgagaaatactatgtagactctgtgaag...ggccgattgaccatctccagagacaatgccaagaactccctctatctgcaagtgaacagcctgagagctgaggacatgaccgtgtattactgtgtgaga >IGHV3-52*03 gaggtgcagctggtcgagtctgggtga...ggcttggtacagcctggagggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctcctggatgcactgggtctgccaggctccggagaaggggctggagtgggtggccgacataaagtgtgac......ggaagtgagaaatactatgtagactctgtgaag...ggccgattgaccatctccagagacaatgccaagaactccctctatctgcaagtgaacagcctgagagctgaggacatgaccgtgtattactgtgtgaga >IGHV3-53*01 gaggtgcagctggtggagtctggagga...ggcttgatccagcctggggggtccctgagactctcctgtgcagcctctgggttcaccgtc............agtagcaactacatgagctgggtccgccaggctccagggaaggggctggagtgggtctcagttatttatagcggt.........ggtagcacatactacgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatcttcaaatgaacagcctgagagccgaggacacggccgtgtattactgtgcgagaga >IGHV3-53*02 gaggtgcagctggtggagactggagga...ggcttgatccagcctggggggtccctgagactctcctgtgcagcctctgggttcaccgtc............agtagcaactacatgagctgggtccgccaggctccagggaaggggctggagtgggtctcagttatttatagcggt.........ggtagcacatactacgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatcttcaaatgaacagcctgagagccgaggacacggccgtgtattactgtgcgagaga >IGHV3-53*03 gaggtgcagctggtggagtctggagga...ggcttgatccagcctggggggtccctgagactctcctgtgcagcctctgggttcaccgtc............agtagcaactacatgagctgggtccgccagcctccagggaaggggctggagtgggtctcagttatttatagcggt.........ggtagcacatactacgcagactctgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatcttcaaatgaacagcctgagagccgaggacacggccgtgtattactgtgctaggga >IGHV3-53*04 gaggtgcagctggtggagtctggagga...ggcttggtccagcctggggggtccctgagactctcctgtgcagcctctgggttcaccgtc............agtagcaactacatgagctgggtccgccaggctccagggaaggggctggagtgggtctcagttatttatagcggt.........ggtagcacatactacgcagactccgtgaag...ggccgattcaccatctccagacacaattccaagaacacgctgtatcttcaaatgaacagcctgagagctgaggacacggccgtgtattactgtgcgagaga >IGHV3-54*01 gaggtacagctggtggagtctgaagaa...aaccaaagacaacttgggggatccctgagactctcctgtgcagactctggattaaccttc............agtagctactgaatgagctcagattcccaagctccagggaaggggctggagtgagtagtagatatatagtaggat......agaagtcagctatgttatgcacaatctgtgaag...agcagattcaccatctccaaagaaaatgccaagaactcactctgtttgcaaatgaacagtctgagagcagagggcacggccgtgtattactgtatgtgagt >IGHV3-54*02 gaggtacagctggtggagtctgaagaa...aaccaaagacaacttgggggatccctgagactctcctgtgcagactctggattaaccttc............agtagctactgaatgagctcagattcccaggctccagggaaggggctggagtgagtagtagatatatagtacgat......agaagtcagatatgttatgcacaatctgtgaag...agcagattcaccatctccaaagaaaatgccaagaactcactccgtttgcaaatgaacagtctgagagcagagggcacggccgtgtattactgtatgtgagg >IGHV3-54*04 gaggtacagctggtggagtctgaagaa...aaccaaagacaacttgggggatccctgagactctcctgtgcagactctggattaaccttc............agtagctactgaatgagctcagattcccaggctccagggaaggggctggagtgagtagtagatatatagtaggat......agaagtcagctatgttatgcacaatctgtgaag...agcagattcaccatctccaaagaaaatgccaagaactcactctgtttgcaaatgaacagtctgagagcagagggcacggccgtgtattactgtatgtgagt >IGHV3-62*01 gaggtgcagctggtggagtctggggaa...ggcttggtccagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctctgctatgcactgggtccgccaggctccaagaaagggtttgtagtgggtctcagttattagtacaagt......ggtgataccgtactctacacagactctgtgaag...ggccgattcaccatctccagagacaatgcccagaattcactgtctctgcaaatgaacagcctgagagccgagggcacagttgtgtactactgtgtgaaaga >IGHV3-63*01 gaggtggagctgatagagtccatagag...ggcctgagacaacttgggaagttcctgagactctcctgtgtagcctctggattcaccttc............agtagctactgaatgagctgggtcaatgagactctagggaaggggctggagggagtaatagatgtaaaatatgat......ggaagtcagatataccatgcagactctgtgaag...ggcagattcaccatctccaaagacaatgctaagaactcaccgtatctccaaacgaacagtctgagagctgaggacatgaccatgcatggctgtacataaggtt >IGHV3-63*02 gaggtggagctgatagagtccatagag...ggcctgagacaacttgggaagttcctgagactctcctgtgtagcctctggattcaccttc............agtagctactgaatgagctgggtcaatgagactctagggaaggggctggagggagtaatagatgtaaaatatgat......ggaagtcagatataccatgcagactctgtgaag...ggcagattcaccatctccaaagacaatgctaagaactcaccgtatctgcaaacgaacagtctgagagctgaggacatgaccatgcatggctgtacataa >IGHV3-64*01 gaggtgcagctggtggagtctggggga...ggcttggtccagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatgctatgcactgggtccgccaggctccagggaagggactggaatatgtttcagctattagtagtaat......gggggtagcacatattatgcaaactctgtgaag...ggcagattcaccatctccagagacaattccaagaacacgctgtatcttcaaatgggcagcctgagagctgaggacatggctgtgtattactgtgcgagaga >IGHV3-64*02 gaggtgcagctggtggagtctggggaa...ggcttggtccagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatgctatgcactgggtccgccaggctccagggaagggactggaatatgtttcagctattagtagtaat......gggggtagcacatattatgcagactctgtgaag...ggcagattcaccatctccagagacaattccaagaacacgctgtatcttcaaatgggcagcctgagagctgaggacatggctgtgtattactgtgcgagaga >IGHV3-64*03 gaggtgcagctggtggagtctggggga...ggcttggtccagcctggggggtccctgagactctcctgttcagcctctggattcaccttc............agtagctatgctatgcactgggtccgccaggctccagggaagggactggaatatgtttcagctattagtagtaat......gggggtagcacatactacgcagactcagtgaag...ggcagattcaccatctccagagacaattccaagaacacgctgtatgtccaaatgagcagtctgagagctgaggacacggctgtgtattactgtgtgaaaga >IGHV3-64*04 caggtgcagctggtggagtctggggga...ggcttggtccagcctggggggtccctgagactctcctgttcagcctctggattcaccttc............agtagctatgctatgcactgggtccgccaggctccagggaagggactggaatatgtttcagctattagtagtaat......gggggtagcacatactacgcagactcagtgaag...ggcagattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgcgagaga >IGHV3-64*05 gaggtgcagctggtggagtctggggga...ggcttggtccagcctggggggtccctgagactctcctgttcagcctctggattcaccttc............agtagctatgctatgcactgggtccgccaggctccagggaagggactggaatatgtttcagctattagtagtaat......gggggtagcacatactacgcagactcagtgaag...ggcagattcaccatctccagagacaattccaagaacacgctgtatgttcaaatgagcagtctgagagctgaggacacggctgtgtattactgtgtgaaaga >IGHV3-64D*06 gaggtgcagctggtggagtctggggga...ggcttggtccagcctggggggtccctgagactctcctgttcagcctctggattcaccttc............agtagctatgctatgcactgggtccgccaggctccagggaagggactggaatatgtttcagctattagtagtaat......gggggtagcacatactacgcagactccgtgaag...ggcagattcaccatctccagagacaattccaagaacacgctgtatcttcaaatgagcagtctgagagctgaggacacggctgtgtattactgtgtgaaaga >IGHV3-66*01 gaggtgcagctggtggagtctggggga...ggcttggtccagcctggggggtccctgagactctcctgtgcagcctctggattcaccgtc............agtagcaactacatgagctgggtccgccaggctccagggaaggggctggagtgggtctcagttatttatagcggt.........ggtagcacatactacgcagactccgtgaag...ggcagattcaccatctccagagacaattccaagaacacgctgtatcttcaaatgaacagcctgagagccgaggacacggctgtgtattactgtgcgagaga >IGHV3-66*02 gaggtgcagctggtggagtctggggga...ggcttggtccagcctggggggtccctgagactctcctgtgcagcctctggattcaccgtc............agtagcaactacatgagctgggtccgccaggctccagggaaggggctggagtgggtctcagttatttatagcggt.........ggtagcacatactacgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatcttcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgcgaga >IGHV3-66*03 gaggtgcagctggtggagtctggagga...ggcttgatccagcctggggggtccctgagactctcctgtgcagcctctgggttcaccgtc............agtagcaactacatgagctgggtccgccaggctccagggaaggggctggagtgggtctcagttatttatagctgt.........ggtagcacatactacgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatcttcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgcgagaga >IGHV3-66*04 gaggtgcagctggtggagtctggggga...ggcttggtccagcctggggggtccctgagactctcctgtgcagcctctggattcaccgtc............agtagcaactacatgagctgggtccgccaggctccagggaaggggctggagtgggtctcagttatttatagcggt.........ggtagcacatactacgcagactccgtgaag...ggcagattcaccatctccagagacaattccaagaacacgctgtatcttcaaatgaacagcctgagagccgaggacacggctgtgtattactgtgcgagaca >IGHV3-69-1*01 gaggtgcagctggtggagtctggggga...ggcttggtaaagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agtgactactacatgaactgggtccgccaggctccagggaaggggctggagtgggtctcatccattagtagtagt.........agtaccatatactacgcagactctgtgaag...ggccgattcaccatctccagagacaacgccaagaactcactgtatctgcaaatgaacagcctgagagccgaggacacggctgtgtattactgtgcgagaga >IGHV3-69-1*02 gaggtgcagctggtggagtctggggga...ggcttggtaaagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agtgactactacatgaactgggtccgccaggctccagggaaggggctggagtgggtctcatccattagtagtagt.........agtaccatatactacgcagactctgtgaag...ggccgattcaccatctccagagacaacgccaagaactcactgtatctgcaaatgaacagcctgagagccgaggacacggctgtttattactgtgcgagaga >IGHV3-7*01 gaggtgcagctggtggagtctggggga...ggcttggtccagcctggggggtccctgagactctcctgtgcagcctctggattcaccttt............agtagctattggatgagctgggtccgccaggctccagggaaggggctggagtgggtggccaacataaagcaagat......ggaagtgagaaatactatgtggactctgtgaag...ggccgattcaccatctccagagacaacgccaagaactcactgtatctgcaaatgaacagcctgagagccgaggacacggctgtgtattactgtgcgagaga >IGHV3-7*02 gaggtgcagctggtggagtctggggga...ggcttggtccagcctggggggtccctgagactctcctgtgcagcctctggattcaccttt............agtagctattggatgagctgggtccgccaggctccagggaaagggctggagtgggtggccaacataaagcaagat......ggaagtgagaaatactatgtggactctgtgaag...ggccgattcaccatctccagagacaacgccaagaactcactgtatctgcaaatgaacagcctgagagccgaggacacggctgtgtattactgtgcgaga >IGHV3-7*03 gaggtgcagctggtggagtctggggga...ggcttggtccagcctggggggtccctgagactctcctgtgcagcctctggattcaccttt............agtagctattggatgagctgggtccgccaggctccagggaaggggctggagtgggtggccaacataaagcaagat......ggaagtgagaaatactatgtggactctgtgaag...ggccgattcaccatctccagagacaacgccaagaactcactgtatctgcaaatgaacagcctgagagccgaggacacggccgtgtattactgtgcgagaga >IGHV3-71*01 gaggtgcagctggtggagtccggggga...ggcttggtccagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agtgactactacatgagctgggtccgccaggctcccgggaaggggctggagtgggtaggtttcattagaaacaaagctaatggtgggacaacagaatagaccacgtctgtgaaa...ggcagattcacaatctcaagagatgattccaaaagcatcacctatctgcaaatgaacagcctgagagccgaggacacggccgtgtattactgtgcgagaga >IGHV3-71*02 gaggtgcagctggtggagtctggggga...ggcttggtccagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agtgactactacatgagctgggtccgccaggctcccgggaaggggctggagtgggtaggtttcattagaaacaaagctaatggtgggacaacagaatagaccacgtctgtgaaa...ggcagattcacaatctcaagagatgattccaaaagcatcacctatctgcaaatgaacagcctgagagccgaggacatggctgtgtattactgtgcgagaga >IGHV3-71*03 gaggtgcagctggtggagtctggggga...ggcttggtccagcctggggggtccctgagactctcctgtgcagcctctggtttcaccttc............agtgactactacatgagctgggtccgccaggctcccgggaaggggctggagtgggtaggtttcattagaaacaaagctaatggtgggacaacagaatagaccacgtctgtgaaa...ggcagattcacaatctcaagagatgattccaaaagcatcacctatctgcaaatgaacagcctgagagccgaggacacggctgtgtattactgtgcgagaga >IGHV3-72*01 gaggtgcagctggtggagtctggggga...ggcttggtccagcctggagggtccctgagactctcctgtgcagcctctggattcaccttc............agtgaccactacatggactgggtccgccaggctccagggaaggggctggagtgggttggccgtactagaaacaaagctaacagttacaccacagaatacgccgcgtctgtgaaa...ggcagattcaccatctcaagagatgattcaaagaactcactgtatctgcaaatgaacagcctgaaaaccgaggacacggccgtgtattactgtgctagaga >IGHV3-72*02 ....................................................................................accttc............agtgaccactacatggactgggtccgccaggctccagggaaggggctggagtgggttggccgtactagaaacaaagctaacagctacaccacagaatacgccgcgtctgtgaaa...ggcagattcaccatctcaagagatgattcaaagaactcactgtat >IGHV3-73*01 gaggtgcagctggtggagtctggggga...ggcttggtccagcctggggggtccctgaaactctcctgtgcagcctctgggttcaccttc............agtggctctgctatgcactgggtccgccaggcttccgggaaagggctggagtgggttggccgtattagaagcaaagctaacagttacgcgacagcatatgctgcgtcggtgaaa...ggcaggttcaccatctccagagatgattcaaagaacacggcgtatctgcaaatgaacagcctgaaaaccgaggacacggccgtgtattactgtactagaca >IGHV3-73*02 gaggtgcagctggtggagtccggggga...ggcttggtccagcctggggggtccctgaaactctcctgtgcagcctctgggttcaccttc............agtggctctgctatgcactgggtccgccaggcttccgggaaagggctggagtgggttggccgtattagaagcaaagctaacagttacgcgacagcatatgctgcgtcggtgaaa...ggcaggttcaccatctccagagatgattcaaagaacacggcgtatctgcaaatgaacagcctgaaaaccgaggacacggccgtgtattactgtactagaca >IGHV3-74*01 gaggtgcagctggtggagtccggggga...ggcttagttcagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctactggatgcactgggtccgccaagctccagggaaggggctggtgtgggtctcacgtattaatagtgat......gggagtagcacaagctacgcggactccgtgaag...ggccgattcaccatctccagagacaacgccaagaacacgctgtatctgcaaatgaacagtctgagagccgaggacacggctgtgtattactgtgcaagaga >IGHV3-74*02 gaggtgcagctggtggagtctggggga...ggcttagttcagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctactggatgcactgggtccgccaagctccagggaaggggctggtgtgggtctcacgtattaatagtgat......gggagtagcacaagctacgcggactccgtgaag...ggccgattcaccatctccagagacaacgccaagaacacgctgtatctgcaaatgaacagtctgagagccgaggacacggctgtgtattactgtgcaaga >IGHV3-74*03 gaggtgcagctggtggagtccggggga...ggcttagttcagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctactggatgcactgggtccgccaagctccagggaaggggctggtgtgggtctcacgtattaatagtgat......gggagtagcacaacgtacgcggactccgtgaag...ggccgattcaccatctccagagacaacgccaagaacacgctgtatctgcaaatgaacagtctgagagccgaggacacggctgtgtattactgtgcaagaga >IGHV3-9*01 gaagtgcagctggtggagtctggggga...ggcttggtacagcctggcaggtccctgagactctcctgtgcagcctctggattcaccttt............gatgattatgccatgcactgggtccggcaagctccagggaagggcctggagtgggtctcaggtattagttggaat......agtggtagcataggctatgcggactctgtgaag...ggccgattcaccatctccagagacaacgccaagaactccctgtatctgcaaatgaacagtctgagagctgaggacacggccttgtattactgtgcaaaagata >IGHV3-9*02 gaagtgcagctggtggagtctggggga...ggcttggtacagcctggcaggtccctgagactctcctgtgcagcctctggattcacctct............gatgattatgccatgcactgggtccggcaagctccagggaagggcctggagtgggtctcaggtattagttggaat......agtggtagcataggctatgcggactctgtgaag...ggccgattcaccatctccagagacaacgccaagaactccctgtatctgcaaatgaacagtctgagagctgaggacacggccttgtattactgtgcaaaagata >IGHV3-9*03 gaagtgcagctggtggagtctggggga...ggcttggtacagcctggcaggtccctgagactctcctgtgcagcctctggattcaccttt............gatgattatgccatgcactgggtccggcaagctccagggaagggcctggagtgggtctcaggtattagttggaat......agtggtagcataggctatgcggactctgtgaag...ggccgattcaccatctccagagacaacgccaagaactccctgtatctgcaaatgaacagtctgagagctgaggacatggccttgtattactgtgcaaaagata >IGHV3-NL1*01 caggtgcagctggtggagtctggggga...ggcgtggtccagcctggggggtccctgagactctcctgtgcagcgtctggattcaccttc............agtagctatggcatgcactgggtccgccaggctccaggcaaggggctggagtgggtctcagttatttatagcggt......ggtagtagcacatactatgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgcgaaaga >IGHV3/OR15-7*01 gaggtgcagctggtggagtctggggga...ggcttggtccagcctgggggttctctgagactctcatgtgcagcctctggattcaccttc............agtgaccactacatgagctgggtccgccaggctcaagggaaagggctagagttggtaggtttaataagaaacaaagctaacagttacacgacagaatatgctgcgtctgtgaaa...ggcagacttaccatctcaagagaggattcaaagaacacgatgtatctgcaaatgagcaacctgaaaaccgaggacttggccgtgtattactgtgctaga >IGHV3/OR15-7*02 gaggtgcagctgttggagtctggggga...ggcttggtccagcctgggggttctctgagactctcatgtgctgcctctggattcaccttc............agtgaccactacatgagctgggtccgccaggctcaagggaaagggctagagttggtaggtttaataagaaacaaagctaacagttacacgacagaatatgctgcgtctgtgaaa...ggcagacttaccatctcaagagaggattcaaagaacacgctgtatctgcaaatgagcagcctgaaaaccgaggacttggccgtgtattactgtgctaga >IGHV3/OR15-7*03 gaggtgcagctggtggagtctggggga...ggcttggtccagcctgggggttctctgagactctcatgtgcagcctctggattcaccttc............agtgaccactacatgagctgggtccgccaggctcaagggaaagggctagagttggtaggtttaataagaaacaaagctaacagttacacgacagaatatgctgcgtctgtgaaa...ggcagacttaccatctcaagagaggattcaaagaacacgctgtatctgcaaatgagcagcctgaaaaccgaggacttggccgtgtattactgtgctaga >IGHV3/OR15-7*05 gaggtgcagctggtggagtctggggga...ggcttggtccagcctgggggttctctgagactctcatgtgcagcctctggattcaccttc............agtgaccactacatgagctgggtccgccaggctcaagggaaagggctagagttggtaggtttaataagaaacaaagctaacagttacacgacagaatatgctgcgtctgtgaaa...ggcagacttaccatctcaagagaggattcaaagaacacgctgtatctgcaaatgagcaacctgaaaaccgaggacttggccgtgtattactgtgctagaga >IGHV3/OR16-10*01 gaggttcagctggtgcagtctggggga...ggcttggtacatcctggggggtccctgagactctcctgtgcaggctctggattcaccttc............agtagctatgctatgcactgggttcgccaggctccaggaaaaggtctggagtgggtatcagctattggtactggt.........ggtggcacatactatgcagactccgtgaag...ggccgattcaccatctccagagacaatgccaagaactccttgtatcttcaaatgaacagcctgagagccgaggacatggctgtgtattactgtgcaaga >IGHV3/OR16-10*02 gaggttcagctggtgcagtctggggga...ggcttggtacagcctggggggtccctgagactctcctgtgcaggctctggattcaccttc............agtagctatgctatgcactgggttcgccaggctccaggaaaaggtctggagtgggtatcagctattggtactggt.........ggtggcacatactatgcagactccgtgaag...ggccgattcaccatctccagagacaatgccaagaactccttgtatcttcaaatgaacagcctgagagccgaggacatggctgtgtattactgtgcaaga >IGHV3/OR16-10*03 gaggtgcagctggtggagtctggggga...ggcttggtacagcctggggggtccctgagactctcctgtgcaggctctggattcaccttc............agtagctatgctatgcactgggttcgccaggctccaggaaaaggtctggagtgggtatcagctattggtactggt.........ggtggcacatactatgcagactccgtgaag...ggccgattcaccatctccagagacaatgccaagaactccttgtatcttcaaatgaacagcctgagagccgaggacatggctgtgtattactgtgcaagaga >IGHV3/OR16-12*01 gaggtgcagctggtagagtctgggaga...ggcttggcccagcctggggggtacctaaaactctccggtgcagcctctggattcaccgtc............ggtagctggtacatgagctggatccaccaggctccagggaagggtctggagtgggtctcatacattagtagtagt......ggttgtagcacaaactacgcagactctgtgaag...ggcagattcaccatctccacagacaactcaaagaacacgctctacctgcaaatgaacagcctgagagtggaggacacggccgtgtattactgtgcaaga >IGHV3/OR16-13*01 gaggtgcagctggtggagtctggggga...ggcttagtacagcctggagggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctactggatgcactgggtccgccaagctccagggaaggggctggtgtgggtctcacgtattaatagtgat......gggagtagcacaagctacgcagactccatgaag...ggccaattcaccatctccagagacaatgctaagaacacgctgtatctgcaaatgaacagtctgagagctgaggacatggctgtgtattactgtactaga >IGHV3/OR16-14*01 gaggtgcagctggaggagtctggggga...ggcttagtacagcctggagggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctactggatgcactgggtccgccaatctccagggaaggggctggtgtgagtctcacgtattaatagtgat......gggagtagcacaagctacgcagactccttgaag...ggccaattcaccatctccagagacaatgctaagaacacgctgtatctgcaaatgaacagtctgagagctgaggacatggctgtgtattactgtactaga >IGHV3/OR16-15*01 gaagtgcagctggtggagtctggggga...ggcttggtccagcctggggggtccctgagactctcctgtgcagcctctgtattcaccttc............agtaacagtgacataaactgggtcctctaggctccaggaaaggggctggagtgggtctcgggtattagttggaat......ggcggtaagacgcactatgtggactccgtgaag...ggccaattttccatctccagagacaattccagcaagtccctgtatctgcaaaagaacagacagagagccaaggacatggccgtgtattactgtgtgagaaa >IGHV3/OR16-15*02 gaggtgcagctggtggagtctggggga...ggcttggtccagcctggggggtccctgagacactcctgtgcagcctctggattcaccttc............agtaacagtgacatgaactgggtcctctaggctccaggaaaggggctggagtgggtctcgggtattagttggaat......ggcggtaagacgcactatgtggactccgtgaag...ggccaatttaccatctccagagacaattccagcaagtccctgtatctgcaaaagaacagacagagagccaaagacatggccgtgtattactgtgtgaga >IGHV3/OR16-16*01 gaggtgcagctggtggagtctggggga...ggcttggtccagcctggggggtccctgagacactcctgtgcagcctctggattcaccttc............agtaacagtgacatgaactgggtcctctaggctccaggaaaggggctggagtgggtctcggatattagttggaat......ggcggtaagacgcactatgtggactccgtgaag...ggccaatttaccatctccagagacaattccagcaagtccctgtatctgcaaaagaacagacagagagccaaggacatggccgtgtattactgtgtgaga >IGHV3/OR16-6*02 gaggtgcagctggtggagtctgcggga...ggccttggtacagcctgggggtcccttagactctcctgtgcagcctctggattcacttgc............agtaacgcctggatgagctgggtccgccaggctccagggaaggggctggagtgggttggctgtattaaaagcaaagctaatggtgggacaacagactacgctgcacctgtgaaa...ggcagattcaccatctcaagagatgattcaaaaaacacgctgtatctgcaaatgatcagcctgaaaaccgaggacacggccgtgtattactgtaccacagg >IGHV3/OR16-8*01 gaggtgcagctggtggagtctggggga...ggcttggtacagcctggggggtccctgagactgtcctgtccagcctctggattcaccttc............agtaaccactacatgagctgggtccgccaggctccagggaagggactggagtgggtttcatacattagtggtgat......agtggttacacaaactacgcagactctgtgaag...ggccgattcaccatctccagggacaacgccaataactcaccgtatctgcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgtgaaa >IGHV3/OR16-8*02 gaggtgcagctggtggagtctggggga...ggcttggtacagcctggggggtccctgagactgtcctgtccagactctggattcaccttc............agtaaccactacatgagctgggtccgccaggctccagggaagggactggagtggatttcatacattagtggtgat......agtggttacacaaactacgcagactctgtgaag...ggccgattcaccatctccagggacaacgccaataactcaccgtatctgcaaatgaacagcttgagagctgaggacacggctgtgtattactgtgtgaaaca >IGHV3/OR16-9*01 gaggtgcagctggtggagtctggagga...ggcttggtacagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agtaaccactacacgagctgggtccgccaggctccagggaagggactggagtgggtttcatacagtagtggtaat......agtggttacacaaactacgcagactctgtgaaa...ggccgattcaccatctccagggacaacgccaagaactcactgtatctgcaaatgaacagcctgagagccgaggacacggctgtgtattactgtgtgaaa >IGHV4-28*01 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggacaccctgtccctcacctgcgctgtctctggttactccatcagc.........agtagtaactggtggggctggatccggcagcccccagggaagggactggagtggattgggtacatctattatagt.........gggagcacctactacaacccgtccctcaag...agtcgagtcaccatgtcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgtggacacggccgtgtattactgtgcgagaaa >IGHV4-28*02 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcacagaccctgtccctcacctgcgctgtctctggttactccatcagc.........agtagtaactggtggggctggatccggcagcccccagggaagggactggagtggattgggtacatctattatagt.........gggagcatctactacaacccgtccctcaag...agtcgagtcaccatgtcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgtggacacggccgtgtattactgtgcgagaaa >IGHV4-28*03 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggacaccctgtccctcacctgcgctgtctctggttactccatcagc.........agtagtaactggtggggctggatccggcagcccccagggaagggactggagtggattgggtacatctattatagt.........gggagcacctactacaacccgtccctcaag...agtcgagtcaccatgtcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgtggacacggccgtgtattactgtgcgagaga >IGHV4-28*04 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggacaccctgtccctcacctgcgctgtctctggttactccatcagc.........agtagtaactggtggggctggatccggcagcccccagggaagggactggagtggattgggtacatctattatagt.........gggagcacctactacaacccgtccctcaag...agtcgagtcaccatgtcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgtggacaccggcgtgtattactgtgcgaga >IGHV4-28*05 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggacaccctgtccctcacctgcgctgtctctggttactccatcagc.........agtagtaactggtggggctggatccggcagcccccagggaagggactggagtggattgggtacatctattatagt.........gggagcatctactacaacccgtccctcaag...agtcgagtcaccatgtcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgtggacacggccgtgtattactgtgcgagaaa >IGHV4-28*06 caggtgcagctacaggagtcgggccca...ggactggtgaagccttcggacaccctgtccctcacctgcgctgtctctggttactccatcagc.........agtagtaactggtggggctggatccggcagcccccagggaagggactggagtggattgggtacatctattatagt.........gggagcaccaactacaacccgtccctcaag...agtcgagtcaccatgtcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccttggacacggccgtgtattactgtgcgagaaa >IGHV4-28*07 caggtacagctgcaggagtcgggccca...ggactggtgaagccttcggacaccctgtccctcacctgcgctgtctctggttactccatcagc.........agtagtaactggtggggctggatccggcagcccccagggaagggactggagtggattgggtacatctattatagt.........gggagcacctactacaacccgtccctcaag...agtcgagtcaccatgtcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgtggacacggccgtgtattactgtgcgagaaa >IGHV4-30-2*01 cagctgcagctgcaggagtccggctca...ggactggtgaagccttcacagaccctgtccctcacctgcgctgtctctggtggctccatcagc......agtggtggttactcctggagctggatccggcagccaccagggaagggcctggagtggattgggtacatctatcatagt.........gggagcacctactacaacccgtccctcaag...agtcgagtcaccatatcagtagacaggtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggccgtgtattactgtgccagaga >IGHV4-30-2*02 cagctgcagctgcaggagtccggctca...ggactggtgaagccttcacagaccctgtccctcacctgcgctgtctctggtggctccatcagc......agtggtggttactcctggagctggatccggcagccaccagggaagggcctggagtggattgggtacatctatcatagt.........gggagcacctactacaacccgtccctcaag...agtcgagtcaccatatcagtagacaggtccaagaaccagttctccctgaagctgagctctgtgaccgctgcggacacggccgtgtattactgtgcg >IGHV4-30-2*03 cagctgcagctgcaggagtccggctca...ggactggtgaagccttcacagaccctgtccctcacctgcgctgtctctggtggctccatcagc......agtggtggttactcctggagctggatccggcagccaccagggaagggcctggagtggattgggagtatctattatagt.........gggagcacctactacaacccgtccctcaag...agtcgagtcaccatatccgtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgctgcagacacggctgtgtattactgtgcgagaca >IGHV4-30-2*04 ...........................................................................tctggtggctccatcagc......agtggtggttactcctggagctggatccggcagccaccagggaagggcctggagtggattgggtacatctatcatagt.........gggagcacctactacaacccgtccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcagacacggccgtgtattactgtgcgagaga >IGHV4-30-2*05 cagctgcagctgcaggagtccggctca...ggactggtgaagccttcacagaccctgtccctcacctgcgctgtctctggtggctccatcagc......agtggtggttactcctggagctggatccggcagccaccagggaagggcctggagtggattgggtacatctatcatagt.........gggagcacctactacaacccgtccctcaag...agtcgagttaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgactgccgcagacacggccgtgtattactgtgccagaga >IGHV4-30-2*06 cagctgcagctgcaggagtccggctca...ggactggtgaagccttcacagaccctgtccctcacctgcgctgtctctggtggctccatcagc......agtggtggttactcctggagctggatccggcagtcaccagggaagggcctggagtggattgggtacatctatcatagt.........gggagcacctactacaacccgtccctcaag...agtcgagtcaccatatcagtagacaggtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggccgtgtattactgtgccagaga >IGHV4-30-4*01 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcacagaccctgtccctcacctgcactgtctctggtggctccatcagc......agtggtgattactactggagttggatccgccagcccccagggaagggcctggagtggattgggtacatctattacagt.........gggagcacctactacaacccgtccctcaag...agtcgagttaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgactgccgcagacacggccgtgtattactgtgccagaga >IGHV4-30-4*02 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggacaccctgtccctcacctgcactgtctctggtggctccatcagc......agtggtgattactactggagttggatccgccagcccccagggaagggcctggagtggattgggtacatctattacagt.........gggagcacctactacaacccgtccctcaag...agtcgagttaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgactgcagcagacacggccgtgtattactgtgccagaga >IGHV4-30-4*03 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcacagaccctgtccctcacctgcactgtctctggtggctccatcagc......agtggtgattactactggagttggatccgccagcccccagggaagggcctggagtggattgggtacatctattacagt.........gggagcacctactacaacccgtccctcaag...agtcgagttaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgactgccgcggacacggccgtgtattactg >IGHV4-30-4*04 caggtgcagctgcaggactcgggccca...ggactggtgaagccttcacagaccctgtccctcacctgcactgtctctggtggctccatcagc......agtggtgattactactggagttggatccgccagcccccagggaagggcctggagtggattgggtacttctattacagt.........gggagcacctactacaacccgtccctcaag...agtcgagttaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgactgccgcagacacggccgtgtattactg >IGHV4-30-4*05 ..........................................................................ctctggtggctccatcagc......agtggtgattactactggagttggatccgccagcncccagggaagggcctggagtggattgggtacatctattacagt.........gggagcacctactacaacccgtccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgactgccgcagacacggccgtgtattactgtgccagaga >IGHV4-30-4*06 ...........................................................................tctggtggctccatcagc......agtggtgattactactggagttggatccgccagcacccagggaagggcctggagtggattgggtacatctattacagt.........gggagcacctactacaacccgtccctcaag...agtcgagttaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgactgccgcagacacggccgtgtattactgtgccagaga >IGHV4-30-4*07 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcacagaccctgtccctcacctgcgctgtctctggtggctccatcagc......agtggtggttactcctggagctggatccggcagccaccagggaagggactggagtggattgggtatatctattacagt.........gggagcacctactacaacccgtccctcaag...agtcgagttaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggccgtgtattactgtgccagaga >IGHV4-31*01 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcacagaccctgtccctcacctgcactgtctctggtggctccatcagc......agtggtggttactactggagctggatccgccagcacccagggaagggcctggagtggattgggtacatctattacagt.........gggagcacctactacaacccgtccctcaag...agtctagttaccatatcagtagacacgtctaagaaccagttctccctgaagctgagctctgtgactgccgcggacacggccgtgtattactgtgcgagaga >IGHV4-31*02 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcacagaccctgtccctcacctgtactgtctctggtggctccatcagc......agtggtggttactactggagctggatccgccagcacccagggaagggcctggagtggattgggtacatctattacagt.........gggagcacctactacaacccgtccctcaag...agtcgagttaccatatcagtagacacgtctaagaaccagttctccctgaagctgagctctgtgactgccgcggacacggccgtgtattactgtgcgagaga >IGHV4-31*03 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcacagaccctgtccctcacctgcactgtctctggtggctccatcagc......agtggtggttactactggagctggatccgccagcacccagggaagggcctggagtggattgggtacatctattacagt.........gggagcacctactacaacccgtccctcaag...agtcgagttaccatatcagtagacacgtctaagaaccagttctccctgaagctgagctctgtgactgccgcggacacggccgtgtattactgtgcgagaga >IGHV4-31*04 caggtgcggctgcaggagtcgggccca...ggactggtgaagccttcacagaccctgtccctcacctgcactgtctctggtggctccatcagc......agtggtggttactactggagctggatccgccagcacccagggaagggcctggagtggattgggtacatctattacagt.........gggagcacctactacaacccgtccctcaag...agtcgagttaccatatcagtagacacgtctaagaaccagttctccctgaagctgagctctgtgactgccgcggacacggccgtgtattactgtgcg >IGHV4-31*05 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcacagaccctgtccctcacctgcactgtctctggtggctccatcagc......agtggtggttactactggagctggatccgccagcacccagggaagggcctggagtggattgggtacatctattacagt.........gggagcacctactacaacccgtccctcaag...agtcgagttaccatatcagtagacacgtctaagaaccagttctccctgaagctgagctctgtgacc...gcggacgcggccgtgtattactgtgcg >IGHV4-31*06 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcacagaccctgtccctcacctgcactgtctctggtggctccatcagc......agtggtagttactactggagctggatccgccagcacccagggaagggcctggagtggattgggtacatctattacagt.........gggagcacctactacaacccgtccctcaag...agtcgagttaccatatcagtagacacgtctaagaaccagttctccctgaagctgagctctgtgactgccgcggacacggccgtgtattactg >IGHV4-31*07 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcacagaccctgtccctcacctgcactgtctctggtggatccatcagc......agtggtggttactactggagctggatccgccagcacccagggaagggcctggagtggattgggtacatctattacagt.........gggagcacctactacaacccgtccctcaag...agtcgagttaccatatcagtagacacgtctaagaaccagttctccctgaagctgagctctgtgactgccgcggacacggccgtgtattactg >IGHV4-31*08 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcacagaccctgtccctcacctgcactgtctctggtggctccatcagc......agtggtggttactactggagctggatccgccagcacccagggaagggcctggagtggattgggtacatctattacagt.........gggagcacctactacaacccgtccctcaag...agtcgagttaccatatccgtagacacgtccaagaaccagttctccctgaagctgagctctgtgactgccgcggacacggccgtgtattactg >IGHV4-31*09 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcacagaccctgtccctcacctgcactgtctctggtggctccatcagc......agtggtggttactactggagctggatccgccagcacccagggaagggcctggagtggattgggtacatctattacagt.........gggagcacctactacaacccgtccctcaag...agtcgagttaccatatcagtagacaagtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggccgtgtattactg >IGHV4-31*10 caggtgcagctgcaggagtcgggccca...ggactgttgaagccttcacagaccctgtccctcacctgcactgtctctggtggctccatcagc......agtggtggttactactggagctggatccgccagcacccagggaagggcctggagtggattgggtgcatctattacagt.........gggagcacctactacaacccgtccctcaag...agtcgagttaccatatcagtagacccgtccaagaaccagttctccctgaagccgagctctgtgactgccgcggacacggccgtggattactgtgcgagaga >IGHV4-34*01 caggtgcagctacagcagtggggcgca...ggactgttgaagccttcggagaccctgtccctcacctgcgctgtctatggtgggtccttc............agtggttactactggagctggatccgccagcccccagggaaggggctggagtggattggggaaatcaatcatagt.........ggaagcaccaactacaacccgtccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggctgtgtattactgtgcgagagg >IGHV4-34*02 caggtgcagctacaacagtggggcgca...ggactgttgaagccttcggagaccctgtccctcacctgcgctgtctatggtgggtccttc............agtggttactactggagctggatccgccagcccccagggaaggggctggagtggattggggaaatcaatcatagt.........ggaagcaccaactacaacccgtccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggctgtgtattactgtgcgagagg >IGHV4-34*03 caggtgcagctacagcagtggggcgca...ggactgttgaagccttcggagaccctgtccctcacctgcgctgtctatggtgggtccttc............agtggttactactggagctggatccgccagcccccagggaaggggctggagtggattggggaaatcaatcatagt.........ggaagcaccaactacaacccgtccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggccgtgtattactg >IGHV4-34*04 caggtgcagctacagcagtggggcgca...ggactgttgaagccttcggagaccctgtccctcacctgcgctgtctatggtgggtccttc............agtggttactactggagctggatccgccagcccccagggaaggggctggagtggattggggaaatcaatcatagt.........ggaagcaccaacaacaacccgtccctcaag...agtcgagccaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggctgtgtattactgtgcgagagg >IGHV4-34*05 caggtgcagctacagcagtggggcgca...ggactgttgaagccttcggagaccctgtccctcacctgcgctgtctatggtgggtccttc............agtggttactactggtgctggatccgccagcccctagggaaggggctggagtggattggggaaatcaatcatagt.........ggaagcaccaacaacaacccgtccctcaag...agtcgagccaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggctgtgtattactgtgcgagagg >IGHV4-34*06 caggtgcagctacagcagtggggcgca...ggactgttgaagccttcggagaccctgtccctcacctgcgctgtctatggtgggtccttc............agtggttactactggagctggatccgccagcccccagggaaggggctggagtggattggggaaatcaatcatagt.........ggaagcaccaactacaacccgtccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttctccctgaagctgggctctgtgaccgccgcggacacggccgtgtattactg >IGHV4-34*07 caggtgcagctacagcagtggggcgca...ggactgttgaagccttcggagaccctgtccctcacctgcgctgtctatggtgggtccttc............agtggttactactggagctggatccgccagcccccagggaaggggctggagtggattggggaaatcaaccatagt.........ggaagcaccaactacaacccgtccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggccgtgtattactg >IGHV4-34*08 caggtgcagctacagcagtggggcgca...ggactgttgaagccttcggagaccctgtccctcacctgcgctgtctatggtgggaccttc............agtggttactactggagctggatccgccagcccccagggaaggggctggagtggattggggaaatcaatcatagt.........ggaagcaccaactacaacccgtccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggctgtgtattactgtgcg >IGHV4-34*09 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcacagaccctgtccctcacctgcgctgtctatggtgggtccttc............agtggttactactggagctggatccgccagcccccagggaagggactggagtggattggggaaatcaatcatagt.........ggaagcaccaactacaacccgtccctcaag...agtcgagttaccatatcagtagacacgtctaagaaccagttctccctgaagctgagctctgtgactgccgcggacacggccgtgtattactgtgcgagaga >IGHV4-34*10 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcgctgtctatggtgggtccttc............agtggttactactggagctggatccgccagcccccagggaagggactggagtggattggggaaatcaatcatagt.........ggaagcaccaactacaacccgtccctcaag...agtcgaatcaccatgtcagtagacacgtccaagaaccagttctacctgaagctgagctctgtgaccgccgcggacacggccgtgtattactgtgcgagata >IGHV4-34*11 caggtgcagctacagcagtggggcgca...ggactgttgaagccttcggagaccctgtccctcacctgcgctgtctatggtgggtccgtc............agtggttactactggagctggatccggcagcccccagggaaggggctggagtggattgggtatatctattatagt.........gggagcaccaacaacaacccctccctcaag...agtcgagccaccatatcagtagacacgtccaagaaccagttctccctgaacctgagctctgtgaccgccgcggacacggccgtgtattgctgtgcgagaga >IGHV4-34*12 caggtgcagctacagcagtggggcgca...ggactgttgaagccttcggagaccctgtccctcacctgcgctgtctatggtgggtccttc............agtggttactactggagctggatccgccagcccccagggaaggggctggagtggattggggaaatcattcatagt.........ggaagcaccaactacaacccgtccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggctgtgtattactgtgcgaga >IGHV4-34*13 ...........................................................................tatggtgggtccttc............agtggttactactggagctggatccgccagcccccagggaaggggctggagtggattggggaaatcaatcatagt.........ggaagcaccaactacaacccctccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggctgtgtattactgtgcgagagg >IGHV4-38-2*01 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcgctgtctctggttactccatcagc.........agtggttactactggggctggatccggcagcccccagggaaggggctggagtggattgggagtatctatcatagt.........gggagcacctactacaacccgtccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcagacacggccgtgtattactgtgcgaga >IGHV4-38-2*02 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcactgtctctggttactccatcagc.........agtggttactactggggctggatccggcagcccccagggaaggggctggagtggattgggagtatctatcatagt.........gggagcacctactacaacccgtccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcagacacggccgtgtattactgtgcgagaga >IGHV4-39*01 cagctgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcactgtctctggtggctccatcagc......agtagtagttactactggggctggatccgccagcccccagggaaggggctggagtggattgggagtatctattatagt.........gggagcacctactacaacccgtccctcaag...agtcgagtcaccatatccgtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcagacacggctgtgtattactgtgcgagaca >IGHV4-39*02 cagctgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcactgtctctggtggctccatcagc......agtagtagttactactggggctggatccgccagcccccagggaaggggctggagtggattgggagtatctattatagt.........gggagcacctactacaacccgtccctcaag...agtcgagtcaccatatccgtagacacgtccaagaaccacttctccctgaagctgagctctgtgaccgccgcagacacggctgtgtattactgtgcgagaga >IGHV4-39*03 cagctgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcactgtctctggtggctccatcagc......agtagtagttactactggggctggatccgccagcccccagggaaggggctggagtggattgggagtatctattatagt.........gggagcacctactacaacccgtccctcaag...agtcgagtcaccatatccgtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcagacacggccgtgtattactg >IGHV4-39*04 ..................................................................................gctccatcagc......agtagtagttactactggggctggatccgccagcccccagggaaggggctggagtggattgggagtatctattatagt.........gggagcacctactacaacccgtccctcaag...agtcgagtcaccatatccgtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacac >IGHV4-39*05 cagctgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccccgtccctcacctgcactgtctctggtggctccatcagc......agtagtagttactactggggctggatccgccagcccccagggaaggggctggagtggattgggagtatctattatagt.........gggagcacctactacaacccgtccctcaag...agtcgagtcaccatatccgtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcagacacggctgtgtattactgtgcg >IGHV4-39*06 cggctgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcactgtctctggtggctccatcagc......agtagtagttactactggggctggatccgccagcccccagggaaggggctggagtggattgggagtatctattatagt.........gggagcacctactacaacccgtccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttccccctgaagctgagctctgtgaccgccgcggacacggccgtgtattactgtgcgagaga >IGHV4-39*07 cagctgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcactgtctctggtggctccatcagc......agtagtagttactactggggctggatccgccagcccccagggaaggggctggagtggattgggagtatctattatagt.........gggagcacctactacaacccgtccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggccgtgtattactgtgcgagaga >IGHV4-4*01 caggtgcagctgcaggagtcgggccca...ggactggtgaagcctccggggaccctgtccctcacctgcgctgtctctggtggctccatcagc.........agtagtaactggtggagttgggtccgccagcccccagggaaggggctggagtggattggggaaatctatcatagt.........gggagcaccaactacaacccgtccctcaag...agtcgagtcaccatatcagtagacaagtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggccgtgtattgctgtgcgagaga >IGHV4-4*02 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggggaccctgtccctcacctgcgctgtctctggtggctccatcagc.........agtagtaactggtggagttgggtccgccagcccccagggaaggggctggagtggattggggaaatctatcatagt.........gggagcaccaactacaacccgtccctcaag...agtcgagtcaccatatcagtagacaagtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggccgtgtattactgtgcgagaga >IGHV4-4*03 caggtgcagctgcaggagtcgggccca...ggactggtgaagcctccggggaccctgtccctcacctgcgctgtctctggtggctccatcagc.........agtagtaactggtggagttgggtccgccagcccccagggaaggggctggagtggattggggaaatctatcatagt.........gggagcaccaactacaacccgtccctcaag...agtcgagtcaccatatcagtagacaagtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggccgtgtattactg >IGHV4-4*04 caggtgcagctgcaggagtcgggccca...ggactggtgaagcctccggggaccctgtccctcacctgcgctatctctggtggctccatcagc.........agtagtaactggtggagttgggtccgccagcccccagggaaggggctggagtggattggggaaatctatcatagt.........gggagcaccaactacaacccgtccctcaag...agtcgagtcaccatatcagtagacaagtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggccgtgtattactg >IGHV4-4*05 caggtgcagctgcaggagttgggccca...ggactggtgaagcctccggggaccctgtccctcacctgcgctgtctctggtggctccatcagc.........agtagtaactggtggagttgggtccgccagcccccagggaaggggctggagtggattggggaaatctatcatagt.........gggagcaccaactacaacccgtccctcaag...agtcgagtcaccatatcagtagacaagtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggccgtgtattactg >IGHV4-4*06 ...........................................................................tctggtggctccatcagc.........agtagtaactggtggagttgggtccgccagcccccagggannnggctggagtggattggggaaatctatcatagt.........gggagcaccaactacaacccgtccctcaag...agtcgagtcaccatgtcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggccgtgtattactgtgcgagaga >IGHV4-4*07 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcactgtctctggtggctccatc............agtagttactactggagctggatccggcagcccgccgggaagggactggagtggattgggcgtatctataccagt.........gggagcaccaactacaacccctccctcaag...agtcgagtcaccatgtcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggccgtgtattactgtgcgagaga >IGHV4-4*08 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcactgtctctggtggctccatc............agtagttactactggagctggatccggcagcccccagggaagggactggagtggattgggtatatctataccagt.........gggagcaccaactacaacccctccctcaag...agtcgagtcaccatatccgtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcagacacggccgtgtattactgtgcgagaga >IGHV4-55*01 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcatctgcgctgtctctggtgactccatcagc.........agtggtaactggtgaatctgggtccgccagcccccagggaaggggctggagtggattggggaaatccatcatagt.........gggagcacctactacaacccgtccctcaag...agtcgaatcaccatgtccgtagacacgtccaagaaccagttctacctgaagctgagctctgtgaccgccgcggacacggccgtgtattactgtgcgagata >IGHV4-55*02 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcatctgcgctgtctctggtgactccatcagc.........agtggtaactggtgaatctgggtccgccagcccccagggaaggggctggagtggattggggaaatccatcatagt.........gggagcacctactacaacccgtccctcaag...agtcgaatcaccatgtcagtagacacgtccaagaaccagttctacctgaagctgagctctgtgaccgccgcggacacggccgtgtattactgtgcgagata >IGHV4-55*03 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcatctgcgctgtctctggtgactccatcagc.........agtggtaactggtgaatctgggtccgccagcccccagggaaggggctggagtggattggggaaatccatcatagt.........gggagcacctactacaacccgtccctcaag...agtcgaatcaccatgtcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggccgtgtattactg >IGHV4-55*04 caggtgcagctgcaggagtcgggccca...ggactggtgaagctttcggagaccctgtccctcatctgcgctgtctctggtgactccatcagc.........agtggtaactggtgaatctgggtccgccagcccccagggaaggggctggagtggattggggaaatccatcatagt.........gggagcacctactacaacccgtccctcaag...agtcgaatcaccatgtcagtagacacgtccaagaaccagttctacctgaagctgagctctgtgaccgccgcggacacggccgtgtattactg >IGHV4-55*05 caggtgcagctgcaggagtcgggccca...ggactggtgaagctttcggagaccctgtccctcatctgcgctgtctctggtgactccatcagc.........agtggtaactggtgaatctgggtccgccagcccccagggaaggggctggagtggattggggaaatccatcatagt.........gggagcacctactacaacccgtccctcaag...agtcgaatcaccatgtccgtagacacgtccaagaaccagttctacctgaagctgagctctgtgaccgccgcggacacggccgtgtattactg >IGHV4-55*06 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcatctgcgctgtctctggtgactccatcagc.........agtggtaactggtgaatctgggtccgccagcccccagggaaggggctggagtggattggggaaatccatcatagt.........gggagcacctactacaacccgtccctcaag...agtcgaatcaccatgtccgtagacacgtccaagaagcagttctacctgaagctgagctctgtgaccgctgcggacacggccgtgtattactg >IGHV4-55*07 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcatctgcgctgtctctggtgactccatcagc.........agtggtaactggtgaatctgggtccgccagcccccagggaaggggctggagtggattggggaaatccatcatagt.........gggagcacctactacaacccgtccctcaag...agtcgaatcaccatgtccgtagacacgtccaggaaccagttctccctgaagctgagctctgtgaccgccgcagacacggccgtgtattactg >IGHV4-55*08 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcatctgcgctgtctctggtgactccatcagc.........agtggtaactggtgaatctgggtccgccagcccccagggaaggggctggagtggattggggaaatccatcatagt.........gggagcacctactacaacccgtccctcaag...agtcgaatcaccatgtcagtagacacgtccaagaaccagttctacctgaagctgagctctgtgaccgccgcggacacggccgtgtattactgtgcgagaga >IGHV4-55*09 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcatctgcgctgtctctggtgactccatcagc.........agtggtaactggtgaatctgggtccgccagcccccagggaaggggctggagtggattggggaaatccatcatagt.........gggagcacctactacaacccgtccctcaag...agtcgaatcaccatgtccgtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgtggacacggccgtgtattactgtgcgagaaa >IGHV4-59*01 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcactgtctctggtggctccatc............agtagttactactggagctggatccggcagcccccagggaagggactggagtggattgggtatatctattacagt.........gggagcaccaactacaacccctccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgctgcggacacggccgtgtattactgtgcgagaga >IGHV4-59*02 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcactgtctctggtggctccgtc............agtagttactactggagctggatccggcagcccccagggaagggactggagtggattgggtatatctattacagt.........gggagcaccaactacaacccctccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgctgcggacacggccgtgtattactgtgcgagaga >IGHV4-59*03 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcactgtctctggtggctccatc............agtagttactactggagctggatccggcagcccccagggaagggactggagtggattgggtatatctattacagt.........gggagcaccaactacaacccctccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccaattctccctgaagctgagctctgtgaccgctgcggacacggccgtgtattactgtgcg >IGHV4-59*04 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcactgtctctggtggctccatc............agtagttactactggagctggatccggcagcccccagggaagggactggagtggattgggtatatctattatagt.........gggagcacctactacaacccgtccctcaag...agtcgagtcaccatgtcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcagacacggctgtgtattactgtgcg >IGHV4-59*05 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcactgtctctggtggctccatc............agtagttactactggagctggatccggcagccgccggggaagggactggagtggattgggcgtatctattatagt.........gggagcacctactacaacccgtccctcaag...agtcgagtcaccatatccgtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcagacacggctgtgtattactgtgcg >IGHV4-59*06 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcactgtcactggtggctccatc............agtagttactactggagctggatccggcagcccgctgggaagggcctggagtggattgggtacatctattacagt.........gggagcacctactacaacccgtccctcaag...agtcgagttaccatatcagtagacacgtctaagaaccagttctccctgaagctgagctctgtgactgccgcggacacggccgtgtattactgtgcg >IGHV4-59*07 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggacaccctgtccctcacctgcactgtctctggtggctccatc............agtagttactactggagctggatccggcagcccccagggaagggactggagtggattgggtatatctattacagt.........gggagcaccaactacaacccctccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgctgcggacacggccgtgtattactgtgcgaga >IGHV4-59*08 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcactgtctctggtggctccatc............agtagttactactggagctggatccggcagcccccagggaagggactggagtggattgggtatatctattacagt.........gggagcaccaactacaacccctccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcagacacggccgtgtattactgtgcgagaca >IGHV4-59*09 ...........................................................................tctggtggctccatc............agtagttactactggagctggatccggcagcccccaggnannngactggagtggattgggtatatctattacagt.........gggagcaccaactacaacccctccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgctgcggacacggccgtgtattactgtgcgagagg >IGHV4-59*10 caggtgcagctacagcagtggggcgca...ggactgttgaagccttcggagaccctgtccctcacctgcgctgtctatggtggctccatc............agtagttactactggagctggatccggcagcccgccgggaaggggctggagtggattgggcgtatctataccagt.........gggagcaccaactacaacccctccctcaag...agtcgagtcaccatgtcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggccgtgtattactgtgcgagata >IGHV4-61*01 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcactgtctctggtggctccgtcagc......agtggtagttactactggagctggatccggcagcccccagggaagggactggagtggattgggtatatctattacagt.........gggagcaccaactacaacccctccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgctgcggacacggccgtgtattactgtgcgagaga >IGHV4-61*02 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcacagaccctgtccctcacctgcactgtctctggtggctccatcagc......agtggtagttactactggagctggatccggcagcccgccgggaagggactggagtggattgggcgtatctataccagt.........gggagcaccaactacaacccctccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcagacacggccgtgtattactgtgcgagaga >IGHV4-61*03 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcactgtctctggtggctccgtcagc......agtggtagttactactggagctggatccggcagcccccagggaagggactggagtggattgggtatatctattacagt.........gggagcaccaactacaacccctccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccacttctccctgaagctgagctctgtgaccgctgcggacacggccgtgtattactgtgcgagaga >IGHV4-61*04 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcactgtctctggtggctccgtcagc......agtggtagttactactggagctggatccggcagcccccagggaagggactggagtggattggatatatctattacagt.........gggagcaccaactacaacccctccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgct...gacacggccgtgtattactg >IGHV4-61*05 cagctgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcactgtctctggtggctccatcagc......agtagtagttactactggggctggatccggcagcccccagggaagggactggagtggattgggtatatctattacagt.........gggagcaccaactacaacccctccctcaag...agtcgagtcaccatatcagtagacaagtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggccgtgtattactgtgcgaga >IGHV4-61*06 ...........................................................................tctggtggctccgtcagc......agtggtagttactactggagctggatccggcagcccccagggaagggactggagtggattgggtatatctattacagt.........gggagcaccaactacaacccctccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggccgtgtattactgtgccagaga >IGHV4-61*07 ...........................................................................tctggtggctccgtcagc......agtggtagttactactggagctggatccggcagcccccagggaagggactggagtggattgggtatatctattacagt.........gggagcaccaactacaacccctccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgctgcggacacggccgtgtattactgtgcgagaca >IGHV4-61*08 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcactgtctctggtggctccgtcagc......agtggtggttactactggagctggatccggcagcccccagggaagggactggagtggattgggtatatctattacagt.........gggagcaccaactacaacccctccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgctgcggacacggccgtgtattactgtgcgagaga >IGHV4/OR15-8*01 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcgttgtctctggtggctccatcagc.........agtagtaactggtggagctgggtccgccagcccccagggaaggggctggagtggattggggaaatctatcatagt.........gggagccccaactacaacccgtccctcaag...agtcgagtcaccatatcagtagacaagtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggccgtgtattactgtgcgagaga >IGHV4/OR15-8*02 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcgttgtctctggtggctccatcagc.........agtagtaactggtggagctgggtccgccagcccccagggaaggggctggagtggattggggaaatctatcatagt.........gggaaccccaactacaacccgtccctcaag...agtcgagtcaccatatcaatagacaagtccaagaaccaattctccctgaagctgagctctgtgaccgccgcggacacggccgtgtattactgtgcgagaga >IGHV4/OR15-8*03 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcgttgtctctggtggctccatcagc.........agtagtaactggtggagctgggtccgccagcccccagggaaggggctggagtggattggggaaatctatcatagt.........gggagccccaactacaacccatccctcaag...agtcgagtcaccatatcagtagacaagtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggccgtgtattactgtgcgagaga >IGHV5-10-1*01 gaagtgcagctggtgcagtctggagca...gaggtgaaaaagcccggggagtctctgaggatctcctgtaagggttctggatacagcttt............accagctactggatcagctgggtgcgccagatgcccgggaaaggcctggagtggatggggaggattgatcctagt......gactcttataccaactacagcccgtccttccaa...ggccacgtcaccatctcagctgacaagtccatcagcactgcctacctgcagtggagcagcctgaaggcctcggacaccgccatgtattactgtgcgaga >IGHV5-10-1*02 gaagtgcagctggtgcagtctggagca...gaggtgaaaaagcccggggagtctctgaggatctcctgtaagggttctggatacagcttt............accagctactggatcagctgggtgcgccagatgcccgggaaaggcttggagtggatggggaggattgatcctagt......gactcttataccaactacagcccgtccttccaa...ggccacgtcaccatctcagctgacaagtccatcagcactgcctacctgcagtggagcagcctgaaggc.tcggacaccgccatgtattactgtgcgagaca >IGHV5-10-1*03 gaagtgcagctggtgcagtccggagca...gaggtgaaaaagcccggggagtctctgaggatctcctgtaagggttctggatacagcttt............accagctactggatcagctgggtgcgccagatgcccgggaaaggcctggagtggatggggaggattgatcctagt......gactcttataccaactacagcccgtccttccaa...ggccacgtcaccatctcagctgacaagtccatcagcactgcctacctgcagtggagcagcctgaaggcctcggacaccgccatgtattactgtgcgaga >IGHV5-10-1*04 gaagtgcagctggtgcagtctggagca...gaggtgaaaaagcccggggagtctctgaggatctcctgtaagggttctggatacagcttt............accagctactggatcagctgggtgcgccagatgcccgggaaaggcctggagtggatggggaggattgatcctagt......gactcttataccaactacagcccgtccttccaa...ggccaggtcaccatctcagctgacaagtccatcagcactgcctacctgcagtggagcagcctgaaggcctcggacaccgccatgtattactgtgcgaga >IGHV5-51*01 gaggtgcagctggtgcagtctggagca...gaggtgaaaaagcccggggagtctctgaagatctcctgtaagggttctggatacagcttt............accagctactggatcggctgggtgcgccagatgcccgggaaaggcctggagtggatggggatcatctatcctggt......gactctgataccagatacagcccgtccttccaa...ggccaggtcaccatctcagccgacaagtccatcagcaccgcctacctgcagtggagcagcctgaaggcctcggacaccgccatgtattactgtgcgagaca >IGHV5-51*02 gaggtgcagctggtgcagtctggagca...gaggtgaaaaagcccggggagtctctgaagatctcctgtaagggttctggatacagcttt............accagctactggaccggctgggtgcgccagatgcccgggaaaggcttggagtggatggggatcatctatcctggt......gactctgataccagatacagcccgtccttccaa...ggccaggtcaccatctcagccgacaagtccatcagcaccgcctacctgcagtggagcagcctgaaggcctcggacaccgccatgtattactgtgcgagaca >IGHV5-51*03 gaggtgcagctggtgcagtctggagca...gaggtgaaaaagccgggggagtctctgaagatctcctgtaagggttctggatacagcttt............accagctactggatcggctgggtgcgccagatgcccgggaaaggcctggagtggatggggatcatctatcctggt......gactctgataccagatacagcccgtccttccaa...ggccaggtcaccatctcagccgacaagtccatcagcaccgcctacctgcagtggagcagcctgaaggcctcggacaccgccatgtattactgtgcgaga >IGHV5-51*04 gaggtgcagctggtgcagtctggagca...gaggtgaaaaagccgggggagtctctgaagatctcctgtaagggttctggatacagcttt............accagctactggatcggctgggtgcgccagatgcccgggaaaggcctggagtggatggggatcatctatcctggt......gactctgataccagatacagcccgtccttccaa...ggccaggtcaccatctcagccgacaagcccatcagcaccgcctacctgcagtggagcagcctgaaggcctcggacaccgccatgtattactgtgcgaga >IGHV5-51*05 .....................................aaaagcccggggagtctctgaagatctcctgtaagggttctggatacagcttt............accagctactggatcggctgggtgcgccagatgcccaggaaaggcctggagtggatggggatcatctatcctggt......gactctgataccagatacagcccgtccttccaa...ggccaggtcaccatctcagccgacaagtccatcagcaccgcctacctgcagtggagcagcctgaaggcctcggacaccgccatg >IGHV5-78*01 gaggtgcagctgttgcagtctgcagca...gaggtgaaaagacccggggagtctctgaggatctcctgtaagacttctggatacagcttt............accagctactggatccactgggtgcgccagatgcccgggaaagaactggagtggatggggagcatctatcctggg......aactctgataccagatacagcccatccttccaa...ggccacgtcaccatctcagccgacagctccagcagcaccgcctacctgcagtggagcagcctgaaggcctcggacgccgccatgtattattgtgtgaga >IGHV6-1*01 caggtacagctgcagcagtcaggtcca...ggactggtgaagccctcgcagaccctctcactcacctgtgccatctccggggacagtgtctct......agcaacagtgctgcttggaactggatcaggcagtccccatcgagaggccttgagtggctgggaaggacatactacaggtcc...aagtggtataatgattatgcagtatctgtgaaa...agtcgaataaccatcaacccagacacatccaagaaccagttctccctgcagctgaactctgtgactcccgaggacacggctgtgtattactgtgcaagaga >IGHV6-1*02 caggtacagctgcagcagtcaggtccg...ggactggtgaagccctcgcagaccctctcactcacctgtgccatctccggggacagtgtctct......agcaacagtgctgcttggaactggatcaggcagtccccatcgagaggccttgagtggctgggaaggacatactacaggtcc...aagtggtataatgattatgcagtatctgtgaaa...agtcgaataaccatcaacccagacacatccaagaaccagttctccctgcagctgaactctgtgactcccgaggacacggctgtgtattactgtgcaagaga >IGHV7-34-1*01 ...ctgcagctggtgcagtctgggcct...gaggtgaagaagcctggggcctcagtgaaggtctcctataagtcttctggttacaccttc............accatctatggtatgaattgggtatgatagacccctggacagggctttgagtggatgtgatggatcatcacctac......actgggaacccaacgtatacccacggcttcaca...ggatggtttgtcttctccatggacacgtctgtcagcacggcgtgtcttcagatcagcagcctaaaggctgaggacacggccgagtattactgtgcgaagta >IGHV7-34-1*02 ...ctgcagctggtgcagtctgggcct...gaggtgaagaagcctggggcctcagtgaaggtctcctataagtcttctggttacaccttc............accatctatggtatgaattgggtatgatagacccctggacagggctttgagtggatgtgatggatcatcacctac......aatgggaacccaacgtatacccacggcttcaca...ggatggtttgtcttctccatggacacgtctgtcagcacggcgtgtcttcagatcagcagcctaaaggctgaggacacggccgagtattactgtgcgaagta >IGHV7-4-1*01 caggtgcagctggtgcaatctgggtct...gagttgaagaagcctggggcctcagtgaaggtttcctgcaaggcttctggatacaccttc............actagctatgctatgaattgggtgcgacaggcccctggacaagggcttgagtggatgggatggatcaacaccaac......actgggaacccaacgtatgcccagggcttcaca...ggacggtttgtcttctccttggacacctctgtcagcacggcatatctgcagatctgcagcctaaaggctgaggacactgccgtgtattactgtgcgaga >IGHV7-4-1*02 caggtgcagctggtgcaatctgggtct...gagttgaagaagcctggggcctcagtgaaggtttcctgcaaggcttctggatacaccttc............actagctatgctatgaattgggtgcgacaggcccctggacaagggcttgagtggatgggatggatcaacaccaac......actgggaacccaacgtatgcccagggcttcaca...ggacggtttgtcttctccttggacacctctgtcagcacggcatatctgcagatcagcagcctaaaggctgaggacactgccgtgtattactgtgcgagaga >IGHV7-4-1*03 caggtgcagctggtgcaatctgggtct...gagttgaagaagcctggggcctcagtgaaggtttcctgcaaggcttctggatacaccttc............actagctatgctatgaattgggtgcgacaggcccctggacaagggcttgagtggatgggatggatcaacaccaac......actgggaacccaacgtatgcccagggcttcaca...ggacggtttgtcttctccttggacacctctgtcagcacggcatatctgcagatcagcacgctaaaggctgaggacactg >IGHV7-4-1*04 caggtgcagctggtgcaatctgggtct...gagttgaagaagcctggggcctcagtgaaggtttcctgcaaggcttctggatacaccttc............actagctatgctatgaattgggtgcgacaggcccctggacaagggcttgagtggatgggatggatcaacaccaac......actgggaacccaacgtatgcccagggcttcaca...ggacggtttgtcttctccttggacacctctgtcagcatggcatatctgcagatcagcagcctaaaggctgaggacactgccgtgtattactgtgcgagaga >IGHV7-4-1*05 caggtgcagctggtgcaatctgggtct...gagttgaagaagcctggggcctcagtgaaggtttcctgcaaggcttctggatacaccttc............actagctatgctatgaattgggtgcgacaggcccctggacaagggcttgagtggatgggatggatcaacaccaac......actgggaacccaacgtatgcccagggcttcaca...ggacggtttgtcttctccttggacacctctgtcagcatggcatatctgcagatcagcagcctaaaggctgaggacactgccgtgtgttactgtgcgagaga >IGHV7-40*03 ttttcaatagaaaagtcaaataatcta...agtgtcaatcagtggatgattagataaaatatgatatatgtaaatcatggaatactatgc............agccagtatggtatgaattcagtgtgaccagcccctggacaagggcttgagtggatgggatggatcatcacctac......actgggaacccaacatataccaacggcttcaca...ggacggtttctattctccatggacacctctgtcagcatggcgtatctgcagatcagcagcctaaaggctgaggacacggccgtgtatgactgtatgagaga >IGHV7-81*01 caggtgcagctggtgcagtctggccat...gaggtgaagcagcctggggcctcagtgaaggtctcctgcaaggcttctggttacagtttc............accacctatggtatgaattgggtgccacaggcccctggacaagggcttgagtggatgggatggttcaacacctac......actgggaacccaacatatgcccagggcttcaca...ggacggtttgtcttctccatggacacctctgccagcacagcatacctgcagatcagcagcctaaaggctgaggacatggccatgtattactgtgcgagata
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/shm_csr/baseline/IMGTVHreferencedataset20161215.fasta Fri Feb 19 15:08:51 2021 +0000 @@ -0,0 +1,1 @@ +>IGHV1-18*01 caggttcagctggtgcagtctggagct...gaggtgaagaagcctggggcctcagtgaaggtctcctgcaaggcttctggttacaccttt............accagctatggtatcagctgggtgcgacaggcccctggacaagggcttgagtggatgggatggatcagcgcttac......aatggtaacacaaactatgcacagaagctccag...ggcagagtcaccatgaccacagacacatccacgagcacagcctacatggagctgaggagcctgagatctgacgacacggccgtgtattactgtgcgagaga >IGHV1-18*02 caggttcagctggtgcagtctggagct...gaggtgaagaagcctggggcctcagtgaaggtctcctgcaaggcttctggttacaccttt............accagctatggtatcagctgggtgcgacaggcccctggacaagggcttgagtggatgggatggatcagcgcttac......aatggtaacacaaactatgcacagaagctccag...ggcagagtcaccatgaccacagacacatccacgagcacagcctacatggagctgaggagcctaagatctgacgacacggcc >IGHV1-18*03 caggttcagctggtgcagtctggagct...gaggtgaagaagcctggggcctcagtgaaggtctcctgcaaggcttctggttacaccttt............accagctatggtatcagctgggtgcgacaggcccctggacaagggcttgagtggatgggatggatcagcgcttac......aatggtaacacaaactatgcacagaagctccag...ggcagagtcaccatgaccacagacacatccacgagcacagcctacatggagctgaggagcctgagatctgacgacatggccgtgtattactgtgcgagaga >IGHV1-18*04 caggttcagctggtgcagtctggagct...gaggtgaagaagcctggggcctcagtgaaggtctcctgcaaggcttctggttacaccttt............accagctacggtatcagctgggtgcgacaggcccctggacaagggcttgagtggatgggatggatcagcgcttac......aatggtaacacaaactatgcacagaagctccag...ggcagagtcaccatgaccacagacacatccacgagcacagcctacatggagctgaggagcctgagatctgacgacacggccgtgtattactgtgcgagaga >IGHV1-2*01 caggtgcagctggtgcagtctggggct...gaggtgaagaagcctggggcctcagtgaaggtctcctgcaaggcttctggatacaccttc............accggctactatatgcactgggtgcgacaggcccctggacaagggcttgagtggatgggacggatcaaccctaac......agtggtggcacaaactatgcacagaagtttcag...ggcagggtcaccagtaccagggacacgtccatcagcacagcctacatggagctgagcaggctgagatctgacgacacggtcgtgtattactgtgcgagaga >IGHV1-2*02 caggtgcagctggtgcagtctggggct...gaggtgaagaagcctggggcctcagtgaaggtctcctgcaaggcttctggatacaccttc............accggctactatatgcactgggtgcgacaggcccctggacaagggcttgagtggatgggatggatcaaccctaac......agtggtggcacaaactatgcacagaagtttcag...ggcagggtcaccatgaccagggacacgtccatcagcacagcctacatggagctgagcaggctgagatctgacgacacggccgtgtattactgtgcgagaga >IGHV1-2*03 caggtgcagctggtgcagtctggggct...gaggtgaagaagcttggggcctcagtgaaggtctcctgcaaggcttctggatacaccttc............accggctactatatgcactgggtgcnacaggcccctggacaagggcttgagtggatgggatggatcaaccctaac......agtggtggcacaaactatgcacagaagtttcag...ggcagggtcaccatgaccagggacacgtccatcagcacagcctacatggagctgagcaggctgagatctgacgacacggccgtgtattactgtgcgagaga >IGHV1-2*04 caggtgcagctggtgcagtctggggct...gaggtgaagaagcctggggcctcagtgaaggtctcctgcaaggcttctggatacaccttc............accggctactatatgcactgggtgcgacaggcccctggacaagggcttgagtggatgggatggatcaaccctaac......agtggtggcacaaactatgcacagaagtttcag...ggctgggtcaccatgaccagggacacgtccatcagcacagcctacatggagctgagcaggctgagatctgacgacacggccgtgtattactgtgcgagaga >IGHV1-2*05 caggtgcagctggtgcagtctggggct...gaggtgaagaagcctggggcctcagtgaaggtctcctgcaaggcttctggatacaccttc............accggctactatatgcactgggtgcgacaggcccctggacaagggcttgagtggatgggacggatcaaccctaac......agtggtggcacaaactatgcacagaagtttcag...ggcagggtcaccatgaccagggacacgtccatcagcacagcctacatggagctgagcaggctgagatctgacgacacggtcgtgtattactgtgcgagaga >IGHV1-24*01 caggtccagctggtacagtctggggct...gaggtgaagaagcctggggcctcagtgaaggtctcctgcaaggtttccggatacaccctc............actgaattatccatgcactgggtgcgacaggctcctggaaaagggcttgagtggatgggaggttttgatcctgaa......gatggtgaaacaatctacgcacagaagttccag...ggcagagtcaccatgaccgaggacacatctacagacacagcctacatggagctgagcagcctgagatctgaggacacggccgtgtattactgtgcaacaga >IGHV1-3*01 caggtccagcttgtgcagtctggggct...gaggtgaagaagcctggggcctcagtgaaggtttcctgcaaggcttctggatacaccttc............actagctatgctatgcattgggtgcgccaggcccccggacaaaggcttgagtggatgggatggatcaacgctggc......aatggtaacacaaaatattcacagaagttccag...ggcagagtcaccattaccagggacacatccgcgagcacagcctacatggagctgagcagcctgagatctgaagacacggctgtgtattactgtgcgagaga >IGHV1-3*02 caggttcagctggtgcagtctggggct...gaggtgaagaagcctggggcctcagtgaaggtttcctgcaaggcttctggatacaccttc............actagctatgctatgcattgggtgcgccaggcccccggacaaaggcttgagtggatgggatggagcaacgctggc......aatggtaacacaaaatattcacaggagttccag...ggcagagtcaccattaccagggacacatccgcgagcacagcctacatggagctgagcagcctgagatctgaggacatggctgtgtattactgtgcgagaga >IGHV1-38-4*01 caggtccagctggtgcagtcttgggct...gaggtgaggaagtctggggcctcagtgaaagtctcctgtagtttttctgggtttaccatc............accagctacggtatacattgggtgcaacagtcccctggacaagggcttgagtggatgggatggatcaaccctggc......aatggtagcccaagctatgccaagaagtttcag...ggcagattcaccatgaccagggacatgtccacaaccacagcctacacagacctgagcagcctgacatctgaggacatggctgtgtattactatgcaagaca >IGHV1-45*01 cagatgcagctggtgcagtctggggct...gaggtgaagaagactgggtcctcagtgaaggtttcctgcaaggcttccggatacaccttc............acctaccgctacctgcactgggtgcgacaggcccccggacaagcgcttgagtggatgggatggatcacacctttc......aatggtaacaccaactacgcacagaaattccag...gacagagtcaccattactagggacaggtctatgagcacagcctacatggagctgagcagcctgagatctgaggacacagccatgtattactgtgcaagana >IGHV1-45*02 cagatgcagctggtgcagtctggggct...gaggtgaagaagactgggtcctcagtgaaggtttcctgcaaggcttccggatacaccttc............acctaccgctacctgcactgggtgcgacaggcccccggacaagcgcttgagtggatgggatggatcacacctttc......aatggtaacaccaactacgcacagaaattccag...gacagagtcaccattaccagggacaggtctatgagcacagcctacatggagctgagcagcctgagatctgaggacacagccatgtattactgtgcaagata >IGHV1-45*03 .....................................agaagactgggtcctcagtgaaggtttcctgcaaggcttccggatacaccttc............acctaccgctacctgcactgggtgcgacaggcccccagacaagcgcttgagtggatgggatggatcacacctttc......aatggtaacaccaactacgcacagaaattccag...gacagagtcaccattaccagggacaggtctatgagcacagcctacatggagctgagcagcctgagatctgaggacacagccatgtattactgtgcaaga >IGHV1-46*01 caggtgcagctggtgcagtctggggct...gaggtgaagaagcctggggcctcagtgaaggtttcctgcaaggcatctggatacaccttc............accagctactatatgcactgggtgcgacaggcccctggacaagggcttgagtggatgggaataatcaaccctagt......ggtggtagcacaagctacgcacagaagttccag...ggcagagtcaccatgaccagggacacgtccacgagcacagtctacatggagctgagcagcctgagatctgaggacacggccgtgtattactgtgcgagaga >IGHV1-46*02 caggtgcagctggtgcagtctggggct...gaggtgaagaagcctggggcctcagtgaaggtttcctgcaaggcatctggatacaccttc............aacagctactatatgcactgggtgcgacaggcccctggacaagggcttgagtggatgggaataatcaaccctagt......ggtggtagcacaagctacgcacagaagttccag...ggcagagtcaccatgaccagggacacgtccacgagcacagtctacatggagctgagcagcctgagatctgaggacacggccgtgtattactgtgcgagaga >IGHV1-46*03 caggtgcagctggtgcagtctggggct...gaggtgaagaagcctggggcctcagtgaaggtttcctgcaaggcatctggatacaccttc............accagctactatatgcactgggtgcgacaggcccctggacaagggcttgagtggatgggaataatcaaccctagt......ggtggtagcacaagctacgcacagaagttccag...ggcagagtcaccatgaccagggacacgtccacgagcacagtctacatggagctgagcagcctgagatctgaggacacggccgtgtattactgtgctagaga >IGHV1-58*01 caaatgcagctggtgcagtctgggcct...gaggtgaagaagcctgggacctcagtgaaggtctcctgcaaggcttctggattcaccttt............actagctctgctgtgcagtgggtgcgacaggctcgtggacaacgccttgagtggataggatggatcgtcgttggc......agtggtaacacaaactacgcacagaagttccag...gaaagagtcaccattaccagggacatgtccacaagcacagcctacatggagctgagcagcctgagatccgaggacacggccgtgtattactgtgcggcaga >IGHV1-58*02 caaatgcagctggtgcagtctgggcct...gaggtgaagaagcctgggacctcagtgaaggtctcctgcaaggcttctggattcaccttt............actagctctgctatgcagtgggtgcgacaggctcgtggacaacgccttgagtggataggatggatcgtcgttggc......agtggtaacacaaactacgcacagaagttccag...gaaagagtcaccattaccagggacatgtccacaagcacagcctacatggagctgagcagcctgagatccgaggacacggccgtgtattactgtgcggcaga >IGHV1-68*01 caggtgcagctggggcagtctgaggct...gaggtaaagaagcctggggcctcagtgaaggtctcctgcaaggcttccggatacaccttc............acttgctgctccttgcactggttgcaacaggcccctggacaagggcttgaaaggatgagatggatcacactttac......aatggtaacaccaactatgcaaagaagttccag...ggcagagtcaccattaccagggacatgtccctgaggacagcctacatagagctgagcagcctgagatctgaggactcggctgtgtattactgggcaagata >IGHV1-69*01 caggtgcagctggtgcagtctggggct...gaggtgaagaagcctgggtcctcggtgaaggtctcctgcaaggcttctggaggcaccttc............agcagctatgctatcagctgggtgcgacaggcccctggacaagggcttgagtggatgggagggatcatccctatc......tttggtacagcaaactacgcacagaagttccag...ggcagagtcacgattaccgcggacgaatccacgagcacagcctacatggagctgagcagcctgagatctgaggacacggccgtgtattactgtgcgagaga >IGHV1-69*02 caggtccagctggtgcaatctggggct...gaggtgaagaagcctgggtcctcggtgaaggtctcctgcaaggcttctggaggcaccttc............agcagctatactatcagctgggtgcgacaggcccctggacaagggcttgagtggatgggaaggatcatccctatc......cttggtatagcaaactacgcacagaagttccag...ggcagagtcacgattaccgcggacaaatccacgagcacagcctacatggagctgagcagcctgagatctgaggacacggccgtgtattactgtgcgaga >IGHV1-69*03 caggtgcagctggtgcagtctggggct...gaggtgaagaagcctgggtcctcggtgaaggtctcctgcaaggcttctggaggcaccttc............agcagctatgctatcagctgggtgcgacaggcccctggacaagggcttgagtggatgggagggatcatccctatc......tttggtacagcaaactacgcacagaagttccag...ggcagagtcacgattaccgcggacgaatccacgagcacagcctacatggagctgagcagcctgagatctgatgacacggc >IGHV1-69*04 caggtccagctggtgcagtctggggct...gaggtgaagaagcctgggtcctcggtgaaggtctcctgcaaggcttctggaggcaccttc............agcagctatgctatcagctgggtgcgacaggcccctggacaagggcttgagtggatgggaaggatcatccctatc......cttggtatagcaaactacgcacagaagttccag...ggcagagtcacgattaccgcggacaaatccacgagcacagcctacatggagctgagcagcctgagatctgaggacacggccgtgtattactgtgcgagaga >IGHV1-69*05 caggtccagctggtgcagtctggggct...gaggtgaagaagcctgggtcctcggtgaaggtctcctgcaaggcttctggaggcaccttc............agcagctatgctatcagctgggtgcgacaggcccctggacaagggcttgagtggatgggagggatcatccctatc......tttggtacagcaaactacgcacagaagttccag...ggcagagtcacgattaccacggacgaatccacgagcacagcctacatggagctgagcagcctgagatctgaggacacggccgtgtattactgtgcgaga >IGHV1-69*06 caggtgcagctggtgcagtctggggct...gaggtgaagaagcctgggtcctcggtgaaggtctcctgcaaggcttctggaggcaccttc............agcagctatgctatcagctgggtgcgacaggcccctggacaagggcttgagtggatgggagggatcatccctatc......tttggtacagcaaactacgcacagaagttccag...ggcagagtcacgattaccgcggacaaatccacgagcacagcctacatggagctgagcagcctgagatctgaggacacggccgtgtattactgtgcgagaga >IGHV1-69*07 .....................................agaagcctgggtcctcggtgaaggtctcctgcaaggcttctggaggcaccttc............agcagctatgctatcagctgggtgcgacaggcccctggacaagggcttgagtggatgggaaggatcatccctatc......tttggtacagcaaactacgcacagaagttccag...ggcagagtcacgattaccgcggacgaatccacgagcacagcctacatggagctgagcagcctgagatctgag >IGHV1-69*08 caggtccagctggtgcaatctggggct...gaggtgaagaagcctgggtcctcggtgaaggtctcctgcaaggcttctggaggcaccttc............agcagctatactatcagctgggtgcgacaggcccctggacaagggcttgagtggatgggaaggatcatccctatc......cttggtacagcaaactacgcacagaagttccag...ggcagagtcacgattaccgcggacaaatccacgagcacagcctacatggagctgagcagcctgagatctgaggacacggccgtgtattactgtgcgagaga >IGHV1-69*09 caggtgcagctggtgcagtctggggct...gaggtgaagaagcctgggtcctcggtgaaggtctcctgcaaggcttctggaggcaccttc............agcagctatgctatcagctgggtgcgacaggcccctggacaagggcttgagtggatgggaaggatcatccctatc......cttggtatagcaaactacgcacagaagttccag...ggcagagtcacgattaccgcggacaaatccacgagcacagcctacatggagctgagcagcctgagatctgaggacacggccgtgtattactgtgcgagaga >IGHV1-69*10 caggtccagctggtgcagtctggggct...gaggtgaagaagcctgggtcctcagtgaaggtctcctgcaaggcttctggaggcaccttc............agcagctatgctatcagctgggtgcgacaggcccctggacaagggcttgagtggatgggagggatcatccctatc......cttggtatagcaaactacgcacagaagttccag...ggcagagtcacgattaccgcggacaaatccacgagcacagcctacatggagctgagcagcctgagatctgaggacacggccgtgtattactgtgcgagaga >IGHV1-69*11 caggtccagctggtgcagtctggggct...gaggtgaagaagcctgggtcctcggtgaaggtctcctgcaaggcttctggaggcaccttc............agcagctatgctatcagctgggtgcgacaggcccctggacaagggcttgagtggatgggaaggatcatccctatc......cttggtacagcaaactacgcacagaagttccag...ggcagagtcacgattaccgcggacgaatccacgagcacagcctacatggagctgagcagcctgagatctgaggacacggccgtgtattactgtgcgagaga >IGHV1-69*12 caggtccagctggtgcagtctggggct...gaggtgaagaagcctgggtcctcggtgaaggtctcctgcaaggcttctggaggcaccttc............agcagctatgctatcagctgggtgcgacaggcccctggacaagggcttgagtggatgggagggatcatccctatc......tttggtacagcaaactacgcacagaagttccag...ggcagagtcacgattaccgcggacgaatccacgagcacagcctacatggagctgagcagcctgagatctgaggacacggccgtgtattactgtgcgagaga >IGHV1-69*13 caggtccagctggtgcagtctggggct...gaggtgaagaagcctgggtcctcagtgaaggtctcctgcaaggcttctggaggcaccttc............agcagctatgctatcagctgggtgcgacaggcccctggacaagggcttgagtggatgggagggatcatccctatc......tttggtacagcaaactacgcacagaagttccag...ggcagagtcacgattaccgcggacgaatccacgagcacagcctacatggagctgagcagcctgagatctgaggacacggccgtgtattactgtgcgagaga >IGHV1-69*14 caggtccagctggtgcagtctggggct...gaggtgaagaagcctgggtcctcggtgaaggtctcctgcaaggcttctggaggcaccttc............agcagctatgctatcagctgggtgcgacaggcccctggacaagggcttgagtggatgggagggatcatccctatc......tttggtacagcaaactacgcacagaagttccag...ggcagagtcacgattaccgcggacaaatccacgagcacagcctacatggagctgagcagcctgagatctgaggacacggccgtgtattactgtgcgagaga >IGHV1-69-2*01 gaggtccagctggtacagtctggggct...gaggtgaagaagcctggggctacagtgaaaatctcctgcaaggtttctggatacaccttc............accgactactacatgcactgggtgcaacaggcccctggaaaagggcttgagtggatgggacttgttgatcctgaa......gatggtgaaacaatatacgcagagaagttccag...ggcagagtcaccataaccgcggacacgtctacagacacagcctacatggagctgagcagcctgagatctgaggacacggccgtgtattactgtgcaacaga >IGHV1-69-2*02 .....................................agaagcctggggctacagtgaaaatctcctgcaaggtttctggatacaccttc............accgactactacatgcactgggtgcaacaggcccctggaaaagggcttgagtggatgggacttgttgatcctgaa......gatggtgaaacaatatatgcagagaagttccag...ggcagagtcaccataaccgcggacacgtctacagacacagcctacatggagctgagcagcctgagatctgag >IGHV1-69D*01 caggtgcagctggtgcagtctggggct...gaggtgaagaagcctgggtcctcggtgaaggtctcctgcaaggcttctggaggcaccttc............agcagctatgctatcagctgggtgcgacaggcccctggacaagggcttgagtggatgggagggatcatccctatc......tttggtacagcaaactacgcacagaagttccag...ggcagagtcacgattaccgcggacgaatccacgagcacagcctacatggagctgagcagcctgagatctgaggacacggccgtgtattactgtgcgagaga >IGHV1-8*01 caggtgcagctggtgcagtctggggct...gaggtgaagaagcctggggcctcagtgaaggtctcctgcaaggcttctggatacaccttc............accagttatgatatcaactgggtgcgacaggccactggacaagggcttgagtggatgggatggatgaaccctaac......agtggtaacacaggctatgcacagaagttccag...ggcagagtcaccatgaccaggaacacctccataagcacagcctacatggagctgagcagcctgagatctgaggacacggccgtgtattactgtgcgagagg >IGHV1-8*02 caggtgcagctggtgcagtctggggct...gaggtgaagaagcctggggcctcagtgaaggtctcctgcaaggcttctggatacaccttc............accagctatgatatcaactgggtgcgacaggccactggacaagggcttgagtggatgggatggatgaaccctaac......agtggtaacacaggctatgcacagaagttccag...ggcagagtcaccatgaccaggaacacctccataagcacagcctacatggagctgagcagcctgagatctgaggacacggccgtgtattactgtgcgagagg >IGHV1-NL1*01 caggttcagctgttgcagcctggggtc...caggtgaagaagcctgggtcctcagtgaaggtctcctgctaggcttccagatacaccttc............accaaatactttacacggtgggtgtgacaaagccctggacaagggcatnagtggatgggatgaatcaacccttac......aacgataacacacactacgcacagacgttctgg...ggcagagtcaccattaccagtgacaggtccatgagcacagcctacatggagctgagcngcctgagatccgaagacatggtcgtgtattactgtgtgagaga >IGHV1/OR15-1*01 caggtgcagctggtgcagtctggggct...gaggtgaagaagcctggggcctcagtgaaggtctcctgcaaggcttctggatacatcttc............accgactactatatgcactgggtgcgacaggcccctggacaagagcttgggtggatgggacggatcaaccctaac......agtggtggcacaaactatgcacagaagtttcag...ggcagagtcaccatgaccagggacacgtccatcagcacagcctacacggagctgagcagcctgagatctgaggacacggccacgtattactgtgcgaga >IGHV1/OR15-1*02 caggtgcagctggtgcagtctggggct...gaggtgaagaagcctggggcctcagtgaaggtctcctgcaaggcttctggatacatcttc............accgactactatatgcactgggtgcgacaggcccctggacaagagcttgggtggatgggacggatcaaccctaac......agtggtggcacaaactatgcacagaagtttcag...ggcagagtcaccatgaccagggacacgtccatcagcacagcctgcacggagctgagcagcctgagatctgaggacacggccacgtattactgtgcgagaga >IGHV1/OR15-1*03 caggtgcagctggtgcagtctggggct...gaggtgaagaagcctggggcctcagtgaaggtctcctgcaaggcttctggatacatcttc............accgactactatatgcactgggtgcgacaggcccctggacaagagcttgggtggatgggacggatcaaccctaac......agtggtggcacaaactatgcacagaagtttcag...ggcagagtcaccatgaccagggacacgtccatcagcacagcctacacggagctgagcagcctgagatctgaggacacagccacgtattactgtgcgagaga >IGHV1/OR15-1*04 caggtgcagctggtgcagtctggggct...gaggtgaagaagcctggggcctcagtgaaggtctcctgcaaggcttctggatacatcttc............accgactactatatgcactgggtgcgacaggcccctggacaagagcttgggtggatgggacggatcaaccctaac......agtggtggcacaaactatgcacagaagtttcag...ggcagagtcaccatgaccagggacacgtccatcagcacagcctacatggagctgagcagcctgagatctgaggacacggccacgtattactgtgcgagaga >IGHV1/OR15-2*01 caggtgcagctggtgcagtctggagct...gaggtgaagaagcctagagcctcagtgaaggtctcctgcaaggcttctggttacaccttt............accagctactatatgcactgggtgtgacaggcccctgaacaagggcttgagtggatgggatggatcaacacttac......aatggtaacacaaactacccacagaagctccag...ggcagagtcaccatgaccagagacacatccacgagcacagcctacatggagctgagcaggctgagatctgacgacatggccgtgtattactgtgcgagaga >IGHV1/OR15-2*02 caggtgcagctggtgcagtctggagct...gaggtgaagaagcctggagcctcagtgaaggtctcctgcaaggcttctggttacaccttt............accagctactatatgcactgggtgtgacaggcccctgaacaagggcttgagtggatgggatggatcaacacttac......aatggtaacacaaactacccacagaagctccag...ggcagagtcaccatgaccagagacacatccacgagcacagcctacatggagctgagcagcctgagatctgacgacatggccgtgtattactgtgcgagaga >IGHV1/OR15-2*03 caggtgcagctggtgcagtctggagct...gaggtgaagaagcctagagcctcagtgaaggtctcctgcaaggcttctggttacaccttt............accagctactatatgcactgggtgtgacaggcccctgaacaagggcttgagtggatgggatggatcaacacttac......aatggtaacacaaactacccacagaagctccag...ggcagagtcaccatgaccagagacacatccacgagcacagcctacatggagctgagcagcctgagatctgacgacatggccgtgtattactgtgcgagaga >IGHV1/OR15-3*01 caggtccaactggtgtagtctggagct...gaggtgaagaagcctggggcctcagtgaaggtctcctgcaaggcttctggatacaccttc............accgactactttatgaactggatgcgccaggcccctggacaaaggcttgagtggatgggatggatcaacgctggc......aatggtaacacaaaatattcacagaagctccag...ggcagagtcaccattaccagggacacatcttcgagcacagcctacatgcagctgagcagcctgagatctgaggacacggccgtgtattactgtgcgaga >IGHV1/OR15-3*02 caggtccaactggtgtagtctggagct...gaggtgaagaagcctggggcctcagtgaaggtctcctgcaaggcttctggatacaccttc............accgactactttatgaactggatgcgccaggcccctggacaaaggcttgagtggatgggatggatcaacgctggc......aatggtaacacaaaatattcacagaagctccag...ggcagagtcaccattaccagggacacatctgcgagcacagcctacatgcagctgagcagcctgagatctgaggacacggccgtgtattactgtgcgagaga >IGHV1/OR15-3*03 caggtccaactggtgtagtctggagct...gaggtgaagaagcctggggcctcagtgaaggtctcctgcaaggcttctggatacaccttc............accagctactatatgaactggatgcgccaggcccctggacaaggcttcgagtggatgggatggatcaacgctggc......aatggtaacacaaagtattcacagaagctccag...ggcagagtcaccattaccagggacacatctgcgagcacagcctacatgcagctgagcagcctgagatctgaggacacggccgtgtattactgtgcgaga >IGHV1/OR15-4*01 caggaccagttggtgcagtctggggct...gaggtgaagaagcctctgtcctcagtgaaggtctccttcaaggcttctggatacaccttc............accaacaactttatgcactgggtgtgacaggcccctggacaaggacttgagtggatgggatggatcaatgctggc......aatggtaacacaacatatgcacagaagttccag...ggcagagtcaccataaccagggacacgtccatgagcacagcctacacggagctgagcagcctgagatctgaggacacggccgtgtattactgtgcgaga >IGHV1/OR15-5*01 .....................................agaagcctggggcctcagtgaaggtctcctgcaaggcttctggatacaccttc............accagctactgtatgcactgggtgcaccaggtccatgcacaagggcttgagtggatgggattggtgtgccctagt......gatggcagcacaagctatgcacagaagttccag...gccagagtcaccataaccagggacacatccatgagcacagcctacatggagctaagcagtctgagatctgaggacacggccatgtattactgtgtgaga >IGHV1/OR15-5*02 caggtacagctggtgcagtctggggct...gaggtgaagaagcctggggcctcagtgaaggtctcctgcaaggcttctggatacaccttc............accaactactgtatgcactgggtgcgccaggtccatgcacaagggcttgagtggatgggattggtgtgccctagt......gatggcagcacaagctatgcacaaaagttccag...gccagagtcaccataaccagggacacatccatgagcacagcctacatggagctaagcagtctgagatctgaggacacggccatgtattactgtgtgaga >IGHV1/OR15-9*01 caggtacagctgatgcagtctggggct...gaggtgaagaagcctggggcctcagtgaggatctcctgcaaggcttctggatacaccttc............accagctactgtatgcactgggtgtgccaggcccatgcacaagggcttgagtggatgggattggtgtgccctagt......gatggcagcacaagctatgcacagaagttccag...ggcagagtcaccataaccagggacacatccatgggcacagcctacatggagctaagcagcctgagatctgaggacacggccatgtattactgtgtgagaga >IGHV1/OR21-1*01 caggtacagctggtgcagtctggggct...gaggtgaagaagcctggggcctcagtgaaggtctcctgcaaggcttctggatacaccatc............accagctactgtatgcactgggtgcaccaggtccatgcacaagggcttgagtggatgggattggtgtgccctagt......gatggcagcacaagctatgcacagaagttccag...gccagagtcaccataaccagggacacatccatgagcacagcctacatggagctaagcagtctgagatctgaggacacggccatgtattactgtgtgagaga >IGHV2-10*01 caggtcaccttgaaggagtctggtcct...gcactggtgaaacccacacagaccctcatgctgacctgcaccttctctgggttctcactcagc......acttctggaatgggtgtgggttagatctgtcagccctcagcaaaggccctggagtggcttgcacacatttattagaat.........gataataaatactacagcccatctctgaag...agtaggctcattatctccaaggacacctccaagaatgaagtggttctaacagtgatcaacatggacattgtggacacagccacacattactgtgcaaggagac >IGHV2-26*01 caggtcaccttgaaggagtctggtcct...gtgctggtgaaacccacagagaccctcacgctgacctgcaccgtctctgggttctcactcagc......aatgctagaatgggtgtgagctggatccgtcagcccccagggaaggccctggagtggcttgcacacattttttcgaat.........gacgaaaaatcctacagcacatctctgaag...agcaggctcaccatctccaaggacacctccaaaagccaggtggtccttaccatgaccaacatggaccctgtggacacagccacatattactgtgcacggatac >IGHV2-5*01 cagatcaccttgaaggagtctggtcct...acgctggtgaaacccacacagaccctcacgctgacctgcaccttctctgggttctcactcagc......actagtggagtgggtgtgggctggatccgtcagcccccaggaaaggccctggagtggcttgcactcatttattggaat.........gatgataagcgctacagcccatctctgaag...agcaggctcaccatcaccaaggacacctccaaaaaccaggtggtccttacaatgaccaacatggaccctgtggacacagccacatattactgtgcacacagac >IGHV2-5*02 cagatcaccttgaaggagtctggtcct...acgctggtgaaacccacacagaccctcacgctgacctgcaccttctctgggttctcactcagc......actagtggagtgggtgtgggctggatccgtcagcccccaggaaaggccctggagtggcttgcactcatttattgggat.........gatgataagcgctacagcccatctctgaag...agcaggctcaccatcaccaaggacacctccaaaaaccaggtggtccttacaatgaccaacatggaccctgtggacacagccacatattactgtgcacacagac >IGHV2-5*03 ................................gctggtgaaacccacacagaccctcacgctgacctgcaccttctctgggttctcactcagc......actagtggagtgggtgtgggctggatccgtcagcccccaggaaaggccctggagtggcttgcactcatttattgggat.........gatgataagcgctacagcccatctctgaag...agcaggctcaccattaccaaggacacctccaaaaaccaggt >IGHV2-5*04 cagatcaccttgaaggagtctggtcct...acgctggtgaaacccacacagaccctcacgctgacctgcaccttctctgggttctcactcagc......actagtggagtgggtgtgggctggatccgtcagcccccaggaaaggccctggagtggcttgcactcatttattggaat.........gatgataagcgctacagcccatctctgaag...agcaggctcaccatcaccaaggacacctccaaaaaccaggtggtccttacaatgaccaacatggaccctgtggacacaggcacatattactgtgtac >IGHV2-5*05 cagatcaccttgaaggagtctggtcct...acgctggtgaaacccacacagaccctcacgctgacctgcaccttctctgggttctcactcagc......actagtggagtgggtgtgggctggatccgtcagcccccaggaaaggccctggagtggcttgcactcatttattgggat.........gatgataagcgctacggcccatctctgaag...agcaggctcaccatcaccaaggacacctccaaaaaccaggtggtccttacaatgaccaacatggaccctgtggacacagccacatattactgtgcacacagac >IGHV2-5*06 cagatcaccttgaaggagtctggtcct...acgctggtaaaacccacacagaccctcacgctgacctgcaccttctctgggttctcactcagc......actagtggagtgggtgtgggctggatccgtcagcccccaggaaaggccctggagtggcttgcactcatttattgggat.........gatgataagcgctacggcccatctctgaag...agcaggctcaccatcaccaaggacacctccaaaaaccaggtggtccttacaatgaccaacatggaccctgtggacacagccacatattactgtgcacacaga >IGHV2-5*08 caggtcaccttgaaggagtctggtcct...gcgctggtgaaacccacacagaccctcacactgacctgcaccttctctgggttctcactcagc......actagtggaatgcgtgtgagctggatccgtcagcccccaggaaaggccctggagtggcttgcactcatttattgggat.........gatgataagcgctacagcccatctctgaag...agcaggctcaccatcaccaaggacacctccaaaaaccaggtggtccttacaatgaccaacatggaccctgtggacacagccacatattactgtgcacacagac >IGHV2-5*09 caggtcaccttgaaggagtctggtcct...acgctggtgaaacccacacagaccctcacgctgacctgcaccttctctgggttctcactcagc......actagtggagtgggtgtgggctggatccgtcagcccccaggaaaggccctggagtggcttgcactcatttattgggat.........gatgataagcgctacggcccatctctgaag...agcaggctcaccatcaccaaggacacctccaaaaaccaggtggtccttacaatgaccaacatggaccctgtggacacagccacatattactgtgcacacagac >IGHV2-70*01 caggtcaccttgagggagtctggtcct...gcgctggtgaaacccacacagaccctcacactgacctgcaccttctctgggttctcactcagc......actagtggaatgtgtgtgagctggatccgtcagcccccagggaaggccctggagtggcttgcactcattgattgggat.........gatgataaatactacagcacatctctgaag...accaggctcaccatctccaaggacacctccaaaaaccaggtggtccttacaatgaccaacatggaccctgtggacacagccacgtattactgtgcacggatac >IGHV2-70*02 caggtcaccttgagggagtctggtcct...gcgctggtgaaacccacacagaccctcacactgacctgcaccttctctgggttctcactcagc......actagtggaatgtgtgtgagctggatccgtcagcccccagggaaggccctggagtggcttgcactcattgattgggat.........gatgataaatactacagcacatctctgaag...accaggctcaccatctccaaggacacctccaaaaaccaggtggtccttacaatgaccaacatggaccctgtggacacggccgtgtattactg >IGHV2-70*03 caggtcaccttgaaggagtctggtcct...gcgctggtgaaacccacacagaccctcacactgacctgcaccttctctgggttctcactcagc......actagtggaatgcgtgtgagctggatccgtcagcccccagggaaggccctggagtggcttgcacgcattgattgggat.........gatgataaattctacagcacatctctgaag...accaggctcaccatctccaaggacacctccaaaaaccaggtggtccttacaatgaccaacatggaccctgtggacacggccgtgtattactg >IGHV2-70*04 caggtcaccttgaaggagtctggtcct...gcgctggtgaaacccacacagaccctcacactgacctgcaccttctctgggttctcactcagc......actagtggaatgcgtgtgagctggatccgtcagcccccagggaaggccctggagtggcttgcacgcattgattgggat.........gatgataaattctacagcacatctctgaag...accaggctcaccatctccaaggacacctccaaaaaccaggtggtccttacaatgaccaacatggaccctgtggacacagccacgtattac >IGHV2-70*05 ..........................t...gcgctggtgaaacccacacagaccctcacactgacctgcaccttctctgggttctcactcagc......actagtggaatgcgtgcgagctggatccgtcagcccccagggaaggccctggagtggcttgcacgcattgattgggat.........gatgataaattctacagcacatctctgaag...accaggctcaccatctccaaggacacctccaaaaaccaggtggtccttacaatgaccaacatgga >IGHV2-70*06 caggtcaccttgaaggagtctggtcct...gcgctggtgaaacccacacagaccctcacactgacctgcaccttctctgggttctcactcagc......actagtggaatgcgtgtgagctggatccgtcagcccccagggaaggccctggagtggcttgcacgcattgattgggat.........gatgataaattctacagcacatccctgaag...accaggctcaccatctccaaggacacctccaaaaaccaggtggtccttacaatgaccaacatggaccctgtggacacggccgtgtattactg >IGHV2-70*07 caggtcaccttgagggagtctggtcct...gcgctggtgaaacccacacagaccctcacactgacctgcaccttctctgggttctcactcagc......actagtggaatgtgtgtgagctggatccgtcagcccccggggaaggccctggagtggcttgcactcattgattgggat.........gatgataaatactacagcacatctctgaag...accaggctcaccatctccaaggacacctccaaaaaccaggtggtccttacaatgaccaacatggaccctgtggacacggccgtgtattactg >IGHV2-70*08 caggtcaccttgagggagtctggtcct...gcgctggtgaaacccacacagaccctcacactgacctgcgccttctctgggttctcactcagc......actagtggaatgtgtgtgagctggatccgtcagcccccagggaaggccctggagtggcttgcacgcattgattgggat.........gatgataaatactacagcacatctctgaag...accaggctcaccatctccaaggacacctccaaaaaccaggtggtccttacaatgaccaacatggaccctgtggacacggccgtgtattactg >IGHV2-70*09 cagatcaccttgaaggagtctggtcct...acgctggtgaaacccacacagaccctcacgctgacccgcaccttctctgggttctcactcagc......actagtggaatgtgtgtgagctggatccgtcagcccccagggaaggccctggagtggcttgcactcattgattgggat.........gatgataaatactacagcacatctctgaac...accaggctcaccatctccaaggacacctccaaaaaccaggtggtccttacaatgaccaacatggaccctgtggacacaggcacatattactgtgtacgg >IGHV2-70*10 caggtcaccttgaaggagtctggtcct...gcgctggtgaaacccacacagaccctcacactgacctgcaccttctctgggttctcactcagc......actagtggaatgcgtgtgagctggatccgtcagcccccagggaaggccctggagtggattgcacgcattgattgggat.........gatgataaatactacagcacatctctgaag...accaggctcaccatctccaaggacacctccaaaaaccaggtggtccttacaatgaccaacatggaccctgtggacacagccacgtattactgtgcacggatac >IGHV2-70*11 cgggtcaccttgagggagtctggtcct...gcgctggtgaaacccacacagaccctcacactgacctgcaccttctctgggttctcactcagc......actagtggaatgtgtgtgagctggatccgtcagcccccagggaaggccctggagtggcttgcacgcattgattgggat.........gatgataaatactacagcacatctctgaag...accaggctcaccatctccaaggacacctccaaaaaccaggtggtccttacaatgaccaacatggaccctgtggacacagccacgtattactgtgcacggatac >IGHV2-70*12 cagatcaccttgaaggagtctggtcct...acgctggtgaaacccacacagaccctcacgctgacctgcaccttctctgggttctcactcagc......actagtggaatgtgtgtgagctggatccgtcagcccccagggaaggccctggagtggcttgcactcattgattgggat.........gatgataaatactacagcacatctctgaag...accaggctcaccatctccaaggacacctccaaaaaccaggtggtccttacaatgaccaacatggaccctgtggacacagccacatattactgtgcacacagac >IGHV2-70*13 caggtcaccttgagggagtctggtcct...gcgctggtgaaacccacacagaccctcacactgacctgcaccttctctgggttctcactcagc......actagtggaatgtgtgtgagctggatccgtcagcccccagggaaggccctggagtggcttgcactcattgattgggat.........gatgataaatactacagcacatctctgaag...accaggctcaccatctccaaggacacctccaaaaaccaggtggtccttacaatgaccaacatggaccctgtggacacagccacgtattattgtgcacggatac >IGHV2-70D*04 caggtcaccttgaaggagtctggtcct...gcgctggtgaaacccacacagaccctcacactgacctgcaccttctctgggttctcactcagc......actagtggaatgcgtgtgagctggatccgtcagcccccagggaaggccctggagtggcttgcacgcattgattgggat.........gatgataaattctacagcacatctctgaag...accaggctcaccatctccaaggacacctccaaaaaccaggtggtccttacaatgaccaacatggaccctgtggacacagccacgtattactgtgcacggatac >IGHV2-70D*14 caggtcaccttgaaggagtctggtcct...gcgctggtgaaacccacacagaccctcacactgacctgcaccttctctgggttctcactcagc......actagtggaatgcgtgtgagctggatccgtcagcccccaggtaaggccctggagtggcttgcacgcattgattgggat.........gatgataaattctacagcacatctctgaag...accaggctcaccatctccaaggacacctccaaaaaccaggtggtccttacaatgaccaacatggaccctgtggacacagccacgtattactgtgcacggatac >IGHV2/OR16-5*01 caggtcaccttgaaggagtctggtcct...gcgctggtgaaacccacagagaccctcacgctgacctgcactctctctgggttctcactcagc......acttctggaatgggtatgagctggatccgtcagcccccagggaaggccctggagtggcttgctcacatttttttgaat.........gacaaaaaatcctacagcacgtctctgaag...aacaggctcatcatctccaaggacacctccaaaagccaggtggtccttaccatgaccaacatggaccctgtggacacagccacgtattactgtgcatggagag >IGHV3-11*01 caggtgcagctggtggagtctggggga...ggcttggtcaagcctggagggtccctgagactctcctgtgcagcctctggattcaccttc............agtgactactacatgagctggatccgccaggctccagggaaggggctggagtgggtttcatacattagtagtagt......ggtagtaccatatactacgcagactctgtgaag...ggccgattcaccatctccagggacaacgccaagaactcactgtatctgcaaatgaacagcctgagagccgaggacacggccgtgtattactgtgcgagaga >IGHV3-11*03 caggtgcagctgttggagtctggggga...ggcttggtcaagcctggagggtccctgagactctcctgtgcagcctctggattcaccttc............agtgactactacatgagctggatccgccaggctccagggaaggggctggagtgggtttcatacattagtagtagt......agtagttacacaaactacgcagactctgtgaag...ggccgattcaccatctccagagacaacgccaagaactcactgtatctgcaaatgaacagcctgagagccgaggacacggccgtgtattactgtgcgaga >IGHV3-11*04 caggtgcagctggtggagtctggggga...ggcttggtcaagcctggagggtccctgagactctcctgtgcagcctctggattcaccttc............agtgactactacatgagctggatccgccaggctccagggaaggggctggagtgggtttcatacattagtagtagt......ggtagtaccatatactacgcagactctgtgaag...ggccgattcaccatctccagggacaacgccaagaactcactgtatctgcaaatgaacagcctgagagccgaggacacggctgtgtattactgtgcgagaga >IGHV3-11*05 caggtgcagctggtggagtctggggga...ggcttggtcaagcctggagggtccctgagactctcctgtgcagcctctggattcaccttc............agtgactactacatgagctggatccgccaggctccagggaaggggctggagtgggtttcatacattagtagtagt......agtagttacacaaactacgcagactctgtgaag...ggccgattcaccatctccagagacaacgccaagaactcactgtatctgcaaatgaacagcctgagagccgaggacacggccgtgtattactgtgcgagaga >IGHV3-11*06 caggtgcagctggtggagtctggggga...ggcttggtcaagcctggagggtccctgagactctcctgtgcagcctctggattcaccttc............agtgactactacatgagctggatccgccaggctccagggaaggggctggagtgggtttcatacattagtagtagt......agtagttacacaaactacgcagactctgtgaag...ggccgattcaccatctccagagacaacgccaagaactcactgtatctgcaaatgaacagcctgagagccgaggacacggctgtgtattactgtgcgagaga >IGHV3-13*01 gaggtgcagctggtggagtctggggga...ggcttggtacagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctacgacatgcactgggtccgccaagctacaggaaaaggtctggagtgggtctcagctattggtactgct.........ggtgacacatactatccaggctccgtgaag...ggccgattcaccatctccagagaaaatgccaagaactccttgtatcttcaaatgaacagcctgagagccggggacacggctgtgtattactgtgcaagaga >IGHV3-13*02 gaggtgcatctggtggagtctggggga...ggcttggtacagcctgggggggccctgagactctcctgtgcagcctctggattcaccttc............agtaactacgacatgcactgggtccgccaagctacaggaaaaggtctggagtgggtctcagccaatggtactgct.........ggtgacacatactatccaggctccgtgaag...gggcgattcaccatctccagagaaaatgccaagaactccttgtatcttcaaatgaacagcctgagagccggggacacggctgtgtattactgtgcaagaga >IGHV3-13*03 gaggtgcagctggtggagtctggggga...ggcttggtacagcctggggggtccctgagactctcctgtgcagcctgtggattcaccttc............agtagctacgacatgcactgggtccgccaagctacaggaaaaggtctggagtgggtctcagctattggtactgct.........ggtgacacatactatccaggctccgtgaag...ggccaattcaccatctccagagaaaatgccaagaactccttgtatcttcaaatgaacagcctgagagccggggacacggctgtgtattactgtgcaaga >IGHV3-13*04 gaggtgcagctggtggagtctggggga...ggcttggtacagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctacgacatgcactgggtccgccaagctacaggaaaaggtctggaatgggtctcagctattggtactgct.........ggtgacacatactatccaggctccgtgaag...ggccgattcaccatctccagagaaaatgccaagaactccttgtatcttcaaatgaacagcctgagagccggggacacggctgtgtattactgtgcaagaga >IGHV3-13*05 gaggtgcagctggtggagtctggggga...ggcttggtacagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctacgacatgcactgggtccgccaagctacaggaaaaggtctggagtgggtctcagctattggtactgct.........ggtgacccatactatccaggctccgtgaag...ggccgattcaccatctccagagaaaatgccaagaactccttgtatcttcaaatgaacagcctgagagccggggacacggctgtgtattactgtgcaagaga >IGHV3-15*01 gaggtgcagctggtggagtctggggga...ggcttggtaaagcctggggggtcccttagactctcctgtgcagcctctggattcactttc............agtaacgcctggatgagctgggtccgccaggctccagggaaggggctggagtgggttggccgtattaaaagcaaaactgatggtgggacaacagactacgctgcacccgtgaaa...ggcagattcaccatctcaagagatgattcaaaaaacacgctgtatctgcaaatgaacagcctgaaaaccgaggacacagccgtgtattactgtaccacaga >IGHV3-15*02 gaggtgcagctggtggagtctggggga...gccttggtaaagcctggggggtcccttagactctcctgtgcagcctctggattcactttc............agtaacgcctggatgagctgggtccgccaggctccagggaaggggctggagtgggttggccgtattaaaagcaaaactgatggtgggacaacagactacgctgcacccgtgaaa...ggcagattcaccatctcaagagatgattcaaaaaacacgctgtatctgcaaatgaacagcctgaaaaccgaggacacagccgtgtattactgtaccacaga >IGHV3-15*03 gaggtgcagctggtggagtctgccgga...gccttggtacagcctggggggtcccttagactctcctgtgcagcctctggattcacttgc............agtaacgcctggatgagctgggtccgccaggctccagggaaggggctggagtgggttggccgtattaaaagcaaagctaatggtgggacaacagactacgctgcacctgtgaaa...ggcagattcaccatctcaagagttgattcaaaaaacacgctgtatctgcaaatgaacagcctgaaaaccgaggacacagccgtgtattactgtaccacaga >IGHV3-15*04 gaggtgcagctggtggagtctggggga...ggcttggtaaagcctggggggtcccttagactctcctgtgcagcctctggattcactttc............agtaacgcctggatgagctgggtccgccaggctccagggaaggggctggagtgggttggccgtattgaaagcaaaactgatggtgggacaacagactacgctgcacccgtgaaa...ggcagattcaccatctcaagagatgattcaaaaaacacgctgtatctgcaaatgaacagcctgaaaaccgaggacacagccgtgtattactgtaccacaga >IGHV3-15*05 gaggtgcagctggtggagtctggggga...ggcttggtaaagcctggggggtcccttagactctcctgtgcagcctctggattcactttc............agtaacgcctggatgagctgggtccgccaggctccagggaaggggctggagtgggttggccgtattaaaagcaaaactgatggtgggacaacagactacgctgcacccgtgaaa...ggcagattcaccatctcaagagatgattcaaaaaacacgctgtatctgcaaatgaacagtctgaaaaccgaggacacagccgtgtattactgtaccacaga >IGHV3-15*06 gaggtgcagctggtggagtctggggga...ggcttggtaaagcctggggggtcccttagactctcctgtgcagcctctggattcactttc............agtaacgcctggatgagctgggtccgccaggctccagggaaggggctggagtgggtcggccgtattaaaagcaaaactgatggtgggacaacaaactacgctgcacccgtgaaa...ggcagattcaccatctcaagagatgattcaaaaaacacgctgtatctgcaaatgaacagcctgaaaaccgaggacacagccgtgtattactgtaccacaga >IGHV3-15*07 gaggtgcagctggtggagtctggggga...ggcttggtaaagcctggggggtcccttagactctcctgtgcagcctctggtttcactttc............agtaacgcctggatgaactgggtccgccaggctccagggaaggggctggagtgggtcggccgtattaaaagcaaaactgatggtgggacaacagactacgctgcacccgtgaaa...ggcagattcaccatctcaagagatgattcaaaaaacacgctgtatctgcaaatgaacagcctgaaaaccgaggacacagccgtgtattactgtaccacaga >IGHV3-15*08 gaggtgcagctggtggagtctgcggga...ggcttggtacagcctggggggtcccttagactctcctgtgcagcctctggattcacttgc............agtaacgcctggatgagctgggtccgccaggctccagggaaggggctggagtgggttggctgtattaaaagcaaagctaatggtgggacaacagactacgctgcacctgtgaaa...ggcagattcaccatctcaagagatgattcaaaaaacacgctgtatctgcaaatgatcagcctgaaaaccgaggacacggccgtgtattactgtaccacagg >IGHV3-16*01 gaggtacaactggtggagtctggggga...ggcttggtacagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agtaacagtgacatgaactgggcccgcaaggctccaggaaaggggctggagtgggtatcgggtgttagttggaat......ggcagtaggacgcactatgtggactccgtgaag...cgccgattcatcatctccagagacaattccaggaactccctgtatctgcaaaagaacagacggagagccgaggacatggctgtgtattactgtgtgagaaa >IGHV3-16*02 gaggtgcagctggtggagtctggggga...ggcttggtacagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agtaacagtgacatgaactgggcccgcaaggctccaggaaaggggctggagtgggtatcgggtgttagttggaat......ggcagtaggacgcactatgtggactccgtgaag...cgccgattcatcatctccagagacaattccaggaactccctgtatctgcaaaagaacagacggagagccgaggacatggctgtgtattactgtgtgagaaa >IGHV3-19*01 acagtgcagctggtggagtctggggga...ggcttggtagagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agtaacagtgacatgaactgggtccgccaggctccaggaaaggggctggagtgggtatcgggtgttagttggaat......ggcagtaggacgcactatgcagactctgtgaag...ggccgattcatcatctccagagacaattccaggaacttcctgtatcagcaaatgaacagcctgaggcccgaggacatggctgtgtattactgtgtgagaaa >IGHV3-20*01 gaggtgcagctggtggagtctggggga...ggtgtggtacggcctggggggtccctgagactctcctgtgcagcctctggattcaccttt............gatgattatggcatgagctgggtccgccaagctccagggaaggggctggagtgggtctctggtattaattggaat......ggtggtagcacaggttatgcagactctgtgaag...ggccgattcaccatctccagagacaacgccaagaactccctgtatctgcaaatgaacagtctgagagccgaggacacggccttgtatcactgtgcgagaga >IGHV3-20*02 gaggtgcagctggtggagtctggggga...ggtgtggtacggcctggggggtccctgagactctcctttgcagcctctggattcaccttt............gatgattatggcatgagctgggtccgccaagctccagggaaggggctggagtgggtctctggtattaattggaat......ggtggtagcacaggttatgcagactctgtgaag...ggccgattcaccatctccagagacaacgccaagaactccctgtatctgcaaatgaacagtctgagagccgaggacacggccttgtatcactgtgcgagaga >IGHV3-21*01 gaggtgcagctggtggagtctggggga...ggcctggtcaagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatagcatgaactgggtccgccaggctccagggaaggggctggagtgggtctcatccattagtagtagt......agtagttacatatactacgcagactcagtgaag...ggccgattcaccatctccagagacaacgccaagaactcactgtatctgcaaatgaacagcctgagagccgaggacacggctgtgtattactgtgcgagaga >IGHV3-21*02 gaggtgcaactggtggagtctggggga...ggcctggtcaagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatagcatgaactgggtccgccaggctccagggaaggggctggagtgggtctcatccattagtagtagt......agtagttacatatactacgcagactcagtgaag...ggccgattcaccatctccagagacaacgccaagaactcactgtatctgcaaatgaacagcctgagagccgaggacacggctgtgtattactgtgcgagaga >IGHV3-21*03 gaggtgcagctggtggagtctggggga...ggcctggtcaagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatagcatgaactgggtccgccaggctccagggaaggggctggagtgggtctcatccattagtagtagt......agtagttacatatactacgcagactcagtgaag...ggccgattcaccatctccagagacaacgccaagaactcactgtatctgcaaatgaacagcctgagagccgaggacacagctgtgtattactgtgcgagaga >IGHV3-21*04 gaggtgcagctggtggagtctggggga...ggcctggtcaagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatagcatgaactgggtccgccaggctccagggaaggggctggagtgggtctcatccattagtagtagt......agtagttacatatactacgcagactcagtgaag...ggccgattcaccatctccagagacaacgccaagaactcactgtatctgcaaatgaacagcctgagagccgaggacacggccgtgtattactgtgcgagaga >IGHV3-22*01 gaggtgcatctggtggagtctggggga...gccttggtacagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agttactactacatgagcggggtccgccaggctcccgggaaggggctggaatgggtaggtttcattagaaacaaagctaatggtgggacaacagaatagaccacgtctgtgaaa...ggcagattcacaatctcaagagatgattccaaaagcatcacctatctgcaaatgaagagcctgaaaaccgaggacacggccgtgtattactgttccagaga >IGHV3-22*02 gaggtgcagctggtggagtctggggga...ggcttggtacagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agttactactacatgagcggggtccgccaggctcccgggaaggggctggaatgggtaggtttcattagaaacaaagctaatggtgggacaacagaatagaccacgtctgtgaaa...ggcagattcacaatctcaagagatgattccaaaagcatcacctatctgcaaatgaagagcctgaaaaccgaggacacggccgtgtattactgttccagaga >IGHV3-23*01 gaggtgcagctgttggagtctggggga...ggcttggtacagcctggggggtccctgagactctcctgtgcagcctctggattcaccttt............agcagctatgccatgagctgggtccgccaggctccagggaaggggctggagtgggtctcagctattagtggtagt......ggtggtagcacatactacgcagactccgtgaag...ggccggttcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagccgaggacacggccgtatattactgtgcgaaaga >IGHV3-23*02 gaggtgcagctgttggagtctggggga...ggcttggtacagcctggggggtccctgagactctcctgtgcagcctctggattcaccttt............agcagctatgccatgagctgggtccgccaggctccagggaaggggctggagtgggtctcagctattagtggtagt......ggtggtagcacatactacggagactccgtgaag...ggccggttcaccatctcaagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagccgaggacacggccgtatattactgtgcgaaaga >IGHV3-23*03 gaggtgcagctgttggagtctggggga...ggcttggtacagcctggggggtccctgagactctcctgtgcagcctctggattcaccttt............agcagctatgccatgagctgggtccgccaggctccagggaaggggctggagtgggtctcagttatttatagcggt......ggtagtagcacatactatgcagactccgtgaag...ggccggttcaccatctccagagataattccaagaacacgctgtatctgcaaatgaacagcctgagagccgaggacacggccgtatattactgtgcgaaaga >IGHV3-23*04 gaggtgcagctggtggagtctggggga...ggcttggtacagcctggggggtccctgagactctcctgtgcagcctctggattcaccttt............agcagctatgccatgagctgggtccgccaggctccagggaaggggctggagtgggtctcagctattagtggtagt......ggtggtagcacatactacgcagactccgtgaag...ggccggttcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagccgaggacacggccgtatattactgtgcgaaaga >IGHV3-23*05 gaggtgcagctgttggagtctggggga...ggcttggtacagcctggggggtccctgagactctcctgtgcagcctctggattcaccttt............agcagctatgccatgagctgggtccgccaggctccagggaaggggctggagtgggtctcagctatttatagcagt......ggtagtagcacatactatgcagactccgtgaag...ggccggttcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagccgaggacacggccgtatattactgtgcgaaa >IGHV3-23D*01 gaggtgcagctgttggagtctggggga...ggcttggtacagcctggggggtccctgagactctcctgtgcagcctctggattcaccttt............agcagctatgccatgagctgggtccgccaggctccagggaaggggctggagtgggtctcagctattagtggtagt......ggtggtagcacatactacgcagactccgtgaag...ggccggttcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagccgaggacacggccgtatattactgtgcgaaaga >IGHV3-25*01 gagatgcagctggtggagtctggggga...ggcttgcaaaagcctgcgtggtccccgagactctcctgtgcagcctctcaattcaccttc............agtagctactacatgaactgtgtccgccaggctccagggaatgggctggagttggtttgacaagttaatcctaat......gggggtagcacatacctcatagactccggtaag...gaccgattcaatacctccagagataacgccaagaacacacttcatctgcaaatgaacagcctgaaaaccgaggacacggccctctattagtgtaccagaga >IGHV3-25*02 gagatgcagctggtggagtctggggga...ggcttggcaaagcctgcgtggtccccgagactctcctgtgcagcctctcaattcaccttc............agtagctactacatgaactgtgtccgccaggctccagggaatgggctggagttggtttgacaagttaatcctaat......gggggtagcacatacctcatagactccggtaag...gaccgattcaatacctccagagataacgccaagaacacacttcatctgcaaatgaacagcctgaaaaccgaggacacggccctctattagtgtaccagaga >IGHV3-25*03 gagatgcagctggtggagtctggggga...ggcttggcaaagcctgcgtggtccccgagactctcctgtgcagcctctcaattcaccttc............agtagctactacatgaactgtgtccgccaggctccagggaatgggctggagttggttggacaagttaatcctaat......gggggtagcacatacctcatagactccggtaag...gaccgattcaatacctccagagataacgccaagaacacacttcatctgcaaatgaacagcctgaaaaccgaggacacggccctgtattagtgtaccaga >IGHV3-25*04 gagacgcagctggtggagtctggggga...ggcttggcaaagcctgggcggtccccgagactctcctgtgcagcctctcaattcaccttc............agtagctactacatgaactgtgtccgccaggctccagggaatgggctggagttggttggacaagttaatcctaat......gggggtagcacatacctcatagactccggtaag...gaccgattcaatacctccagagataacgccaagaacacacttcatctgcaaatgaacagcctgaaaaccgaggacacggccctgtattactgtaccagaga >IGHV3-25*05 gagatgcagctggtggagtctggggga...ggcttggcaaagcctgcgtggtccccgagactctcctgtgcagcctctcaattcaccttc............agtagctactacatgaactgtgtccgccaggctccagggaatgggctggagttggttggacaagttaatcctaat......gggggtagcacatacctcatagactccggtaag...gaccgattcaatacctccagagataacgccaagaacacacttcatctgcaaatgaacagcctgaaaaccgaggacacggccctctattagtgtaccagaga >IGHV3-29*01 gaggtggagctgatagagcccacagag...gacctgagacaacctgggaagttcctgagactctcctgtgtagcctctagattcgccttc............agtagcttctgaatgagcccagttcaccagtctgcaggcaaggggctggagtgagtaatagatataaaagatgat......ggaagtcagatacaccatgcagactctgtgaag...ggcagattctccatctccaaagacaatgctaagaactctctgtatctgcaaatgaacagtcagagaactgaggacatggctgtgtatggctgtacataaggtt >IGHV3-30*01 caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatgctatgcactgggtccgccaggctccaggcaaggggctagagtgggtggcagttatatcatatgat......ggaagtaataaatactacgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgcgagaga >IGHV3-30*02 caggtgcagctggtggagtctggggga...ggcgtggtccagcctggggggtccctgagactctcctgtgcagcgtctggattcaccttc............agtagctatggcatgcactgggtccgccaggctccaggcaaggggctggagtgggtggcatttatacggtatgat......ggaagtaataaatactatgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgcgaaaga >IGHV3-30*03 caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatggcatgcactgggtccgccaggctccaggcaaggggctggagtgggtggcagttatatcatatgat......ggaagtaataaatactatgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgcgagaga >IGHV3-30*04 caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatgctatgcactgggtccgccaggctccaggcaaggggctggagtgggtggcagttatatcatatgat......ggaagtaataaatactacgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgcgagaga >IGHV3-30*05 caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatggcatgcactgggtccgccaggctccaggcaaggggctagagtgggtggcagttatatcatatgat......ggaagtaataaatactacgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagctgagggcacggctgtgtattactgtgcgagaga >IGHV3-30*06 caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcgtctggattcaccttc............agtagctatggcatgcactgggtccgccaggctccaggcaaggggctagagtgggtggcagttatatcatatgat......ggaagtaataaatactacgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgcgagaga >IGHV3-30*07 caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatgctatgcactgggtccgccaggctccaggcaaggggctagagtgggtggcagttatatcatatgat......ggaagtaataaatactacgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagccgaggacacggctgtgtattactgtgcgagaga >IGHV3-30*08 caggtgcagctggtggactctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcctctgcattcaccttc............agtagctatgctatgcactgggtccgccaggctccaggcaaggggctagagtgggtggcagttatatcatatgat......ggaagtaataaatactacgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgcgaga >IGHV3-30*09 caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatgctatgcactgggtccgccaggctccaggcaaggggctggagtgggtggcagttatatcatatgat......ggaagtaataaatactacgcagactccgtgaag...ggccgattcgccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgcgagaga >IGHV3-30*10 caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatgctatgcactgggtccgccaggctccaggcaaggggctagagtgggtggcagttatatcatatgat......ggaagtaataaatactacacagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgcgagaga >IGHV3-30*11 caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcgtctggattcaccttc............agtagctatgctatgcactgggtccgccaggctccaggcaaggggctagagtgggtggcagttatatcatatgat......ggaagtaataaatactacgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgcgagaga >IGHV3-30*12 caggtgcagctggtggagtctgggggg...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcgtctggattcaccttc............agtagctatggcatgcactgggtccgccaggctccaggcaaggggctagagtgggtggcagttatatcatatgat......ggaagtaataaatactacgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagccgaggacacggctgtgtattactgtgcgagaga >IGHV3-30*13 caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatggcatgcactgggtccgccaggctccaggcaaggggctagagtgggtggcagttatatcatatgat......ggaagtaataaatactacgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacaggctgtatctgcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgcgagaga >IGHV3-30*14 caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatgctatgcactgggtccgccaggctccaggcaaggggctggagtgggtggcagttatatcatatgat......ggaagtaataaatactacgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatcttcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgcgagaga >IGHV3-30*15 caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatgctatgcactgggtccgccaggctccaggcaaggggctagagtgggtggcagttatatcatatgat......ggaagtaataaatactacgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgagcagcctgagagctgaggacacggctgtgtattactgtgcgagaga >IGHV3-30*16 caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatgctatgcactgggtccgccaggccccaggcaaggggctagagtgggtggcagttatatcatatgat......ggaagtaataaatactacgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgcgagaga >IGHV3-30*17 caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatgctatgcactgggtccgccaggctccgggcaaggggctagagtgggtggcagttatatcatatgat......ggaagtaataaatactacgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgcgagaga >IGHV3-30*18 caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatggcatgcactgggtccgccaggctccaggcaaggggctggagtgggtggcagttatatcatatgat......ggaagtaataaatactatgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgcgaaaga >IGHV3-30*19 caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcgtctggattcaccttc............agtagctatggcatgcactgggtccgccaggctccaggcaaggggctggagtgggtggcagttatatcatatgat......ggaagtaataaatactacgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgcgagaga >IGHV3-30-2*01 gaggtacagctcgtggagtccggagag...gacccaagacaacctgggggatccctgagactctcctgtgcagactctggattaaccttc............agtagctactgaaggaactcggtttcccaggctccagggaaggggctggagtgagtagtagatatacagtgtgat......ggaagtcagatatgttatgcataatctttgaag...agcaaattcaccatctccaaagaaaatgccaagaactcactgtatttgctaatgaacagtctgagagcagcgggcacagctgtgtgttactgtatgtgaggca >IGHV3-30-22*01 gaggtggagctgatagagtccatagag...gacctgagacaacctgggaagttcctgagactctcctgtgtagcctctagattcgccttc............agtagcttctgaatgagccgagttcaccagtctccaggcaaggggctggagtgagtaatagatataaaagatgat......ggaagtcagatacaccatgcagactctgtgaag...ggcagattctccatctccaaagacaatgctaagaactctctgtatctgcaaatgaacagtcagagagctgaggacatggacgtgtatggctgtacataaggtc >IGHV3-30-3*01 caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatgctatgcactgggtccgccaggctccaggcaaggggctggagtgggtggcagttatatcatatgat......ggaagcaataaatactacgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgcgagaga >IGHV3-30-3*02 caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcgtctggattcaccttc............agtagctatgctatgcactgggtccgccaggctccaggcaaggggctggagtgggtggcagttatatcatatgat......ggaagcaataaatactacgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgcgaaaga >IGHV3-30-3*03 caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatgctatgcactgggtccgccaggctccaggcaaggggctggagtgggtggcagttatatcatatgat......ggaagtaataaatactacgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgcgagaga >IGHV3-30-33*01 gaggtacagctcgtggagtccggagag...gacccaagacaacctgggggatccctgagactctcctgtgcagactctggattaaccttc............agtagctactgaaggagctcggtttcccaggctccagggaaggggctggagtgagtagtagatatacagtgtgat......ggaagtcagatatgttatgcataatctttgaag...agcaaattcaccatctccaaagaaaatgccaagaactcactgtatttgctaatgaacagtctgagagcagagggcacagctgtgtgttactgtatgtgagg >IGHV3-30-42*01 gaggtggagctgatagagcccacagag...gacctgagacaacctgggaagttcctgagactctcctgtgtagcctctagattcgccttc............agtagcttctgaatgagcccagttcaccagtctgcaggcaaggggctggagtgagtaatagatataaaagatgat......ggaagtcagatacaccatgcagactctgtgaag...ggcagattctccatctccaaagacaatgctaagaactctctgtatctgcaaatgaacagtcagagaactgaggacatggctgtgtatggctgtacataaggtt >IGHV3-30-5*01 caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatggcatgcactgggtccgccaggctccaggcaaggggctggagtgggtggcagttatatcatatgat......ggaagtaataaatactatgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgcgaaaga >IGHV3-30-5*02 caggtgcagctggtggagtctggggga...ggcgtggtccagcctggggggtccctgagactctcctgtgcagcgtctggattcaccttc............agtagctatggcatgcactgggtccgccaggctccaggcaaggggctggagtgggtggcatttatacggtatgat......ggaagtaataaatactatgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgcgaaaga >IGHV3-30-52*01 gaggtacagctcgtggagtccggagag...gacccaagacaacctgggggatccctgagactctcctgtgcagactctggattaaccttc............agtagctactgaaggaactcggtttcccaggctccagggaaggggctggagtgagtagtagatatacagtgtgat......ggaagtcagatatgttatgcataatctttgaag...agcaaattcaccatctccaaagaaaatgccaagaactcactgtatttgctaatgaacagtctgagagcagcgggcacagctgtgtgttactgtatgtgagg >IGHV3-32*01 gaggtggagctgatagagtccatagag...gacctgagacaacctgggaagttcctgagactctcctgtgtagcctctagattcgccttc............agtagcttctgaatgagccgagttcaccagtctccaggcaaggggctggagtgagtaatagatataaaagatgat......ggaagtcagatacaccatgcagactctgtgaag...ggcagattctccatctccaaagacaatgctaagaactctctgtatctgcaaatgaacactcagagagctgaggacgtggccgtgtatggctatacataaggtc >IGHV3-33*01 caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcgtctggattcaccttc............agtagctatggcatgcactgggtccgccaggctccaggcaaggggctggagtgggtggcagttatatggtatgat......ggaagtaataaatactatgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagccgaggacacggctgtgtattactgtgcgagaga >IGHV3-33*02 caggtacagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcgtctggattcaccttc............agtagctatggcatgcactgggtccgccaggctccaggcaaggggctggagtgggtggcagttatatggtatgat......ggaagtaataaatactatgcagactccgcgaag...ggccgattcaccatctccagagacaattccacgaacacgctgtttctgcaaatgaacagcctgagagccgaggacacggctgtgtattactgtgcgagaga >IGHV3-33*03 caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcgtctggattcaccttc............agtagctatggcatgcactgggtccgccaggctccaggcaaggggctggagtgggtggcagttatatggtatgat......ggaagtaataaatactatgcagactccgtgaag...ggccgattcaccatctccagagacaactccaagaacacgctgtatctgcaaatgaacagcctgagagccgaggacacggctgtgtattactgtgcgaaaga >IGHV3-33*04 caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcgtctggattcaccttc............agtagctatggcatgcactgggtccgccaggctccaggcaaggggctagagtgggtggcagttatatggtatgac......ggaagtaataaatactatgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagccgaggacacggctgtgtattactgtgcgagaga >IGHV3-33*05 caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcgtctggattcaccttc............agtagctatggcatgcactgggtccgccaggctccaggcaaggggctggagtgggtggcagttatatcatatgat......ggaagtaataaatactatgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagccgaggacacggctgtgtattactgtgcgagaga >IGHV3-33*06 caggtgcagctggtggagtctggggga...ggcgtggtccagcctgggaggtccctgagactctcctgtgcagcgtctggattcaccttc............agtagctatggcatgcactgggtccgccaggctccaggcaaggggctggagtgggtggcagttatatggtatgat......ggaagtaataaatactatgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagccgaggacacggctgtgtattactgtgcgaaaga >IGHV3-33-2*01 gaggtacagctcgtggagtccggagag...gacccaagacaacctgggggatccttgagactctcctgtgcagactctggattaaccttc............agtagctactgaatgagctcggtttcccaggctccagggaaggggctggagtgagtagtagatatacagtgtgat......ggaagtcagatatgttatgcccaatctgtgaag...agcaaattcaccatctccaaagaaaatgccaagaactcactgtatttgcaaatgaacagtctgagagcagagggcacagctgtgtgttactgtatgtgaggca >IGHV3-35*01 gaggtgcagctggtggagtctggggga...ggcttggtacagcctgggggatccctgagactctcctgtgcagcctctggattcaccttc............agtaacagtgacatgaactgggtccatcaggctccaggaaaggggctggagtgggtatcgggtgttagttggaat......ggcagtaggacgcactatgcagactctgtgaag...ggccgattcatcatctccagagacaattccaggaacaccctgtatctgcaaacgaatagcctgagggccgaggacacggctgtgtattactgtgtgagaaa >IGHV3-38*01 gaggtgcagctggtggagtctggggga...ggcttggtacagcctagggggtccctgagactctcctgtgcagcctctggattcaccgtc............agtagcaatgagatgagctggatccgccaggctccagggaaggggctggagtgggtctcatccattagtggt............ggtagcacatactacgcagactccaggaag...ggcagattcaccatctccagagacaattccaagaacacgctgtatcttcaaatgaacaacctgagagctgagggcacggccgcgtattactgtgccagatata >IGHV3-38*02 gaggtgcagctggtggagtctggggga...ggcttggtacagcctagggggtccctgagactctcctgtgcagcctctggattcaccgtc............agtagcaatgagatgagctggatccgccaggctccagggaaggggctggagtgggtctcatccattagtggt............ggtagcacatactacgcagactccaggaag...ggcagattcaccatctccagagacaattccaagaacacgctgtatcttcaaatgaacaacctgagagctgagggcacggccgtgtattactgtgccagatata >IGHV3-38*03 gaggtgcagctggtggagtctggggga...ggcttggtacagcctagggggtccctgagactctcctgtgcagcctctggattcaccgtc............agtagcaatgagatgagctggatccgccaggctccagggaagggtctggagtgggtctcatccattagtggt............ggtagcacatactacgcagactccaggaag...ggcagattcaccatctccagagacaattccaagaacacgctgtatcttcaaatgaacaacctgagagctgagggcacggccgtgtattactgtgccagatata >IGHV3-38-3*01 gaggtgcagctggtggagtctcgggga...gtcttggtacagcctggggggtccctgagactctcctgtgcagcctctggattcaccgtc............agtagcaatgagatgagctgggtccgccaggctccagggaagggtctggagtgggtctcatccattagtggt............ggtagcacatactacgcagactccaggaag...ggcagattcaccatctccagagacaattccaagaacacgctgcatcttcaaatgaacagcctgagagctgaggacacggctgtgtattactgtaagaaaga >IGHV3-43*01 gaagtgcagctggtggagtctggggga...gtcgtggtacagcctggggggtccctgagactctcctgtgcagcctctggattcaccttt............gatgattataccatgcactgggtccgtcaagctccggggaagggtctggagtgggtctctcttattagttgggat......ggtggtagcacatactatgcagactctgtgaag...ggccgattcaccatctccagagacaacagcaaaaactccctgtatctgcaaatgaacagtctgagaactgaggacaccgccttgtattactgtgcaaaagata >IGHV3-43*02 gaagtgcagctggtggagtctggggga...ggcgtggtacagcctggggggtccctgagactctcctgtgcagcctctggattcaccttt............gatgattatgccatgcactgggtccgtcaagctccagggaagggtctggagtgggtctctcttattagtggggat......ggtggtagcacatactatgcagactctgtgaag...ggccgattcaccatctccagagacaacagcaaaaactccctgtatctgcaaatgaacagtctgagaactgaggacaccgccttgtattactgtgcaaaagata >IGHV3-43D*01 gaagtgcagctggtggagtctggggga...gtcgtggtacagcctggggggtccctgagactctcctgtgcagcctctggattcaccttt............gatgattatgccatgcactgggtccgtcaagctccggggaagggtctggagtgggtctctcttattagttgggat......ggtggtagcacctactatgcagactctgtgaag...ggtcgattcaccatctccagagacaacagcaaaaactccctgtatctgcaaatgaacagtctgagagctgaggacaccgccttgtattactgtgcaaaagata >IGHV3-47*01 gaggatcagctggtggagtctggggga...ggcttggtacagcctggggggtccctgcgaccctcctgtgcagcctctggattcgccttc............agtagctatgctctgcactgggttcgccgggctccagggaagggtctggagtgggtatcagctattggtactggt.........ggtgatacatactatgcagactccgtgatg...ggccgattcaccatctccagagacaacgccaagaagtccttgtatcttcatatgaacagcctgatagctgaggacatggctgtgtattattgtgcaaga >IGHV3-47*02 gaggatcagctggtggagtctggggga...ggcttggtacagcctggggggtccctgagaccctcctgtgcagcctctggattcgccttc............agtagctatgttctgcactgggttcgccgggctccagggaagggtccggagtgggtatcagctattggtactggt.........ggtgatacatactatgcagactccgtgatg...ggccgattcaccatctccagagacaacgccaagaagtccttgtatcttcaaatgaacagcctgatagctgaggacatggctgtgtattattgtgcaagaga >IGHV3-48*01 gaggtgcagctggtggagtctggggga...ggcttggtacagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatagcatgaactgggtccgccaggctccagggaaggggctggagtgggtttcatacattagtagtagt......agtagtaccatatactacgcagactctgtgaag...ggccgattcaccatctccagagacaatgccaagaactcactgtatctgcaaatgaacagcctgagagccgaggacacggctgtgtattactgtgcgagaga >IGHV3-48*02 gaggtgcagctggtggagtctggggga...ggcttggtacagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatagcatgaactgggtccgccaggctccagggaaggggctggagtgggtttcatacattagtagtagt......agtagtaccatatactacgcagactctgtgaag...ggccgattcaccatctccagagacaatgccaagaactcactgtatctgcaaatgaacagcctgagagacgaggacacggctgtgtattactgtgcgagaga >IGHV3-48*03 gaggtgcagctggtggagtctggggga...ggcttggtacagcctggagggtccctgagactctcctgtgcagcctctggattcaccttc............agtagttatgaaatgaactgggtccgccaggctccagggaaggggctggagtgggtttcatacattagtagtagt......ggtagtaccatatactacgcagactctgtgaag...ggccgattcaccatctccagagacaacgccaagaactcactgtatctgcaaatgaacagcctgagagccgaggacacggctgtttattactgtgcgagaga >IGHV3-48*04 gaggtgcagctggtggagtctggggga...ggcttggtacagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatagcatgaactgggtccgccaggctccagggaaggggctggagtgggtttcatacattagtagtagt......agtagtaccatatactacgcagactctgtgaag...ggccgattcaccatctccagagacaacgccaagaactcactgtatctgcaaatgaacagcctgagagccgaggacacggctgtgtattactgtgcgagaga >IGHV3-49*01 gaggtgcagctggtggagtctggggga...ggcttggtacagccagggcggtccctgagactctcctgtacagcttctggattcaccttt............ggtgattatgctatgagctggttccgccaggctccagggaaggggctggagtgggtaggtttcattagaagcaaagcttatggtgggacaacagaatacaccgcgtctgtgaaa...ggcagattcaccatctcaagagatggttccaaaagcatcgcctatctgcaaatgaacagcctgaaaaccgaggacacagccgtgtattactgtactagaga >IGHV3-49*02 gaggtgcagctggtggagtctggggga...ggcttggtacagccagggccgtccctgagactctcctgtacagcttctggattcaccttt............gggtattatcctatgagctgggtccgccaggctccagggaaggggctggagtgggtaggtttcattagaagcaaagcttatggtgggacaacagaatacgccgcgtctgtgaaa...ggcagattcaccatctcaagagatgattccaaaagcatcgcctatctgcaaatgaacagcctgaaaaccgaggacacagccgtgtattactgtactagaga >IGHV3-49*03 gaggtgcagctggtggagtctggggga...ggcttggtacagccagggcggtccctgagactctcctgtacagcttctggattcaccttt............ggtgattatgctatgagctggttccgccaggctccagggaaggggctggagtgggtaggtttcattagaagcaaagcttatggtgggacaacagaatacgccgcgtctgtgaaa...ggcagattcaccatctcaagagatgattccaaaagcatcgcctatctgcaaatgaacagcctgaaaaccgaggacacagccgtgtattactgtactagaga >IGHV3-49*04 gaggtgcagctggtggagtctggggga...ggcttggtacagccagggcggtccctgagactctcctgtacagcttctggattcaccttt............ggtgattatgctatgagctgggtccgccaggctccagggaaggggctggagtgggtaggtttcattagaagcaaagcttatggtgggacaacagaatacgccgcgtctgtgaaa...ggcagattcaccatctcaagagatgattccaaaagcatcgcctatctgcaaatgaacagcctgaaaaccgaggacacagccgtgtattactgtactagaga >IGHV3-49*05 gaggtgcagctggtggagtctggggga...ggcttggtaaagccagggcggtccctgagactctcctgtacagcttctggattcaccttt............ggtgattatgctatgagctggttccgccaggctccagggaaggggctggagtgggtaggtttcattagaagcaaagcttatggtgggacaacagaatacgccgcgtctgtgaaa...ggcagattcaccatctcaagagatgattccaaaagcatcgcctatctgcaaatgaacagcctgaaaaccgaggacacagccgtgtattactgtactagaga >IGHV3-52*01 gaggtgcagctggtggagtctgggtga...ggcttggtacagcctggagggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctcctggatgcactgggtctgccaggctccggagaaggggctggagtgggtggccgacataaagtgtgac......ggaagtgagaaatactatgtagactctgtgaag...ggccgattgaccatctccagagacaatgccaagaactccctctatctgcaagtgaacagcctgagagctgaggacatgaccgtgtattactgtgtgagagg >IGHV3-52*02 gaggtgcagctggtggagtctgggtga...ggcttggtacagcctggagggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctcctggatgcactgggtctgccaggctccggagaaggggcaggagtgggtggccgacataaagtgtgac......ggaagtgagaaatactatgtagactctgtgaag...ggccgattgaccatctccagagacaatgccaagaactccctctatctgcaagtgaacagcctgagagctgaggacatgaccgtgtattactgtgtgaga >IGHV3-52*03 gaggtgcagctggtcgagtctgggtga...ggcttggtacagcctggagggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctcctggatgcactgggtctgccaggctccggagaaggggctggagtgggtggccgacataaagtgtgac......ggaagtgagaaatactatgtagactctgtgaag...ggccgattgaccatctccagagacaatgccaagaactccctctatctgcaagtgaacagcctgagagctgaggacatgaccgtgtattactgtgtgaga >IGHV3-53*01 gaggtgcagctggtggagtctggagga...ggcttgatccagcctggggggtccctgagactctcctgtgcagcctctgggttcaccgtc............agtagcaactacatgagctgggtccgccaggctccagggaaggggctggagtgggtctcagttatttatagcggt.........ggtagcacatactacgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatcttcaaatgaacagcctgagagccgaggacacggccgtgtattactgtgcgagaga >IGHV3-53*02 gaggtgcagctggtggagactggagga...ggcttgatccagcctggggggtccctgagactctcctgtgcagcctctgggttcaccgtc............agtagcaactacatgagctgggtccgccaggctccagggaaggggctggagtgggtctcagttatttatagcggt.........ggtagcacatactacgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatcttcaaatgaacagcctgagagccgaggacacggccgtgtattactgtgcgagaga >IGHV3-53*03 gaggtgcagctggtggagtctggagga...ggcttgatccagcctggggggtccctgagactctcctgtgcagcctctgggttcaccgtc............agtagcaactacatgagctgggtccgccagcctccagggaaggggctggagtgggtctcagttatttatagcggt.........ggtagcacatactacgcagactctgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatcttcaaatgaacagcctgagagccgaggacacggccgtgtattactgtgctaggga >IGHV3-53*04 gaggtgcagctggtggagtctggagga...ggcttggtccagcctggggggtccctgagactctcctgtgcagcctctgggttcaccgtc............agtagcaactacatgagctgggtccgccaggctccagggaaggggctggagtgggtctcagttatttatagcggt.........ggtagcacatactacgcagactccgtgaag...ggccgattcaccatctccagacacaattccaagaacacgctgtatcttcaaatgaacagcctgagagctgaggacacggccgtgtattactgtgcgagaga >IGHV3-54*01 gaggtacagctggtggagtctgaagaa...aaccaaagacaacttgggggatccctgagactctcctgtgcagactctggattaaccttc............agtagctactgaatgagctcagattcccaagctccagggaaggggctggagtgagtagtagatatatagtaggat......agaagtcagctatgttatgcacaatctgtgaag...agcagattcaccatctccaaagaaaatgccaagaactcactctgtttgcaaatgaacagtctgagagcagagggcacggccgtgtattactgtatgtgagt >IGHV3-54*02 gaggtacagctggtggagtctgaagaa...aaccaaagacaacttgggggatccctgagactctcctgtgcagactctggattaaccttc............agtagctactgaatgagctcagattcccaggctccagggaaggggctggagtgagtagtagatatatagtacgat......agaagtcagatatgttatgcacaatctgtgaag...agcagattcaccatctccaaagaaaatgccaagaactcactccgtttgcaaatgaacagtctgagagcagagggcacggccgtgtattactgtatgtgagg >IGHV3-54*04 gaggtacagctggtggagtctgaagaa...aaccaaagacaacttgggggatccctgagactctcctgtgcagactctggattaaccttc............agtagctactgaatgagctcagattcccaggctccagggaaggggctggagtgagtagtagatatatagtaggat......agaagtcagctatgttatgcacaatctgtgaag...agcagattcaccatctccaaagaaaatgccaagaactcactctgtttgcaaatgaacagtctgagagcagagggcacggccgtgtattactgtatgtgagt >IGHV3-62*01 gaggtgcagctggtggagtctggggaa...ggcttggtccagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctctgctatgcactgggtccgccaggctccaagaaagggtttgtagtgggtctcagttattagtacaagt......ggtgataccgtactctacacagactctgtgaag...ggccgattcaccatctccagagacaatgcccagaattcactgtctctgcaaatgaacagcctgagagccgagggcacagttgtgtactactgtgtgaaaga >IGHV3-63*01 gaggtggagctgatagagtccatagag...ggcctgagacaacttgggaagttcctgagactctcctgtgtagcctctggattcaccttc............agtagctactgaatgagctgggtcaatgagactctagggaaggggctggagggagtaatagatgtaaaatatgat......ggaagtcagatataccatgcagactctgtgaag...ggcagattcaccatctccaaagacaatgctaagaactcaccgtatctccaaacgaacagtctgagagctgaggacatgaccatgcatggctgtacataaggtt >IGHV3-63*02 gaggtggagctgatagagtccatagag...ggcctgagacaacttgggaagttcctgagactctcctgtgtagcctctggattcaccttc............agtagctactgaatgagctgggtcaatgagactctagggaaggggctggagggagtaatagatgtaaaatatgat......ggaagtcagatataccatgcagactctgtgaag...ggcagattcaccatctccaaagacaatgctaagaactcaccgtatctgcaaacgaacagtctgagagctgaggacatgaccatgcatggctgtacataa >IGHV3-64*01 gaggtgcagctggtggagtctggggga...ggcttggtccagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatgctatgcactgggtccgccaggctccagggaagggactggaatatgtttcagctattagtagtaat......gggggtagcacatattatgcaaactctgtgaag...ggcagattcaccatctccagagacaattccaagaacacgctgtatcttcaaatgggcagcctgagagctgaggacatggctgtgtattactgtgcgagaga >IGHV3-64*02 gaggtgcagctggtggagtctggggaa...ggcttggtccagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctatgctatgcactgggtccgccaggctccagggaagggactggaatatgtttcagctattagtagtaat......gggggtagcacatattatgcagactctgtgaag...ggcagattcaccatctccagagacaattccaagaacacgctgtatcttcaaatgggcagcctgagagctgaggacatggctgtgtattactgtgcgagaga >IGHV3-64*03 gaggtgcagctggtggagtctggggga...ggcttggtccagcctggggggtccctgagactctcctgttcagcctctggattcaccttc............agtagctatgctatgcactgggtccgccaggctccagggaagggactggaatatgtttcagctattagtagtaat......gggggtagcacatactacgcagactcagtgaag...ggcagattcaccatctccagagacaattccaagaacacgctgtatgtccaaatgagcagtctgagagctgaggacacggctgtgtattactgtgtgaaaga >IGHV3-64*04 caggtgcagctggtggagtctggggga...ggcttggtccagcctggggggtccctgagactctcctgttcagcctctggattcaccttc............agtagctatgctatgcactgggtccgccaggctccagggaagggactggaatatgtttcagctattagtagtaat......gggggtagcacatactacgcagactcagtgaag...ggcagattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgcgagaga >IGHV3-64*05 gaggtgcagctggtggagtctggggga...ggcttggtccagcctggggggtccctgagactctcctgttcagcctctggattcaccttc............agtagctatgctatgcactgggtccgccaggctccagggaagggactggaatatgtttcagctattagtagtaat......gggggtagcacatactacgcagactcagtgaag...ggcagattcaccatctccagagacaattccaagaacacgctgtatgttcaaatgagcagtctgagagctgaggacacggctgtgtattactgtgtgaaaga >IGHV3-64D*06 gaggtgcagctggtggagtctggggga...ggcttggtccagcctggggggtccctgagactctcctgttcagcctctggattcaccttc............agtagctatgctatgcactgggtccgccaggctccagggaagggactggaatatgtttcagctattagtagtaat......gggggtagcacatactacgcagactccgtgaag...ggcagattcaccatctccagagacaattccaagaacacgctgtatcttcaaatgagcagtctgagagctgaggacacggctgtgtattactgtgtgaaaga >IGHV3-66*01 gaggtgcagctggtggagtctggggga...ggcttggtccagcctggggggtccctgagactctcctgtgcagcctctggattcaccgtc............agtagcaactacatgagctgggtccgccaggctccagggaaggggctggagtgggtctcagttatttatagcggt.........ggtagcacatactacgcagactccgtgaag...ggcagattcaccatctccagagacaattccaagaacacgctgtatcttcaaatgaacagcctgagagccgaggacacggctgtgtattactgtgcgagaga >IGHV3-66*02 gaggtgcagctggtggagtctggggga...ggcttggtccagcctggggggtccctgagactctcctgtgcagcctctggattcaccgtc............agtagcaactacatgagctgggtccgccaggctccagggaaggggctggagtgggtctcagttatttatagcggt.........ggtagcacatactacgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatcttcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgcgaga >IGHV3-66*03 gaggtgcagctggtggagtctggagga...ggcttgatccagcctggggggtccctgagactctcctgtgcagcctctgggttcaccgtc............agtagcaactacatgagctgggtccgccaggctccagggaaggggctggagtgggtctcagttatttatagctgt.........ggtagcacatactacgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatcttcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgcgagaga >IGHV3-66*04 gaggtgcagctggtggagtctggggga...ggcttggtccagcctggggggtccctgagactctcctgtgcagcctctggattcaccgtc............agtagcaactacatgagctgggtccgccaggctccagggaaggggctggagtgggtctcagttatttatagcggt.........ggtagcacatactacgcagactccgtgaag...ggcagattcaccatctccagagacaattccaagaacacgctgtatcttcaaatgaacagcctgagagccgaggacacggctgtgtattactgtgcgagaca >IGHV3-69-1*01 gaggtgcagctggtggagtctggggga...ggcttggtaaagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agtgactactacatgaactgggtccgccaggctccagggaaggggctggagtgggtctcatccattagtagtagt.........agtaccatatactacgcagactctgtgaag...ggccgattcaccatctccagagacaacgccaagaactcactgtatctgcaaatgaacagcctgagagccgaggacacggctgtgtattactgtgcgagaga >IGHV3-69-1*02 gaggtgcagctggtggagtctggggga...ggcttggtaaagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agtgactactacatgaactgggtccgccaggctccagggaaggggctggagtgggtctcatccattagtagtagt.........agtaccatatactacgcagactctgtgaag...ggccgattcaccatctccagagacaacgccaagaactcactgtatctgcaaatgaacagcctgagagccgaggacacggctgtttattactgtgcgagaga >IGHV3-7*01 gaggtgcagctggtggagtctggggga...ggcttggtccagcctggggggtccctgagactctcctgtgcagcctctggattcaccttt............agtagctattggatgagctgggtccgccaggctccagggaaggggctggagtgggtggccaacataaagcaagat......ggaagtgagaaatactatgtggactctgtgaag...ggccgattcaccatctccagagacaacgccaagaactcactgtatctgcaaatgaacagcctgagagccgaggacacggctgtgtattactgtgcgagaga >IGHV3-7*02 gaggtgcagctggtggagtctggggga...ggcttggtccagcctggggggtccctgagactctcctgtgcagcctctggattcaccttt............agtagctattggatgagctgggtccgccaggctccagggaaagggctggagtgggtggccaacataaagcaagat......ggaagtgagaaatactatgtggactctgtgaag...ggccgattcaccatctccagagacaacgccaagaactcactgtatctgcaaatgaacagcctgagagccgaggacacggctgtgtattactgtgcgaga >IGHV3-7*03 gaggtgcagctggtggagtctggggga...ggcttggtccagcctggggggtccctgagactctcctgtgcagcctctggattcaccttt............agtagctattggatgagctgggtccgccaggctccagggaaggggctggagtgggtggccaacataaagcaagat......ggaagtgagaaatactatgtggactctgtgaag...ggccgattcaccatctccagagacaacgccaagaactcactgtatctgcaaatgaacagcctgagagccgaggacacggccgtgtattactgtgcgagaga >IGHV3-71*01 gaggtgcagctggtggagtccggggga...ggcttggtccagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agtgactactacatgagctgggtccgccaggctcccgggaaggggctggagtgggtaggtttcattagaaacaaagctaatggtgggacaacagaatagaccacgtctgtgaaa...ggcagattcacaatctcaagagatgattccaaaagcatcacctatctgcaaatgaacagcctgagagccgaggacacggccgtgtattactgtgcgagaga >IGHV3-71*02 gaggtgcagctggtggagtctggggga...ggcttggtccagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agtgactactacatgagctgggtccgccaggctcccgggaaggggctggagtgggtaggtttcattagaaacaaagctaatggtgggacaacagaatagaccacgtctgtgaaa...ggcagattcacaatctcaagagatgattccaaaagcatcacctatctgcaaatgaacagcctgagagccgaggacatggctgtgtattactgtgcgagaga >IGHV3-71*03 gaggtgcagctggtggagtctggggga...ggcttggtccagcctggggggtccctgagactctcctgtgcagcctctggtttcaccttc............agtgactactacatgagctgggtccgccaggctcccgggaaggggctggagtgggtaggtttcattagaaacaaagctaatggtgggacaacagaatagaccacgtctgtgaaa...ggcagattcacaatctcaagagatgattccaaaagcatcacctatctgcaaatgaacagcctgagagccgaggacacggctgtgtattactgtgcgagaga >IGHV3-72*01 gaggtgcagctggtggagtctggggga...ggcttggtccagcctggagggtccctgagactctcctgtgcagcctctggattcaccttc............agtgaccactacatggactgggtccgccaggctccagggaaggggctggagtgggttggccgtactagaaacaaagctaacagttacaccacagaatacgccgcgtctgtgaaa...ggcagattcaccatctcaagagatgattcaaagaactcactgtatctgcaaatgaacagcctgaaaaccgaggacacggccgtgtattactgtgctagaga >IGHV3-72*02 ....................................................................................accttc............agtgaccactacatggactgggtccgccaggctccagggaaggggctggagtgggttggccgtactagaaacaaagctaacagctacaccacagaatacgccgcgtctgtgaaa...ggcagattcaccatctcaagagatgattcaaagaactcactgtat >IGHV3-73*01 gaggtgcagctggtggagtctggggga...ggcttggtccagcctggggggtccctgaaactctcctgtgcagcctctgggttcaccttc............agtggctctgctatgcactgggtccgccaggcttccgggaaagggctggagtgggttggccgtattagaagcaaagctaacagttacgcgacagcatatgctgcgtcggtgaaa...ggcaggttcaccatctccagagatgattcaaagaacacggcgtatctgcaaatgaacagcctgaaaaccgaggacacggccgtgtattactgtactagaca >IGHV3-73*02 gaggtgcagctggtggagtccggggga...ggcttggtccagcctggggggtccctgaaactctcctgtgcagcctctgggttcaccttc............agtggctctgctatgcactgggtccgccaggcttccgggaaagggctggagtgggttggccgtattagaagcaaagctaacagttacgcgacagcatatgctgcgtcggtgaaa...ggcaggttcaccatctccagagatgattcaaagaacacggcgtatctgcaaatgaacagcctgaaaaccgaggacacggccgtgtattactgtactagaca >IGHV3-74*01 gaggtgcagctggtggagtccggggga...ggcttagttcagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctactggatgcactgggtccgccaagctccagggaaggggctggtgtgggtctcacgtattaatagtgat......gggagtagcacaagctacgcggactccgtgaag...ggccgattcaccatctccagagacaacgccaagaacacgctgtatctgcaaatgaacagtctgagagccgaggacacggctgtgtattactgtgcaagaga >IGHV3-74*02 gaggtgcagctggtggagtctggggga...ggcttagttcagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctactggatgcactgggtccgccaagctccagggaaggggctggtgtgggtctcacgtattaatagtgat......gggagtagcacaagctacgcggactccgtgaag...ggccgattcaccatctccagagacaacgccaagaacacgctgtatctgcaaatgaacagtctgagagccgaggacacggctgtgtattactgtgcaaga >IGHV3-74*03 gaggtgcagctggtggagtccggggga...ggcttagttcagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctactggatgcactgggtccgccaagctccagggaaggggctggtgtgggtctcacgtattaatagtgat......gggagtagcacaacgtacgcggactccgtgaag...ggccgattcaccatctccagagacaacgccaagaacacgctgtatctgcaaatgaacagtctgagagccgaggacacggctgtgtattactgtgcaagaga >IGHV3-9*01 gaagtgcagctggtggagtctggggga...ggcttggtacagcctggcaggtccctgagactctcctgtgcagcctctggattcaccttt............gatgattatgccatgcactgggtccggcaagctccagggaagggcctggagtgggtctcaggtattagttggaat......agtggtagcataggctatgcggactctgtgaag...ggccgattcaccatctccagagacaacgccaagaactccctgtatctgcaaatgaacagtctgagagctgaggacacggccttgtattactgtgcaaaagata >IGHV3-9*02 gaagtgcagctggtggagtctggggga...ggcttggtacagcctggcaggtccctgagactctcctgtgcagcctctggattcacctct............gatgattatgccatgcactgggtccggcaagctccagggaagggcctggagtgggtctcaggtattagttggaat......agtggtagcataggctatgcggactctgtgaag...ggccgattcaccatctccagagacaacgccaagaactccctgtatctgcaaatgaacagtctgagagctgaggacacggccttgtattactgtgcaaaagata >IGHV3-9*03 gaagtgcagctggtggagtctggggga...ggcttggtacagcctggcaggtccctgagactctcctgtgcagcctctggattcaccttt............gatgattatgccatgcactgggtccggcaagctccagggaagggcctggagtgggtctcaggtattagttggaat......agtggtagcataggctatgcggactctgtgaag...ggccgattcaccatctccagagacaacgccaagaactccctgtatctgcaaatgaacagtctgagagctgaggacatggccttgtattactgtgcaaaagata >IGHV3-NL1*01 caggtgcagctggtggagtctggggga...ggcgtggtccagcctggggggtccctgagactctcctgtgcagcgtctggattcaccttc............agtagctatggcatgcactgggtccgccaggctccaggcaaggggctggagtgggtctcagttatttatagcggt......ggtagtagcacatactatgcagactccgtgaag...ggccgattcaccatctccagagacaattccaagaacacgctgtatctgcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgcgaaaga >IGHV3/OR15-7*01 gaggtgcagctggtggagtctggggga...ggcttggtccagcctgggggttctctgagactctcatgtgcagcctctggattcaccttc............agtgaccactacatgagctgggtccgccaggctcaagggaaagggctagagttggtaggtttaataagaaacaaagctaacagttacacgacagaatatgctgcgtctgtgaaa...ggcagacttaccatctcaagagaggattcaaagaacacgatgtatctgcaaatgagcaacctgaaaaccgaggacttggccgtgtattactgtgctaga >IGHV3/OR15-7*02 gaggtgcagctgttggagtctggggga...ggcttggtccagcctgggggttctctgagactctcatgtgctgcctctggattcaccttc............agtgaccactacatgagctgggtccgccaggctcaagggaaagggctagagttggtaggtttaataagaaacaaagctaacagttacacgacagaatatgctgcgtctgtgaaa...ggcagacttaccatctcaagagaggattcaaagaacacgctgtatctgcaaatgagcagcctgaaaaccgaggacttggccgtgtattactgtgctaga >IGHV3/OR15-7*03 gaggtgcagctggtggagtctggggga...ggcttggtccagcctgggggttctctgagactctcatgtgcagcctctggattcaccttc............agtgaccactacatgagctgggtccgccaggctcaagggaaagggctagagttggtaggtttaataagaaacaaagctaacagttacacgacagaatatgctgcgtctgtgaaa...ggcagacttaccatctcaagagaggattcaaagaacacgctgtatctgcaaatgagcagcctgaaaaccgaggacttggccgtgtattactgtgctaga >IGHV3/OR15-7*05 gaggtgcagctggtggagtctggggga...ggcttggtccagcctgggggttctctgagactctcatgtgcagcctctggattcaccttc............agtgaccactacatgagctgggtccgccaggctcaagggaaagggctagagttggtaggtttaataagaaacaaagctaacagttacacgacagaatatgctgcgtctgtgaaa...ggcagacttaccatctcaagagaggattcaaagaacacgctgtatctgcaaatgagcaacctgaaaaccgaggacttggccgtgtattactgtgctagaga >IGHV3/OR16-10*01 gaggttcagctggtgcagtctggggga...ggcttggtacatcctggggggtccctgagactctcctgtgcaggctctggattcaccttc............agtagctatgctatgcactgggttcgccaggctccaggaaaaggtctggagtgggtatcagctattggtactggt.........ggtggcacatactatgcagactccgtgaag...ggccgattcaccatctccagagacaatgccaagaactccttgtatcttcaaatgaacagcctgagagccgaggacatggctgtgtattactgtgcaaga >IGHV3/OR16-10*02 gaggttcagctggtgcagtctggggga...ggcttggtacagcctggggggtccctgagactctcctgtgcaggctctggattcaccttc............agtagctatgctatgcactgggttcgccaggctccaggaaaaggtctggagtgggtatcagctattggtactggt.........ggtggcacatactatgcagactccgtgaag...ggccgattcaccatctccagagacaatgccaagaactccttgtatcttcaaatgaacagcctgagagccgaggacatggctgtgtattactgtgcaaga >IGHV3/OR16-10*03 gaggtgcagctggtggagtctggggga...ggcttggtacagcctggggggtccctgagactctcctgtgcaggctctggattcaccttc............agtagctatgctatgcactgggttcgccaggctccaggaaaaggtctggagtgggtatcagctattggtactggt.........ggtggcacatactatgcagactccgtgaag...ggccgattcaccatctccagagacaatgccaagaactccttgtatcttcaaatgaacagcctgagagccgaggacatggctgtgtattactgtgcaagaga >IGHV3/OR16-12*01 gaggtgcagctggtagagtctgggaga...ggcttggcccagcctggggggtacctaaaactctccggtgcagcctctggattcaccgtc............ggtagctggtacatgagctggatccaccaggctccagggaagggtctggagtgggtctcatacattagtagtagt......ggttgtagcacaaactacgcagactctgtgaag...ggcagattcaccatctccacagacaactcaaagaacacgctctacctgcaaatgaacagcctgagagtggaggacacggccgtgtattactgtgcaaga >IGHV3/OR16-13*01 gaggtgcagctggtggagtctggggga...ggcttagtacagcctggagggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctactggatgcactgggtccgccaagctccagggaaggggctggtgtgggtctcacgtattaatagtgat......gggagtagcacaagctacgcagactccatgaag...ggccaattcaccatctccagagacaatgctaagaacacgctgtatctgcaaatgaacagtctgagagctgaggacatggctgtgtattactgtactaga >IGHV3/OR16-14*01 gaggtgcagctggaggagtctggggga...ggcttagtacagcctggagggtccctgagactctcctgtgcagcctctggattcaccttc............agtagctactggatgcactgggtccgccaatctccagggaaggggctggtgtgagtctcacgtattaatagtgat......gggagtagcacaagctacgcagactccttgaag...ggccaattcaccatctccagagacaatgctaagaacacgctgtatctgcaaatgaacagtctgagagctgaggacatggctgtgtattactgtactaga >IGHV3/OR16-15*01 gaagtgcagctggtggagtctggggga...ggcttggtccagcctggggggtccctgagactctcctgtgcagcctctgtattcaccttc............agtaacagtgacataaactgggtcctctaggctccaggaaaggggctggagtgggtctcgggtattagttggaat......ggcggtaagacgcactatgtggactccgtgaag...ggccaattttccatctccagagacaattccagcaagtccctgtatctgcaaaagaacagacagagagccaaggacatggccgtgtattactgtgtgagaaa >IGHV3/OR16-15*02 gaggtgcagctggtggagtctggggga...ggcttggtccagcctggggggtccctgagacactcctgtgcagcctctggattcaccttc............agtaacagtgacatgaactgggtcctctaggctccaggaaaggggctggagtgggtctcgggtattagttggaat......ggcggtaagacgcactatgtggactccgtgaag...ggccaatttaccatctccagagacaattccagcaagtccctgtatctgcaaaagaacagacagagagccaaagacatggccgtgtattactgtgtgaga >IGHV3/OR16-16*01 gaggtgcagctggtggagtctggggga...ggcttggtccagcctggggggtccctgagacactcctgtgcagcctctggattcaccttc............agtaacagtgacatgaactgggtcctctaggctccaggaaaggggctggagtgggtctcggatattagttggaat......ggcggtaagacgcactatgtggactccgtgaag...ggccaatttaccatctccagagacaattccagcaagtccctgtatctgcaaaagaacagacagagagccaaggacatggccgtgtattactgtgtgaga >IGHV3/OR16-6*02 gaggtgcagctggtggagtctgcggga...ggccttggtacagcctgggggtcccttagactctcctgtgcagcctctggattcacttgc............agtaacgcctggatgagctgggtccgccaggctccagggaaggggctggagtgggttggctgtattaaaagcaaagctaatggtgggacaacagactacgctgcacctgtgaaa...ggcagattcaccatctcaagagatgattcaaaaaacacgctgtatctgcaaatgatcagcctgaaaaccgaggacacggccgtgtattactgtaccacagg >IGHV3/OR16-8*01 gaggtgcagctggtggagtctggggga...ggcttggtacagcctggggggtccctgagactgtcctgtccagcctctggattcaccttc............agtaaccactacatgagctgggtccgccaggctccagggaagggactggagtgggtttcatacattagtggtgat......agtggttacacaaactacgcagactctgtgaag...ggccgattcaccatctccagggacaacgccaataactcaccgtatctgcaaatgaacagcctgagagctgaggacacggctgtgtattactgtgtgaaa >IGHV3/OR16-8*02 gaggtgcagctggtggagtctggggga...ggcttggtacagcctggggggtccctgagactgtcctgtccagactctggattcaccttc............agtaaccactacatgagctgggtccgccaggctccagggaagggactggagtggatttcatacattagtggtgat......agtggttacacaaactacgcagactctgtgaag...ggccgattcaccatctccagggacaacgccaataactcaccgtatctgcaaatgaacagcttgagagctgaggacacggctgtgtattactgtgtgaaaca >IGHV3/OR16-9*01 gaggtgcagctggtggagtctggagga...ggcttggtacagcctggggggtccctgagactctcctgtgcagcctctggattcaccttc............agtaaccactacacgagctgggtccgccaggctccagggaagggactggagtgggtttcatacagtagtggtaat......agtggttacacaaactacgcagactctgtgaaa...ggccgattcaccatctccagggacaacgccaagaactcactgtatctgcaaatgaacagcctgagagccgaggacacggctgtgtattactgtgtgaaa >IGHV4-28*01 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggacaccctgtccctcacctgcgctgtctctggttactccatcagc.........agtagtaactggtggggctggatccggcagcccccagggaagggactggagtggattgggtacatctattatagt.........gggagcacctactacaacccgtccctcaag...agtcgagtcaccatgtcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgtggacacggccgtgtattactgtgcgagaaa >IGHV4-28*02 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcacagaccctgtccctcacctgcgctgtctctggttactccatcagc.........agtagtaactggtggggctggatccggcagcccccagggaagggactggagtggattgggtacatctattatagt.........gggagcatctactacaacccgtccctcaag...agtcgagtcaccatgtcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgtggacacggccgtgtattactgtgcgagaaa >IGHV4-28*03 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggacaccctgtccctcacctgcgctgtctctggttactccatcagc.........agtagtaactggtggggctggatccggcagcccccagggaagggactggagtggattgggtacatctattatagt.........gggagcacctactacaacccgtccctcaag...agtcgagtcaccatgtcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgtggacacggccgtgtattactgtgcgagaga >IGHV4-28*04 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggacaccctgtccctcacctgcgctgtctctggttactccatcagc.........agtagtaactggtggggctggatccggcagcccccagggaagggactggagtggattgggtacatctattatagt.........gggagcacctactacaacccgtccctcaag...agtcgagtcaccatgtcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgtggacaccggcgtgtattactgtgcgaga >IGHV4-28*05 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggacaccctgtccctcacctgcgctgtctctggttactccatcagc.........agtagtaactggtggggctggatccggcagcccccagggaagggactggagtggattgggtacatctattatagt.........gggagcatctactacaacccgtccctcaag...agtcgagtcaccatgtcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgtggacacggccgtgtattactgtgcgagaaa >IGHV4-28*06 caggtgcagctacaggagtcgggccca...ggactggtgaagccttcggacaccctgtccctcacctgcgctgtctctggttactccatcagc.........agtagtaactggtggggctggatccggcagcccccagggaagggactggagtggattgggtacatctattatagt.........gggagcaccaactacaacccgtccctcaag...agtcgagtcaccatgtcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccttggacacggccgtgtattactgtgcgagaaa >IGHV4-28*07 caggtacagctgcaggagtcgggccca...ggactggtgaagccttcggacaccctgtccctcacctgcgctgtctctggttactccatcagc.........agtagtaactggtggggctggatccggcagcccccagggaagggactggagtggattgggtacatctattatagt.........gggagcacctactacaacccgtccctcaag...agtcgagtcaccatgtcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgtggacacggccgtgtattactgtgcgagaaa >IGHV4-30-2*01 cagctgcagctgcaggagtccggctca...ggactggtgaagccttcacagaccctgtccctcacctgcgctgtctctggtggctccatcagc......agtggtggttactcctggagctggatccggcagccaccagggaagggcctggagtggattgggtacatctatcatagt.........gggagcacctactacaacccgtccctcaag...agtcgagtcaccatatcagtagacaggtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggccgtgtattactgtgccagaga >IGHV4-30-2*02 cagctgcagctgcaggagtccggctca...ggactggtgaagccttcacagaccctgtccctcacctgcgctgtctctggtggctccatcagc......agtggtggttactcctggagctggatccggcagccaccagggaagggcctggagtggattgggtacatctatcatagt.........gggagcacctactacaacccgtccctcaag...agtcgagtcaccatatcagtagacaggtccaagaaccagttctccctgaagctgagctctgtgaccgctgcggacacggccgtgtattactgtgcg >IGHV4-30-2*03 cagctgcagctgcaggagtccggctca...ggactggtgaagccttcacagaccctgtccctcacctgcgctgtctctggtggctccatcagc......agtggtggttactcctggagctggatccggcagccaccagggaagggcctggagtggattgggagtatctattatagt.........gggagcacctactacaacccgtccctcaag...agtcgagtcaccatatccgtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgctgcagacacggctgtgtattactgtgcgagaca >IGHV4-30-2*04 ...........................................................................tctggtggctccatcagc......agtggtggttactcctggagctggatccggcagccaccagggaagggcctggagtggattgggtacatctatcatagt.........gggagcacctactacaacccgtccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcagacacggccgtgtattactgtgcgagaga >IGHV4-30-2*05 cagctgcagctgcaggagtccggctca...ggactggtgaagccttcacagaccctgtccctcacctgcgctgtctctggtggctccatcagc......agtggtggttactcctggagctggatccggcagccaccagggaagggcctggagtggattgggtacatctatcatagt.........gggagcacctactacaacccgtccctcaag...agtcgagttaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgactgccgcagacacggccgtgtattactgtgccagaga >IGHV4-30-2*06 cagctgcagctgcaggagtccggctca...ggactggtgaagccttcacagaccctgtccctcacctgcgctgtctctggtggctccatcagc......agtggtggttactcctggagctggatccggcagtcaccagggaagggcctggagtggattgggtacatctatcatagt.........gggagcacctactacaacccgtccctcaag...agtcgagtcaccatatcagtagacaggtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggccgtgtattactgtgccagaga >IGHV4-30-4*01 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcacagaccctgtccctcacctgcactgtctctggtggctccatcagc......agtggtgattactactggagttggatccgccagcccccagggaagggcctggagtggattgggtacatctattacagt.........gggagcacctactacaacccgtccctcaag...agtcgagttaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgactgccgcagacacggccgtgtattactgtgccagaga >IGHV4-30-4*02 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggacaccctgtccctcacctgcactgtctctggtggctccatcagc......agtggtgattactactggagttggatccgccagcccccagggaagggcctggagtggattgggtacatctattacagt.........gggagcacctactacaacccgtccctcaag...agtcgagttaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgactgcagcagacacggccgtgtattactgtgccagaga >IGHV4-30-4*03 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcacagaccctgtccctcacctgcactgtctctggtggctccatcagc......agtggtgattactactggagttggatccgccagcccccagggaagggcctggagtggattgggtacatctattacagt.........gggagcacctactacaacccgtccctcaag...agtcgagttaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgactgccgcggacacggccgtgtattactg >IGHV4-30-4*04 caggtgcagctgcaggactcgggccca...ggactggtgaagccttcacagaccctgtccctcacctgcactgtctctggtggctccatcagc......agtggtgattactactggagttggatccgccagcccccagggaagggcctggagtggattgggtacttctattacagt.........gggagcacctactacaacccgtccctcaag...agtcgagttaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgactgccgcagacacggccgtgtattactg >IGHV4-30-4*05 ..........................................................................ctctggtggctccatcagc......agtggtgattactactggagttggatccgccagcncccagggaagggcctggagtggattgggtacatctattacagt.........gggagcacctactacaacccgtccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgactgccgcagacacggccgtgtattactgtgccagaga >IGHV4-30-4*06 ...........................................................................tctggtggctccatcagc......agtggtgattactactggagttggatccgccagcacccagggaagggcctggagtggattgggtacatctattacagt.........gggagcacctactacaacccgtccctcaag...agtcgagttaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgactgccgcagacacggccgtgtattactgtgccagaga >IGHV4-30-4*07 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcacagaccctgtccctcacctgcgctgtctctggtggctccatcagc......agtggtggttactcctggagctggatccggcagccaccagggaagggactggagtggattgggtatatctattacagt.........gggagcacctactacaacccgtccctcaag...agtcgagttaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggccgtgtattactgtgccagaga >IGHV4-31*01 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcacagaccctgtccctcacctgcactgtctctggtggctccatcagc......agtggtggttactactggagctggatccgccagcacccagggaagggcctggagtggattgggtacatctattacagt.........gggagcacctactacaacccgtccctcaag...agtctagttaccatatcagtagacacgtctaagaaccagttctccctgaagctgagctctgtgactgccgcggacacggccgtgtattactgtgcgagaga >IGHV4-31*02 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcacagaccctgtccctcacctgtactgtctctggtggctccatcagc......agtggtggttactactggagctggatccgccagcacccagggaagggcctggagtggattgggtacatctattacagt.........gggagcacctactacaacccgtccctcaag...agtcgagttaccatatcagtagacacgtctaagaaccagttctccctgaagctgagctctgtgactgccgcggacacggccgtgtattactgtgcgagaga >IGHV4-31*03 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcacagaccctgtccctcacctgcactgtctctggtggctccatcagc......agtggtggttactactggagctggatccgccagcacccagggaagggcctggagtggattgggtacatctattacagt.........gggagcacctactacaacccgtccctcaag...agtcgagttaccatatcagtagacacgtctaagaaccagttctccctgaagctgagctctgtgactgccgcggacacggccgtgtattactgtgcgagaga >IGHV4-31*04 caggtgcggctgcaggagtcgggccca...ggactggtgaagccttcacagaccctgtccctcacctgcactgtctctggtggctccatcagc......agtggtggttactactggagctggatccgccagcacccagggaagggcctggagtggattgggtacatctattacagt.........gggagcacctactacaacccgtccctcaag...agtcgagttaccatatcagtagacacgtctaagaaccagttctccctgaagctgagctctgtgactgccgcggacacggccgtgtattactgtgcg >IGHV4-31*05 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcacagaccctgtccctcacctgcactgtctctggtggctccatcagc......agtggtggttactactggagctggatccgccagcacccagggaagggcctggagtggattgggtacatctattacagt.........gggagcacctactacaacccgtccctcaag...agtcgagttaccatatcagtagacacgtctaagaaccagttctccctgaagctgagctctgtgacc...gcggacgcggccgtgtattactgtgcg >IGHV4-31*06 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcacagaccctgtccctcacctgcactgtctctggtggctccatcagc......agtggtagttactactggagctggatccgccagcacccagggaagggcctggagtggattgggtacatctattacagt.........gggagcacctactacaacccgtccctcaag...agtcgagttaccatatcagtagacacgtctaagaaccagttctccctgaagctgagctctgtgactgccgcggacacggccgtgtattactg >IGHV4-31*07 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcacagaccctgtccctcacctgcactgtctctggtggatccatcagc......agtggtggttactactggagctggatccgccagcacccagggaagggcctggagtggattgggtacatctattacagt.........gggagcacctactacaacccgtccctcaag...agtcgagttaccatatcagtagacacgtctaagaaccagttctccctgaagctgagctctgtgactgccgcggacacggccgtgtattactg >IGHV4-31*08 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcacagaccctgtccctcacctgcactgtctctggtggctccatcagc......agtggtggttactactggagctggatccgccagcacccagggaagggcctggagtggattgggtacatctattacagt.........gggagcacctactacaacccgtccctcaag...agtcgagttaccatatccgtagacacgtccaagaaccagttctccctgaagctgagctctgtgactgccgcggacacggccgtgtattactg >IGHV4-31*09 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcacagaccctgtccctcacctgcactgtctctggtggctccatcagc......agtggtggttactactggagctggatccgccagcacccagggaagggcctggagtggattgggtacatctattacagt.........gggagcacctactacaacccgtccctcaag...agtcgagttaccatatcagtagacaagtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggccgtgtattactg >IGHV4-31*10 caggtgcagctgcaggagtcgggccca...ggactgttgaagccttcacagaccctgtccctcacctgcactgtctctggtggctccatcagc......agtggtggttactactggagctggatccgccagcacccagggaagggcctggagtggattgggtgcatctattacagt.........gggagcacctactacaacccgtccctcaag...agtcgagttaccatatcagtagacccgtccaagaaccagttctccctgaagccgagctctgtgactgccgcggacacggccgtggattactgtgcgagaga >IGHV4-34*01 caggtgcagctacagcagtggggcgca...ggactgttgaagccttcggagaccctgtccctcacctgcgctgtctatggtgggtccttc............agtggttactactggagctggatccgccagcccccagggaaggggctggagtggattggggaaatcaatcatagt.........ggaagcaccaactacaacccgtccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggctgtgtattactgtgcgagagg >IGHV4-34*02 caggtgcagctacaacagtggggcgca...ggactgttgaagccttcggagaccctgtccctcacctgcgctgtctatggtgggtccttc............agtggttactactggagctggatccgccagcccccagggaaggggctggagtggattggggaaatcaatcatagt.........ggaagcaccaactacaacccgtccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggctgtgtattactgtgcgagagg >IGHV4-34*03 caggtgcagctacagcagtggggcgca...ggactgttgaagccttcggagaccctgtccctcacctgcgctgtctatggtgggtccttc............agtggttactactggagctggatccgccagcccccagggaaggggctggagtggattggggaaatcaatcatagt.........ggaagcaccaactacaacccgtccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggccgtgtattactg >IGHV4-34*04 caggtgcagctacagcagtggggcgca...ggactgttgaagccttcggagaccctgtccctcacctgcgctgtctatggtgggtccttc............agtggttactactggagctggatccgccagcccccagggaaggggctggagtggattggggaaatcaatcatagt.........ggaagcaccaacaacaacccgtccctcaag...agtcgagccaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggctgtgtattactgtgcgagagg >IGHV4-34*05 caggtgcagctacagcagtggggcgca...ggactgttgaagccttcggagaccctgtccctcacctgcgctgtctatggtgggtccttc............agtggttactactggtgctggatccgccagcccctagggaaggggctggagtggattggggaaatcaatcatagt.........ggaagcaccaacaacaacccgtccctcaag...agtcgagccaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggctgtgtattactgtgcgagagg >IGHV4-34*06 caggtgcagctacagcagtggggcgca...ggactgttgaagccttcggagaccctgtccctcacctgcgctgtctatggtgggtccttc............agtggttactactggagctggatccgccagcccccagggaaggggctggagtggattggggaaatcaatcatagt.........ggaagcaccaactacaacccgtccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttctccctgaagctgggctctgtgaccgccgcggacacggccgtgtattactg >IGHV4-34*07 caggtgcagctacagcagtggggcgca...ggactgttgaagccttcggagaccctgtccctcacctgcgctgtctatggtgggtccttc............agtggttactactggagctggatccgccagcccccagggaaggggctggagtggattggggaaatcaaccatagt.........ggaagcaccaactacaacccgtccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggccgtgtattactg >IGHV4-34*08 caggtgcagctacagcagtggggcgca...ggactgttgaagccttcggagaccctgtccctcacctgcgctgtctatggtgggaccttc............agtggttactactggagctggatccgccagcccccagggaaggggctggagtggattggggaaatcaatcatagt.........ggaagcaccaactacaacccgtccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggctgtgtattactgtgcg >IGHV4-34*09 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcacagaccctgtccctcacctgcgctgtctatggtgggtccttc............agtggttactactggagctggatccgccagcccccagggaagggactggagtggattggggaaatcaatcatagt.........ggaagcaccaactacaacccgtccctcaag...agtcgagttaccatatcagtagacacgtctaagaaccagttctccctgaagctgagctctgtgactgccgcggacacggccgtgtattactgtgcgagaga >IGHV4-34*10 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcgctgtctatggtgggtccttc............agtggttactactggagctggatccgccagcccccagggaagggactggagtggattggggaaatcaatcatagt.........ggaagcaccaactacaacccgtccctcaag...agtcgaatcaccatgtcagtagacacgtccaagaaccagttctacctgaagctgagctctgtgaccgccgcggacacggccgtgtattactgtgcgagata >IGHV4-34*11 caggtgcagctacagcagtggggcgca...ggactgttgaagccttcggagaccctgtccctcacctgcgctgtctatggtgggtccgtc............agtggttactactggagctggatccggcagcccccagggaaggggctggagtggattgggtatatctattatagt.........gggagcaccaacaacaacccctccctcaag...agtcgagccaccatatcagtagacacgtccaagaaccagttctccctgaacctgagctctgtgaccgccgcggacacggccgtgtattgctgtgcgagaga >IGHV4-34*12 caggtgcagctacagcagtggggcgca...ggactgttgaagccttcggagaccctgtccctcacctgcgctgtctatggtgggtccttc............agtggttactactggagctggatccgccagcccccagggaaggggctggagtggattggggaaatcattcatagt.........ggaagcaccaactacaacccgtccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggctgtgtattactgtgcgaga >IGHV4-34*13 ...........................................................................tatggtgggtccttc............agtggttactactggagctggatccgccagcccccagggaaggggctggagtggattggggaaatcaatcatagt.........ggaagcaccaactacaacccctccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggctgtgtattactgtgcgagagg >IGHV4-38-2*01 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcgctgtctctggttactccatcagc.........agtggttactactggggctggatccggcagcccccagggaaggggctggagtggattgggagtatctatcatagt.........gggagcacctactacaacccgtccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcagacacggccgtgtattactgtgcgaga >IGHV4-38-2*02 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcactgtctctggttactccatcagc.........agtggttactactggggctggatccggcagcccccagggaaggggctggagtggattgggagtatctatcatagt.........gggagcacctactacaacccgtccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcagacacggccgtgtattactgtgcgagaga >IGHV4-39*01 cagctgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcactgtctctggtggctccatcagc......agtagtagttactactggggctggatccgccagcccccagggaaggggctggagtggattgggagtatctattatagt.........gggagcacctactacaacccgtccctcaag...agtcgagtcaccatatccgtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcagacacggctgtgtattactgtgcgagaca >IGHV4-39*02 cagctgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcactgtctctggtggctccatcagc......agtagtagttactactggggctggatccgccagcccccagggaaggggctggagtggattgggagtatctattatagt.........gggagcacctactacaacccgtccctcaag...agtcgagtcaccatatccgtagacacgtccaagaaccacttctccctgaagctgagctctgtgaccgccgcagacacggctgtgtattactgtgcgagaga >IGHV4-39*03 cagctgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcactgtctctggtggctccatcagc......agtagtagttactactggggctggatccgccagcccccagggaaggggctggagtggattgggagtatctattatagt.........gggagcacctactacaacccgtccctcaag...agtcgagtcaccatatccgtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcagacacggccgtgtattactg >IGHV4-39*04 ..................................................................................gctccatcagc......agtagtagttactactggggctggatccgccagcccccagggaaggggctggagtggattgggagtatctattatagt.........gggagcacctactacaacccgtccctcaag...agtcgagtcaccatatccgtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacac >IGHV4-39*05 cagctgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccccgtccctcacctgcactgtctctggtggctccatcagc......agtagtagttactactggggctggatccgccagcccccagggaaggggctggagtggattgggagtatctattatagt.........gggagcacctactacaacccgtccctcaag...agtcgagtcaccatatccgtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcagacacggctgtgtattactgtgcg >IGHV4-39*06 cggctgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcactgtctctggtggctccatcagc......agtagtagttactactggggctggatccgccagcccccagggaaggggctggagtggattgggagtatctattatagt.........gggagcacctactacaacccgtccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttccccctgaagctgagctctgtgaccgccgcggacacggccgtgtattactgtgcgagaga >IGHV4-39*07 cagctgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcactgtctctggtggctccatcagc......agtagtagttactactggggctggatccgccagcccccagggaaggggctggagtggattgggagtatctattatagt.........gggagcacctactacaacccgtccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggccgtgtattactgtgcgagaga >IGHV4-4*01 caggtgcagctgcaggagtcgggccca...ggactggtgaagcctccggggaccctgtccctcacctgcgctgtctctggtggctccatcagc.........agtagtaactggtggagttgggtccgccagcccccagggaaggggctggagtggattggggaaatctatcatagt.........gggagcaccaactacaacccgtccctcaag...agtcgagtcaccatatcagtagacaagtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggccgtgtattgctgtgcgagaga >IGHV4-4*02 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggggaccctgtccctcacctgcgctgtctctggtggctccatcagc.........agtagtaactggtggagttgggtccgccagcccccagggaaggggctggagtggattggggaaatctatcatagt.........gggagcaccaactacaacccgtccctcaag...agtcgagtcaccatatcagtagacaagtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggccgtgtattactgtgcgagaga >IGHV4-4*03 caggtgcagctgcaggagtcgggccca...ggactggtgaagcctccggggaccctgtccctcacctgcgctgtctctggtggctccatcagc.........agtagtaactggtggagttgggtccgccagcccccagggaaggggctggagtggattggggaaatctatcatagt.........gggagcaccaactacaacccgtccctcaag...agtcgagtcaccatatcagtagacaagtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggccgtgtattactg >IGHV4-4*04 caggtgcagctgcaggagtcgggccca...ggactggtgaagcctccggggaccctgtccctcacctgcgctatctctggtggctccatcagc.........agtagtaactggtggagttgggtccgccagcccccagggaaggggctggagtggattggggaaatctatcatagt.........gggagcaccaactacaacccgtccctcaag...agtcgagtcaccatatcagtagacaagtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggccgtgtattactg >IGHV4-4*05 caggtgcagctgcaggagttgggccca...ggactggtgaagcctccggggaccctgtccctcacctgcgctgtctctggtggctccatcagc.........agtagtaactggtggagttgggtccgccagcccccagggaaggggctggagtggattggggaaatctatcatagt.........gggagcaccaactacaacccgtccctcaag...agtcgagtcaccatatcagtagacaagtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggccgtgtattactg >IGHV4-4*06 ...........................................................................tctggtggctccatcagc.........agtagtaactggtggagttgggtccgccagcccccagggannnggctggagtggattggggaaatctatcatagt.........gggagcaccaactacaacccgtccctcaag...agtcgagtcaccatgtcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggccgtgtattactgtgcgagaga >IGHV4-4*07 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcactgtctctggtggctccatc............agtagttactactggagctggatccggcagcccgccgggaagggactggagtggattgggcgtatctataccagt.........gggagcaccaactacaacccctccctcaag...agtcgagtcaccatgtcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggccgtgtattactgtgcgagaga >IGHV4-4*08 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcactgtctctggtggctccatc............agtagttactactggagctggatccggcagcccccagggaagggactggagtggattgggtatatctataccagt.........gggagcaccaactacaacccctccctcaag...agtcgagtcaccatatccgtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcagacacggccgtgtattactgtgcgagaga >IGHV4-55*01 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcatctgcgctgtctctggtgactccatcagc.........agtggtaactggtgaatctgggtccgccagcccccagggaaggggctggagtggattggggaaatccatcatagt.........gggagcacctactacaacccgtccctcaag...agtcgaatcaccatgtccgtagacacgtccaagaaccagttctacctgaagctgagctctgtgaccgccgcggacacggccgtgtattactgtgcgagata >IGHV4-55*02 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcatctgcgctgtctctggtgactccatcagc.........agtggtaactggtgaatctgggtccgccagcccccagggaaggggctggagtggattggggaaatccatcatagt.........gggagcacctactacaacccgtccctcaag...agtcgaatcaccatgtcagtagacacgtccaagaaccagttctacctgaagctgagctctgtgaccgccgcggacacggccgtgtattactgtgcgagata >IGHV4-55*03 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcatctgcgctgtctctggtgactccatcagc.........agtggtaactggtgaatctgggtccgccagcccccagggaaggggctggagtggattggggaaatccatcatagt.........gggagcacctactacaacccgtccctcaag...agtcgaatcaccatgtcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggccgtgtattactg >IGHV4-55*04 caggtgcagctgcaggagtcgggccca...ggactggtgaagctttcggagaccctgtccctcatctgcgctgtctctggtgactccatcagc.........agtggtaactggtgaatctgggtccgccagcccccagggaaggggctggagtggattggggaaatccatcatagt.........gggagcacctactacaacccgtccctcaag...agtcgaatcaccatgtcagtagacacgtccaagaaccagttctacctgaagctgagctctgtgaccgccgcggacacggccgtgtattactg >IGHV4-55*05 caggtgcagctgcaggagtcgggccca...ggactggtgaagctttcggagaccctgtccctcatctgcgctgtctctggtgactccatcagc.........agtggtaactggtgaatctgggtccgccagcccccagggaaggggctggagtggattggggaaatccatcatagt.........gggagcacctactacaacccgtccctcaag...agtcgaatcaccatgtccgtagacacgtccaagaaccagttctacctgaagctgagctctgtgaccgccgcggacacggccgtgtattactg >IGHV4-55*06 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcatctgcgctgtctctggtgactccatcagc.........agtggtaactggtgaatctgggtccgccagcccccagggaaggggctggagtggattggggaaatccatcatagt.........gggagcacctactacaacccgtccctcaag...agtcgaatcaccatgtccgtagacacgtccaagaagcagttctacctgaagctgagctctgtgaccgctgcggacacggccgtgtattactg >IGHV4-55*07 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcatctgcgctgtctctggtgactccatcagc.........agtggtaactggtgaatctgggtccgccagcccccagggaaggggctggagtggattggggaaatccatcatagt.........gggagcacctactacaacccgtccctcaag...agtcgaatcaccatgtccgtagacacgtccaggaaccagttctccctgaagctgagctctgtgaccgccgcagacacggccgtgtattactg >IGHV4-55*08 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcatctgcgctgtctctggtgactccatcagc.........agtggtaactggtgaatctgggtccgccagcccccagggaaggggctggagtggattggggaaatccatcatagt.........gggagcacctactacaacccgtccctcaag...agtcgaatcaccatgtcagtagacacgtccaagaaccagttctacctgaagctgagctctgtgaccgccgcggacacggccgtgtattactgtgcgagaga >IGHV4-55*09 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcatctgcgctgtctctggtgactccatcagc.........agtggtaactggtgaatctgggtccgccagcccccagggaaggggctggagtggattggggaaatccatcatagt.........gggagcacctactacaacccgtccctcaag...agtcgaatcaccatgtccgtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgtggacacggccgtgtattactgtgcgagaaa >IGHV4-59*01 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcactgtctctggtggctccatc............agtagttactactggagctggatccggcagcccccagggaagggactggagtggattgggtatatctattacagt.........gggagcaccaactacaacccctccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgctgcggacacggccgtgtattactgtgcgagaga >IGHV4-59*02 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcactgtctctggtggctccgtc............agtagttactactggagctggatccggcagcccccagggaagggactggagtggattgggtatatctattacagt.........gggagcaccaactacaacccctccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgctgcggacacggccgtgtattactgtgcgagaga >IGHV4-59*03 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcactgtctctggtggctccatc............agtagttactactggagctggatccggcagcccccagggaagggactggagtggattgggtatatctattacagt.........gggagcaccaactacaacccctccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccaattctccctgaagctgagctctgtgaccgctgcggacacggccgtgtattactgtgcg >IGHV4-59*04 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcactgtctctggtggctccatc............agtagttactactggagctggatccggcagcccccagggaagggactggagtggattgggtatatctattatagt.........gggagcacctactacaacccgtccctcaag...agtcgagtcaccatgtcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcagacacggctgtgtattactgtgcg >IGHV4-59*05 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcactgtctctggtggctccatc............agtagttactactggagctggatccggcagccgccggggaagggactggagtggattgggcgtatctattatagt.........gggagcacctactacaacccgtccctcaag...agtcgagtcaccatatccgtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcagacacggctgtgtattactgtgcg >IGHV4-59*06 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcactgtcactggtggctccatc............agtagttactactggagctggatccggcagcccgctgggaagggcctggagtggattgggtacatctattacagt.........gggagcacctactacaacccgtccctcaag...agtcgagttaccatatcagtagacacgtctaagaaccagttctccctgaagctgagctctgtgactgccgcggacacggccgtgtattactgtgcg >IGHV4-59*07 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggacaccctgtccctcacctgcactgtctctggtggctccatc............agtagttactactggagctggatccggcagcccccagggaagggactggagtggattgggtatatctattacagt.........gggagcaccaactacaacccctccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgctgcggacacggccgtgtattactgtgcgaga >IGHV4-59*08 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcactgtctctggtggctccatc............agtagttactactggagctggatccggcagcccccagggaagggactggagtggattgggtatatctattacagt.........gggagcaccaactacaacccctccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcagacacggccgtgtattactgtgcgagaca >IGHV4-59*09 ...........................................................................tctggtggctccatc............agtagttactactggagctggatccggcagcccccaggnannngactggagtggattgggtatatctattacagt.........gggagcaccaactacaacccctccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgctgcggacacggccgtgtattactgtgcgagagg >IGHV4-59*10 caggtgcagctacagcagtggggcgca...ggactgttgaagccttcggagaccctgtccctcacctgcgctgtctatggtggctccatc............agtagttactactggagctggatccggcagcccgccgggaaggggctggagtggattgggcgtatctataccagt.........gggagcaccaactacaacccctccctcaag...agtcgagtcaccatgtcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggccgtgtattactgtgcgagata >IGHV4-61*01 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcactgtctctggtggctccgtcagc......agtggtagttactactggagctggatccggcagcccccagggaagggactggagtggattgggtatatctattacagt.........gggagcaccaactacaacccctccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgctgcggacacggccgtgtattactgtgcgagaga >IGHV4-61*02 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcacagaccctgtccctcacctgcactgtctctggtggctccatcagc......agtggtagttactactggagctggatccggcagcccgccgggaagggactggagtggattgggcgtatctataccagt.........gggagcaccaactacaacccctccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcagacacggccgtgtattactgtgcgagaga >IGHV4-61*03 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcactgtctctggtggctccgtcagc......agtggtagttactactggagctggatccggcagcccccagggaagggactggagtggattgggtatatctattacagt.........gggagcaccaactacaacccctccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccacttctccctgaagctgagctctgtgaccgctgcggacacggccgtgtattactgtgcgagaga >IGHV4-61*04 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcactgtctctggtggctccgtcagc......agtggtagttactactggagctggatccggcagcccccagggaagggactggagtggattggatatatctattacagt.........gggagcaccaactacaacccctccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgct...gacacggccgtgtattactg >IGHV4-61*05 cagctgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcactgtctctggtggctccatcagc......agtagtagttactactggggctggatccggcagcccccagggaagggactggagtggattgggtatatctattacagt.........gggagcaccaactacaacccctccctcaag...agtcgagtcaccatatcagtagacaagtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggccgtgtattactgtgcgaga >IGHV4-61*06 ...........................................................................tctggtggctccgtcagc......agtggtagttactactggagctggatccggcagcccccagggaagggactggagtggattgggtatatctattacagt.........gggagcaccaactacaacccctccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggccgtgtattactgtgccagaga >IGHV4-61*07 ...........................................................................tctggtggctccgtcagc......agtggtagttactactggagctggatccggcagcccccagggaagggactggagtggattgggtatatctattacagt.........gggagcaccaactacaacccctccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgctgcggacacggccgtgtattactgtgcgagaca >IGHV4-61*08 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcactgtctctggtggctccgtcagc......agtggtggttactactggagctggatccggcagcccccagggaagggactggagtggattgggtatatctattacagt.........gggagcaccaactacaacccctccctcaag...agtcgagtcaccatatcagtagacacgtccaagaaccagttctccctgaagctgagctctgtgaccgctgcggacacggccgtgtattactgtgcgagaga >IGHV4/OR15-8*01 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcgttgtctctggtggctccatcagc.........agtagtaactggtggagctgggtccgccagcccccagggaaggggctggagtggattggggaaatctatcatagt.........gggagccccaactacaacccgtccctcaag...agtcgagtcaccatatcagtagacaagtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggccgtgtattactgtgcgagaga >IGHV4/OR15-8*02 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcgttgtctctggtggctccatcagc.........agtagtaactggtggagctgggtccgccagcccccagggaaggggctggagtggattggggaaatctatcatagt.........gggaaccccaactacaacccgtccctcaag...agtcgagtcaccatatcaatagacaagtccaagaaccaattctccctgaagctgagctctgtgaccgccgcggacacggccgtgtattactgtgcgagaga >IGHV4/OR15-8*03 caggtgcagctgcaggagtcgggccca...ggactggtgaagccttcggagaccctgtccctcacctgcgttgtctctggtggctccatcagc.........agtagtaactggtggagctgggtccgccagcccccagggaaggggctggagtggattggggaaatctatcatagt.........gggagccccaactacaacccatccctcaag...agtcgagtcaccatatcagtagacaagtccaagaaccagttctccctgaagctgagctctgtgaccgccgcggacacggccgtgtattactgtgcgagaga >IGHV5-10-1*01 gaagtgcagctggtgcagtctggagca...gaggtgaaaaagcccggggagtctctgaggatctcctgtaagggttctggatacagcttt............accagctactggatcagctgggtgcgccagatgcccgggaaaggcctggagtggatggggaggattgatcctagt......gactcttataccaactacagcccgtccttccaa...ggccacgtcaccatctcagctgacaagtccatcagcactgcctacctgcagtggagcagcctgaaggcctcggacaccgccatgtattactgtgcgaga >IGHV5-10-1*02 gaagtgcagctggtgcagtctggagca...gaggtgaaaaagcccggggagtctctgaggatctcctgtaagggttctggatacagcttt............accagctactggatcagctgggtgcgccagatgcccgggaaaggcttggagtggatggggaggattgatcctagt......gactcttataccaactacagcccgtccttccaa...ggccacgtcaccatctcagctgacaagtccatcagcactgcctacctgcagtggagcagcctgaaggc.tcggacaccgccatgtattactgtgcgagaca >IGHV5-10-1*03 gaagtgcagctggtgcagtccggagca...gaggtgaaaaagcccggggagtctctgaggatctcctgtaagggttctggatacagcttt............accagctactggatcagctgggtgcgccagatgcccgggaaaggcctggagtggatggggaggattgatcctagt......gactcttataccaactacagcccgtccttccaa...ggccacgtcaccatctcagctgacaagtccatcagcactgcctacctgcagtggagcagcctgaaggcctcggacaccgccatgtattactgtgcgaga >IGHV5-10-1*04 gaagtgcagctggtgcagtctggagca...gaggtgaaaaagcccggggagtctctgaggatctcctgtaagggttctggatacagcttt............accagctactggatcagctgggtgcgccagatgcccgggaaaggcctggagtggatggggaggattgatcctagt......gactcttataccaactacagcccgtccttccaa...ggccaggtcaccatctcagctgacaagtccatcagcactgcctacctgcagtggagcagcctgaaggcctcggacaccgccatgtattactgtgcgaga >IGHV5-51*01 gaggtgcagctggtgcagtctggagca...gaggtgaaaaagcccggggagtctctgaagatctcctgtaagggttctggatacagcttt............accagctactggatcggctgggtgcgccagatgcccgggaaaggcctggagtggatggggatcatctatcctggt......gactctgataccagatacagcccgtccttccaa...ggccaggtcaccatctcagccgacaagtccatcagcaccgcctacctgcagtggagcagcctgaaggcctcggacaccgccatgtattactgtgcgagaca >IGHV5-51*02 gaggtgcagctggtgcagtctggagca...gaggtgaaaaagcccggggagtctctgaagatctcctgtaagggttctggatacagcttt............accagctactggaccggctgggtgcgccagatgcccgggaaaggcttggagtggatggggatcatctatcctggt......gactctgataccagatacagcccgtccttccaa...ggccaggtcaccatctcagccgacaagtccatcagcaccgcctacctgcagtggagcagcctgaaggcctcggacaccgccatgtattactgtgcgagaca >IGHV5-51*03 gaggtgcagctggtgcagtctggagca...gaggtgaaaaagccgggggagtctctgaagatctcctgtaagggttctggatacagcttt............accagctactggatcggctgggtgcgccagatgcccgggaaaggcctggagtggatggggatcatctatcctggt......gactctgataccagatacagcccgtccttccaa...ggccaggtcaccatctcagccgacaagtccatcagcaccgcctacctgcagtggagcagcctgaaggcctcggacaccgccatgtattactgtgcgaga >IGHV5-51*04 gaggtgcagctggtgcagtctggagca...gaggtgaaaaagccgggggagtctctgaagatctcctgtaagggttctggatacagcttt............accagctactggatcggctgggtgcgccagatgcccgggaaaggcctggagtggatggggatcatctatcctggt......gactctgataccagatacagcccgtccttccaa...ggccaggtcaccatctcagccgacaagcccatcagcaccgcctacctgcagtggagcagcctgaaggcctcggacaccgccatgtattactgtgcgaga >IGHV5-51*05 .....................................aaaagcccggggagtctctgaagatctcctgtaagggttctggatacagcttt............accagctactggatcggctgggtgcgccagatgcccaggaaaggcctggagtggatggggatcatctatcctggt......gactctgataccagatacagcccgtccttccaa...ggccaggtcaccatctcagccgacaagtccatcagcaccgcctacctgcagtggagcagcctgaaggcctcggacaccgccatg >IGHV5-78*01 gaggtgcagctgttgcagtctgcagca...gaggtgaaaagacccggggagtctctgaggatctcctgtaagacttctggatacagcttt............accagctactggatccactgggtgcgccagatgcccgggaaagaactggagtggatggggagcatctatcctggg......aactctgataccagatacagcccatccttccaa...ggccacgtcaccatctcagccgacagctccagcagcaccgcctacctgcagtggagcagcctgaaggcctcggacgccgccatgtattattgtgtgaga >IGHV6-1*01 caggtacagctgcagcagtcaggtcca...ggactggtgaagccctcgcagaccctctcactcacctgtgccatctccggggacagtgtctct......agcaacagtgctgcttggaactggatcaggcagtccccatcgagaggccttgagtggctgggaaggacatactacaggtcc...aagtggtataatgattatgcagtatctgtgaaa...agtcgaataaccatcaacccagacacatccaagaaccagttctccctgcagctgaactctgtgactcccgaggacacggctgtgtattactgtgcaagaga >IGHV6-1*02 caggtacagctgcagcagtcaggtccg...ggactggtgaagccctcgcagaccctctcactcacctgtgccatctccggggacagtgtctct......agcaacagtgctgcttggaactggatcaggcagtccccatcgagaggccttgagtggctgggaaggacatactacaggtcc...aagtggtataatgattatgcagtatctgtgaaa...agtcgaataaccatcaacccagacacatccaagaaccagttctccctgcagctgaactctgtgactcccgaggacacggctgtgtattactgtgcaagaga >IGHV7-34-1*01 ...ctgcagctggtgcagtctgggcct...gaggtgaagaagcctggggcctcagtgaaggtctcctataagtcttctggttacaccttc............accatctatggtatgaattgggtatgatagacccctggacagggctttgagtggatgtgatggatcatcacctac......actgggaacccaacgtatacccacggcttcaca...ggatggtttgtcttctccatggacacgtctgtcagcacggcgtgtcttcagatcagcagcctaaaggctgaggacacggccgagtattactgtgcgaagta >IGHV7-34-1*02 ...ctgcagctggtgcagtctgggcct...gaggtgaagaagcctggggcctcagtgaaggtctcctataagtcttctggttacaccttc............accatctatggtatgaattgggtatgatagacccctggacagggctttgagtggatgtgatggatcatcacctac......aatgggaacccaacgtatacccacggcttcaca...ggatggtttgtcttctccatggacacgtctgtcagcacggcgtgtcttcagatcagcagcctaaaggctgaggacacggccgagtattactgtgcgaagta >IGHV7-4-1*01 caggtgcagctggtgcaatctgggtct...gagttgaagaagcctggggcctcagtgaaggtttcctgcaaggcttctggatacaccttc............actagctatgctatgaattgggtgcgacaggcccctggacaagggcttgagtggatgggatggatcaacaccaac......actgggaacccaacgtatgcccagggcttcaca...ggacggtttgtcttctccttggacacctctgtcagcacggcatatctgcagatctgcagcctaaaggctgaggacactgccgtgtattactgtgcgaga >IGHV7-4-1*02 caggtgcagctggtgcaatctgggtct...gagttgaagaagcctggggcctcagtgaaggtttcctgcaaggcttctggatacaccttc............actagctatgctatgaattgggtgcgacaggcccctggacaagggcttgagtggatgggatggatcaacaccaac......actgggaacccaacgtatgcccagggcttcaca...ggacggtttgtcttctccttggacacctctgtcagcacggcatatctgcagatcagcagcctaaaggctgaggacactgccgtgtattactgtgcgagaga >IGHV7-4-1*03 caggtgcagctggtgcaatctgggtct...gagttgaagaagcctggggcctcagtgaaggtttcctgcaaggcttctggatacaccttc............actagctatgctatgaattgggtgcgacaggcccctggacaagggcttgagtggatgggatggatcaacaccaac......actgggaacccaacgtatgcccagggcttcaca...ggacggtttgtcttctccttggacacctctgtcagcacggcatatctgcagatcagcacgctaaaggctgaggacactg >IGHV7-4-1*04 caggtgcagctggtgcaatctgggtct...gagttgaagaagcctggggcctcagtgaaggtttcctgcaaggcttctggatacaccttc............actagctatgctatgaattgggtgcgacaggcccctggacaagggcttgagtggatgggatggatcaacaccaac......actgggaacccaacgtatgcccagggcttcaca...ggacggtttgtcttctccttggacacctctgtcagcatggcatatctgcagatcagcagcctaaaggctgaggacactgccgtgtattactgtgcgagaga >IGHV7-4-1*05 caggtgcagctggtgcaatctgggtct...gagttgaagaagcctggggcctcagtgaaggtttcctgcaaggcttctggatacaccttc............actagctatgctatgaattgggtgcgacaggcccctggacaagggcttgagtggatgggatggatcaacaccaac......actgggaacccaacgtatgcccagggcttcaca...ggacggtttgtcttctccttggacacctctgtcagcatggcatatctgcagatcagcagcctaaaggctgaggacactgccgtgtgttactgtgcgagaga >IGHV7-40*03 ttttcaatagaaaagtcaaataatcta...agtgtcaatcagtggatgattagataaaatatgatatatgtaaatcatggaatactatgc............agccagtatggtatgaattcagtgtgaccagcccctggacaagggcttgagtggatgggatggatcatcacctac......actgggaacccaacatataccaacggcttcaca...ggacggtttctattctccatggacacctctgtcagcatggcgtatctgcagatcagcagcctaaaggctgaggacacggccgtgtatgactgtatgagaga >IGHV7-81*01 caggtgcagctggtgcagtctggccat...gaggtgaagcagcctggggcctcagtgaaggtctcctgcaaggcttctggttacagtttc............accacctatggtatgaattgggtgccacaggcccctggacaagggcttgagtggatgggatggttcaacacctac......actgggaacccaacatatgcccagggcttcaca...ggacggtttgtcttctccatggacacctctgccagcacagcatacctgcagatcagcagcctaaaggctgaggacatggccatgtattactgtgcgagata
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/shm_csr/baseline/baseline_url.txt Fri Feb 19 15:08:51 2021 +0000 @@ -0,0 +1,1 @@ +http://selection.med.yale.edu/baseline/ \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/shm_csr/baseline/comparePDFs.r Fri Feb 19 15:08:51 2021 +0000 @@ -0,0 +1,225 @@ +options("warn"=-1) + +#from http://selection.med.yale.edu/baseline/Archive/Baseline%20Version%201.3/Baseline_Functions_Version1.3.r +# Compute p-value of two distributions +compareTwoDistsFaster <-function(sigma_S=seq(-20,20,length.out=4001), N=10000, dens1=runif(4001,0,1), dens2=runif(4001,0,1)){ +#print(c(length(dens1),length(dens2))) +if(length(dens1)>1 & length(dens2)>1 ){ + dens1<-dens1/sum(dens1) + dens2<-dens2/sum(dens2) + cum2 <- cumsum(dens2)-dens2/2 + tmp<- sum(sapply(1:length(dens1),function(i)return(dens1[i]*cum2[i]))) + #print(tmp) + if(tmp>0.5)tmp<-tmp-1 + return( tmp ) + } + else { + return(NA) + } + #return (sum(sapply(1:N,function(i)(sample(sigma_S,1,prob=dens1)>sample(sigma_S,1,prob=dens2))))/N) +} + + +require("grid") +arg <- commandArgs(TRUE) +#arg <- c("300143","4","5") +arg[!arg=="clonal"] +input <- arg[1] +output <- arg[2] +rowIDs <- as.numeric( sapply(arg[3:(max(3,length(arg)))],function(x){ gsub("chkbx","",x) } ) ) + +numbSeqs = length(rowIDs) + +if ( is.na(rowIDs[1]) | numbSeqs>10 ) { + stop( paste("Error: Please select between one and 10 seqeunces to compare.") ) +} + +#load( paste("output/",sessionID,".RData",sep="") ) +load( input ) +#input + +xMarks = seq(-20,20,length.out=4001) + +plot_grid_s<-function(pdf1,pdf2,Sample=100,cex=1,xlim=NULL,xMarks = seq(-20,20,length.out=4001)){ + yMax = max(c(abs(as.numeric(unlist(listPDFs[pdf1]))),abs(as.numeric(unlist(listPDFs[pdf2]))),0),na.rm=T) * 1.1 + + if(length(xlim==2)){ + xMin=xlim[1] + xMax=xlim[2] + } else { + xMin_CDR = xMarks[listPDFs[pdf1][[1]][["CDR"]]>0.001][1] + xMin_FWR = xMarks[listPDFs[pdf1][[1]][["FWR"]]>0.001][1] + xMax_CDR = xMarks[listPDFs[pdf1][[1]][["CDR"]]>0.001][length(xMarks[listPDFs[pdf1][[1]][["CDR"]]>0.001])] + xMax_FWR = xMarks[listPDFs[pdf1][[1]][["FWR"]]>0.001][length(xMarks[listPDFs[pdf1][[1]][["FWR"]]>0.001])] + + xMin_CDR2 = xMarks[listPDFs[pdf2][[1]][["CDR"]]>0.001][1] + xMin_FWR2 = xMarks[listPDFs[pdf2][[1]][["FWR"]]>0.001][1] + xMax_CDR2 = xMarks[listPDFs[pdf2][[1]][["CDR"]]>0.001][length(xMarks[listPDFs[pdf2][[1]][["CDR"]]>0.001])] + xMax_FWR2 = xMarks[listPDFs[pdf2][[1]][["FWR"]]>0.001][length(xMarks[listPDFs[pdf2][[1]][["FWR"]]>0.001])] + + xMin=min(c(xMin_CDR,xMin_FWR,xMin_CDR2,xMin_FWR2,0),na.rm=TRUE) + xMax=max(c(xMax_CDR,xMax_FWR,xMax_CDR2,xMax_FWR2,0),na.rm=TRUE) + } + + sigma<-approx(xMarks,xout=seq(xMin,xMax,length.out=Sample))$x + grid.rect(gp = gpar(col=gray(0.6),fill="white",cex=cex)) + x <- sigma + pushViewport(viewport(x=0.175,y=0.175,width=0.825,height=0.825,just=c("left","bottom"),default.units="npc")) + #pushViewport(plotViewport(c(1.8, 1.8, 0.25, 0.25)*cex)) + pushViewport(dataViewport(x, c(yMax,-yMax),gp = gpar(cex=cex),extension=c(0.05))) + grid.polygon(c(0,0,1,1),c(0,0.5,0.5,0),gp=gpar(col=grey(0.95),fill=grey(0.95)),default.units="npc") + grid.polygon(c(0,0,1,1),c(1,0.5,0.5,1),gp=gpar(col=grey(0.9),fill=grey(0.9)),default.units="npc") + grid.rect() + grid.xaxis(gp = gpar(cex=cex/1.1)) + yticks = pretty(c(-yMax,yMax),8) + yticks = yticks[yticks>(-yMax) & yticks<(yMax)] + grid.yaxis(at=yticks,label=abs(yticks),gp = gpar(cex=cex/1.1)) + if(length(listPDFs[pdf1][[1]][["CDR"]])>1){ + ycdr<-approx(xMarks,listPDFs[pdf1][[1]][["CDR"]],xout=seq(xMin,xMax,length.out=Sample),yleft=0,yright=0)$y + grid.lines(unit(x,"native"), unit(ycdr,"native"),gp=gpar(col=2,lwd=2)) + } + if(length(listPDFs[pdf1][[1]][["FWR"]])>1){ + yfwr<-approx(xMarks,listPDFs[pdf1][[1]][["FWR"]],xout=seq(xMin,xMax,length.out=Sample),yleft=0,yright=0)$y + grid.lines(unit(x,"native"), unit(-yfwr,"native"),gp=gpar(col=4,lwd=2)) + } + + if(length(listPDFs[pdf2][[1]][["CDR"]])>1){ + ycdr2<-approx(xMarks,listPDFs[pdf2][[1]][["CDR"]],xout=seq(xMin,xMax,length.out=Sample),yleft=0,yright=0)$y + grid.lines(unit(x,"native"), unit(ycdr2,"native"),gp=gpar(col=2,lwd=2,lty=2)) + } + if(length(listPDFs[pdf2][[1]][["FWR"]])>1){ + yfwr2<-approx(xMarks,listPDFs[pdf2][[1]][["FWR"]],xout=seq(xMin,xMax,length.out=Sample),yleft=0,yright=0)$y + grid.lines(unit(x,"native"), unit(-yfwr2,"native"),gp=gpar(col=4,lwd=2,lty=2)) + } + + grid.lines(unit(c(0,1),"npc"), unit(c(0.5,0.5),"npc"),gp=gpar(col=1)) + grid.lines(unit(c(0,0),"native"), unit(c(0,1),"npc"),gp=gpar(col=1,lwd=1,lty=3)) + + grid.text("All", x = unit(-2.5, "lines"), rot = 90,gp = gpar(cex=cex)) + grid.text( expression(paste("Selection Strength (", Sigma, ")", sep="")) , y = unit(-2.5, "lines"),gp = gpar(cex=cex)) + + if(pdf1==pdf2 & length(listPDFs[pdf2][[1]][["FWR"]])>1 & length(listPDFs[pdf2][[1]][["CDR"]])>1 ){ + pCDRFWR = compareTwoDistsFaster(sigma_S=xMarks, N=10000, dens1=listPDFs[[pdf1]][["CDR"]], dens2=listPDFs[[pdf1]][["FWR"]]) + pval = formatC(as.numeric(pCDRFWR),digits=3) + grid.text( substitute(expression(paste(P[CDR/FWR], "=", x, sep="")),list(x=pval))[[2]] , x = unit(0.02, "npc"),y = unit(0.98, "npc"),just=c("left", "top"),gp = gpar(cex=cex*1.2)) + } + grid.text(paste("CDR"), x = unit(0.98, "npc"),y = unit(0.98, "npc"),just=c("right", "top"),gp = gpar(cex=cex*1.5)) + grid.text(paste("FWR"), x = unit(0.98, "npc"),y = unit(0.02, "npc"),just=c("right", "bottom"),gp = gpar(cex=cex*1.5)) + popViewport(2) +} +#plot_grid_s(1) + + +p2col<-function(p=0.01){ + breaks=c(-.51,-0.1,-.05,-0.01,-0.005,0,0.005,0.01,0.05,0.1,0.51) + i<-findInterval(p,breaks) + cols = c( rgb(0.8,1,0.8), rgb(0.6,1,0.6), rgb(0.4,1,0.4), rgb(0.2,1,0.2) , rgb(0,1,0), + rgb(1,0,0), rgb(1,.2,.2), rgb(1,.4,.4), rgb(1,.6,.6) , rgb(1,.8,.8) ) + return(cols[i]) +} + + +plot_pvals<-function(pdf1,pdf2,cex=1,upper=TRUE){ + if(upper){ + pCDR1FWR2 = compareTwoDistsFaster(sigma_S=xMarks, N=10000, dens1=listPDFs[[pdf1]][["CDR"]], dens2=listPDFs[[pdf2]][["FWR"]]) + pFWR1FWR2 = compareTwoDistsFaster(sigma_S=xMarks, N=10000, dens1=listPDFs[[pdf1]][["FWR"]], dens2=listPDFs[[pdf2]][["FWR"]]) + pFWR1CDR2 = compareTwoDistsFaster(sigma_S=xMarks, N=10000, dens2=listPDFs[[pdf2]][["CDR"]], dens1=listPDFs[[pdf1]][["FWR"]]) + pCDR1CDR2 = compareTwoDistsFaster(sigma_S=xMarks, N=10000, dens2=listPDFs[[pdf2]][["CDR"]], dens1=listPDFs[[pdf1]][["CDR"]]) + grid.polygon(c(0.5,0.5,1,1),c(0,0.5,0.5,0),gp=gpar(col=p2col(pFWR1FWR2),fill=p2col(pFWR1FWR2)),default.units="npc") + grid.polygon(c(0.5,0.5,1,1),c(1,0.5,0.5,1),gp=gpar(col=p2col(pCDR1FWR2),fill=p2col(pCDR1FWR2)),default.units="npc") + grid.polygon(c(0.5,0.5,0,0),c(1,0.5,0.5,1),gp=gpar(col=p2col(pCDR1CDR2),fill=p2col(pCDR1CDR2)),default.units="npc") + grid.polygon(c(0.5,0.5,0,0),c(0,0.5,0.5,0),gp=gpar(col=p2col(pFWR1CDR2),fill=p2col(pFWR1CDR2)),default.units="npc") + + grid.lines(c(0,1),0.5,gp=gpar(lty=2,col=gray(0.925))) + grid.lines(0.5,c(0,1),gp=gpar(lty=2,col=gray(0.925))) + + grid.text(formatC(as.numeric(pFWR1FWR2),digits=3), x = unit(0.75, "npc"),y = unit(0.25, "npc"),just=c("center", "center"),gp = gpar(cex=cex)) + grid.text(formatC(as.numeric(pCDR1FWR2),digits=3), x = unit(0.75, "npc"),y = unit(0.75, "npc"),just=c("center", "center"),gp = gpar(cex=cex)) + grid.text(formatC(as.numeric(pCDR1CDR2),digits=3), x = unit(0.25, "npc"),y = unit(0.75, "npc"),just=c("center", "center"),gp = gpar(cex=cex)) + grid.text(formatC(as.numeric(pFWR1CDR2),digits=3), x = unit(0.25, "npc"),y = unit(0.25, "npc"),just=c("center", "center"),gp = gpar(cex=cex)) + + + # grid.text(paste("P = ",formatC(pCDRFWR,digits=3)), x = unit(0.5, "npc"),y = unit(0.98, "npc"),just=c("center", "top"),gp = gpar(cex=cex)) + # grid.text(paste("P = ",formatC(pFWRFWR,digits=3)), x = unit(0.5, "npc"),y = unit(0.02, "npc"),just=c("center", "bottom"),gp = gpar(cex=cex)) + } + else{ + } +} + + +################################################################################## +################## The whole OCD's matrix ######################################## +################################################################################## + +#pdf(width=4*numbSeqs+1/3,height=4*numbSeqs+1/3) +pdf( output ,width=4*numbSeqs+1/3,height=4*numbSeqs+1/3) + +pushViewport(viewport(x=0.02,y=0.02,just = c("left", "bottom"),w =0.96,height=0.96,layout = grid.layout(numbSeqs+1,numbSeqs+1,widths=unit.c(unit(rep(1,numbSeqs),"null"),unit(4,"lines")),heights=unit.c(unit(4,"lines"),unit(rep(1,numbSeqs),"null"))))) + +for( seqOne in 1:numbSeqs+1){ + pushViewport(viewport(layout.pos.col = seqOne-1, layout.pos.row = 1)) + if(seqOne>2){ + grid.polygon(c(0,0,0.5,0.5),c(0,0.5,0.5,0),gp=gpar(col=grey(0.5),fill=grey(0.9)),default.units="npc") + grid.polygon(c(1,1,0.5,0.5),c(0,0.5,0.5,0),gp=gpar(col=grey(0.5),fill=grey(0.95)),default.units="npc") + grid.polygon(c(0,0,1,1),c(1,0.5,0.5,1),gp=gpar(col=grey(0.5)),default.units="npc") + + grid.text(y=.25,x=0.75,"FWR",gp = gpar(cex=1.5),just="center") + grid.text(y=.25,x=0.25,"CDR",gp = gpar(cex=1.5),just="center") + } + grid.rect(gp = gpar(col=grey(0.9))) + grid.text(y=.75,substr(paste(names(listPDFs)[rowIDs[seqOne-1]]),1,16),gp = gpar(cex=2),just="center") + popViewport(1) +} + +for( seqOne in 1:numbSeqs+1){ + pushViewport(viewport(layout.pos.row = seqOne, layout.pos.col = numbSeqs+1)) + if(seqOne<=numbSeqs){ + grid.polygon(c(0,0.5,0.5,0),c(0,0,0.5,0.5),gp=gpar(col=grey(0.5),fill=grey(0.95)),default.units="npc") + grid.polygon(c(0,0.5,0.5,0),c(1,1,0.5,0.5),gp=gpar(col=grey(0.5),fill=grey(0.9)),default.units="npc") + grid.polygon(c(1,0.5,0.5,1),c(0,0,1,1),gp=gpar(col=grey(0.5)),default.units="npc") + grid.text(x=.25,y=0.75,"CDR",gp = gpar(cex=1.5),just="center",rot=270) + grid.text(x=.25,y=0.25,"FWR",gp = gpar(cex=1.5),just="center",rot=270) + } + grid.rect(gp = gpar(col=grey(0.9))) + grid.text(x=0.75,substr(paste(names(listPDFs)[rowIDs[seqOne-1]]),1,16),gp = gpar(cex=2),rot=270,just="center") + popViewport(1) +} + +for( seqOne in 1:numbSeqs+1){ + for(seqTwo in 1:numbSeqs+1){ + pushViewport(viewport(layout.pos.col = seqTwo-1, layout.pos.row = seqOne)) + if(seqTwo>seqOne){ + plot_pvals(rowIDs[seqOne-1],rowIDs[seqTwo-1],cex=2) + grid.rect() + } + popViewport(1) + } +} + + +xMin=0 +xMax=0.01 +for(pdf1 in rowIDs){ + xMin_CDR = xMarks[listPDFs[pdf1][[1]][["CDR"]]>0.001][1] + xMin_FWR = xMarks[listPDFs[pdf1][[1]][["FWR"]]>0.001][1] + xMax_CDR = xMarks[listPDFs[pdf1][[1]][["CDR"]]>0.001][length(xMarks[listPDFs[pdf1][[1]][["CDR"]]>0.001])] + xMax_FWR = xMarks[listPDFs[pdf1][[1]][["FWR"]]>0.001][length(xMarks[listPDFs[pdf1][[1]][["FWR"]]>0.001])] + xMin=min(c(xMin_CDR,xMin_FWR,xMin),na.rm=TRUE) + xMax=max(c(xMax_CDR,xMax_FWR,xMax),na.rm=TRUE) +} + + + +for(i in 1:numbSeqs+1){ + for(j in (i-1):numbSeqs){ + pushViewport(viewport(layout.pos.col = i-1, layout.pos.row = j+1)) + grid.rect() + plot_grid_s(rowIDs[i-1],rowIDs[j],cex=1) + popViewport(1) + } +} + +dev.off() + +cat("Success", paste(rowIDs,collapse="_"),sep=":") +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/shm_csr/baseline/filter.r Fri Feb 19 15:08:51 2021 +0000 @@ -0,0 +1,55 @@ +arg = commandArgs(TRUE) +summaryfile = arg[1] +gappedfile = arg[2] +selection = arg[3] +output = arg[4] +print(paste("selection = ", selection)) + + +summarydat = read.table(summaryfile, header=T, sep="\t", fill=T, stringsAsFactors=F, quote = "") +gappeddat = read.table(gappedfile, header=T, sep="\t", fill=T, stringsAsFactors=F, quote = "") + +fix_column_names = function(df){ + if("V.DOMAIN.Functionality" %in% names(df)){ + names(df)[names(df) == "V.DOMAIN.Functionality"] = "Functionality" + print("found V.DOMAIN.Functionality, changed") + } + if("V.DOMAIN.Functionality.comment" %in% names(df)){ + names(df)[names(df) == "V.DOMAIN.Functionality.comment"] = "Functionality.comment" + print("found V.DOMAIN.Functionality.comment, changed") + } + return(df) +} + +gappeddat = fix_column_names(gappeddat) + +#dat = data.frame(merge(gappeddat, summarydat, by="Sequence.ID", all.x=T)) + +dat = cbind(gappeddat, summarydat$AA.JUNCTION) + +colnames(dat)[length(dat)] = "AA.JUNCTION" + +dat$VGene = gsub("^Homsap ", "", dat$V.GENE.and.allele) +dat$VGene = gsub("[*].*", "", dat$VGene) + +dat$DGene = gsub("^Homsap ", "", dat$D.GENE.and.allele) +dat$DGene = gsub("[*].*", "", dat$DGene) + +dat$JGene = gsub("^Homsap ", "", dat$J.GENE.and.allele) +dat$JGene = gsub("[*].*", "", dat$JGene) + +print(str(dat)) + +dat$past = do.call(paste, c(dat[unlist(strsplit(selection, ","))], sep = ":")) + +dat = dat[!duplicated(dat$past), ] + +print(paste("Sequences remaining after duplicate filter:", nrow(dat))) + +dat = dat[dat$Functionality != "No results" & dat$Functionality != "unproductive",] + +print(paste("Sequences remaining after functionality filter:", nrow(dat))) + +print(paste("Sequences remaining:", nrow(dat))) + +write.table(x=dat, file=output, sep="\t",quote=F,row.names=F,col.names=T)
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/shm_csr/baseline/script_imgt.py Fri Feb 19 15:08:51 2021 +0000 @@ -0,0 +1,86 @@ +#import xlrd #avoid dep +import argparse +import re + +parser = argparse.ArgumentParser() +parser.add_argument("--input", help="Excel input file containing one or more sheets where column G has the gene annotation, H has the sequence id and J has the sequence") +parser.add_argument("--ref", help="Reference file") +parser.add_argument("--output", help="Output file") +parser.add_argument("--id", help="ID to be used at the '>>>' line in the output") + +args = parser.parse_args() + +print "script_imgt.py" +print "input:", args.input +print "ref:", args.ref +print "output:", args.output +print "id:", args.id + +refdic = dict() +with open(args.ref, 'rU') as ref: + currentSeq = "" + currentId = "" + for line in ref: + if line.startswith(">"): + if currentSeq is not "" and currentId is not "": + refdic[currentId[1:]] = currentSeq + currentId = line.rstrip() + currentSeq = "" + else: + currentSeq += line.rstrip() + refdic[currentId[1:]] = currentSeq + +print "Have", str(len(refdic)), "reference sequences" + +vPattern = [r"(IGHV[0-9]-[0-9ab]+-?[0-9]?D?\*\d{1,2})"]#, +# r"(TRBV[0-9]{1,2}-?[0-9]?-?[123]?)", +# r"(IGKV[0-3]D?-[0-9]{1,2})", +# r"(IGLV[0-9]-[0-9]{1,2})", +# r"(TRAV[0-9]{1,2}(-[1-46])?(/DV[45678])?)", +# r"(TRGV[234589])", +# r"(TRDV[1-3])"] + +#vPattern = re.compile(r"|".join(vPattern)) +vPattern = re.compile("|".join(vPattern)) + +def filterGene(s, pattern): + if type(s) is not str: + return None + res = pattern.search(s) + if res: + return res.group(0) + return None + + + +currentSeq = "" +currentId = "" +first=True +with open(args.input, 'r') as i: + with open(args.output, 'a') as o: + o.write(">>>" + args.id + "\n") + outputdic = dict() + for line in i: + if first: + first = False + continue + linesplt = line.split("\t") + ref = filterGene(linesplt[1], vPattern) + if not ref or not linesplt[2].rstrip(): + continue + if ref in outputdic: + outputdic[ref] += [(linesplt[0].replace(">", ""), linesplt[2].replace(">", "").rstrip())] + else: + outputdic[ref] = [(linesplt[0].replace(">", ""), linesplt[2].replace(">", "").rstrip())] + #print outputdic + + for k in outputdic.keys(): + if k in refdic: + o.write(">>" + k + "\n") + o.write(refdic[k] + "\n") + for seq in outputdic[k]: + #print seq + o.write(">" + seq[0] + "\n") + o.write(seq[1] + "\n") + else: + print k + " not in reference, skipping " + k
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/shm_csr/baseline/script_xlsx.py Fri Feb 19 15:08:51 2021 +0000 @@ -0,0 +1,58 @@ +import xlrd +import argparse + +parser = argparse.ArgumentParser() +parser.add_argument("--input", help="Excel input file containing one or more sheets where column G has the gene annotation, H has the sequence id and J has the sequence") +parser.add_argument("--ref", help="Reference file") +parser.add_argument("--output", help="Output file") + +args = parser.parse_args() + +gene_column = 6 +id_column = 7 +seq_column = 8 +LETTERS = [x for x in "ABCDEFGHIJKLMNOPQRSTUVWXYZ"] + + +refdic = dict() +with open(args.ref, 'r') as ref: + currentSeq = "" + currentId = "" + for line in ref.readlines(): + if line[0] is ">": + if currentSeq is not "" and currentId is not "": + refdic[currentId[1:]] = currentSeq + currentId = line.rstrip() + currentSeq = "" + else: + currentSeq += line.rstrip() + refdic[currentId[1:]] = currentSeq + +currentSeq = "" +currentId = "" +with xlrd.open_workbook(args.input, 'r') as wb: + with open(args.output, 'a') as o: + for sheet in wb.sheets(): + if sheet.cell(1,gene_column).value.find("IGHV") < 0: + print "Genes not in column " + LETTERS[gene_column] + ", skipping sheet " + sheet.name + continue + o.write(">>>" + sheet.name + "\n") + outputdic = dict() + for rowindex in range(1, sheet.nrows): + ref = sheet.cell(rowindex, gene_column).value.replace(">", "") + if ref in outputdic: + outputdic[ref] += [(sheet.cell(rowindex, id_column).value.replace(">", ""), sheet.cell(rowindex, seq_column).value)] + else: + outputdic[ref] = [(sheet.cell(rowindex, id_column).value.replace(">", ""), sheet.cell(rowindex, seq_column).value)] + #print outputdic + + for k in outputdic.keys(): + if k in refdic: + o.write(">>" + k + "\n") + o.write(refdic[k] + "\n") + for seq in outputdic[k]: + #print seq + o.write(">" + seq[0] + "\n") + o.write(seq[1] + "\n") + else: + print k + " not in reference, skipping " + k
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/shm_csr/baseline/wrapper.sh Fri Feb 19 15:08:51 2021 +0000 @@ -0,0 +1,92 @@ +#!/bin/bash +dir="$(cd "$(dirname "$0")" && pwd)" + +testID=$1 +species=$2 +substitutionModel=$3 +mutabilityModel=$4 +clonal=$5 +fixIndels=$6 +region=$7 +inputs=$8 +inputs=($inputs) +IDs=$9 +IDs=($IDs) +ref=${10} +output=${11} +selection=${12} +output_table=${13} +outID="result" + +echo "$PWD" + +echo "testID = $testID" +echo "species = $species" +echo "substitutionModel = $substitutionModel" +echo "mutabilityModel = $mutabilityModel" +echo "clonal = $clonal" +echo "fixIndels = $fixIndels" +echo "region = $region" +echo "inputs = ${inputs[@]}" +echo "IDs = ${IDs[@]}" +echo "ref = $ref" +echo "output = $output" +echo "outID = $outID" + +fasta="$PWD/baseline.fasta" + + +count=0 +for current in ${inputs[@]} +do + f=$(file $current) + zipType="Zip archive" + if [[ "$f" == *"Zip archive"* ]] || [[ "$f" == *"XZ compressed data"* ]] + then + id=${IDs[$count]} + echo "id=$id" + if [[ "$f" == *"Zip archive"* ]] ; then + echo "Zip archive" + echo "unzip $input -d $PWD/files/" + unzip $current -d "$PWD/$id/" + elif [[ "$f" == *"XZ compressed data"* ]] ; then + echo "ZX archive" + echo "tar -xJf $input -C $PWD/files/" + mkdir -p "$PWD/$id/files" + tar -xJf $current -C "$PWD/$id/files/" + fi + filtered="$PWD/filtered_${id}.txt" + imgt_1_file="`find $PWD/$id -name '1_*.txt'`" + imgt_2_file="`find $PWD/$id -name '2_*.txt'`" + echo "1_Summary file: ${imgt_1_file}" + echo "2_IMGT-gapped file: ${imgt_2_file}" + echo "filter.r for $id" + Rscript $dir/filter.r ${imgt_1_file} ${imgt_2_file} "$selection" $filtered 2>&1 + + final="$PWD/final_${id}.txt" + cat $filtered | cut -f2,4,7 > $final + python $dir/script_imgt.py --input $final --ref $ref --output $fasta --id $id + else + python $dir/script_xlsx.py --input $current --ref $ref --output $fasta + fi + count=$((count+1)) +done +workdir="$PWD" +cd $dir +echo "file: ${inputs[0]}" +#Rscript --verbose $dir/Baseline_Main.r $testID $species $substitutionModel $mutabilityModel $clonal $fixIndels $region ${inputs[0]} $workdir/ $outID 2>&1 +Rscript --verbose $dir/Baseline_Main.r $testID $species $substitutionModel $mutabilityModel $clonal $fixIndels $region $fasta $workdir/ $outID 2>&1 + +echo "$workdir/${outID}.txt" + +rows=`tail -n +2 $workdir/${outID}.txt | grep -v "All sequences combined" | grep -n 'Group' | grep -Eoh '^[0-9]+' | tr '\n' ' '` +rows=($rows) +#unset rows[${#rows[@]}-1] + +cd $dir +Rscript --verbose $dir/comparePDFs.r $workdir/${outID}.RData $output ${rows[@]} 2>&1 +cp $workdir/result.txt ${output_table} + + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/shm_csr/change_o/change_o_url.txt Fri Feb 19 15:08:51 2021 +0000 @@ -0,0 +1,1 @@ +https://changeo.readthedocs.io/en/version-0.4.4/ \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/shm_csr/change_o/define_clones.r Fri Feb 19 15:08:51 2021 +0000 @@ -0,0 +1,15 @@ +args <- commandArgs(trailingOnly = TRUE) + +input=args[1] +output=args[2] + +change.o = read.table(input, header=T, sep="\t", quote="", stringsAsFactors=F) + +freq = data.frame(table(change.o$CLONE)) +freq2 = data.frame(table(freq$Freq)) + +freq2$final = as.numeric(freq2$Freq) * as.numeric(as.character(freq2$Var1)) + +names(freq2) = c("Clone size", "Nr of clones", "Nr of sequences") + +write.table(x=freq2, file=output, sep="\t",quote=F,row.names=F,col.names=T)
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/shm_csr/change_o/define_clones.sh Fri Feb 19 15:08:51 2021 +0000 @@ -0,0 +1,39 @@ +#!/bin/bash +dir="$(cd "$(dirname "$0")" && pwd)" + +#define_clones.sh $input $noparse $scores $regions $out_file + +type=$1 +input=$2 + +mkdir -p $PWD/outdir + +cp $input $PWD/input.tab #file has to have a ".tab" extension + +if [ "bygroup" == "$type" ] ; then + mode=$3 + act=$4 + model=$5 + norm=$6 + sym=$7 + link=$8 + dist=$9 + output=${10} + output2=${11} + + DefineClones.py -d $PWD/input.tab --nproc 4 --outdir $PWD/outdir --outname output --mode $mode --act $act --model $model --dist $dist --norm $norm --sym $sym --link $link + + Rscript $dir/define_clones.r $PWD/outdir/output_clone-pass.tab $output2 2>&1 +else + method=$3 + output=$4 + output2=$5 + + DefineClones.py hclust -d $PWD/input.tab --nproc 4 --outdir $PWD/outdir --outname output --method $method + + Rscript $dir/define_clones.r $PWD/outdir/output_clone-pass.tab $output2 2>&1 +fi + +cp $PWD/outdir/output_clone-pass.tab $output + +rm -rf $PWD/outdir/
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/shm_csr/change_o/makedb.sh Fri Feb 19 15:08:51 2021 +0000 @@ -0,0 +1,36 @@ +#!/bin/bash +dir="$(cd "$(dirname "$0")" && pwd)" + +input=$1 +noparse=$2 +scores=$3 +regions=$4 +output=$5 + +if [ "true" == "$noparse" ] ; then + noparse="--noparse" +else + noparse="" +fi + +if [ "true" == "$scores" ] ; then + scores="--scores" +else + scores="" +fi + +if [ "true" == "$regions" ] ; then + regions="--regions" +else + regions="" +fi + +mkdir $PWD/outdir + +echo "makedb: $PWD/outdir" + +MakeDb.py imgt -i $input --outdir $PWD/outdir --outname output $noparse $scores $regions + +mv $PWD/outdir/output_db-pass.tab $output + +rm -rf $PWD/outdir/
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/shm_csr/change_o/select_first_in_clone.r Fri Feb 19 15:08:51 2021 +0000 @@ -0,0 +1,16 @@ +args <- commandArgs(trailingOnly = TRUE) + +input.file = args[1] +output.file = args[2] + +print("select_in_first_clone.r") +print(input.file) +print(output.file) + +input = read.table(input.file, header=T, sep="\t", fill=T, stringsAsFactors=F, quote="") + +input = input[!duplicated(input$CLONE),] + +names(input)[1] = "Sequence.ID" + +write.table(input, output.file, quote=F, sep="\t", row.names=F, col.names=T, na="")
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/shm_csr/check_unique_id.r Fri Feb 19 15:08:51 2021 +0000 @@ -0,0 +1,25 @@ +args <- commandArgs(trailingOnly = TRUE) #first argument must be the summary file so it can grab the + +current_file = args[1] + +current = read.table(current_file, header=T, sep="\t", fill=T, stringsAsFactors=F, quote="", check.names=F) + +if(!("Sequence number" %in% names(current))){ + stop("First argument doesn't contain the 'Sequence number' column") +} + +tbl = table(current[,"Sequence ID"]) +l_tbl = length(tbl) +check = any(tbl > 1) + +#if(l_tbl != nrow(current)){ # non unique IDs? +if(check){ + print("Sequence.ID is not unique for every sequence, adding sequence number to IDs") + for(i in 1:length(args)){ + current_file = args[i] + print(paste("Appending 'Sequence number' column to 'Sequence ID' column in", current_file)) + current = read.table(current_file, header=T, sep="\t", fill=T, stringsAsFactors=F, quote="", check.names=F) + current[,"Sequence ID"] = paste(current[,"Sequence ID"], current[,"Sequence number"], sep="_") + write.table(x = current, file = current_file, quote = F, sep = "\t", na = "", row.names = F, col.names = T) + } +}
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/shm_csr/datatypes_conf.xml Fri Feb 19 15:08:51 2021 +0000 @@ -0,0 +1,6 @@ +<?xml version="1.0"?> +<datatypes> + <registration> + <datatype extension="imgt_archive" type="galaxy.datatypes.binary:CompressedArchive" display_in_upload="True" subclass="True"/> + </registration> +</datatypes>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/shm_csr/gene_identification.py Fri Feb 19 15:08:51 2021 +0000 @@ -0,0 +1,226 @@ +import re +import argparse +import time +starttime= int(time.time() * 1000) + +parser = argparse.ArgumentParser() +parser.add_argument("--input", help="The 1_Summary file from an IMGT zip file") +parser.add_argument("--output", help="The annotated output file to be merged back with the summary file") + +args = parser.parse_args() + +infile = args.input +#infile = "test_VH-Ca_Cg_25nt/1_Summary_test_VH-Ca_Cg_25nt_241013.txt" +output = args.output +#outfile = "identified.txt" + +dic = dict() +total = 0 + + +first = True +IDIndex = 0 +seqIndex = 0 + +with open(infile, 'r') as f: #read all sequences into a dictionary as key = ID, value = sequence + for line in f: + total += 1 + linesplt = line.split("\t") + if first: + print "linesplt", linesplt + IDIndex = linesplt.index("Sequence ID") + seqIndex = linesplt.index("Sequence") + first = False + continue + + ID = linesplt[IDIndex] + if len(linesplt) < 28: #weird rows without a sequence + dic[ID] = "" + else: + dic[ID] = linesplt[seqIndex] + +print "Number of input sequences:", len(dic) + +#old cm sequence: gggagtgcatccgccccaacccttttccccctcgtctcctgtgagaattccc +#old cg sequence: ctccaccaagggcccatcggtcttccccctggcaccctcctccaagagcacctctgggggcacagcggccctgggctgcctggtcaaggactacttccccgaaccggtgacggtgtcgtggaactcaggcgccctgaccag + +#lambda/kappa reference sequence +searchstrings = {"ca": "catccccgaccagccccaaggtcttcccgctgagcctctgcagcacccagccagatgggaacgtggtcatcgcctgcctgg", + "cg": "ctccaccaagggcccatcggtcttccccctggcaccctcctccaagagcacctctgggggcacagcggcc", + "ce": "gcctccacacagagcccatccgtcttccccttgacccgctgctgcaaaaacattccctcc", + "cm": "gggagtgcatccgccccaacc"} #new (shorter) cm sequence + +compiledregex = {"ca": [], + "cg": [], + "ce": [], + "cm": []} + +#lambda/kappa reference sequence variable nucleotides +ca1 = {38: 't', 39: 'g', 48: 'a', 49: 'g', 51: 'c', 68: 'a', 73: 'c'} +ca2 = {38: 'g', 39: 'a', 48: 'c', 49: 'c', 51: 'a', 68: 'g', 73: 'a'} +cg1 = {0: 'c', 33: 'a', 38: 'c', 44: 'a', 54: 't', 56: 'g', 58: 'g', 66: 'g', 132: 'c'} +cg2 = {0: 'c', 33: 'g', 38: 'g', 44: 'g', 54: 'c', 56: 'a', 58: 'a', 66: 'g', 132: 't'} +cg3 = {0: 't', 33: 'g', 38: 'g', 44: 'g', 54: 't', 56: 'g', 58: 'g', 66: 'g', 132: 'c'} +cg4 = {0: 't', 33: 'g', 38: 'g', 44: 'g', 54: 'c', 56: 'a', 58: 'a', 66: 'c', 132: 'c'} + +#remove last snp for shorter cg sequence --- note, also change varsInCG +del cg1[132] +del cg2[132] +del cg3[132] +del cg4[132] + +#reference sequences are cut into smaller parts of 'chunklength' length, and with 'chunklength' / 2 overlap +chunklength = 8 + +#create the chunks of the reference sequence with regular expressions for the variable nucleotides +for i in range(0, len(searchstrings["ca"]) - chunklength, chunklength / 2): + pos = i + chunk = searchstrings["ca"][i:i+chunklength] + result = "" + varsInResult = 0 + for c in chunk: + if pos in ca1.keys(): + varsInResult += 1 + result += "[" + ca1[pos] + ca2[pos] + "]" + else: + result += c + pos += 1 + compiledregex["ca"].append((re.compile(result), varsInResult)) + +for i in range(0, len(searchstrings["cg"]) - chunklength, chunklength / 2): + pos = i + chunk = searchstrings["cg"][i:i+chunklength] + result = "" + varsInResult = 0 + for c in chunk: + if pos in cg1.keys(): + varsInResult += 1 + result += "[" + "".join(set([cg1[pos], cg2[pos], cg3[pos], cg4[pos]])) + "]" + else: + result += c + pos += 1 + compiledregex["cg"].append((re.compile(result), varsInResult)) + +for i in range(0, len(searchstrings["cm"]) - chunklength, chunklength / 2): + compiledregex["cm"].append((re.compile(searchstrings["cm"][i:i+chunklength]), False)) + +for i in range(0, len(searchstrings["ce"]) - chunklength + 1, chunklength / 2): + compiledregex["ce"].append((re.compile(searchstrings["ce"][i:i+chunklength]), False)) + +def removeAndReturnMaxIndex(x): #simplifies a list comprehension + m = max(x) + index = x.index(m) + x[index] = 0 + return index + + +start_location = dict() +hits = dict() +alltotal = 0 +for key in compiledregex.keys(): #for ca/cg/cm/ce + regularexpressions = compiledregex[key] #get the compiled regular expressions + for ID in dic.keys()[0:]: #for every ID + if ID not in hits.keys(): #ensure that the dictionairy that keeps track of the hits for every gene exists + hits[ID] = {"ca_hits": 0, "cg_hits": 0, "cm_hits": 0, "ce_hits": 0, "ca1": 0, "ca2": 0, "cg1": 0, "cg2": 0, "cg3": 0, "cg4": 0} + currentIDHits = hits[ID] + seq = dic[ID] + lastindex = 0 + start_zero = len(searchstrings[key]) #allows the reference sequence to start before search sequence (start_locations of < 0) + start = [0] * (len(seq) + start_zero) + for i, regexp in enumerate(regularexpressions): #for every regular expression + relativeStartLocation = lastindex - (chunklength / 2) * i + if relativeStartLocation >= len(seq): + break + regex, hasVar = regexp + matches = regex.finditer(seq[lastindex:]) + for match in matches: #for every match with the current regex, only uses the first hit because of the break at the end of this loop + lastindex += match.start() + start[relativeStartLocation + start_zero] += 1 + if hasVar: #if the regex has a variable nt in it + chunkstart = chunklength / 2 * i #where in the reference does this chunk start + chunkend = chunklength / 2 * i + chunklength #where in the reference does this chunk end + if key == "ca": #just calculate the variable nt score for 'ca', cheaper + currentIDHits["ca1"] += len([1 for x in ca1 if chunkstart <= x < chunkend and ca1[x] == seq[lastindex + x - chunkstart]]) + currentIDHits["ca2"] += len([1 for x in ca2 if chunkstart <= x < chunkend and ca2[x] == seq[lastindex + x - chunkstart]]) + elif key == "cg": #just calculate the variable nt score for 'cg', cheaper + currentIDHits["cg1"] += len([1 for x in cg1 if chunkstart <= x < chunkend and cg1[x] == seq[lastindex + x - chunkstart]]) + currentIDHits["cg2"] += len([1 for x in cg2 if chunkstart <= x < chunkend and cg2[x] == seq[lastindex + x - chunkstart]]) + currentIDHits["cg3"] += len([1 for x in cg3 if chunkstart <= x < chunkend and cg3[x] == seq[lastindex + x - chunkstart]]) + currentIDHits["cg4"] += len([1 for x in cg4 if chunkstart <= x < chunkend and cg4[x] == seq[lastindex + x - chunkstart]]) + else: #key == "cm" #no variable regions in 'cm' or 'ce' + pass + break #this only breaks when there was a match with the regex, breaking means the 'else:' clause is skipped + else: #only runs if there were no hits + continue + #print "found ", regex.pattern , "at", lastindex, "adding one to", (lastindex - chunklength / 2 * i), "to the start array of", ID, "gene", key, "it's now:", start[lastindex - chunklength / 2 * i] + currentIDHits[key + "_hits"] += 1 + start_location[ID + "_" + key] = str([(removeAndReturnMaxIndex(start) + 1 - start_zero) for x in range(5) if len(start) > 0 and max(start) > 1]) + #start_location[ID + "_" + key] = str(start.index(max(start))) + + +varsInCA = float(len(ca1.keys()) * 2) +varsInCG = float(len(cg1.keys()) * 2) - 2 # -2 because the sliding window doesn't hit the first and last nt twice +varsInCM = 0 +varsInCE = 0 + +def round_int(val): + return int(round(val)) + +first = True +seq_write_count=0 +with open(infile, 'r') as f: #read all sequences into a dictionary as key = ID, value = sequence + with open(output, 'w') as o: + for line in f: + total += 1 + if first: + o.write("Sequence ID\tbest_match\tnt_hit_percentage\tchunk_hit_percentage\tstart_locations\n") + first = False + continue + linesplt = line.split("\t") + if linesplt[2] == "No results": + pass + ID = linesplt[1] + currentIDHits = hits[ID] + possibleca = float(len(compiledregex["ca"])) + possiblecg = float(len(compiledregex["cg"])) + possiblecm = float(len(compiledregex["cm"])) + possiblece = float(len(compiledregex["ce"])) + cahits = currentIDHits["ca_hits"] + cghits = currentIDHits["cg_hits"] + cmhits = currentIDHits["cm_hits"] + cehits = currentIDHits["ce_hits"] + if cahits >= cghits and cahits >= cmhits and cahits >= cehits: #its a ca gene + ca1hits = currentIDHits["ca1"] + ca2hits = currentIDHits["ca2"] + if ca1hits >= ca2hits: + o.write(ID + "\tIGA1\t" + str(round_int(ca1hits / varsInCA * 100)) + "\t" + str(round_int(cahits / possibleca * 100)) + "\t" + start_location[ID + "_ca"] + "\n") + else: + o.write(ID + "\tIGA2\t" + str(round_int(ca2hits / varsInCA * 100)) + "\t" + str(round_int(cahits / possibleca * 100)) + "\t" + start_location[ID + "_ca"] + "\n") + elif cghits >= cahits and cghits >= cmhits and cghits >= cehits: #its a cg gene + cg1hits = currentIDHits["cg1"] + cg2hits = currentIDHits["cg2"] + cg3hits = currentIDHits["cg3"] + cg4hits = currentIDHits["cg4"] + if cg1hits >= cg2hits and cg1hits >= cg3hits and cg1hits >= cg4hits: #cg1 gene + o.write(ID + "\tIGG1\t" + str(round_int(cg1hits / varsInCG * 100)) + "\t" + str(round_int(cghits / possiblecg * 100)) + "\t" + start_location[ID + "_cg"] + "\n") + elif cg2hits >= cg1hits and cg2hits >= cg3hits and cg2hits >= cg4hits: #cg2 gene + o.write(ID + "\tIGG2\t" + str(round_int(cg2hits / varsInCG * 100)) + "\t" + str(round_int(cghits / possiblecg * 100)) + "\t" + start_location[ID + "_cg"] + "\n") + elif cg3hits >= cg1hits and cg3hits >= cg2hits and cg3hits >= cg4hits: #cg3 gene + o.write(ID + "\tIGG3\t" + str(round_int(cg3hits / varsInCG * 100)) + "\t" + str(round_int(cghits / possiblecg * 100)) + "\t" + start_location[ID + "_cg"] + "\n") + else: #cg4 gene + o.write(ID + "\tIGG4\t" + str(round_int(cg4hits / varsInCG * 100)) + "\t" + str(round_int(cghits / possiblecg * 100)) + "\t" + start_location[ID + "_cg"] + "\n") + else: #its a cm or ce gene + if cmhits >= cehits: + o.write(ID + "\tIGM\t100\t" + str(round_int(cmhits / possiblecm * 100)) + "\t" + start_location[ID + "_cm"] + "\n") + else: + o.write(ID + "\tIGE\t100\t" + str(round_int(cehits / possiblece * 100)) + "\t" + start_location[ID + "_ce"] + "\n") + seq_write_count += 1 + +print "Time: %i" % (int(time.time() * 1000) - starttime) + +print "Number of sequences written to file:", seq_write_count + + + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/shm_csr/imgt_loader.r Fri Feb 19 15:08:51 2021 +0000 @@ -0,0 +1,98 @@ +args <- commandArgs(trailingOnly = TRUE) + +summ.file = args[1] +aa.file = args[2] +junction.file = args[3] +out.file = args[4] + +summ = read.table(summ.file, sep="\t", header=T, quote="", fill=T) +aa = read.table(aa.file, sep="\t", header=T, quote="", fill=T) +junction = read.table(junction.file, sep="\t", header=T, quote="", fill=T) + +fix_column_names = function(df){ + if("V.DOMAIN.Functionality" %in% names(df)){ + names(df)[names(df) == "V.DOMAIN.Functionality"] = "Functionality" + print("found V.DOMAIN.Functionality, changed") + } + if("V.DOMAIN.Functionality.comment" %in% names(df)){ + names(df)[names(df) == "V.DOMAIN.Functionality.comment"] = "Functionality.comment" + print("found V.DOMAIN.Functionality.comment, changed") + } + return(df) +} + +summ = fix_column_names(summ) +aa = fix_column_names(aa) +junction = fix_column_names(junction) + +old_summary_columns=c('Sequence.ID','JUNCTION.frame','V.GENE.and.allele','D.GENE.and.allele','J.GENE.and.allele','CDR1.IMGT.length','CDR2.IMGT.length','CDR3.IMGT.length','Orientation') +old_sequence_columns=c('CDR1.IMGT','CDR2.IMGT','CDR3.IMGT') +old_junction_columns=c('JUNCTION') + +added_summary_columns=c('Functionality','V.REGION.identity..','V.REGION.identity.nt','D.REGION.reading.frame','AA.JUNCTION','Functionality.comment','Sequence') +added_sequence_columns=c('FR1.IMGT','FR2.IMGT','FR3.IMGT','CDR3.IMGT','JUNCTION','J.REGION','FR4.IMGT') + +added_junction_columns=c('P3.V.nt.nb','N.REGION.nt.nb','N1.REGION.nt.nb','P5.D.nt.nb','P3.D.nt.nb','N2.REGION.nt.nb','P5.J.nt.nb','X3.V.REGION.trimmed.nt.nb','X5.D.REGION.trimmed.nt.nb','X3.D.REGION.trimmed.nt.nb','X5.J.REGION.trimmed.nt.nb','N.REGION','N1.REGION','N2.REGION') +added_junction_columns=c(added_junction_columns, 'P5.D1.nt.nb', 'P3.D1.nt.nb', 'N2.REGION.nt.nb', 'P5.D2.nt.nb', 'P3.D2.nt.nb', 'N3.REGION.nt.nb', 'P5.D3.nt.nb', 'P3.D2.nt.nb', 'N4.REGION.nt.nb', 'X5.D1.REGION.trimmed.nt.nb', 'X3.D1.REGION.trimmed.nt.nb', 'X5.D2.REGION.trimmed.nt.nb', 'X3.D2.REGION.trimmed.nt.nb', 'X5.D3.REGION.trimmed.nt.nb', 'X3.D3.REGION.trimmed.nt.nb', 'D.REGION.nt.nb', 'D1.REGION.nt.nb', 'D2.REGION.nt.nb', 'D3.REGION.nt.nb') + +out=summ[,c("Sequence.ID","JUNCTION.frame","V.GENE.and.allele","D.GENE.and.allele","J.GENE.and.allele")] + +out[,"CDR1.Seq"] = aa[,"CDR1.IMGT"] +out[,"CDR1.Length"] = summ[,"CDR1.IMGT.length"] + +out[,"CDR2.Seq"] = aa[,"CDR2.IMGT"] +out[,"CDR2.Length"] = summ[,"CDR2.IMGT.length"] + +out[,"CDR3.Seq"] = aa[,"CDR3.IMGT"] +out[,"CDR3.Length"] = summ[,"CDR3.IMGT.length"] + +out[,"CDR3.Seq.DNA"] = junction[,"JUNCTION"] +out[,"CDR3.Length.DNA"] = nchar(as.character(junction[,"JUNCTION"])) +out[,"Strand"] = summ[,"Orientation"] +out[,"CDR3.Found.How"] = "a" + +out[,added_summary_columns] = summ[,added_summary_columns] + +out[,added_sequence_columns] = aa[,added_sequence_columns] + +out[,added_junction_columns] = junction[,added_junction_columns] + +out[,"Top V Gene"] = gsub(".* ", "", gsub("\\*.*", "", summ[,"V.GENE.and.allele"])) +out[,"Top D Gene"] = gsub(".* ", "", gsub("\\*.*", "", summ[,"D.GENE.and.allele"])) +out[,"Top J Gene"] = gsub(".* ", "", gsub("\\*.*", "", summ[,"J.GENE.and.allele"])) + +out = out[,c('Sequence.ID','JUNCTION.frame','Top V Gene','Top D Gene','Top J Gene','CDR1.Seq','CDR1.Length','CDR2.Seq','CDR2.Length','CDR3.Seq','CDR3.Length','CDR3.Seq.DNA','CDR3.Length.DNA','Strand','CDR3.Found.How','Functionality','V.REGION.identity..','V.REGION.identity.nt','D.REGION.reading.frame','AA.JUNCTION','Functionality.comment','Sequence','FR1.IMGT','FR2.IMGT','FR3.IMGT','CDR3.IMGT','JUNCTION','J.REGION','FR4.IMGT','P3.V.nt.nb','N.REGION.nt.nb','N1.REGION.nt.nb','P5.D.nt.nb','P3.D.nt.nb','N2.REGION.nt.nb','P5.J.nt.nb','X3.V.REGION.trimmed.nt.nb','X5.D.REGION.trimmed.nt.nb','X3.D.REGION.trimmed.nt.nb','X5.J.REGION.trimmed.nt.nb','N.REGION','N1.REGION','N2.REGION', 'P5.D1.nt.nb', 'P3.D1.nt.nb', 'N2.REGION.nt.nb', 'P5.D2.nt.nb', 'P3.D2.nt.nb', 'N3.REGION.nt.nb', 'P5.D3.nt.nb', 'P3.D2.nt.nb', 'N4.REGION.nt.nb', 'X5.D1.REGION.trimmed.nt.nb', 'X3.D1.REGION.trimmed.nt.nb', 'X5.D2.REGION.trimmed.nt.nb', 'X3.D2.REGION.trimmed.nt.nb', 'X5.D3.REGION.trimmed.nt.nb', 'X3.D3.REGION.trimmed.nt.nb', 'D.REGION.nt.nb', 'D1.REGION.nt.nb', 'D2.REGION.nt.nb', 'D3.REGION.nt.nb')] + +names(out) = c('ID','VDJ Frame','Top V Gene','Top D Gene','Top J Gene','CDR1 Seq','CDR1 Length','CDR2 Seq','CDR2 Length','CDR3 Seq','CDR3 Length','CDR3 Seq DNA','CDR3 Length DNA','Strand','CDR3 Found How','Functionality','V-REGION identity %','V-REGION identity nt','D-REGION reading frame','AA JUNCTION','Functionality comment','Sequence','FR1-IMGT','FR2-IMGT','FR3-IMGT','CDR3-IMGT','JUNCTION','J-REGION','FR4-IMGT','P3V-nt nb','N-REGION-nt nb','N1-REGION-nt nb','P5D-nt nb','P3D-nt nb','N2-REGION-nt nb','P5J-nt nb','3V-REGION trimmed-nt nb','5D-REGION trimmed-nt nb','3D-REGION trimmed-nt nb','5J-REGION trimmed-nt nb','N-REGION','N1-REGION','N2-REGION', 'P5.D1.nt.nb', 'P3.D1.nt.nb', 'N2.REGION.nt.nb', 'P5.D2.nt.nb', 'P3.D2.nt.nb', 'N3.REGION.nt.nb', 'P5.D3.nt.nb', 'P3.D2.nt.nb', 'N4.REGION.nt.nb', 'X5.D1.REGION.trimmed.nt.nb', 'X3.D1.REGION.trimmed.nt.nb', 'X5.D2.REGION.trimmed.nt.nb', 'X3.D2.REGION.trimmed.nt.nb', 'X5.D3.REGION.trimmed.nt.nb', 'X3.D3.REGION.trimmed.nt.nb', 'D.REGION.nt.nb', 'D1.REGION.nt.nb', 'D2.REGION.nt.nb', 'D3.REGION.nt.nb') + +out[,"VDJ Frame"] = as.character(out[,"VDJ Frame"]) + +fltr = out[,"VDJ Frame"] == "in-frame" +if(any(fltr, na.rm = T)){ + out[fltr, "VDJ Frame"] = "In-frame" +} + +fltr = out[,"VDJ Frame"] == "null" +if(any(fltr, na.rm = T)){ + out[fltr, "VDJ Frame"] = "Out-of-frame" +} + +fltr = out[,"VDJ Frame"] == "out-of-frame" +if(any(fltr, na.rm = T)){ + out[fltr, "VDJ Frame"] = "Out-of-frame" +} + +fltr = out[,"VDJ Frame"] == "" +if(any(fltr, na.rm = T)){ + out[fltr, "VDJ Frame"] = "Out-of-frame" +} + +for(col in c('Top V Gene','Top D Gene','Top J Gene')){ + out[,col] = as.character(out[,col]) + fltr = out[,col] == "" + if(any(fltr, na.rm = T)){ + out[fltr,col] = "NA" + } +} + +write.table(out, out.file, sep="\t", quote=F, row.names=F, col.names=T)
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/shm_csr/merge.r Fri Feb 19 15:08:51 2021 +0000 @@ -0,0 +1,27 @@ +args <- commandArgs(trailingOnly = TRUE) + +input.1 = args[1] +input.2 = args[2] + +fields.1 = args[3] +fields.2 = args[4] + +field.1 = args[5] +field.2 = args[6] + +output = args[7] + +dat1 = read.table(input.1, header=T, sep="\t", quote="", stringsAsFactors=F, fill=T, row.names=NULL) +if(fields.1 != "all"){ + fields.1 = unlist(strsplit(fields.1, ",")) + dat1 = dat1[,fields.1] +} +dat2 = read.table(input.2, header=T, sep="\t", quote="", stringsAsFactors=F, fill=T, row.names=NULL) +if(fields.2 != "all"){ + fields.2 = unlist(strsplit(fields.2, ",")) + dat2 = dat2[,fields.2] +} + +dat3 = merge(dat1, dat2, by.x=field.1, by.y=field.2) + +write.table(dat3, output, sep="\t",quote=F,row.names=F,col.names=T)
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/shm_csr/merge_and_filter.r Fri Feb 19 15:08:51 2021 +0000 @@ -0,0 +1,304 @@ +args <- commandArgs(trailingOnly = TRUE) + + +summaryfile = args[1] +sequencesfile = args[2] +mutationanalysisfile = args[3] +mutationstatsfile = args[4] +hotspotsfile = args[5] +aafile = args[6] +gene_identification_file= args[7] +output = args[8] +before.unique.file = args[9] +unmatchedfile = args[10] +method=args[11] +functionality=args[12] +unique.type=args[13] +filter.unique=args[14] +filter.unique.count=as.numeric(args[15]) +class.filter=args[16] +empty.region.filter=args[17] + +print(paste("filter.unique.count:", filter.unique.count)) + +summ = read.table(summaryfile, header=T, sep="\t", fill=T, stringsAsFactors=F, quote="") +sequences = read.table(sequencesfile, header=T, sep="\t", fill=T, stringsAsFactors=F, quote="") +mutationanalysis = read.table(mutationanalysisfile, header=T, sep="\t", fill=T, stringsAsFactors=F, quote="") +mutationstats = read.table(mutationstatsfile, header=T, sep="\t", fill=T, stringsAsFactors=F, quote="") +hotspots = read.table(hotspotsfile, header=T, sep="\t", fill=T, stringsAsFactors=F, quote="") +AAs = read.table(aafile, header=T, sep="\t", fill=T, stringsAsFactors=F, quote="") +gene_identification = read.table(gene_identification_file, header=T, sep="\t", fill=T, stringsAsFactors=F, quote="") + +fix_column_names = function(df){ + if("V.DOMAIN.Functionality" %in% names(df)){ + names(df)[names(df) == "V.DOMAIN.Functionality"] = "Functionality" + print("found V.DOMAIN.Functionality, changed") + } + if("V.DOMAIN.Functionality.comment" %in% names(df)){ + names(df)[names(df) == "V.DOMAIN.Functionality.comment"] = "Functionality.comment" + print("found V.DOMAIN.Functionality.comment, changed") + } + return(df) +} + +fix_non_unique_ids = function(df){ + df$Sequence.ID = paste(df$Sequence.ID, 1:nrow(df)) + return(df) +} + +summ = fix_column_names(summ) +sequences = fix_column_names(sequences) +mutationanalysis = fix_column_names(mutationanalysis) +mutationstats = fix_column_names(mutationstats) +hotspots = fix_column_names(hotspots) +AAs = fix_column_names(AAs) + +if(method == "blastn"){ + #"qseqid\tsseqid\tpident\tlength\tmismatch\tgapopen\tqstart\tqend\tsstart\tsend\tevalue\tbitscore" + gene_identification = gene_identification[!duplicated(gene_identification$qseqid),] + ref_length = data.frame(sseqid=c("ca1", "ca2", "cg1", "cg2", "cg3", "cg4", "cm"), ref.length=c(81,81,141,141,141,141,52)) + gene_identification = merge(gene_identification, ref_length, by="sseqid", all.x=T) + gene_identification$chunk_hit_percentage = (gene_identification$length / gene_identification$ref.length) * 100 + gene_identification = gene_identification[,c("qseqid", "chunk_hit_percentage", "pident", "qstart", "sseqid")] + colnames(gene_identification) = c("Sequence.ID", "chunk_hit_percentage", "nt_hit_percentage", "start_locations", "best_match") +} + +#print("Summary analysis files columns") +#print(names(summ)) + + + +input.sequence.count = nrow(summ) +print(paste("Number of sequences in summary file:", input.sequence.count)) + +filtering.steps = data.frame(character(0), numeric(0)) + +filtering.steps = rbind(filtering.steps, c("Input", input.sequence.count)) + +filtering.steps[,1] = as.character(filtering.steps[,1]) +filtering.steps[,2] = as.character(filtering.steps[,2]) +#filtering.steps[,3] = as.numeric(filtering.steps[,3]) + +#print("summary files columns") +#print(names(summ)) + +summ = merge(summ, gene_identification, by="Sequence.ID") + +print(paste("Number of sequences after merging with gene identification:", nrow(summ))) + +summ = summ[summ$Functionality != "No results",] + +print(paste("Number of sequences after 'No results' filter:", nrow(summ))) + +filtering.steps = rbind(filtering.steps, c("After 'No results' filter", nrow(summ))) + +if(functionality == "productive"){ + summ = summ[summ$Functionality == "productive (see comment)" | summ$Functionality == "productive",] +} else if (functionality == "unproductive"){ + summ = summ[summ$Functionality == "unproductive (see comment)" | summ$Functionality == "unproductive",] +} else if (functionality == "remove_unknown"){ + summ = summ[summ$Functionality != "No results" & summ$Functionality != "unknown (see comment)" & summ$Functionality != "unknown",] +} + +print(paste("Number of sequences after functionality filter:", nrow(summ))) + +filtering.steps = rbind(filtering.steps, c("After functionality filter", nrow(summ))) + +if(F){ #to speed up debugging + set.seed(1) + summ = summ[sample(nrow(summ), floor(nrow(summ) * 0.03)),] + print(paste("Number of sequences after sampling 3%:", nrow(summ))) + + filtering.steps = rbind(filtering.steps, c("Number of sequences after sampling 3%", nrow(summ))) +} + +print("mutation analysis files columns") +print(names(mutationanalysis[,!(names(mutationanalysis) %in% names(summ)[-1])])) + +result = merge(summ, mutationanalysis[,!(names(mutationanalysis) %in% names(summ)[-1])], by="Sequence.ID") + +print(paste("Number of sequences after merging with mutation analysis file:", nrow(result))) + +#print("mutation stats files columns") +#print(names(mutationstats[,!(names(mutationstats) %in% names(result)[-1])])) + +result = merge(result, mutationstats[,!(names(mutationstats) %in% names(result)[-1])], by="Sequence.ID") + +print(paste("Number of sequences after merging with mutation stats file:", nrow(result))) + +print("hotspots files columns") +print(names(hotspots[,!(names(hotspots) %in% names(result)[-1])])) + +result = merge(result, hotspots[,!(names(hotspots) %in% names(result)[-1])], by="Sequence.ID") + +print(paste("Number of sequences after merging with hotspots file:", nrow(result))) + +print("sequences files columns") +print(c("FR1.IMGT", "CDR1.IMGT", "FR2.IMGT", "CDR2.IMGT", "FR3.IMGT", "CDR3.IMGT")) + +sequences = sequences[,c("Sequence.ID", "FR1.IMGT", "CDR1.IMGT", "FR2.IMGT", "CDR2.IMGT", "FR3.IMGT", "CDR3.IMGT")] +names(sequences) = c("Sequence.ID", "FR1.IMGT.seq", "CDR1.IMGT.seq", "FR2.IMGT.seq", "CDR2.IMGT.seq", "FR3.IMGT.seq", "CDR3.IMGT.seq") +result = merge(result, sequences, by="Sequence.ID", all.x=T) + +AAs = AAs[,c("Sequence.ID", "CDR3.IMGT")] +names(AAs) = c("Sequence.ID", "CDR3.IMGT.AA") +result = merge(result, AAs, by="Sequence.ID", all.x=T) + +print(paste("Number of sequences in result after merging with sequences:", nrow(result))) + +result$VGene = gsub("^Homsap ", "", result$V.GENE.and.allele) +result$VGene = gsub("[*].*", "", result$VGene) +result$DGene = gsub("^Homsap ", "", result$D.GENE.and.allele) +result$DGene = gsub("[*].*", "", result$DGene) +result$JGene = gsub("^Homsap ", "", result$J.GENE.and.allele) +result$JGene = gsub("[*].*", "", result$JGene) + +splt = strsplit(class.filter, "_")[[1]] +chunk_hit_threshold = as.numeric(splt[1]) +nt_hit_threshold = as.numeric(splt[2]) + +higher_than=(result$chunk_hit_percentage >= chunk_hit_threshold & result$nt_hit_percentage >= nt_hit_threshold) + +if(!all(higher_than, na.rm=T)){ #check for no unmatched + result[!higher_than,"best_match"] = paste("unmatched,", result[!higher_than,"best_match"]) +} + +if(class.filter == "101_101"){ + result$best_match = "all" +} + +write.table(x=result, file=gsub("merged.txt$", "before_filters.txt", output), sep="\t",quote=F,row.names=F,col.names=T) + +print(paste("Number of empty CDR1 sequences:", sum(result$CDR1.IMGT.seq == "", na.rm=T))) +print(paste("Number of empty FR2 sequences:", sum(result$FR2.IMGT.seq == "", na.rm=T))) +print(paste("Number of empty CDR2 sequences:", sum(result$CDR2.IMGT.seq == "", na.rm=T))) +print(paste("Number of empty FR3 sequences:", sum(result$FR3.IMGT.seq == "", na.rm=T))) + +if(empty.region.filter == "leader"){ + result = result[result$FR1.IMGT.seq != "" & result$CDR1.IMGT.seq != "" & result$FR2.IMGT.seq != "" & result$CDR2.IMGT.seq != "" & result$FR3.IMGT.seq != "", ] +} else if(empty.region.filter == "FR1"){ + result = result[result$CDR1.IMGT.seq != "" & result$FR2.IMGT.seq != "" & result$CDR2.IMGT.seq != "" & result$FR3.IMGT.seq != "", ] +} else if(empty.region.filter == "CDR1"){ + result = result[result$FR2.IMGT.seq != "" & result$CDR2.IMGT.seq != "" & result$FR3.IMGT.seq != "", ] +} else if(empty.region.filter == "FR2"){ + result = result[result$CDR2.IMGT.seq != "" & result$FR3.IMGT.seq != "", ] +} + +print(paste("After removal sequences that are missing a gene region:", nrow(result))) +filtering.steps = rbind(filtering.steps, c("After removal sequences that are missing a gene region", nrow(result))) + +if(empty.region.filter == "leader"){ + result = result[!(grepl("n|N", result$FR1.IMGT.seq) | grepl("n|N", result$FR2.IMGT.seq) | grepl("n|N", result$FR3.IMGT.seq) | grepl("n|N", result$CDR1.IMGT.seq) | grepl("n|N", result$CDR2.IMGT.seq) | grepl("n|N", result$CDR3.IMGT.seq)),] +} else if(empty.region.filter == "FR1"){ + result = result[!(grepl("n|N", result$FR2.IMGT.seq) | grepl("n|N", result$FR3.IMGT.seq) | grepl("n|N", result$CDR1.IMGT.seq) | grepl("n|N", result$CDR2.IMGT.seq) | grepl("n|N", result$CDR3.IMGT.seq)),] +} else if(empty.region.filter == "CDR1"){ + result = result[!(grepl("n|N", result$FR2.IMGT.seq) | grepl("n|N", result$FR3.IMGT.seq) | grepl("n|N", result$CDR2.IMGT.seq) | grepl("n|N", result$CDR3.IMGT.seq)),] +} else if(empty.region.filter == "FR2"){ + result = result[!(grepl("n|N", result$FR3.IMGT.seq) | grepl("n|N", result$CDR2.IMGT.seq) | grepl("n|N", result$CDR3.IMGT.seq)),] +} + +print(paste("Number of sequences in result after n filtering:", nrow(result))) +filtering.steps = rbind(filtering.steps, c("After N filter", nrow(result))) + +cleanup_columns = c("FR1.IMGT.Nb.of.mutations", + "CDR1.IMGT.Nb.of.mutations", + "FR2.IMGT.Nb.of.mutations", + "CDR2.IMGT.Nb.of.mutations", + "FR3.IMGT.Nb.of.mutations") + +for(col in cleanup_columns){ + result[,col] = gsub("\\(.*\\)", "", result[,col]) + result[,col] = as.numeric(result[,col]) + result[is.na(result[,col]),] = 0 +} + +write.table(result, before.unique.file, sep="\t", quote=F,row.names=F,col.names=T) + + +if(filter.unique != "no"){ + clmns = names(result) + if(filter.unique == "remove_vjaa"){ + result$unique.def = paste(result$VGene, result$JGene, result$CDR3.IMGT.AA) + } else if(empty.region.filter == "leader"){ + result$unique.def = paste(result$FR1.IMGT.seq, result$CDR1.IMGT.seq, result$FR2.IMGT.seq, result$CDR2.IMGT.seq, result$FR3.IMGT.seq, result$CDR3.IMGT.seq) + } else if(empty.region.filter == "FR1"){ + result$unique.def = paste(result$CDR1.IMGT.seq, result$FR2.IMGT.seq, result$CDR2.IMGT.seq, result$FR3.IMGT.seq, result$CDR3.IMGT.seq) + } else if(empty.region.filter == "CDR1"){ + result$unique.def = paste(result$FR2.IMGT.seq, result$CDR2.IMGT.seq, result$FR3.IMGT.seq, result$CDR3.IMGT.seq) + } else if(empty.region.filter == "FR2"){ + result$unique.def = paste(result$CDR2.IMGT.seq, result$FR3.IMGT.seq, result$CDR3.IMGT.seq) + } + + if(grepl("remove", filter.unique)){ + result = result[duplicated(result$unique.def) | duplicated(result$unique.def, fromLast=T),] + unique.defs = data.frame(table(result$unique.def)) + unique.defs = unique.defs[unique.defs$Freq >= filter.unique.count,] + result = result[result$unique.def %in% unique.defs$Var1,] + } + + if(filter.unique != "remove_vjaa"){ + result$unique.def = paste(result$unique.def, gsub(",.*", "", result$best_match)) #keep the unique sequences that are in multiple classes, gsub so the unmatched don't have a class after it + } + + result = result[!duplicated(result$unique.def),] +} + +write.table(result, gsub("before_unique_filter.txt", "after_unique_filter.txt", before.unique.file), sep="\t", quote=F,row.names=F,col.names=T) + +filtering.steps = rbind(filtering.steps, c("After filter unique sequences", nrow(result))) + +print(paste("Number of sequences in result after unique filtering:", nrow(result))) + +if(nrow(summ) == 0){ + stop("No data remaining after filter") +} + +result$best_match_class = gsub(",.*", "", result$best_match) #gsub so the unmatched don't have a class after it + +#result$past = "" +#cls = unlist(strsplit(unique.type, ",")) +#for (i in 1:nrow(result)){ +# result[i,"past"] = paste(result[i,cls], collapse=":") +#} + + + +result$past = do.call(paste, c(result[unlist(strsplit(unique.type, ","))], sep = ":")) + +result.matched = result[!grepl("unmatched", result$best_match),] +result.unmatched = result[grepl("unmatched", result$best_match),] + +result = rbind(result.matched, result.unmatched) + +result = result[!(duplicated(result$past)), ] + +result = result[,!(names(result) %in% c("past", "best_match_class"))] + +print(paste("Number of sequences in result after", unique.type, "filtering:", nrow(result))) + +filtering.steps = rbind(filtering.steps, c("After remove duplicates based on filter", nrow(result))) + +unmatched = result[grepl("^unmatched", result$best_match),c("Sequence.ID", "chunk_hit_percentage", "nt_hit_percentage", "start_locations", "best_match")] + +print(paste("Number of rows in result:", nrow(result))) +print(paste("Number of rows in unmatched:", nrow(unmatched))) + +matched.sequences = result[!grepl("^unmatched", result$best_match),] + +write.table(x=matched.sequences, file=gsub("merged.txt$", "filtered.txt", output), sep="\t",quote=F,row.names=F,col.names=T) + +matched.sequences.count = nrow(matched.sequences) +unmatched.sequences.count = sum(grepl("^unmatched", result$best_match)) +if(matched.sequences.count <= unmatched.sequences.count){ + print("WARNING NO MATCHED (SUB)CLASS SEQUENCES!!") +} + +filtering.steps = rbind(filtering.steps, c("Number of matched sequences", matched.sequences.count)) +filtering.steps = rbind(filtering.steps, c("Number of unmatched sequences", unmatched.sequences.count)) +filtering.steps[,2] = as.numeric(filtering.steps[,2]) +filtering.steps$perc = round(filtering.steps[,2] / input.sequence.count * 100, 2) + +write.table(x=filtering.steps, file=gsub("unmatched", "filtering_steps", unmatchedfile), sep="\t",quote=F,row.names=F,col.names=F) + +write.table(x=result, file=output, sep="\t",quote=F,row.names=F,col.names=T) +write.table(x=unmatched, file=unmatchedfile, sep="\t",quote=F,row.names=F,col.names=T)
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/shm_csr/mutation_column_checker.py Fri Feb 19 15:08:51 2021 +0000 @@ -0,0 +1,27 @@ +import re + +mutationMatcher = re.compile("^([nactg])(\d+).([nactg]),?[ ]?([A-Z])?(\d+)?[>]?([A-Z;])?(.*)?") + +with open("7_V-REGION-mutation-and-AA-change-table.txt", 'r') as file_handle: + first = True + fr3_index = -1 + for i, line in enumerate(file_handle): + line_split = line.split("\t") + if first: + fr3_index = line_split.index("FR3-IMGT") + first = False + continue + + if len(line_split) < fr3_index: + continue + + fr3_data = line_split[fr3_index] + if len(fr3_data) > 5: + try: + test = [mutationMatcher.match(x).groups() for x in fr3_data.split("|") if x] + except: + print(line_split[1]) + print("Something went wrong at line {line} with:".format(line=line_split[0])) + #print([x for x in fr3_data.split("|") if not mutationMatcher.match(x)]) + if i % 100000 == 0: + print(i)
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/shm_csr/naive_output.r Fri Feb 19 15:08:51 2021 +0000 @@ -0,0 +1,45 @@ +args <- commandArgs(trailingOnly = TRUE) + +naive.file = args[1] +shm.file = args[2] +output.file.ca = args[3] +output.file.cg = args[4] +output.file.cm = args[5] + +naive = read.table(naive.file, sep="\t", header=T, quote="", fill=T) +shm.merge = read.table(shm.file, sep="\t", header=T, quote="", fill=T) + + +final = merge(naive, shm.merge[,c("Sequence.ID", "best_match")], by.x="ID", by.y="Sequence.ID") +print(paste("nrow final:", nrow(final))) +names(final)[names(final) == "best_match"] = "Sample" +final.numeric = final[,sapply(final, is.numeric)] +final.numeric[is.na(final.numeric)] = 0 +final[,sapply(final, is.numeric)] = final.numeric + +final.ca = final[grepl("^ca", final$Sample),] +final.cg = final[grepl("^cg", final$Sample),] +final.cm = final[grepl("^cm", final$Sample),] + +if(nrow(final.ca) > 0){ + final.ca$Replicate = 1 +} + +if(nrow(final.cg) > 0){ + final.cg$Replicate = 1 +} + +if(nrow(final.cm) > 0){ + final.cm$Replicate = 1 +} + +#print(paste("nrow final:", nrow(final))) +#final2 = final +#final2$Sample = gsub("[0-9]", "", final2$Sample) +#final = rbind(final, final2) +#final$Replicate = 1 + +write.table(final.ca, output.file.ca, quote=F, sep="\t", row.names=F, col.names=T) +write.table(final.cg, output.file.cg, quote=F, sep="\t", row.names=F, col.names=T) +write.table(final.cm, output.file.cm, quote=F, sep="\t", row.names=F, col.names=T) +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/shm_csr/new_imgt.r Fri Feb 19 15:08:51 2021 +0000 @@ -0,0 +1,40 @@ +args <- commandArgs(trailingOnly = TRUE) + +imgt.dir = args[1] +merged.file = args[2] +gene = args[3] + +merged = read.table(merged.file, header=T, sep="\t", fill=T, stringsAsFactors=F, comment.char="", quote="") + +if(!("Sequence.ID" %in% names(merged))){ #change-o db + print("Change-O DB changing 'SEQUENCE_ID' to 'Sequence.ID'") + names(merged)[which(names[merged] == "SEQUENCE_ID")] = "Sequence.ID" +} + +if(gene != "-"){ + merged = merged[grepl(paste("^", gene, sep=""), merged$best_match),] +} + +if("best_match" %in% names(merged)){ + merged = merged[!grepl("unmatched", merged$best_match),] +} + +nrow_dat = 0 + +for(f in list.files(imgt.dir, pattern="*.txt$")){ + #print(paste("filtering", f)) + path = file.path(imgt.dir, f) + dat = read.table(path, header=T, sep="\t", fill=T, quote="", stringsAsFactors=F, check.names=FALSE, comment.char="") + + dat = dat[dat[,"Sequence ID"] %in% merged$Sequence.ID,] + + nrow_dat = nrow(dat) + + if(nrow(dat) > 0 & grepl("^8_", f)){ #change the FR1 columns to 0 in the "8_..." file + dat[,grepl("^FR1", names(dat))] = 0 + } + + write.table(dat, path, quote=F, sep="\t", row.names=F, col.names=T, na="") +} + +print(paste("Creating new zip for ", gene, "with", nrow_dat, "sequences"))
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/shm_csr/pattern_plots.r Fri Feb 19 15:08:51 2021 +0000 @@ -0,0 +1,178 @@ +library(ggplot2) +library(reshape2) +library(scales) + +args <- commandArgs(trailingOnly = TRUE) + +input.file = args[1] #the data that's get turned into the "SHM overview" table in the html report "data_sum.txt" + +plot1.path = args[2] +plot1.png = paste(plot1.path, ".png", sep="") +plot1.txt = paste(plot1.path, ".txt", sep="") +plot1.pdf = paste(plot1.path, ".pdf", sep="") + +plot2.path = args[3] +plot2.png = paste(plot2.path, ".png", sep="") +plot2.txt = paste(plot2.path, ".txt", sep="") +plot2.pdf = paste(plot2.path, ".pdf", sep="") + +plot3.path = args[4] +plot3.png = paste(plot3.path, ".png", sep="") +plot3.txt = paste(plot3.path, ".txt", sep="") +plot3.pdf = paste(plot3.path, ".pdf", sep="") + +clean.output = args[5] + +dat = read.table(input.file, header=F, sep=",", quote="", stringsAsFactors=F, fill=T, row.names=1) + +classes = c("IGA", "IGA1", "IGA2", "IGG", "IGG1", "IGG2", "IGG3", "IGG4", "IGM", "IGE") +xyz = c("x", "y", "z") +new.names = c(paste(rep(classes, each=3), xyz, sep="."), paste("un", xyz, sep="."), paste("all", xyz, sep=".")) + +names(dat) = new.names + +clean.dat = dat +clean.dat = clean.dat[,c(paste(rep(classes, each=3), xyz, sep="."), paste("all", xyz, sep="."), paste("un", xyz, sep="."))] + +write.table(clean.dat, clean.output, quote=F, sep="\t", na="", row.names=T, col.names=NA) + +dat["RGYW.WRCY",] = colSums(dat[c(14,15),], na.rm=T) +dat["TW.WA",] = colSums(dat[c(16,17),], na.rm=T) + +data1 = dat[c("RGYW.WRCY", "TW.WA"),] + +data1 = data1[,names(data1)[grepl(".z", names(data1))]] +names(data1) = gsub("\\..*", "", names(data1)) + +data1 = melt(t(data1)) + +names(data1) = c("Class", "Type", "value") + +chk = is.na(data1$value) +if(any(chk)){ + data1[chk, "value"] = 0 +} + +data1 = data1[order(data1$Type),] + +write.table(data1, plot1.txt, quote=F, sep="\t", na="", row.names=F, col.names=T) + +p = ggplot(data1, aes(Class, value)) + geom_bar(aes(fill=Type), stat="identity", position="dodge", colour = "black") + ylab("% of mutations") + guides(fill=guide_legend(title=NULL)) + ggtitle("Percentage of mutations in AID and pol eta motives") +p = p + theme(panel.background = element_rect(fill = "white", colour="black"),text = element_text(size=15, colour="black"), axis.text.x = element_text(angle = 45, hjust = 1)) + scale_fill_manual(values=c("RGYW.WRCY" = "white", "TW.WA" = "blue4")) +#p = p + scale_colour_manual(values=c("RGYW.WRCY" = "black", "TW.WA" = "blue4")) +png(filename=plot1.png, width=510, height=300) +print(p) +dev.off() + +ggsave(plot1.pdf, p) + +data2 = dat[c(1, 5:8),] + +data2 = data2[,names(data2)[grepl("\\.x", names(data2))]] +names(data2) = gsub(".x", "", names(data2)) + +data2["A/T",] = dat["Targeting of A T (%)",names(dat)[grepl("\\.z", names(dat))]] + +data2["G/C transitions",] = round(data2["Transitions at G C (%)",] / data2["Number of Mutations (%)",] * 100, 1) + +data2["mutation.at.gc",] = dat["Transitions at G C (%)",names(dat)[grepl("\\.y", names(dat))]] +data2["G/C transversions",] = round((data2["mutation.at.gc",] - data2["Transitions at G C (%)",]) / data2["Number of Mutations (%)",] * 100, 1) + +data2["G/C transversions",is.nan(unlist(data2["G/C transversions",]))] = 0 +data2["G/C transversions",is.infinite(unlist(data2["G/C transversions",]))] = 0 +data2["G/C transitions",is.nan(unlist(data2["G/C transitions",]))] = 0 +data2["G/C transitions",is.infinite(unlist(data2["G/C transitions",]))] = 0 + +data2 = melt(t(data2[c("A/T","G/C transitions","G/C transversions"),])) + +names(data2) = c("Class", "Type", "value") + +chk = is.na(data2$value) +if(any(chk)){ + data2[chk, "value"] = 0 +} + +data2 = data2[order(data2$Type),] + +write.table(data2, plot2.txt, quote=F, sep="\t", na="", row.names=F, col.names=T) + +p = ggplot(data2, aes(x=Class, y=value, fill=Type)) + geom_bar(position="fill", stat="identity", colour = "black") + scale_y_continuous(labels=percent_format()) + guides(fill=guide_legend(title=NULL)) + ylab("% of mutations") + ggtitle("Relative mutation patterns") +p = p + theme(panel.background = element_rect(fill = "white", colour="black"), text = element_text(size=15, colour="black"), axis.text.x = element_text(angle = 45, hjust = 1)) + scale_fill_manual(values=c("A/T" = "blue4", "G/C transversions" = "gray74", "G/C transitions" = "white")) +#p = p + scale_colour_manual(values=c("A/T" = "blue4", "G/C transversions" = "gray74", "G/C transitions" = "black")) +png(filename=plot2.png, width=480, height=300) +print(p) +dev.off() + +ggsave(plot2.pdf, p) + +data3 = dat[c(5, 6, 8, 18:21),] +data3 = data3[,names(data3)[grepl("\\.x", names(data3))]] +names(data3) = gsub(".x", "", names(data3)) + +data3["G/C transitions",] = round(data3["Transitions at G C (%)",] / (data3["C",] + data3["G",]) * 100, 1) + +data3["G/C transversions",] = round((data3["Targeting of G C (%)",] - data3["Transitions at G C (%)",]) / (data3["C",] + data3["G",]) * 100, 1) + +data3["A/T",] = round(data3["Targeting of A T (%)",] / (data3["A",] + data3["T",]) * 100, 1) + +data3["G/C transitions",is.nan(unlist(data3["G/C transitions",]))] = 0 +data3["G/C transitions",is.infinite(unlist(data3["G/C transitions",]))] = 0 + +data3["G/C transversions",is.nan(unlist(data3["G/C transversions",]))] = 0 +data3["G/C transversions",is.infinite(unlist(data3["G/C transversions",]))] = 0 + +data3["A/T",is.nan(unlist(data3["A/T",]))] = 0 +data3["A/T",is.infinite(unlist(data3["A/T",]))] = 0 + +data3 = melt(t(data3[8:10,])) +names(data3) = c("Class", "Type", "value") + +chk = is.na(data3$value) +if(any(chk)){ + data3[chk, "value"] = 0 +} + +data3 = data3[order(data3$Type),] + +write.table(data3, plot3.txt, quote=F, sep="\t", na="", row.names=F, col.names=T) + +p = ggplot(data3, aes(Class, value)) + geom_bar(aes(fill=Type), stat="identity", position="dodge", colour = "black") + ylab("% of nucleotides") + guides(fill=guide_legend(title=NULL)) + ggtitle("Absolute mutation patterns") +p = p + theme(panel.background = element_rect(fill = "white", colour="black"), text = element_text(size=15, colour="black"), axis.text.x = element_text(angle = 45, hjust = 1)) + scale_fill_manual(values=c("A/T" = "blue4", "G/C transversions" = "gray74", "G/C transitions" = "white")) +#p = p + scale_colour_manual(values=c("A/T" = "blue4", "G/C transversions" = "gray74", "G/C transitions" = "black")) +png(filename=plot3.png, width=480, height=300) +print(p) +dev.off() + +ggsave(plot3.pdf, p) + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/shm_csr/plot_pdf.r Fri Feb 19 15:08:51 2021 +0000 @@ -0,0 +1,17 @@ +library(ggplot2) + +args <- commandArgs(trailingOnly = TRUE) +print(args) + +input = args[1] +outputdir = args[2] +setwd(outputdir) + +load(input) + +print(names(pdfplots)) + +for(n in names(pdfplots)){ + print(paste("n:", n)) + ggsave(pdfplots[[n]], file=n) +}
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/shm_csr/sequence_overview.r Fri Feb 19 15:08:51 2021 +0000 @@ -0,0 +1,363 @@ +library(reshape2) + +args <- commandArgs(trailingOnly = TRUE) + +before.unique.file = args[1] +merged.file = args[2] +outputdir = args[3] +gene.classes = unlist(strsplit(args[4], ",")) +hotspot.analysis.sum.file = args[5] +NToverview.file = paste(outputdir, "ntoverview.txt", sep="/") +NTsum.file = paste(outputdir, "ntsum.txt", sep="/") +main.html = "index.html" +empty.region.filter = args[6] + + +setwd(outputdir) + +before.unique = read.table(before.unique.file, header=T, sep="\t", fill=T, stringsAsFactors=F, quote="") +merged = read.table(merged.file, header=T, sep="\t", fill=T, stringsAsFactors=F, quote="") +hotspot.analysis.sum = read.table(hotspot.analysis.sum.file, header=F, sep=",", fill=T, stringsAsFactors=F, quote="") + +#before.unique = before.unique[!grepl("unmatched", before.unique$best_match),] + +if(empty.region.filter == "leader"){ + before.unique$seq_conc = paste(before.unique$FR1.IMGT.seq, before.unique$CDR1.IMGT.seq, before.unique$FR2.IMGT.seq, before.unique$CDR2.IMGT.seq, before.unique$FR3.IMGT.seq, before.unique$CDR3.IMGT.seq) +} else if(empty.region.filter == "FR1"){ + before.unique$seq_conc = paste(before.unique$CDR1.IMGT.seq, before.unique$FR2.IMGT.seq, before.unique$CDR2.IMGT.seq, before.unique$FR3.IMGT.seq, before.unique$CDR3.IMGT.seq) +} else if(empty.region.filter == "CDR1"){ + before.unique$seq_conc = paste(before.unique$FR2.IMGT.seq, before.unique$CDR2.IMGT.seq, before.unique$FR3.IMGT.seq, before.unique$CDR3.IMGT.seq) +} else if(empty.region.filter == "FR2"){ + before.unique$seq_conc = paste(before.unique$CDR2.IMGT.seq, before.unique$FR3.IMGT.seq, before.unique$CDR3.IMGT.seq) +} + +IDs = before.unique[,c("Sequence.ID", "seq_conc", "best_match", "Functionality")] +IDs$best_match = as.character(IDs$best_match) + +dat = data.frame(table(before.unique$seq_conc)) + +names(dat) = c("seq_conc", "Freq") + +dat$seq_conc = factor(dat$seq_conc) + +dat = dat[order(as.character(dat$seq_conc)),] + +#writing html from R... +get.bg.color = function(val){ + if(val %in% c("TRUE", "FALSE", "T", "F")){ #if its a logical value, give the background a green/red color + return(ifelse(val,"#eafaf1","#f9ebea")) + } else if (!is.na(as.numeric(val))) { #if its a numerical value, give it a grey tint if its >0 + return(ifelse(val > 0,"#eaecee","white")) + } else { + return("white") + } +} +td = function(val) { + return(paste("<td bgcolor='", get.bg.color(val), "'>", val, "</td>", sep="")) +} +tr = function(val) { + return(paste(c("<tr>", sapply(val, td), "</tr>"), collapse="")) +} + +make.link = function(id, clss, val) { + paste("<a href='", clss, "_", id, ".html'>", val, "</a>", sep="") +} +tbl = function(df) { + res = "<table border='1'>" + for(i in 1:nrow(df)){ + res = paste(res, tr(df[i,]), sep="") + } + res = paste(res, "</table>") +} + +cat("<center><img src='data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAA8AAAAPCAYAAAA71pVKAAAAzElEQVQoka2TwQ2CQBBFpwTshw4ImW8ogJMlUIMmhNCDxgasAi50oSXA8XlAjCG7aqKTzGX/vsnM31mzR0gk7tTudO5MEizpzvQ4ryUSe408J3Xn+grE0p1rnpOamVmWsZG4rS+dzzAMsN8Hi9yyjI1JNGtxu4VxBJgLRLpoTKIPiW0LlwtUVRTubW2OBGUJu92cZRmdfbKQMAw8o+vi5v0fLorZ7Y9waGYJjsf38DJz0O1PsEQffOcv4Sa6YYfDDJ5Obzbsp93+5VfdATueO1fdLdI0AAAAAElFTkSuQmCC'> Please note that this tab is based on all sequences before filter unique sequences and the remove duplicates based on filters are applied. In this table only sequences occuring more than once are included. </center>", file=main.html, append=F) +cat("<table border='1' class='pure-table pure-table-striped'>", file=main.html, append=T) + +if(empty.region.filter == "leader"){ + cat("<caption>FR1+CDR1+FR2+CDR2+FR3+CDR3 sequences that show up more than once</caption>", file=main.html, append=T) +} else if(empty.region.filter == "FR1"){ + cat("<caption>CDR1+FR2+CDR2+FR3+CDR3 sequences that show up more than once</caption>", file=main.html, append=T) +} else if(empty.region.filter == "CDR1"){ + cat("<caption>FR2+CDR2+FR3+CDR3 sequences that show up more than once</caption>", file=main.html, append=T) +} else if(empty.region.filter == "FR2"){ + cat("<caption>CDR2+FR3+CDR3 sequences that show up more than once</caption>", file=main.html, append=T) +} + +cat("<tr>", file=main.html, append=T) +cat("<th>Sequence</th><th>Functionality</th><th>IGA1</th><th>IGA2</th><th>IGG1</th><th>IGG2</th><th>IGG3</th><th>IGG4</th><th>IGM</th><th>IGE</th><th>UN</th>", file=main.html, append=T) +cat("<th>total IGA</th><th>total IGG</th><th>total IGM</th><th>total IGE</th><th>number of subclasses</th><th>present in both IGA and IGG</th><th>present in IGA, IGG and IGM</th><th>present in IGA, IGG and IGE</th><th>present in IGA, IGG, IGM and IGE</th><th>IGA1+IGA2</th>", file=main.html, append=T) +cat("<th>IGG1+IGG2</th><th>IGG1+IGG3</th><th>IGG1+IGG4</th><th>IGG2+IGG3</th><th>IGG2+IGG4</th><th>IGG3+IGG4</th>", file=main.html, append=T) +cat("<th>IGG1+IGG2+IGG3</th><th>IGG2+IGG3+IGG4</th><th>IGG1+IGG2+IGG4</th><th>IGG1+IGG3+IGG4</th><th>IGG1+IGG2+IGG3+IGG4</th>", file=main.html, append=T) +cat("</tr>", file=main.html, append=T) + + + +single.sequences=0 #sequence only found once, skipped +in.multiple=0 #same sequence across multiple subclasses +multiple.in.one=0 #same sequence multiple times in one subclass +unmatched=0 #all of the sequences are unmatched +some.unmatched=0 #one or more sequences in a clone are unmatched +matched=0 #should be the same als matched sequences + +sequence.id.page="by_id.html" + +for(i in 1:nrow(dat)){ + + ca1 = IDs[IDs$seq_conc == dat[i,c("seq_conc")] & grepl("^IGA1", IDs$best_match),] + ca2 = IDs[IDs$seq_conc == dat[i,c("seq_conc")] & grepl("^IGA2", IDs$best_match),] + + cg1 = IDs[IDs$seq_conc == dat[i,c("seq_conc")] & grepl("^IGG1", IDs$best_match),] + cg2 = IDs[IDs$seq_conc == dat[i,c("seq_conc")] & grepl("^IGG2", IDs$best_match),] + cg3 = IDs[IDs$seq_conc == dat[i,c("seq_conc")] & grepl("^IGG3", IDs$best_match),] + cg4 = IDs[IDs$seq_conc == dat[i,c("seq_conc")] & grepl("^IGG4", IDs$best_match),] + + cm = IDs[IDs$seq_conc == dat[i,c("seq_conc")] & grepl("^IGM", IDs$best_match),] + + ce = IDs[IDs$seq_conc == dat[i,c("seq_conc")] & grepl("^IGE", IDs$best_match),] + + un = IDs[IDs$seq_conc == dat[i,c("seq_conc")] & grepl("^unmatched", IDs$best_match),] + + allc = rbind(ca1, ca2, cg1, cg2, cg3, cg4, cm, ce, un) + + ca1.n = nrow(ca1) + ca2.n = nrow(ca2) + + cg1.n = nrow(cg1) + cg2.n = nrow(cg2) + cg3.n = nrow(cg3) + cg4.n = nrow(cg4) + + cm.n = nrow(cm) + + ce.n = nrow(ce) + + un.n = nrow(un) + + classes = c(ca1.n, ca2.n, cg1.n, cg2.n, cg3.n, cg4.n, cm.n, ce.n, un.n) + + classes.sum = sum(classes) + + if(classes.sum == 1){ + single.sequences = single.sequences + 1 + next + } + + if(un.n == classes.sum){ + unmatched = unmatched + 1 + next + } + + classes.no.un = classes[-length(classes)] + + in.classes = sum(classes.no.un > 0) + + matched = matched + in.classes #count in how many subclasses the sequence occurs. + + if(any(classes == classes.sum)){ + multiple.in.one = multiple.in.one + 1 + } else if (un.n > 0) { + some.unmatched = some.unmatched + 1 + } else { + in.multiple = in.multiple + 1 + } + + id = as.numeric(dat[i,"seq_conc"]) + + functionality = paste(unique(allc[,"Functionality"]), collapse=",") + + by.id.row = c() + + if(ca1.n > 0){ + cat(tbl(ca1), file=paste("IGA1_", id, ".html", sep="")) + } + + if(ca2.n > 0){ + cat(tbl(ca2), file=paste("IGA2_", id, ".html", sep="")) + } + + if(cg1.n > 0){ + cat(tbl(cg1), file=paste("IGG1_", id, ".html", sep="")) + } + + if(cg2.n > 0){ + cat(tbl(cg2), file=paste("IGG2_", id, ".html", sep="")) + } + + if(cg3.n > 0){ + cat(tbl(cg3), file=paste("IGG3_", id, ".html", sep="")) + } + + if(cg4.n > 0){ + cat(tbl(cg4), file=paste("IGG4_", id, ".html", sep="")) + } + + if(cm.n > 0){ + cat(tbl(cm), file=paste("IGM_", id, ".html", sep="")) + } + + if(ce.n > 0){ + cat(tbl(ce), file=paste("IGE_", id, ".html", sep="")) + } + + if(un.n > 0){ + cat(tbl(un), file=paste("un_", id, ".html", sep="")) + } + + ca1.html = make.link(id, "IGA1", ca1.n) + ca2.html = make.link(id, "IGA2", ca2.n) + + cg1.html = make.link(id, "IGG1", cg1.n) + cg2.html = make.link(id, "IGG2", cg2.n) + cg3.html = make.link(id, "IGG3", cg3.n) + cg4.html = make.link(id, "IGG4", cg4.n) + + cm.html = make.link(id, "IGM", cm.n) + + ce.html = make.link(id, "IGE", ce.n) + + un.html = make.link(id, "un", un.n) + + #extra columns + ca.n = ca1.n + ca2.n + + cg.n = cg1.n + cg2.n + cg3.n + cg4.n + + #in.classes + + in.ca.cg = (ca.n > 0 & cg.n > 0) + + in.ca.cg.cm = (ca.n > 0 & cg.n > 0 & cm.n > 0) + + in.ca.cg.ce = (ca.n > 0 & cg.n > 0 & ce.n > 0) + + in.ca.cg.cm.ce = (ca.n > 0 & cg.n > 0 & cm.n > 0 & ce.n > 0) + + in.ca1.ca2 = (ca1.n > 0 & ca2.n > 0) + + in.cg1.cg2 = (cg1.n > 0 & cg2.n > 0) + in.cg1.cg3 = (cg1.n > 0 & cg3.n > 0) + in.cg1.cg4 = (cg1.n > 0 & cg4.n > 0) + in.cg2.cg3 = (cg2.n > 0 & cg3.n > 0) + in.cg2.cg4 = (cg2.n > 0 & cg4.n > 0) + in.cg3.cg4 = (cg3.n > 0 & cg4.n > 0) + + in.cg1.cg2.cg3 = (cg1.n > 0 & cg2.n > 0 & cg3.n > 0) + in.cg2.cg3.cg4 = (cg2.n > 0 & cg3.n > 0 & cg4.n > 0) + in.cg1.cg2.cg4 = (cg1.n > 0 & cg2.n > 0 & cg4.n > 0) + in.cg1.cg3.cg4 = (cg1.n > 0 & cg3.n > 0 & cg4.n > 0) + + in.cg.all = (cg1.n > 0 & cg2.n > 0 & cg3.n > 0 & cg4.n > 0) + + #rw = c(as.character(dat[i,"seq_conc"]), functionality, ca1.html, ca2.html, cg1.html, cg2.html, cg3.html, cg4.html, cm.html, un.html) + rw = c(as.character(dat[i,"seq_conc"]), functionality, ca1.html, ca2.html, cg1.html, cg2.html, cg3.html, cg4.html, cm.html, ce.html, un.html) + rw = c(rw, ca.n, cg.n, cm.n, ce.n, in.classes, in.ca.cg, in.ca.cg.cm, in.ca.cg.ce, in.ca.cg.cm.ce, in.ca1.ca2, in.cg1.cg2, in.cg1.cg3, in.cg1.cg4, in.cg2.cg3, in.cg2.cg4, in.cg3.cg4, in.cg1.cg2.cg3, in.cg2.cg3.cg4, in.cg1.cg2.cg4, in.cg1.cg3.cg4, in.cg.all) + + + + cat(tr(rw), file=main.html, append=T) + + + for(i in 1:nrow(allc)){ #generate html by id + html = make.link(id, allc[i,"best_match"], allc[i,"Sequence.ID"]) + cat(paste(html, "<br />"), file=sequence.id.page, append=T) + } +} + +cat("</table>", file=main.html, append=T) + +print(paste("Single sequences:", single.sequences)) +print(paste("Sequences in multiple subclasses:", in.multiple)) +print(paste("Multiple sequences in one subclass:", multiple.in.one)) +print(paste("Matched with unmatched:", some.unmatched)) +print(paste("Count that should match 'matched' sequences:", matched)) + +#ACGT overview + +#NToverview = merged[!grepl("^unmatched", merged$best_match),] +NToverview = merged + +if(empty.region.filter == "leader"){ + NToverview$seq = paste(NToverview$FR1.IMGT.seq, NToverview$CDR1.IMGT.seq, NToverview$FR2.IMGT.seq, NToverview$CDR2.IMGT.seq, NToverview$FR3.IMGT.seq) +} else if(empty.region.filter == "FR1"){ + NToverview$seq = paste(NToverview$CDR1.IMGT.seq, NToverview$FR2.IMGT.seq, NToverview$CDR2.IMGT.seq, NToverview$FR3.IMGT.seq) +} else if(empty.region.filter == "CDR1"){ + NToverview$seq = paste(NToverview$FR2.IMGT.seq, NToverview$CDR2.IMGT.seq, NToverview$FR3.IMGT.seq) +} else if(empty.region.filter == "FR2"){ + NToverview$seq = paste(NToverview$CDR2.IMGT.seq, NToverview$FR3.IMGT.seq) +} + +NToverview$A = nchar(gsub("[^Aa]", "", NToverview$seq)) +NToverview$C = nchar(gsub("[^Cc]", "", NToverview$seq)) +NToverview$G = nchar(gsub("[^Gg]", "", NToverview$seq)) +NToverview$T = nchar(gsub("[^Tt]", "", NToverview$seq)) + +#Nsum = data.frame(Sequence.ID="-", best_match="Sum", seq="-", A = sum(NToverview$A), C = sum(NToverview$C), G = sum(NToverview$G), T = sum(NToverview$T)) + +#NToverview = rbind(NToverview, NTsum) + +NTresult = data.frame(nt=c("A", "C", "T", "G")) + +for(clazz in gene.classes){ + print(paste("class:", clazz)) + NToverview.sub = NToverview[grepl(paste("^", clazz, sep=""), NToverview$best_match),] + print(paste("nrow:", nrow(NToverview.sub))) + new.col.x = c(sum(NToverview.sub$A), sum(NToverview.sub$C), sum(NToverview.sub$T), sum(NToverview.sub$G)) + new.col.y = sum(new.col.x) + new.col.z = round(new.col.x / new.col.y * 100, 2) + + tmp = names(NTresult) + NTresult = cbind(NTresult, data.frame(new.col.x, new.col.y, new.col.z)) + names(NTresult) = c(tmp, paste(clazz, c("x", "y", "z"), sep="")) +} + +NToverview.tmp = NToverview[,c("Sequence.ID", "best_match", "seq", "A", "C", "G", "T")] + +names(NToverview.tmp) = c("Sequence.ID", "best_match", "Sequence of the analysed region", "A", "C", "G", "T") + +write.table(NToverview.tmp, NToverview.file, quote=F, sep="\t", row.names=F, col.names=T) + +NToverview = NToverview[!grepl("unmatched", NToverview$best_match),] + +new.col.x = c(sum(NToverview$A), sum(NToverview$C), sum(NToverview$T), sum(NToverview$G)) +new.col.y = sum(new.col.x) +new.col.z = round(new.col.x / new.col.y * 100, 2) + +tmp = names(NTresult) +NTresult = cbind(NTresult, data.frame(new.col.x, new.col.y, new.col.z)) +names(NTresult) = c(tmp, paste("all", c("x", "y", "z"), sep="")) + +names(hotspot.analysis.sum) = names(NTresult) + +hotspot.analysis.sum = rbind(hotspot.analysis.sum, NTresult) + +write.table(hotspot.analysis.sum, hotspot.analysis.sum.file, quote=F, sep=",", row.names=F, col.names=F, na="0") + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/shm_csr/shm_clonality.htm Fri Feb 19 15:08:51 2021 +0000 @@ -0,0 +1,144 @@ +<html> + +<head> +<meta http-equiv=Content-Type content="text/html; charset=windows-1252"> +<meta name=Generator content="Microsoft Word 14 (filtered)"> +<style> +<!-- + /* Font Definitions */ + @font-face + {font-family:Calibri; + panose-1:2 15 5 2 2 2 4 3 2 4;} +@font-face + {font-family:Tahoma; + panose-1:2 11 6 4 3 5 4 4 2 4;} + /* Style Definitions */ + p.MsoNormal, li.MsoNormal, div.MsoNormal + {margin-top:0in; + margin-right:0in; + margin-bottom:10.0pt; + margin-left:0in; + line-height:115%; + font-size:11.0pt; + font-family:"Calibri","sans-serif";} +a:link, span.MsoHyperlink + {color:blue; + text-decoration:underline;} +a:visited, span.MsoHyperlinkFollowed + {color:purple; + text-decoration:underline;} +p + {margin-right:0in; + margin-left:0in; + font-size:12.0pt; + font-family:"Times New Roman","serif";} +p.MsoAcetate, li.MsoAcetate, div.MsoAcetate + {mso-style-link:"Balloon Text Char"; + margin:0in; + margin-bottom:.0001pt; + font-size:8.0pt; + font-family:"Tahoma","sans-serif";} +p.msochpdefault, li.msochpdefault, div.msochpdefault + {mso-style-name:msochpdefault; + margin-right:0in; + margin-left:0in; + font-size:12.0pt; + font-family:"Calibri","sans-serif";} +p.msopapdefault, li.msopapdefault, div.msopapdefault + {mso-style-name:msopapdefault; + margin-right:0in; + margin-bottom:10.0pt; + margin-left:0in; + line-height:115%; + font-size:12.0pt; + font-family:"Times New Roman","serif";} +span.apple-converted-space + {mso-style-name:apple-converted-space;} +span.BalloonTextChar + {mso-style-name:"Balloon Text Char"; + mso-style-link:"Balloon Text"; + font-family:"Tahoma","sans-serif";} +.MsoChpDefault + {font-size:10.0pt; + font-family:"Calibri","sans-serif";} +.MsoPapDefault + {margin-bottom:10.0pt; + line-height:115%;} +@page WordSection1 + {size:8.5in 11.0in; + margin:1.0in 1.0in 1.0in 1.0in;} +div.WordSection1 + {page:WordSection1;} +--> +</style> + +</head> + +<body lang=EN-US link=blue vlink=purple> + +<div class=WordSection1> + +<p style='margin-top:0in;margin-right:0in;margin-bottom:6.4pt;margin-left:0in; +text-align:justify;background:white'><b><span lang=EN-GB style='color:black'>References</span></b></p> + +<p style='margin-top:0in;margin-right:0in;margin-bottom:6.4pt;margin-left:0in; +text-align:justify;background:white'><span lang=EN-GB style='color:black'>Gupta, +Namita T. and Vander Heiden, Jason A. and Uduman, Mohamed and Gadala-Maria, +Daniel and Yaari, Gur and Kleinstein, Steven H. (2015). <a name="OLE_LINK106"></a><a +name="OLE_LINK107"></a>Change-O: a toolkit for analyzing large-scale B cell +immunoglobulin repertoire sequencing data: Table 1. In<span +class=apple-converted-space> </span><em>Bioinformatics, 31 (20), pp. +3356–3358.</em><span class=apple-converted-space><i> </i></span>[</span><a +href="http://dx.doi.org/10.1093/bioinformatics/btv359" target="_blank"><span +lang=EN-GB style='color:#303030'>doi:10.1093/bioinformatics/btv359</span></a><span +lang=EN-GB style='color:black'>][</span><a +href="http://dx.doi.org/10.1093/bioinformatics/btv359" target="_blank"><span +lang=EN-GB style='color:#303030'>Link</span></a><span lang=EN-GB +style='color:black'>]</span></p> + +<p style='margin-top:0in;margin-right:0in;margin-bottom:6.4pt;margin-left:0in; +text-align:justify;background:white'><span lang=EN-GB style='color:black'> </span></p> + +<p style='margin-top:0in;margin-right:0in;margin-bottom:6.4pt;margin-left:0in; +text-align:justify;background:white'><a name="OLE_LINK110"><u><span lang=EN-GB +style='color:black'>All, IGA, IGG, IGM and IGE tabs</span></u></a></p> + +<p style='margin-top:0in;margin-right:0in;margin-bottom:6.4pt;margin-left:0in; +text-align:justify;background:white'><span lang=EN-GB style='color:black'>In +these tabs information on the clonal relation of transcripts can be found. To +calculate clonal relation Change-O is used (Gupta et al, PMID: 26069265). +Transcripts are considered clonally related if they have maximal three nucleotides +difference in their CDR3 sequence and the same first V segment (as assigned by +IMGT). Results are represented in a table format showing the clone size and the +number of clones or sequences with this clone size. Change-O settings used are +the </span><span lang=EN-GB>nucleotide hamming distance substitution model with +a complete distance of maximal three. For clonal assignment the first gene +segments were used, and the distances were not normalized. In case of +asymmetric distances, the minimal distance was used.<span style='color:black'> </span></span></p> + +<p style='margin-top:0in;margin-right:0in;margin-bottom:6.4pt;margin-left:0in; +text-align:justify;background:white'><span lang=EN-GB style='color:black'> </span></p> + +<p style='margin-top:0in;margin-right:0in;margin-bottom:6.4pt;margin-left:0in; +text-align:justify;background:white'><u><span lang=EN-GB style='color:black'>Overlap +tab</span></u><span lang=EN-GB style='color:black'> </span></p> + +<p style='margin-top:0in;margin-right:0in;margin-bottom:6.4pt;margin-left:0in; +text-align:justify;background:white'><span lang=EN-GB style='color:black'>This +tab gives information on with which (sub)classe(s) each unique analyzed region +(based on the exact nucleotide sequence of the analyzes region and the CDR3 +nucleotide sequence) is found with. This gives information if the combination +of the exact same nucleotide sequence of the analyzed region and the CDR3 +sequence can be found in multiple (sub)classes.</span></p> + +<p style='margin-top:0in;margin-right:0in;margin-bottom:6.4pt;margin-left:0in; +text-align:justify;background:white'><span style='color:black'><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAA8AAAAPCAYAAAA71pVKAAAAzElEQVQoka2TwQ2CQBBFpwTshw4ImW8ogJMlUIMmhNCDxgasAi50oSXA8XlAjCG7aqKTzGX/vsnM31mzR0gk7tTudO5MEizpzvQ4ryUSe408J3Xn+grE0p1rnpOamVmWsZG4rS+dzzAMsN8Hi9yyjI1JNGtxu4VxBJgLRLpoTKIPiW0LlwtUVRTubW2OBGUJu92cZRmdfbKQMAw8o+vi5v0fLorZ7Y9waGYJjsf38DJz0O1PsEQffOcv4Sa6YYfDDJ5Obzbsp93+5VfdATueO1fdLdI0AAAAAElFTkSuQmCC"> Please note that this tab is based on all +sequences before filter unique sequences and the remove duplicates based on +filters are applied. In this table only sequences occuring more than once are +included. </span></p> + +</div> + +</body> + +</html>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/shm_csr/shm_csr.htm Fri Feb 19 15:08:51 2021 +0000 @@ -0,0 +1,95 @@ +<html> + +<head> +<meta http-equiv=Content-Type content="text/html; charset=windows-1252"> +<meta name=Generator content="Microsoft Word 14 (filtered)"> +<style> +<!-- + /* Font Definitions */ + @font-face + {font-family:Calibri; + panose-1:2 15 5 2 2 2 4 3 2 4;} + /* Style Definitions */ + p.MsoNormal, li.MsoNormal, div.MsoNormal + {margin-top:0in; + margin-right:0in; + margin-bottom:10.0pt; + margin-left:0in; + line-height:115%; + font-size:11.0pt; + font-family:"Calibri","sans-serif";} +a:link, span.MsoHyperlink + {color:blue; + text-decoration:underline;} +a:visited, span.MsoHyperlinkFollowed + {color:purple; + text-decoration:underline;} +span.apple-converted-space + {mso-style-name:apple-converted-space;} +.MsoChpDefault + {font-family:"Calibri","sans-serif";} +.MsoPapDefault + {margin-bottom:10.0pt; + line-height:115%;} +@page WordSection1 + {size:8.5in 11.0in; + margin:1.0in 1.0in 1.0in 1.0in;} +div.WordSection1 + {page:WordSection1;} +--> +</style> + +</head> + +<body lang=EN-US link=blue vlink=purple> + +<div class=WordSection1> + +<p class=MsoNormalCxSpFirst style='text-align:justify'><span lang=EN-GB +style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>The +graphs in this tab give insight into the subclass distribution of IGG and IGA +transcripts. </span><span lang=EN-GB style='font-size:12.0pt;line-height:115%; +font-family:"Times New Roman","serif"'>Human Cµ, Cα, Cγ and Cε +constant genes are assigned using a </span><span lang=EN-GB style='font-size: +12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>custom script +specifically designed for human (sub)class assignment in repertoire data as +described in van Schouwenburg and IJspeert et al, submitted for publication. In +this script the reference sequences for the subclasses are divided in 8 +nucleotide chunks which overlap by 4 nucleotides. These overlapping chunks are +then individually aligned in the right order to each input sequence. The +percentage of the chunks identified in each rearrangement is calculated in the +‘chunk hit percentage’. </span><span lang=EN-GB style='font-size:12.0pt; +line-height:115%;font-family:"Times New Roman","serif"'>Cα and Cγ +subclasses are very homologous and only differ in a few nucleotides. To assign +subclasses the </span><span lang=EN-GB style='font-size:12.0pt;line-height: +115%;font-family:"Times New Roman","serif"'>‘nt hit percentage’ is calculated. +This percentage indicates how well the chunks covering the subclass specific +nucleotide match with the different subclasses. </span><span lang=EN-GB +style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>Information +on normal distribution of subclasses in healthy individuals of different ages +can be found in IJspeert and van Schouwenburg et al, PMID: 27799928.</span></p> + +<p class=MsoNormalCxSpMiddle style='text-align:justify'><a name="OLE_LINK100"></a><a +name="OLE_LINK99"></a><a name="OLE_LINK25"><u><span lang=EN-GB +style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>IGA +subclass distribution</span></u></a></p> + +<p class=MsoNormalCxSpMiddle style='text-align:justify'><span lang=EN-GB +style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>Pie +chart showing the relative distribution of IGA1 and IGA2 transcripts in the +sample.</span></p> + +<p class=MsoNormalCxSpMiddle style='text-align:justify'><u><span lang=EN-GB +style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>IGG +subclass distribution</span></u></p> + +<p class=MsoNormalCxSpLast style='text-align:justify'><span lang=EN-GB +style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>Pie +chart showing the relative distribution of IGG1, IGG2, IGG3 and IGG4 +transcripts in the sample.</span></p> + +</div> + +</body> + +</html>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/shm_csr/shm_csr.py Fri Feb 19 15:08:51 2021 +0000 @@ -0,0 +1,508 @@ +import argparse +import logging +import sys +import os +import re + +from collections import defaultdict + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--input", help="The '7_V-REGION-mutation-and-AA-change-table' and '10_V-REGION-mutation-hotspots' merged together, with an added 'best_match' annotation") + parser.add_argument("--genes", help="The genes available in the 'best_match' column") + parser.add_argument("--empty_region_filter", help="Where does the sequence start?", choices=['leader', 'FR1', 'CDR1', 'FR2']) + parser.add_argument("--output", help="Output file") + + args = parser.parse_args() + + infile = args.input + genes = str(args.genes).split(",") + empty_region_filter = args.empty_region_filter + outfile = args.output + + genedic = dict() + + mutationdic = dict() + mutationMatcher = re.compile("^(.)(\d+).(.),?[ ]?(.)?(\d+)?.?(.)?(.?.?.?.?.?)?") + mutationMatcher = re.compile("^([actg])(\d+).([actg]),?[ ]?([A-Z])?(\d+)?.?([A-Z])?(.*)?") + mutationMatcher = re.compile("^([actg])(\d+).([actg]),?[ ]?([A-Z])?(\d+)?[>]?([A-Z;])?(.*)?") + mutationMatcher = re.compile("^([nactg])(\d+).([nactg]),?[ ]?([A-Z])?(\d+)?[>]?([A-Z;])?(.*)?") + NAMatchResult = (None, None, None, None, None, None, '') + geneMatchers = {gene: re.compile("^" + gene + ".*") for gene in genes} + linecount = 0 + + IDIndex = 0 + best_matchIndex = 0 + fr1Index = 0 + cdr1Index = 0 + fr2Index = 0 + cdr2Index = 0 + fr3Index = 0 + first = True + IDlist = [] + mutationList = [] + mutationListByID = {} + cdr1LengthDic = {} + cdr2LengthDic = {} + + fr1LengthDict = {} + fr2LengthDict = {} + fr3LengthDict = {} + + cdr1LengthIndex = 0 + cdr2LengthIndex = 0 + + fr1SeqIndex = 0 + fr2SeqIndex = 0 + fr3SeqIndex = 0 + + tandem_sum_by_class = defaultdict(int) + expected_tandem_sum_by_class = defaultdict(float) + + with open(infile, 'ru') as i: + for line in i: + if first: + linesplt = line.split("\t") + IDIndex = linesplt.index("Sequence.ID") + best_matchIndex = linesplt.index("best_match") + fr1Index = linesplt.index("FR1.IMGT") + cdr1Index = linesplt.index("CDR1.IMGT") + fr2Index = linesplt.index("FR2.IMGT") + cdr2Index = linesplt.index("CDR2.IMGT") + fr3Index = linesplt.index("FR3.IMGT") + cdr1LengthIndex = linesplt.index("CDR1.IMGT.length") + cdr2LengthIndex = linesplt.index("CDR2.IMGT.length") + fr1SeqIndex = linesplt.index("FR1.IMGT.seq") + fr2SeqIndex = linesplt.index("FR2.IMGT.seq") + fr3SeqIndex = linesplt.index("FR3.IMGT.seq") + first = False + continue + linecount += 1 + linesplt = line.split("\t") + ID = linesplt[IDIndex] + genedic[ID] = linesplt[best_matchIndex] + + mutationdic[ID + "_FR1"] = [] + if len(linesplt[fr1Index]) > 5 and empty_region_filter == "leader": + mutationdic[ID + "_FR1"] = [mutationMatcher.match(x).groups() for x in linesplt[fr1Index].split("|") if x] + + mutationdic[ID + "_CDR1"] = [] + if len(linesplt[cdr1Index]) > 5 and empty_region_filter in ["leader", "FR1"]: + mutationdic[ID + "_CDR1"] = [mutationMatcher.match(x).groups() for x in linesplt[cdr1Index].split("|") if x] + + mutationdic[ID + "_FR2"] = [] + if len(linesplt[fr2Index]) > 5 and empty_region_filter in ["leader", "FR1", "CDR1"]: + mutationdic[ID + "_FR2"] = [mutationMatcher.match(x).groups() for x in linesplt[fr2Index].split("|") if x] + + mutationdic[ID + "_CDR2"] = [] + if len(linesplt[cdr2Index]) > 5: + mutationdic[ID + "_CDR2"] = [mutationMatcher.match(x).groups() for x in linesplt[cdr2Index].split("|") if x] + + mutationdic[ID + "_FR2-CDR2"] = mutationdic[ID + "_FR2"] + mutationdic[ID + "_CDR2"] + + mutationdic[ID + "_FR3"] = [] + if len(linesplt[fr3Index]) > 5: + mutationdic[ID + "_FR3"] = [mutationMatcher.match(x).groups() for x in linesplt[fr3Index].split("|") if x] + + mutationList += mutationdic[ID + "_FR1"] + mutationdic[ID + "_CDR1"] + mutationdic[ID + "_FR2"] + mutationdic[ID + "_CDR2"] + mutationdic[ID + "_FR3"] + mutationListByID[ID] = mutationdic[ID + "_FR1"] + mutationdic[ID + "_CDR1"] + mutationdic[ID + "_FR2"] + mutationdic[ID + "_CDR2"] + mutationdic[ID + "_FR3"] + + try: + cdr1Length = int(linesplt[cdr1LengthIndex]) + except: + cdr1Length = 0 + + try: + cdr2Length = int(linesplt[cdr2LengthIndex]) + except: + cdr2Length = 0 + + #print linesplt[fr2SeqIndex] + fr1Length = len(linesplt[fr1SeqIndex]) if empty_region_filter == "leader" else 0 + fr2Length = len(linesplt[fr2SeqIndex]) if empty_region_filter in ["leader", "FR1", "CDR1"] else 0 + fr3Length = len(linesplt[fr3SeqIndex]) + + cdr1LengthDic[ID] = cdr1Length + cdr2LengthDic[ID] = cdr2Length + + fr1LengthDict[ID] = fr1Length + fr2LengthDict[ID] = fr2Length + fr3LengthDict[ID] = fr3Length + + IDlist += [ID] + print "len(mutationdic) =", len(mutationdic) + + with open(os.path.join(os.path.dirname(os.path.abspath(infile)), "mutationdict.txt"), 'w') as out_handle: + for ID, lst in mutationdic.iteritems(): + for mut in lst: + out_handle.write("{0}\t{1}\n".format(ID, "\t".join([str(x) for x in mut]))) + + #tandem mutation stuff + tandem_frequency = defaultdict(int) + mutation_frequency = defaultdict(int) + + mutations_by_id_dic = {} + first = True + mutation_by_id_file = os.path.join(os.path.dirname(outfile), "mutation_by_id.txt") + with open(mutation_by_id_file, 'r') as mutation_by_id: + for l in mutation_by_id: + if first: + first = False + continue + splt = l.split("\t") + mutations_by_id_dic[splt[0]] = int(splt[1]) + + tandem_file = os.path.join(os.path.dirname(outfile), "tandems_by_id.txt") + with open(tandem_file, 'w') as o: + highest_tandem_length = 0 + + o.write("Sequence.ID\tnumber_of_mutations\tnumber_of_tandems\tregion_length\texpected_tandems\tlongest_tandem\ttandems\n") + for ID in IDlist: + mutations = mutationListByID[ID] + if len(mutations) == 0: + continue + last_mut = max(mutations, key=lambda x: int(x[1])) + + last_mut_pos = int(last_mut[1]) + + mut_positions = [False] * (last_mut_pos + 1) + + for mutation in mutations: + frm, where, to, frmAA, whereAA, toAA, thing = mutation + where = int(where) + mut_positions[where] = True + + tandem_muts = [] + tandem_start = -1 + tandem_length = 0 + for i in range(len(mut_positions)): + if mut_positions[i]: + if tandem_start == -1: + tandem_start = i + tandem_length += 1 + #print "".join(["1" if x else "0" for x in mut_positions[:i+1]]) + else: + if tandem_length > 1: + tandem_muts.append((tandem_start, tandem_length)) + #print "{0}{1} {2}:{3}".format(" " * (i - tandem_length), "^" * tandem_length, tandem_start, tandem_length) + tandem_start = -1 + tandem_length = 0 + if tandem_length > 1: # if the sequence ends with a tandem mutation + tandem_muts.append((tandem_start, tandem_length)) + + if len(tandem_muts) > 0: + if highest_tandem_length < len(tandem_muts): + highest_tandem_length = len(tandem_muts) + + region_length = fr1LengthDict[ID] + cdr1LengthDic[ID] + fr2LengthDict[ID] + cdr2LengthDic[ID] + fr3LengthDict[ID] + longest_tandem = max(tandem_muts, key=lambda x: x[1]) if len(tandem_muts) else (0, 0) + num_mutations = mutations_by_id_dic[ID] # len(mutations) + f_num_mutations = float(num_mutations) + num_tandem_muts = len(tandem_muts) + expected_tandem_muts = f_num_mutations * (f_num_mutations - 1.0) / float(region_length) + o.write("{0}\t{1}\t{2}\t{3}\t{4}\t{5}\t{6}\n".format(ID, + str(num_mutations), + str(num_tandem_muts), + str(region_length), + str(round(expected_tandem_muts, 2)), + str(longest_tandem[1]), + str(tandem_muts))) + gene = genedic[ID] + if gene.find("unmatched") == -1: + tandem_sum_by_class[gene] += num_tandem_muts + expected_tandem_sum_by_class[gene] += expected_tandem_muts + + tandem_sum_by_class["all"] += num_tandem_muts + expected_tandem_sum_by_class["all"] += expected_tandem_muts + + gene = gene[:3] + if gene in ["IGA", "IGG"]: + tandem_sum_by_class[gene] += num_tandem_muts + expected_tandem_sum_by_class[gene] += expected_tandem_muts + else: + tandem_sum_by_class["unmatched"] += num_tandem_muts + expected_tandem_sum_by_class["unmatched"] += expected_tandem_muts + + + for tandem_mut in tandem_muts: + tandem_frequency[str(tandem_mut[1])] += 1 + #print "\t".join([ID, str(len(tandem_muts)), str(longest_tandem[1]) , str(tandem_muts)]) + + tandem_freq_file = os.path.join(os.path.dirname(outfile), "tandem_frequency.txt") + with open(tandem_freq_file, 'w') as o: + for frq in sorted([int(x) for x in tandem_frequency.keys()]): + o.write("{0}\t{1}\n".format(frq, tandem_frequency[str(frq)])) + + tandem_row = [] + genes_extra = list(genes) + genes_extra.append("all") + for x, y, in zip([tandem_sum_by_class[x] for x in genes_extra], [expected_tandem_sum_by_class[x] for x in genes_extra]): + if y != 0: + tandem_row += [x, round(y, 2), round(x / y, 2)] + else: + tandem_row += [x, round(y, 2), 0] + + tandem_freq_file = os.path.join(os.path.dirname(outfile), "shm_overview_tandem_row.txt") + with open(tandem_freq_file, 'w') as o: + o.write("Tandems/Expected (ratio),{0}\n".format(",".join([str(x) for x in tandem_row]))) + + #print mutationList, linecount + + AALength = (int(max(mutationList, key=lambda i: int(i[4]) if i[4] and i[5] != ";" else 0)[4]) + 1) # [4] is the position of the AA mutation, None if silent + if AALength < 60: + AALength = 64 + + AA_mutation = [0] * AALength + AA_mutation_dic = {"IGA": AA_mutation[:], "IGG": AA_mutation[:], "IGM": AA_mutation[:], "IGE": AA_mutation[:], "unm": AA_mutation[:], "all": AA_mutation[:]} + AA_mutation_empty = AA_mutation[:] + + print "AALength:", AALength + aa_mutations_by_id_file = outfile[:outfile.rindex("/")] + "/aa_id_mutations.txt" + with open(aa_mutations_by_id_file, 'w') as o: + o.write("ID\tbest_match\t" + "\t".join([str(x) for x in range(1,AALength)]) + "\n") + for ID in mutationListByID.keys(): + AA_mutation_for_ID = AA_mutation_empty[:] + for mutation in mutationListByID[ID]: + if mutation[4] and mutation[5] != ";": + AA_mutation_position = int(mutation[4]) + try: + AA_mutation[AA_mutation_position] += 1 + AA_mutation_for_ID[AA_mutation_position] += 1 + except Exception as e: + print e + print mutation + sys.exit() + clss = genedic[ID][:3] + AA_mutation_dic[clss][AA_mutation_position] += 1 + o.write(ID + "\t" + genedic[ID] + "\t" + "\t".join([str(x) for x in AA_mutation_for_ID[1:]]) + "\n") + + + + #absent AA stuff + absentAACDR1Dic = defaultdict(list) + absentAACDR1Dic[5] = range(29,36) + absentAACDR1Dic[6] = range(29,35) + absentAACDR1Dic[7] = range(30,35) + absentAACDR1Dic[8] = range(30,34) + absentAACDR1Dic[9] = range(31,34) + absentAACDR1Dic[10] = range(31,33) + absentAACDR1Dic[11] = [32] + + absentAACDR2Dic = defaultdict(list) + absentAACDR2Dic[0] = range(55,65) + absentAACDR2Dic[1] = range(56,65) + absentAACDR2Dic[2] = range(56,64) + absentAACDR2Dic[3] = range(57,64) + absentAACDR2Dic[4] = range(57,63) + absentAACDR2Dic[5] = range(58,63) + absentAACDR2Dic[6] = range(58,62) + absentAACDR2Dic[7] = range(59,62) + absentAACDR2Dic[8] = range(59,61) + absentAACDR2Dic[9] = [60] + + absentAA = [len(IDlist)] * (AALength-1) + for k, cdr1Length in cdr1LengthDic.iteritems(): + for c in absentAACDR1Dic[cdr1Length]: + absentAA[c] -= 1 + + for k, cdr2Length in cdr2LengthDic.iteritems(): + for c in absentAACDR2Dic[cdr2Length]: + absentAA[c] -= 1 + + + aa_mutations_by_id_file = outfile[:outfile.rindex("/")] + "/absent_aa_id.txt" + with open(aa_mutations_by_id_file, 'w') as o: + o.write("ID\tcdr1length\tcdr2length\tbest_match\t" + "\t".join([str(x) for x in range(1,AALength)]) + "\n") + for ID in IDlist: + absentAAbyID = [1] * (AALength-1) + cdr1Length = cdr1LengthDic[ID] + for c in absentAACDR1Dic[cdr1Length]: + absentAAbyID[c] -= 1 + + cdr2Length = cdr2LengthDic[ID] + for c in absentAACDR2Dic[cdr2Length]: + absentAAbyID[c] -= 1 + o.write(ID + "\t" + str(cdr1Length) + "\t" + str(cdr2Length) + "\t" + genedic[ID] + "\t" + "\t".join([str(x) for x in absentAAbyID]) + "\n") + + if linecount == 0: + print "No data, exiting" + with open(outfile, 'w') as o: + o.write("RGYW (%)," + ("0,0,0\n" * len(genes))) + o.write("WRCY (%)," + ("0,0,0\n" * len(genes))) + o.write("WA (%)," + ("0,0,0\n" * len(genes))) + o.write("TW (%)," + ("0,0,0\n" * len(genes))) + import sys + + sys.exit() + + hotspotMatcher = re.compile("[actg]+,(\d+)-(\d+)\((.*)\)") + RGYWCount = {} + WRCYCount = {} + WACount = {} + TWCount = {} + + #IDIndex = 0 + ataIndex = 0 + tatIndex = 0 + aggctatIndex = 0 + atagcctIndex = 0 + first = True + with open(infile, 'ru') as i: + for line in i: + if first: + linesplt = line.split("\t") + ataIndex = linesplt.index("X.a.t.a") + tatIndex = linesplt.index("t.a.t.") + aggctatIndex = linesplt.index("X.a.g.g.c.t..a.t.") + atagcctIndex = linesplt.index("X.a.t..a.g.c.c.t.") + first = False + continue + linesplt = line.split("\t") + gene = linesplt[best_matchIndex] + ID = linesplt[IDIndex] + RGYW = [(int(x), int(y), z) for (x, y, z) in + [hotspotMatcher.match(x).groups() for x in linesplt[aggctatIndex].split("|") if x]] + WRCY = [(int(x), int(y), z) for (x, y, z) in + [hotspotMatcher.match(x).groups() for x in linesplt[atagcctIndex].split("|") if x]] + WA = [(int(x), int(y), z) for (x, y, z) in + [hotspotMatcher.match(x).groups() for x in linesplt[ataIndex].split("|") if x]] + TW = [(int(x), int(y), z) for (x, y, z) in + [hotspotMatcher.match(x).groups() for x in linesplt[tatIndex].split("|") if x]] + RGYWCount[ID], WRCYCount[ID], WACount[ID], TWCount[ID] = 0, 0, 0, 0 + + with open(os.path.join(os.path.dirname(os.path.abspath(infile)), "RGYW.txt"), 'a') as out_handle: + for hotspot in RGYW: + out_handle.write("{0}\t{1}\n".format(ID, "\t".join([str(x) for x in hotspot]))) + + mutationList = mutationdic[ID + "_FR1"] + mutationdic[ID + "_CDR1"] + mutationdic[ID + "_FR2"] + mutationdic[ID + "_CDR2"] + mutationdic[ID + "_FR3"] + for mutation in mutationList: + frm, where, to, AAfrm, AAwhere, AAto, junk = mutation + mutation_in_RGYW = any(((start <= int(where) <= end) for (start, end, region) in RGYW)) + mutation_in_WRCY = any(((start <= int(where) <= end) for (start, end, region) in WRCY)) + mutation_in_WA = any(((start <= int(where) <= end) for (start, end, region) in WA)) + mutation_in_TW = any(((start <= int(where) <= end) for (start, end, region) in TW)) + + in_how_many_motifs = sum([mutation_in_RGYW, mutation_in_WRCY, mutation_in_WA, mutation_in_TW]) + + if in_how_many_motifs > 0: + RGYWCount[ID] += (1.0 * int(mutation_in_RGYW)) / in_how_many_motifs + WRCYCount[ID] += (1.0 * int(mutation_in_WRCY)) / in_how_many_motifs + WACount[ID] += (1.0 * int(mutation_in_WA)) / in_how_many_motifs + TWCount[ID] += (1.0 * int(mutation_in_TW)) / in_how_many_motifs + + mutations_in_motifs_file = os.path.join(os.path.dirname(os.path.abspath(infile)), "mutation_in_motifs.txt") + if not os.path.exists(mutation_by_id_file): + with open(mutations_in_motifs_file, 'w') as out_handle: + out_handle.write("{0}\n".format("\t".join([ + "Sequence.ID", + "mutation_position", + "region", + "from_nt", + "to_nt", + "mutation_position_AA", + "from_AA", + "to_AA", + "motif", + "motif_start_nt", + "motif_end_nt", + "rest" + ]))) + + with open(mutations_in_motifs_file, 'a') as out_handle: + motif_dic = {"RGYW": RGYW, "WRCY": WRCY, "WA": WA, "TW": TW} + for mutation in mutationList: + frm, where, to, AAfrm, AAwhere, AAto, junk = mutation + for motif in motif_dic.keys(): + + for start, end, region in motif_dic[motif]: + if start <= int(where) <= end: + out_handle.write("{0}\n".format( + "\t".join([ + ID, + where, + region, + frm, + to, + str(AAwhere), + str(AAfrm), + str(AAto), + motif, + str(start), + str(end), + str(junk) + ]) + )) + + + + def mean(lst): + return (float(sum(lst)) / len(lst)) if len(lst) > 0 else 0.0 + + + def median(lst): + lst = sorted(lst) + l = len(lst) + if l == 0: + return 0 + if l == 1: + return lst[0] + + l = int(l / 2) + + if len(lst) % 2 == 0: + return float(lst[l] + lst[(l - 1)]) / 2.0 + else: + return lst[l] + + funcs = {"mean": mean, "median": median, "sum": sum} + + directory = outfile[:outfile.rfind("/") + 1] + value = 0 + valuedic = dict() + + for fname in funcs.keys(): + for gene in genes: + with open(directory + gene + "_" + fname + "_value.txt", 'r') as v: + valuedic[gene + "_" + fname] = float(v.readlines()[0].rstrip()) + with open(directory + "all_" + fname + "_value.txt", 'r') as v: + valuedic["total_" + fname] = float(v.readlines()[0].rstrip()) + + + def get_xyz(lst, gene, f, fname): + x = round(round(f(lst), 1)) + y = valuedic[gene + "_" + fname] + z = str(round(x / float(y) * 100, 1)) if y != 0 else "0" + return (str(x), str(y), z) + + dic = {"RGYW": RGYWCount, "WRCY": WRCYCount, "WA": WACount, "TW": TWCount} + arr = ["RGYW", "WRCY", "WA", "TW"] + + for fname in funcs.keys(): + func = funcs[fname] + foutfile = outfile[:outfile.rindex("/")] + "/hotspot_analysis_" + fname + ".txt" + with open(foutfile, 'w') as o: + for typ in arr: + o.write(typ + " (%)") + curr = dic[typ] + for gene in genes: + geneMatcher = geneMatchers[gene] + if valuedic[gene + "_" + fname] is 0: + o.write(",0,0,0") + else: + x, y, z = get_xyz([curr[x] for x in [y for y, z in genedic.iteritems() if geneMatcher.match(z)]], gene, func, fname) + o.write("," + x + "," + y + "," + z) + x, y, z = get_xyz([y for x, y in curr.iteritems() if not genedic[x].startswith("unmatched")], "total", func, fname) + #x, y, z = get_xyz([y for x, y in curr.iteritems()], "total", func, fname) + o.write("," + x + "," + y + "," + z + "\n") + + + # for testing + seq_motif_file = outfile[:outfile.rindex("/")] + "/motif_per_seq.txt" + with open(seq_motif_file, 'w') as o: + o.write("ID\tRGYW\tWRCY\tWA\tTW\n") + for ID in IDlist: + #o.write(ID + "\t" + str(round(RGYWCount[ID], 2)) + "\t" + str(round(WRCYCount[ID], 2)) + "\t" + str(round(WACount[ID], 2)) + "\t" + str(round(TWCount[ID], 2)) + "\n") + o.write(ID + "\t" + str(RGYWCount[ID]) + "\t" + str(WRCYCount[ID]) + "\t" + str(WACount[ID]) + "\t" + str(TWCount[ID]) + "\n") + +if __name__ == "__main__": + main()
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/shm_csr/shm_csr.r Fri Feb 19 15:08:51 2021 +0000 @@ -0,0 +1,561 @@ +library(data.table) +library(ggplot2) +library(reshape2) + +args <- commandArgs(trailingOnly = TRUE) + +input = args[1] +genes = unlist(strsplit(args[2], ",")) +outputdir = args[3] +empty.region.filter = args[4] +setwd(outputdir) + +#dat = read.table(input, header=T, sep="\t", fill=T, stringsAsFactors=F) + +dat = data.frame(fread(input, sep="\t", header=T, stringsAsFactors=F)) #fread because read.table suddenly skips certain rows... + +if(length(dat$Sequence.ID) == 0){ + setwd(outputdir) + result = data.frame(x = rep(0, 5), y = rep(0, 5), z = rep(NA, 5)) + row.names(result) = c("Number of Mutations (%)", "Transition (%)", "Transversions (%)", "Transitions at G C (%)", "Targeting of G C (%)") + write.table(x=result, file="mutations.txt", sep=",",quote=F,row.names=T,col.names=F) + transitionTable = data.frame(A=rep(0, 4),C=rep(0, 4),G=rep(0, 4),T=rep(0, 4)) + row.names(transitionTable) = c("A", "C", "G", "T") + transitionTable["A","A"] = NA + transitionTable["C","C"] = NA + transitionTable["G","G"] = NA + transitionTable["T","T"] = NA + + write.table(x=transitionTable, file="transitions.txt", sep=",",quote=F,row.names=T,col.names=NA) + cat("0", file="n.txt") + stop("No data") +} + +cleanup_columns = c("FR1.IMGT.c.a", + "FR2.IMGT.g.t", + "CDR1.IMGT.Nb.of.nucleotides", + "CDR2.IMGT.t.a", + "FR1.IMGT.c.g", + "CDR1.IMGT.c.t", + "FR2.IMGT.a.c", + "FR2.IMGT.Nb.of.mutations", + "FR2.IMGT.g.c", + "FR2.IMGT.a.g", + "FR3.IMGT.t.a", + "FR3.IMGT.t.c", + "FR2.IMGT.g.a", + "FR3.IMGT.c.g", + "FR1.IMGT.Nb.of.mutations", + "CDR1.IMGT.g.a", + "CDR1.IMGT.t.g", + "CDR1.IMGT.g.c", + "CDR2.IMGT.Nb.of.nucleotides", + "FR2.IMGT.a.t", + "CDR1.IMGT.Nb.of.mutations", + "CDR3.IMGT.Nb.of.nucleotides", + "CDR1.IMGT.a.g", + "FR3.IMGT.a.c", + "FR1.IMGT.g.a", + "FR3.IMGT.a.g", + "FR1.IMGT.a.t", + "CDR2.IMGT.a.g", + "CDR2.IMGT.Nb.of.mutations", + "CDR2.IMGT.g.t", + "CDR2.IMGT.a.c", + "CDR1.IMGT.t.c", + "FR3.IMGT.g.c", + "FR1.IMGT.g.t", + "FR3.IMGT.g.t", + "CDR1.IMGT.a.t", + "FR1.IMGT.a.g", + "FR3.IMGT.a.t", + "FR3.IMGT.Nb.of.nucleotides", + "FR2.IMGT.t.c", + "CDR2.IMGT.g.a", + "FR2.IMGT.t.a", + "CDR1.IMGT.t.a", + "FR2.IMGT.t.g", + "FR3.IMGT.t.g", + "FR2.IMGT.Nb.of.nucleotides", + "FR1.IMGT.t.a", + "FR1.IMGT.t.g", + "FR3.IMGT.c.t", + "FR1.IMGT.t.c", + "CDR2.IMGT.a.t", + "FR2.IMGT.c.t", + "CDR1.IMGT.g.t", + "CDR2.IMGT.t.g", + "FR1.IMGT.Nb.of.nucleotides", + "CDR1.IMGT.c.g", + "CDR2.IMGT.t.c", + "FR3.IMGT.g.a", + "CDR1.IMGT.a.c", + "FR2.IMGT.c.a", + "FR3.IMGT.Nb.of.mutations", + "FR2.IMGT.c.g", + "CDR2.IMGT.g.c", + "FR1.IMGT.g.c", + "CDR2.IMGT.c.t", + "FR3.IMGT.c.a", + "CDR1.IMGT.c.a", + "CDR2.IMGT.c.g", + "CDR2.IMGT.c.a", + "FR1.IMGT.c.t", + "FR1.IMGT.Nb.of.silent.mutations", + "FR2.IMGT.Nb.of.silent.mutations", + "FR3.IMGT.Nb.of.silent.mutations", + "FR1.IMGT.Nb.of.nonsilent.mutations", + "FR2.IMGT.Nb.of.nonsilent.mutations", + "FR3.IMGT.Nb.of.nonsilent.mutations") + +print("Cleaning up columns") + +for(col in cleanup_columns){ + dat[,col] = gsub("\\(.*\\)", "", dat[,col]) + #dat[dat[,col] == "",] = "0" + dat[,col] = as.numeric(dat[,col]) + dat[is.na(dat[,col]),col] = 0 +} + +regions = c("FR1", "CDR1", "FR2", "CDR2", "FR3") +if(empty.region.filter == "FR1") { + regions = c("CDR1", "FR2", "CDR2", "FR3") +} else if (empty.region.filter == "CDR1") { + regions = c("FR2", "CDR2", "FR3") +} else if (empty.region.filter == "FR2") { + regions = c("CDR2", "FR3") +} + +pdfplots = list() #save() this later to create the pdf plots in another script (maybe avoids the "address (nil), cause memory not mapped") + +sum_by_row = function(x, columns) { sum(as.numeric(x[columns]), na.rm=T) } + +print("aggregating data into new columns") + +VRegionMutations_columns = paste(regions, ".IMGT.Nb.of.mutations", sep="") +dat$VRegionMutations = apply(dat, FUN=sum_by_row, 1, columns=VRegionMutations_columns) + +VRegionNucleotides_columns = paste(regions, ".IMGT.Nb.of.nucleotides", sep="") +dat$FR3.IMGT.Nb.of.nucleotides = nchar(dat$FR3.IMGT.seq) +dat$VRegionNucleotides = apply(dat, FUN=sum_by_row, 1, columns=VRegionNucleotides_columns) + +transitionMutations_columns = paste(rep(regions, each=4), c(".IMGT.a.g", ".IMGT.g.a", ".IMGT.c.t", ".IMGT.t.c"), sep="") +dat$transitionMutations = apply(dat, FUN=sum_by_row, 1, columns=transitionMutations_columns) + +transversionMutations_columns = paste(rep(regions, each=8), c(".IMGT.a.c",".IMGT.c.a",".IMGT.a.t",".IMGT.t.a",".IMGT.g.c",".IMGT.c.g",".IMGT.g.t",".IMGT.t.g"), sep="") +dat$transversionMutations = apply(dat, FUN=sum_by_row, 1, columns=transversionMutations_columns) + +transitionMutationsAtGC_columns = paste(rep(regions, each=2), c(".IMGT.g.a",".IMGT.c.t"), sep="") +dat$transitionMutationsAtGC = apply(dat, FUN=sum_by_row, 1, columns=transitionMutationsAtGC_columns) + +totalMutationsAtGC_columns = paste(rep(regions, each=6), c(".IMGT.c.g",".IMGT.c.t",".IMGT.c.a",".IMGT.g.c",".IMGT.g.a",".IMGT.g.t"), sep="") +#totalMutationsAtGC_columns = paste(rep(regions, each=6), c(".IMGT.g.a",".IMGT.c.t",".IMGT.c.a",".IMGT.c.g",".IMGT.g.t"), sep="") +dat$totalMutationsAtGC = apply(dat, FUN=sum_by_row, 1, columns=totalMutationsAtGC_columns) + +transitionMutationsAtAT_columns = paste(rep(regions, each=2), c(".IMGT.a.g",".IMGT.t.c"), sep="") +dat$transitionMutationsAtAT = apply(dat, FUN=sum_by_row, 1, columns=transitionMutationsAtAT_columns) + +totalMutationsAtAT_columns = paste(rep(regions, each=6), c(".IMGT.a.g",".IMGT.a.c",".IMGT.a.t",".IMGT.t.g",".IMGT.t.c",".IMGT.t.a"), sep="") +#totalMutationsAtAT_columns = paste(rep(regions, each=5), c(".IMGT.a.g",".IMGT.t.c",".IMGT.a.c",".IMGT.g.c",".IMGT.t.g"), sep="") +dat$totalMutationsAtAT = apply(dat, FUN=sum_by_row, 1, columns=totalMutationsAtAT_columns) + +FRRegions = regions[grepl("FR", regions)] +CDRRegions = regions[grepl("CDR", regions)] + +FR_silentMutations_columns = paste(FRRegions, ".IMGT.Nb.of.silent.mutations", sep="") +dat$silentMutationsFR = apply(dat, FUN=sum_by_row, 1, columns=FR_silentMutations_columns) + +CDR_silentMutations_columns = paste(CDRRegions, ".IMGT.Nb.of.silent.mutations", sep="") +dat$silentMutationsCDR = apply(dat, FUN=sum_by_row, 1, columns=CDR_silentMutations_columns) + +FR_nonSilentMutations_columns = paste(FRRegions, ".IMGT.Nb.of.nonsilent.mutations", sep="") +dat$nonSilentMutationsFR = apply(dat, FUN=sum_by_row, 1, columns=FR_nonSilentMutations_columns) + +CDR_nonSilentMutations_columns = paste(CDRRegions, ".IMGT.Nb.of.nonsilent.mutations", sep="") +dat$nonSilentMutationsCDR = apply(dat, FUN=sum_by_row, 1, columns=CDR_nonSilentMutations_columns) + +mutation.sum.columns = c("Sequence.ID", "VRegionMutations", "VRegionNucleotides", "transitionMutations", "transversionMutations", "transitionMutationsAtGC", "transitionMutationsAtAT", "silentMutationsFR", "nonSilentMutationsFR", "silentMutationsCDR", "nonSilentMutationsCDR") +write.table(dat[,mutation.sum.columns], "mutation_by_id.txt", sep="\t",quote=F,row.names=F,col.names=T) + +setwd(outputdir) + +write.table(dat, input, sep="\t",quote=F,row.names=F,col.names=T) + +base.order.x = data.frame(base=c("A", "C", "G", "T"), order.x=1:4) +base.order.y = data.frame(base=c("T", "G", "C", "A"), order.y=1:4) + +calculate_result = function(i, gene, dat, matrx, f, fname, name){ + tmp = dat[grepl(paste("^", gene, ".*", sep=""), dat$best_match),] + + j = i - 1 + x = (j * 3) + 1 + y = (j * 3) + 2 + z = (j * 3) + 3 + + if(nrow(tmp) > 0){ + if(fname == "sum"){ + matrx[1,x] = round(f(tmp$VRegionMutations, na.rm=T), digits=1) + matrx[1,y] = round(f(tmp$VRegionNucleotides, na.rm=T), digits=1) + matrx[1,z] = round(f(matrx[1,x] / matrx[1,y]) * 100, digits=1) + } else { + matrx[1,x] = round(f(tmp$VRegionMutations, na.rm=T), digits=1) + matrx[1,y] = round(f(tmp$VRegionNucleotides, na.rm=T), digits=1) + matrx[1,z] = round(f(tmp$VRegionMutations / tmp$VRegionNucleotides) * 100, digits=1) + } + + matrx[2,x] = round(f(tmp$transitionMutations, na.rm=T), digits=1) + matrx[2,y] = round(f(tmp$VRegionMutations, na.rm=T), digits=1) + matrx[2,z] = round(matrx[2,x] / matrx[2,y] * 100, digits=1) + + matrx[3,x] = round(f(tmp$transversionMutations, na.rm=T), digits=1) + matrx[3,y] = round(f(tmp$VRegionMutations, na.rm=T), digits=1) + matrx[3,z] = round(matrx[3,x] / matrx[3,y] * 100, digits=1) + + matrx[4,x] = round(f(tmp$transitionMutationsAtGC, na.rm=T), digits=1) + matrx[4,y] = round(f(tmp$totalMutationsAtGC, na.rm=T), digits=1) + matrx[4,z] = round(matrx[4,x] / matrx[4,y] * 100, digits=1) + + matrx[5,x] = round(f(tmp$totalMutationsAtGC, na.rm=T), digits=1) + matrx[5,y] = round(f(tmp$VRegionMutations, na.rm=T), digits=1) + matrx[5,z] = round(matrx[5,x] / matrx[5,y] * 100, digits=1) + + matrx[6,x] = round(f(tmp$transitionMutationsAtAT, na.rm=T), digits=1) + matrx[6,y] = round(f(tmp$totalMutationsAtAT, na.rm=T), digits=1) + matrx[6,z] = round(matrx[6,x] / matrx[6,y] * 100, digits=1) + + matrx[7,x] = round(f(tmp$totalMutationsAtAT, na.rm=T), digits=1) + matrx[7,y] = round(f(tmp$VRegionMutations, na.rm=T), digits=1) + matrx[7,z] = round(matrx[7,x] / matrx[7,y] * 100, digits=1) + + matrx[8,x] = round(f(tmp$nonSilentMutationsFR, na.rm=T), digits=1) + matrx[8,y] = round(f(tmp$silentMutationsFR, na.rm=T), digits=1) + matrx[8,z] = round(matrx[8,x] / matrx[8,y], digits=1) + + matrx[9,x] = round(f(tmp$nonSilentMutationsCDR, na.rm=T), digits=1) + matrx[9,y] = round(f(tmp$silentMutationsCDR, na.rm=T), digits=1) + matrx[9,z] = round(matrx[9,x] / matrx[9,y], digits=1) + + if(fname == "sum"){ + + regions.fr = regions[grepl("FR", regions)] + regions.fr = paste(regions.fr, ".IMGT.Nb.of.nucleotides", sep="") + regions.cdr = regions[grepl("CDR", regions)] + regions.cdr = paste(regions.cdr, ".IMGT.Nb.of.nucleotides", sep="") + + if(length(regions.fr) > 1){ #in case there is only on FR region (rowSums needs >1 column) + matrx[10,x] = round(f(rowSums(tmp[,regions.fr], na.rm=T)), digits=1) + } else { + matrx[10,x] = round(f(tmp[,regions.fr], na.rm=T), digits=1) + } + matrx[10,y] = round(f(tmp$VRegionNucleotides, na.rm=T), digits=1) + matrx[10,z] = round(matrx[10,x] / matrx[10,y] * 100, digits=1) + + if(length(regions.cdr) > 1){ #in case there is only on CDR region + matrx[11,x] = round(f(rowSums(tmp[,regions.cdr], na.rm=T)), digits=1) + } else { + matrx[11,x] = round(f(tmp[,regions.cdr], na.rm=T), digits=1) + } + matrx[11,y] = round(f(tmp$VRegionNucleotides, na.rm=T), digits=1) + matrx[11,z] = round(matrx[11,x] / matrx[11,y] * 100, digits=1) + } + } + + transitionTable = data.frame(A=zeros,C=zeros,G=zeros,T=zeros) + row.names(transitionTable) = c("A", "C", "G", "T") + transitionTable["A","A"] = NA + transitionTable["C","C"] = NA + transitionTable["G","G"] = NA + transitionTable["T","T"] = NA + + if(nrow(tmp) > 0){ + for(nt1 in nts){ + for(nt2 in nts){ + if(nt1 == nt2){ + next + } + NT1 = LETTERS[letters == nt1] + NT2 = LETTERS[letters == nt2] + FR1 = paste("FR1.IMGT.", nt1, ".", nt2, sep="") + CDR1 = paste("CDR1.IMGT.", nt1, ".", nt2, sep="") + FR2 = paste("FR2.IMGT.", nt1, ".", nt2, sep="") + CDR2 = paste("CDR2.IMGT.", nt1, ".", nt2, sep="") + FR3 = paste("FR3.IMGT.", nt1, ".", nt2, sep="") + if (empty.region.filter == "leader"){ + transitionTable[NT1,NT2] = sum(tmp[,c(FR1, CDR1, FR2, CDR2, FR3)]) + } else if (empty.region.filter == "FR1") { + transitionTable[NT1,NT2] = sum(tmp[,c(CDR1, FR2, CDR2, FR3)]) + } else if (empty.region.filter == "CDR1") { + transitionTable[NT1,NT2] = sum(tmp[,c(FR2, CDR2, FR3)]) + } else if (empty.region.filter == "FR2") { + transitionTable[NT1,NT2] = sum(tmp[,c(CDR2, FR3)]) + } + } + } + transition = transitionTable + transition$id = names(transition) + + transition2 = melt(transition, id.vars="id") + + transition2 = merge(transition2, base.order.x, by.x="id", by.y="base") + + transition2 = merge(transition2, base.order.y, by.x="variable", by.y="base") + + transition2[is.na(transition2$value),]$value = 0 + + if(any(transition2$value != 0)){ #having a transition table filled with 0 is bad + print("Plotting heatmap and transition") + png(filename=paste("transitions_stacked_", name, ".png", sep="")) + p = ggplot(transition2, aes(factor(reorder(id, order.x)), y=value, fill=factor(reorder(variable, order.y)))) + geom_bar(position="fill", stat="identity", colour="black") #stacked bar + p = p + xlab("From base") + ylab("") + ggtitle("Bargraph transition information") + guides(fill=guide_legend(title=NULL)) + p = p + theme(panel.background = element_rect(fill = "white", colour="black"), text = element_text(size=16, colour="black")) + scale_fill_manual(values=c("A" = "blue4", "G" = "lightblue1", "C" = "olivedrab3", "T" = "olivedrab4")) + #p = p + scale_colour_manual(values=c("A" = "black", "G" = "black", "C" = "black", "T" = "black")) + print(p) + dev.off() + + pdfplots[[paste("transitions_stacked_", name, ".pdf", sep="")]] <<- p + + png(filename=paste("transitions_heatmap_", name, ".png", sep="")) + p = ggplot(transition2, aes(factor(reorder(variable, -order.y)), factor(reorder(id, -order.x)))) + geom_tile(aes(fill = value)) + scale_fill_gradient(low="white", high="steelblue") #heatmap + p = p + xlab("To base") + ylab("From Base") + ggtitle("Heatmap transition information") + theme(panel.background = element_rect(fill = "white", colour="black"), text = element_text(size=16, colour="black")) + print(p) + dev.off() + + pdfplots[[paste("transitions_heatmap_", name, ".pdf", sep="")]] <<- p + } else { + #print("No data to plot") + } + } + + #print(paste("writing value file: ", name, "_", fname, "_value.txt" ,sep="")) + write.table(x=transitionTable, file=paste("transitions_", name ,"_", fname, ".txt", sep=""), sep=",",quote=F,row.names=T,col.names=NA) + write.table(x=tmp[,c("Sequence.ID", "best_match", "chunk_hit_percentage", "nt_hit_percentage", "start_locations")], file=paste("matched_", name , "_", fname, ".txt", sep=""), sep="\t",quote=F,row.names=F,col.names=T) + cat(matrx[1,x], file=paste(name, "_", fname, "_value.txt" ,sep="")) + cat(nrow(tmp), file=paste(name, "_", fname, "_n.txt" ,sep="")) + #print(paste(fname, name, nrow(tmp))) + matrx +} +nts = c("a", "c", "g", "t") +zeros=rep(0, 4) +funcs = c(median, sum, mean) +fnames = c("median", "sum", "mean") + +print("Creating result tables") + +for(i in 1:length(funcs)){ + func = funcs[[i]] + fname = fnames[[i]] + + print(paste("Creating table for", fname)) + + rows = 9 + if(fname == "sum"){ + rows = 11 + } + matrx = matrix(data = 0, ncol=((length(genes) + 1) * 3),nrow=rows) + for(i in 1:length(genes)){ + matrx = calculate_result(i, genes[i], dat, matrx, func, fname, genes[i]) + } + matrx = calculate_result(i + 1, ".*", dat[!grepl("unmatched", dat$best_match),], matrx, func, fname, name="all") + + result = data.frame(matrx) + if(fname == "sum"){ + row.names(result) = c("Number of Mutations (%)", "Transitions (%)", "Transversions (%)", "Transitions at G C (%)", "Targeting of G C (%)", "Transitions at A T (%)", "Targeting of A T (%)", "FR R/S (ratio)", "CDR R/S (ratio)", "nt in FR", "nt in CDR") + } else { + row.names(result) = c("Number of Mutations (%)", "Transitions (%)", "Transversions (%)", "Transitions at G C (%)", "Targeting of G C (%)", "Transitions at A T (%)", "Targeting of A T (%)", "FR R/S (ratio)", "CDR R/S (ratio)") + } + write.table(x=result, file=paste("mutations_", fname, ".txt", sep=""), sep=",",quote=F,row.names=T,col.names=F) +} + +print("Adding median number of mutations to sum table") +sum.table = read.table("mutations_sum.txt", sep=",", header=F) +median.table = read.table("mutations_median.txt", sep=",", header=F) + +new.table = sum.table[1,] +new.table[2,] = median.table[1,] +new.table[3:12,] = sum.table[2:11,] +new.table[,1] = as.character(new.table[,1]) +new.table[2,1] = "Median of Number of Mutations (%)" + +#sum.table = sum.table[c("Number of Mutations (%)", "Median of Number of Mutations (%)", "Transition (%)", "Transversions (%)", "Transitions at G C (%)", "Targeting of G C (%)", "Transitions at A T (%)", "Targeting of A T (%)", "FR R/S (ratio)", "CDR R/S (ratio)", "nt in FR", "nt in CDR"),] + +write.table(x=new.table, file="mutations_sum.txt", sep=",",quote=F,row.names=F,col.names=F) + +print("Plotting IGA piechart") + +dat = dat[!grepl("^unmatched", dat$best_match),] + +#blegh + +genesForPlot = dat[grepl("IGA", dat$best_match),]$best_match + +if(length(genesForPlot) > 0){ + genesForPlot = data.frame(table(genesForPlot)) + colnames(genesForPlot) = c("Gene","Freq") + genesForPlot$label = paste(genesForPlot$Gene, "-", genesForPlot$Freq) + + pc = ggplot(genesForPlot, aes(x = factor(1), y=Freq, fill=Gene)) + pc = pc + geom_bar(width = 1, stat = "identity") + scale_fill_manual(labels=genesForPlot$label, values=c("IGA1" = "lightblue1", "IGA2" = "blue4")) + pc = pc + coord_polar(theta="y") + scale_y_continuous(breaks=NULL) + pc = pc + theme(panel.background = element_rect(fill = "white", colour="black"), text = element_text(size=16, colour="black"), axis.title=element_blank(), axis.text=element_blank(), axis.ticks=element_blank()) + pc = pc + xlab(" ") + ylab(" ") + ggtitle(paste("IGA subclass distribution", "( n =", sum(genesForPlot$Freq), ")")) + write.table(genesForPlot, "IGA_pie.txt", sep="\t",quote=F,row.names=F,col.names=T) + + png(filename="IGA.png") + print(pc) + dev.off() + + pdfplots[["IGA.pdf"]] <- pc +} + +print("Plotting IGG piechart") + +genesForPlot = dat[grepl("IGG", dat$best_match),]$best_match + +if(length(genesForPlot) > 0){ + genesForPlot = data.frame(table(genesForPlot)) + colnames(genesForPlot) = c("Gene","Freq") + genesForPlot$label = paste(genesForPlot$Gene, "-", genesForPlot$Freq) + + pc = ggplot(genesForPlot, aes(x = factor(1), y=Freq, fill=Gene)) + pc = pc + geom_bar(width = 1, stat = "identity") + scale_fill_manual(labels=genesForPlot$label, values=c("IGG1" = "olivedrab3", "IGG2" = "red", "IGG3" = "gold", "IGG4" = "darkred")) + pc = pc + coord_polar(theta="y") + scale_y_continuous(breaks=NULL) + pc = pc + theme(panel.background = element_rect(fill = "white", colour="black"), text = element_text(size=16, colour="black"), axis.title=element_blank(), axis.text=element_blank(), axis.ticks=element_blank()) + pc = pc + xlab(" ") + ylab(" ") + ggtitle(paste("IGG subclass distribution", "( n =", sum(genesForPlot$Freq), ")")) + write.table(genesForPlot, "IGG_pie.txt", sep="\t",quote=F,row.names=F,col.names=T) + + png(filename="IGG.png") + print(pc) + dev.off() + + pdfplots[["IGG.pdf"]] <- pc +} + +print("Plotting scatterplot") + +dat$percentage_mutations = round(dat$VRegionMutations / dat$VRegionNucleotides * 100, 2) +dat.clss = dat + +dat.clss$best_match = substr(dat.clss$best_match, 0, 3) + +dat.clss = rbind(dat, dat.clss) + +p = ggplot(dat.clss, aes(best_match, percentage_mutations)) +p = p + geom_point(aes(colour=best_match), position="jitter") + geom_boxplot(aes(middle=mean(percentage_mutations)), alpha=0.1, outlier.shape = NA) +p = p + xlab("Subclass") + ylab("Frequency") + ggtitle("Frequency scatter plot") + theme(panel.background = element_rect(fill = "white", colour="black"), text = element_text(size=16, colour="black")) +p = p + scale_fill_manual(values=c("IGA" = "blue4", "IGA1" = "lightblue1", "IGA2" = "blue4", "IGG" = "olivedrab3", "IGG1" = "olivedrab3", "IGG2" = "red", "IGG3" = "gold", "IGG4" = "darkred", "IGM" = "darkviolet", "IGE" = "darkorange", "all" = "blue4")) +p = p + scale_colour_manual(guide = guide_legend(title = "Subclass"), values=c("IGA" = "blue4", "IGA1" = "lightblue1", "IGA2" = "blue4", "IGG" = "olivedrab3", "IGG1" = "olivedrab3", "IGG2" = "red", "IGG3" = "gold", "IGG4" = "darkred", "IGM" = "darkviolet", "IGE" = "darkorange", "all" = "blue4")) + +png(filename="scatter.png") +print(p) +dev.off() + +pdfplots[["scatter.pdf"]] <- p + +write.table(dat[,c("Sequence.ID", "best_match", "VRegionMutations", "VRegionNucleotides", "percentage_mutations")], "scatter.txt", sep="\t",quote=F,row.names=F,col.names=T) + +print("Plotting frequency ranges plot") + +dat$best_match_class = substr(dat$best_match, 0, 3) +freq_labels = c("0", "0-2", "2-5", "5-10", "10-15", "15-20", "20") +dat$frequency_bins = cut(dat$percentage_mutations, breaks=c(-Inf, 0, 2,5,10,15,20, Inf), labels=freq_labels) + +frequency_bins_sum = data.frame(data.table(dat)[, list(class_sum=sum(.N)), by=c("best_match_class")]) + +frequency_bins_data = data.frame(data.table(dat)[, list(frequency_count=.N), by=c("best_match_class", "frequency_bins")]) + +frequency_bins_data = merge(frequency_bins_data, frequency_bins_sum, by="best_match_class") + +frequency_bins_data$frequency = round(frequency_bins_data$frequency_count / frequency_bins_data$class_sum * 100, 2) + +p = ggplot(frequency_bins_data, aes(frequency_bins, frequency)) +p = p + geom_bar(aes(fill=best_match_class), stat="identity", position="dodge") + theme(panel.background = element_rect(fill = "white", colour="black"), text = element_text(size=16, colour="black")) +p = p + xlab("Frequency ranges") + ylab("Frequency") + ggtitle("Mutation Frequencies by class") + scale_fill_manual(guide = guide_legend(title = "Class"), values=c("IGA" = "blue4", "IGG" = "olivedrab3", "IGM" = "darkviolet", "IGE" = "darkorange", "all" = "blue4")) + +png(filename="frequency_ranges.png") +print(p) +dev.off() + +pdfplots[["frequency_ranges.pdf"]] <- p + +save(pdfplots, file="pdfplots.RData") + +frequency_bins_data_by_class = frequency_bins_data + +frequency_bins_data_by_class = frequency_bins_data_by_class[order(frequency_bins_data_by_class$best_match_class, frequency_bins_data_by_class$frequency_bins),] + +frequency_bins_data_by_class$frequency_bins = gsub("-", " to ", frequency_bins_data_by_class$frequency_bins) +frequency_bins_data_by_class[frequency_bins_data_by_class$frequency_bins == "20", c("frequency_bins")] = "20 or higher" +frequency_bins_data_by_class[frequency_bins_data_by_class$frequency_bins == "0", c("frequency_bins")] = "0 or lower" + +write.table(frequency_bins_data_by_class, "frequency_ranges_classes.txt", sep="\t",quote=F,row.names=F,col.names=T) + +frequency_bins_data = data.frame(data.table(dat)[, list(frequency_count=.N), by=c("best_match", "best_match_class", "frequency_bins")]) + +frequency_bins_sum = data.frame(data.table(dat)[, list(class_sum=sum(.N)), by=c("best_match")]) + +frequency_bins_data = merge(frequency_bins_data, frequency_bins_sum, by="best_match") + +frequency_bins_data$frequency = round(frequency_bins_data$frequency_count / frequency_bins_data$class_sum * 100, 2) + +frequency_bins_data = frequency_bins_data[order(frequency_bins_data$best_match, frequency_bins_data$frequency_bins),] +frequency_bins_data$frequency_bins = gsub("-", " to ", frequency_bins_data$frequency_bins) +frequency_bins_data[frequency_bins_data$frequency_bins == "20", c("frequency_bins")] = "20 or higher" +frequency_bins_data[frequency_bins_data$frequency_bins == "0", c("frequency_bins")] = "0 or lower" + +write.table(frequency_bins_data, "frequency_ranges_subclasses.txt", sep="\t",quote=F,row.names=F,col.names=T) + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/shm_csr/shm_csr.xml Fri Feb 19 15:08:51 2021 +0000 @@ -0,0 +1,240 @@ +<tool id="shm_csr" name="SHM & CSR pipeline" version="1.0"> + <description></description> + <requirements> + <requirement type="package" version="2.7">python</requirement> + <requirement type="package" version="1.16.0">numpy</requirement> + <requirement type="package" version="1.2.0">xlrd</requirement> + <requirement type="package" version="3.0.0">r-ggplot2</requirement> + <requirement type="package" version="1.4.3">r-reshape2</requirement> + <requirement type="package" version="0.5.0">r-scales</requirement> + <requirement type="package" version="3.4_5">r-seqinr</requirement> + <requirement type="package" version="1.11.4">r-data.table</requirement> + </requirements> + <command interpreter="bash"> + #if str ( $filter_unique.filter_unique_select ) == "remove": + wrapper.sh $in_file custom $out_file $out_file.files_path "${in_file.name}" "-" $functionality $unique $naive_output_cond.naive_output $naive_output_ca $naive_output_cg $naive_output_cm $naive_output_ce $naive_output_all $filter_unique.filter_unique_select $filter_unique.filter_unique_clone_count $class_filter_cond.class_filter $empty_region_filter $fast + #else: + wrapper.sh $in_file custom $out_file $out_file.files_path "${in_file.name}" "-" $functionality $unique $naive_output_cond.naive_output $naive_output_ca $naive_output_cg $naive_output_cm $naive_output_ce $naive_output_all $filter_unique.filter_unique_select 2 $class_filter_cond.class_filter $empty_region_filter $fast + #end if + </command> + <inputs> + <param name="in_file" type="data" format="data" label="IMGT zip file to be analysed" /> + <param name="empty_region_filter" type="select" label="Sequence starts at" help="" > + <option value="leader" selected="true">Leader: include FR1, CDR1, FR2, CDR2, FR3 in filters</option> + <option value="FR1" selected="true">FR1: include CDR1,FR2,CDR2,FR3 in filters</option> + <option value="CDR1">CDR1: include FR2,CDR2,FR3 in filters</option> + <option value="FR2">FR2: include CDR2,FR3 in filters</option> + </param> + <param name="functionality" type="select" label="Functionality filter" help="" > + <option value="productive" selected="true">Productive (Productive and Productive see comment)</option> + <option value="unproductive">Unproductive (Unproductive and Unproductive see comment)</option> + <option value="remove_unknown">Productive and Unproductive (Productive, Productive see comment, Unproductive, Unproductive and Unproductive see comment)</option> + </param> + <conditional name="filter_unique"> + <param name="filter_unique_select" type="select" label="Filter unique sequences" help="See below for an example."> + <option value="remove" selected="true">Remove uniques (Based on nucleotide sequence + C)</option> + <option value="remove_vjaa">Remove uniques (Based on V+J+CDR3 (AA))</option> + <option value="keep">Keep uniques (Based on nucleotide sequence + C)</option> + <option value="no">No</option> + </param> + <when value="remove"> + <param name="filter_unique_clone_count" size="4" type="integer" label="How many sequences should be in a group to keep 1 of them" value="2" min="2"/> + </when> + <when value="keep"></when> + <when value="no"></when> + </conditional> + <param name="unique" type="select" label="Remove duplicates based on" help="" > + <option value="VGene,CDR3.IMGT.AA,best_match_class">Top.V.Gene, CDR3 (AA), C region</option> + <option value="VGene,CDR3.IMGT.AA">Top.V.Gene, CDR3 (AA)</option> + <option value="CDR3.IMGT.AA,best_match_class">CDR3 (AA), C region</option> + <option value="CDR3.IMGT.AA">CDR3 (AA)</option> + + <option value="VGene,CDR3.IMGT.seq,best_match_class">Top.V.Gene, CDR3 (nt), C region</option> + <option value="VGene,CDR3.IMGT.seq">Top.V.Gene, CDR3 (nt)</option> + <option value="CDR3.IMGT.seq,best_match_class">CDR3 (nt), C region</option> + <option value="CDR3.IMGT.seq">CDR3 (nt)</option> + <option value="Sequence.ID" selected="true">Don't remove duplicates</option> + </param> + <conditional name="class_filter_cond"> + <param name="class_filter" type="select" label="Human Class/Subclass filter" help="" > + <option value="70_70" selected="true">>70% class and >70% subclass</option> + <option value="60_55">>60% class and >55% subclass</option> + <option value="70_0">>70% class</option> + <option value="60_0">>60% class</option> + <option value="19_0">>19% class</option> + <option value="101_101">Do not assign (sub)class</option> + </param> + <when value="70_70"></when> + <when value="60_55"></when> + <when value="70_0"></when> + <when value="60_0"></when> + <when value="19_0"></when> + <when value="101_101"></when> + </conditional> + <conditional name="naive_output_cond"> + <param name="naive_output" type="select" label="Output new IMGT archives per class into your history?"> + <option value="yes">Yes</option> + <option value="no" selected="true">No</option> + </param> + <when value="yes"></when> + <when value="no"></when> + </conditional> + <param name="fast" type="select" label="Fast" help="Skips generating the new ZIP files and Change-O/Baseline" > + <option value="yes">Yes</option> + <option value="no" selected="true">No</option> + </param> + </inputs> + <outputs> + <data format="html" name="out_file" label = "SHM & CSR on ${in_file.name}"/> + <data format="imgt_archive" name="naive_output_ca" label = "Filtered IMGT IGA: ${in_file.name}" > + <filter>naive_output_cond['naive_output'] == "yes"</filter> + <filter>class_filter_cond['class_filter'] != "101_101"</filter> + </data> + <data format="imgt_archive" name="naive_output_cg" label = "Filtered IMGT IGG: ${in_file.name}" > + <filter>naive_output_cond['naive_output'] == "yes"</filter> + <filter>class_filter_cond['class_filter'] != "101_101"</filter> + </data> + <data format="imgt_archive" name="naive_output_cm" label = "Filtered IMGT IGM: ${in_file.name}" > + <filter>naive_output_cond['naive_output'] == "yes"</filter> + <filter>class_filter_cond['class_filter'] != "101_101"</filter> + </data> + <data format="imgt_archive" name="naive_output_ce" label = "Filtered IMGT IGE: ${in_file.name}" > + <filter>naive_output_cond['naive_output'] == "yes"</filter> + <filter>class_filter_cond['class_filter'] != "101_101"</filter> + </data> + <data format="imgt_archive" name="naive_output_all" label = "Filtered IMGT all: ${in_file.name}" > + <filter>naive_output_cond['naive_output'] == "yes"</filter> + <filter>class_filter_cond['class_filter'] == "101_101"</filter> + </data> + </outputs> + <tests> + <test> + <param name="fast" value="yes"/> + <output name="out_file" file="test1.html"/> + </test> + </tests> + <help> +<![CDATA[ +**References** + +Yaari, G. and Uduman, M. and Kleinstein, S. H. (2012). Quantifying selection in high-throughput Immunoglobulin sequencing data sets. In *Nucleic Acids Research, 40 (17), pp. e134–e134.* [`doi:10.1093/nar/gks457`_] + +.. _doi:10.1093/nar/gks457: http://dx.doi.org/10.1093/nar/gks457 + +Gupta, Namita T. and Vander Heiden, Jason A. and Uduman, Mohamed and Gadala-Maria, Daniel and Yaari, Gur and Kleinstein, Steven H. (2015). Change-O: a toolkit for analyzing large-scale B cell immunoglobulin repertoire sequencing data: Table 1. *In Bioinformatics, 31 (20), pp. 3356–3358.* [`doi:10.1093/bioinformatics/btv359`_] + +.. _doi:10.1093/bioinformatics/btv359: http://dx.doi.org/10.1093/bioinformatics/btv359 + +----- + +**Input files** + +IMGT/HighV-QUEST .zip and .txz are accepted as input files. The file to be analysed can be selected using the dropdown menu. + +.. class:: infomark + +Note: Files can be uploaded by using “get data†and “upload file†and selecting “IMGT archive“ as a file type. Special characters should be prevented in the file names of the uploaded samples as these can give errors when running the immune repertoire pipeline. Underscores are allowed in the file names. + +----- + +**Sequence starts at** + +Identifies the region which will be included in the analysis (analysed region) + +- Sequences which are missing a gene region (FR1/CDR1 etc) in the analysed region are excluded. +- Sequences containing an ambiguous base in the analysed region or the CDR3 are excluded. +- All other filtering/analysis is based on the analysed region. + +----- + +**Functionality filter** + +Allows filtering on productive rearrangements, unproductive rearrangements or both based on the assignment provided by IMGT. + +**Filter unique sequences** + +*Remove unique:* + + +This filter consists of two different steps. + +Step 1: removes all sequences of which the nucleotide sequence in the “analysed region†and the CDR3 (see sequence starts at filter) occurs only once. (Sub)classes are not taken into account in this filter step. + +Step 2: removes all duplicate sequences (sequences with the exact same nucleotide sequence in the analysed region, the CDR3 and the same (sub)class). + +.. class:: infomark + +This means that sequences with the same nucleotide sequence but a different (sub)class will be included in the results of both (sub)classes. + +*Keep unique:* + +Removes all duplicate sequences (sequences with the exact same nucleotide sequence in the analysed region and the same (sub)class). + +Example of the sequences that are included using either the “remove unique filter†or the “keep unique filter†+ ++--------------------------+ +| unique filter | ++--------+--------+--------+ +| values | remove | keep | ++--------+--------+--------+ +| A | A | A | ++--------+--------+--------+ +| A | B | B | ++--------+--------+--------+ +| B | D | C | ++--------+--------+--------+ +| B | | D | ++--------+--------+--------+ +| C | | | ++--------+--------+--------+ +| D | | | ++--------+--------+--------+ +| D | | | ++--------+--------+--------+ + +----- + +**Remove duplicates based on** + +Allows the selection of a single sequence per clone. Different definitions of a clone can be chosen. + +.. class:: infomark + +Note: The first sequence (in the data set) of each clone is always included in the analysis. When the first matched sequence is unmatched (no subclass assigned) the first matched sequence will be included. This means that altering the data order (by for instance sorting) can change the sequence which is included in the analysis and therefore slightly influences the results. + +----- + +**Human Class/Subclass filter** + +.. class:: warningmark + +Note: This filter should only be applied when analysing human IGH data in which a (sub)class specific sequence is present. Otherwise please select the do not assign (sub)class option to prevent errors when running the pipeline. + +The class percentage is based on the ‘chunk hit percentage’ (see below). The subclass percentage is based on the ‘nt hit percentage’ (see below). + +The SHM & CSR pipeline identifies human Cµ, Cα, Cγ and Cε constant genes by dividing the reference sequences for the subclasses (NG_001019) in 8 nucleotide chunks which overlap by 4 nucleotides. These overlapping chunks are then individually aligned in the right order to each input sequence. This alignment is used to calculate the chunck hit percentage and the nt hit percentage. + +*Chunk hit percentage*: The percentage of the chunks that is aligned + +*Nt hit percentage*: The percentage of chunks covering the subclass specific nucleotide match with the different subclasses. The most stringent filter for the subclass is 70% ‘nt hit percentage’ which means that 5 out of 7 subclass specific nucleotides for Cα or 6 out of 8 subclass specific nucleotides of Cγ should match with the specific subclass. +The option “>25% class†can be chosen when you only are interested in the class (Cα/Cγ/Cµ/Cɛ) of your sequences and the length of your sequence is not long enough to assign the subclasses. + +----- + +**Output new IMGT archives per class into your history?** + +If yes is selected, additional output files (one for each class) will be added to the history which contain information of the sequences that passed the selected filtering criteria. These files are in the same format as the IMGT/HighV-QUEST output files and therefore are also compatible with many other analysis programs, such as the Immune repertoire pipeline. + +----- + +**Execute** + +Upon pressing execute a new analysis is added to your history (right side of the page). Initially this analysis will be grey, after initiating the analysis colour of the analysis in the history will change to yellow. When the analysis is finished it will turn green in the history. Now the analysis can be opened by clicking on the eye icon on the analysis of interest. When an analysis turns red an error has occurred when running the analysis. If you click on the analysis title additional information can be found on the analysis. In addition a bug icon appears. Here more information on the error can be found. + +]]> + </help> + <citations> + <citation type="doi">10.1093/nar/gks457</citation> + <citation type="doi">10.1093/bioinformatics/btv359</citation> + </citations> +</tool>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/shm_csr/shm_downloads.htm Fri Feb 19 15:08:51 2021 +0000 @@ -0,0 +1,538 @@ +<html> + +<head> +<meta http-equiv=Content-Type content="text/html; charset=windows-1252"> +<meta name=Generator content="Microsoft Word 14 (filtered)"> +<style> +<!-- + /* Font Definitions */ + @font-face + {font-family:Calibri; + panose-1:2 15 5 2 2 2 4 3 2 4;} + /* Style Definitions */ + p.MsoNormal, li.MsoNormal, div.MsoNormal + {margin-top:0in; + margin-right:0in; + margin-bottom:10.0pt; + margin-left:0in; + line-height:115%; + font-size:11.0pt; + font-family:"Calibri","sans-serif";} +a:link, span.MsoHyperlink + {color:blue; + text-decoration:underline;} +a:visited, span.MsoHyperlinkFollowed + {color:purple; + text-decoration:underline;} +p.MsoNoSpacing, li.MsoNoSpacing, div.MsoNoSpacing + {margin:0in; + margin-bottom:.0001pt; + font-size:11.0pt; + font-family:"Calibri","sans-serif";} +.MsoChpDefault + {font-family:"Calibri","sans-serif";} +.MsoPapDefault + {margin-bottom:10.0pt; + line-height:115%;} +@page WordSection1 + {size:8.5in 11.0in; + margin:1.0in 1.0in 1.0in 1.0in;} +div.WordSection1 + {page:WordSection1;} +--> +</style> + +</head> + +<body lang=EN-US link=blue vlink=purple> + +<div class=WordSection1> + +<p class=MsoNoSpacing style='text-align:justify'><b><span lang=EN-GB +style='font-size:12.0pt;font-family:"Times New Roman","serif"'>Info</span></b></p> + +<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB +style='font-size:12.0pt;font-family:"Times New Roman","serif"'>The complete +dataset:</span></u><span lang=EN-GB style='font-size:12.0pt;font-family:"Times New Roman","serif"'> +Allows downloading of the complete parsed data set.</span></p> + +<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB +style='font-size:12.0pt;font-family:"Times New Roman","serif"'>The filtered +dataset:</span></u><span lang=EN-GB style='font-size:12.0pt;font-family:"Times New Roman","serif"'> +Allows downloading of all parsed IMGT information of all transcripts that +passed the chosen filter settings.</span></p> + +<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB +style='font-size:12.0pt;font-family:"Times New Roman","serif"'>The alignment +info on the unmatched sequences:</span></u><span lang=EN-GB style='font-size: +12.0pt;font-family:"Times New Roman","serif"'> Provides information of the subclass +alignment of all unmatched sequences. For each sequence the chunck hit +percentage and the nt hit percentage is shown together with the best matched +subclass.</span></p> + +<p class=MsoNoSpacing style='text-align:justify'><b><span lang=EN-GB +style='font-size:12.0pt;font-family:"Times New Roman","serif"'>SHM Overview</span></b></p> + +<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB +style='font-size:12.0pt;font-family:"Times New Roman","serif"'>The SHM Overview +table as a dataset:</span></u><span lang=EN-GB style='font-size:12.0pt; +font-family:"Times New Roman","serif"'> Allows downloading of the SHM Overview +table as a data set. </span></p> + +<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB +style='font-size:12.0pt;font-family:"Times New Roman","serif"'>Motif data per +sequence ID:</span></u><span lang=EN-GB style='font-size:12.0pt;font-family: +"Times New Roman","serif"'> Provides a file that contains information for each +transcript on the number of mutations present in WA/TW and RGYW/WRCY motives.</span></p> + +<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB +style='font-size:12.0pt;font-family:"Times New Roman","serif"'>Mutation data +per sequence ID: </span></u><span lang=EN-GB style='font-size:12.0pt; +font-family:"Times New Roman","serif"'>Provides a file containing information +on the number of sequences bases, the number and location of mutations and the +type of mutations found in each transcript. </span></p> + +<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB +style='font-size:12.0pt;font-family:"Times New Roman","serif"'>Base count for +every sequence:</span></u><span lang=EN-GB style='font-size:12.0pt;font-family: +"Times New Roman","serif"'> links to a page showing for each transcript the +sequence of the analysed region (as dependent on the sequence starts at filter), +the assigned subclass and the number of sequenced A,C,G and T’s.</span></p> + +<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB +style='font-size:12.0pt;font-family:"Times New Roman","serif"'>The data used to +generate the percentage of mutations in AID and pol eta motives plot:</span></u><span +lang=EN-GB style='font-size:12.0pt;font-family:"Times New Roman","serif"'> +Provides a file containing the values used to generate the percentage of +mutations in AID and pol eta motives plot in the SHM overview tab.</span></p> + +<p class=MsoNormalCxSpFirst style='text-align:justify'><u><span lang=EN-GB +style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>The +data used to generate the relative mutation patterns plot:</span></u><span +lang=EN-GB style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'> +Provides a download with the data used to generate the relative mutation +patterns plot in the SHM overview tab.</span></p> + +<p class=MsoNormalCxSpLast style='text-align:justify'><u><span lang=EN-GB +style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>The +data used to generate the absolute mutation patterns plot:</span></u><span +lang=EN-GB style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'> +Provides a download with the data used to generate the absolute mutation +patterns plot in the SHM overview tab. </span></p> + +<p class=MsoNoSpacing style='text-align:justify'><b><span lang=EN-GB +style='font-size:12.0pt;font-family:"Times New Roman","serif"'>SHM Frequency</span></b></p> + +<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB +style='font-size:12.0pt;font-family:"Times New Roman","serif"'>The data +generate the frequency scatter plot:</span></u><span lang=EN-GB +style='font-size:12.0pt;font-family:"Times New Roman","serif"'> Allows +downloading the data used to generate the frequency scatter plot in the SHM +frequency tab. </span></p> + +<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB +style='font-size:12.0pt;font-family:"Times New Roman","serif"'>The data used to +generate the frequency by class plot:</span></u><span lang=EN-GB +style='font-size:12.0pt;font-family:"Times New Roman","serif"'> Allows +downloading the data used to generate frequency by class plot included in the +SHM frequency tab. </span></p> + +<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB +style='font-size:12.0pt;font-family:"Times New Roman","serif"'>The data for +frequency by subclass:</span></u><span lang=EN-GB style='font-size:12.0pt; +font-family:"Times New Roman","serif"'> Provides information of the number and +percentage of sequences that have 0%, 0-2%, 2-5%, 5-10%, 10-15%, 15-20%, +>20% SHM. Information is provided for each subclass.</span></p> + +<p class=MsoNoSpacing style='text-align:justify'><span lang=EN-GB +style='font-size:12.0pt;font-family:"Times New Roman","serif"'> </span></p> + +<p class=MsoNoSpacing style='text-align:justify'><b><span lang=EN-GB +style='font-size:12.0pt;font-family:"Times New Roman","serif"'>Transition +Tables</span></b></p> + +<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB +style='font-size:12.0pt;font-family:"Times New Roman","serif"'>The data for the +'all' transition plot:</span></u><span lang=EN-GB style='font-size:12.0pt; +font-family:"Times New Roman","serif"'> Contains the information used to +generate the transition table for all sequences.</span></p> + +<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB +style='font-size:12.0pt;font-family:"Times New Roman","serif"'>The data for the +'IGA' transition plot:</span></u><span lang=EN-GB style='font-size:12.0pt; +font-family:"Times New Roman","serif"'> Contains the information used to +generate the transition table for all IGA sequences.</span></p> + +<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB +style='font-size:12.0pt;font-family:"Times New Roman","serif"'>The data for the +'IGA1' transition plot:</span></u><span lang=EN-GB style='font-size:12.0pt; +font-family:"Times New Roman","serif"'> Contains the information used to +generate the transition table for all IGA1 sequences.</span></p> + +<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB +style='font-size:12.0pt;font-family:"Times New Roman","serif"'>The data for the +'IGA2' transition plot:</span></u><span lang=EN-GB style='font-size:12.0pt; +font-family:"Times New Roman","serif"'> Contains the information used to +generate the transition table for all IGA2 sequences.</span></p> + +<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB +style='font-size:12.0pt;font-family:"Times New Roman","serif"'>The data for the +'IGG' transition plot :</span></u><span lang=EN-GB style='font-size:12.0pt; +font-family:"Times New Roman","serif"'> Contains the information used to +generate the transition table for all IGG sequences.</span></p> + +<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB +style='font-size:12.0pt;font-family:"Times New Roman","serif"'>The data for the +'IGG1' transition plot:</span></u><span lang=EN-GB style='font-size:12.0pt; +font-family:"Times New Roman","serif"'> Contains the information used to +generate the transition table for all IGG1 sequences.</span></p> + +<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB +style='font-size:12.0pt;font-family:"Times New Roman","serif"'>The data for the +'IGG2' transition plot:</span></u><span lang=EN-GB style='font-size:12.0pt; +font-family:"Times New Roman","serif"'> Contains the information used to +generate the transition table for all IGG2 sequences.</span></p> + +<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB +style='font-size:12.0pt;font-family:"Times New Roman","serif"'>The data for the +'IGG3' transition plot:</span></u><span lang=EN-GB style='font-size:12.0pt; +font-family:"Times New Roman","serif"'> Contains the information used to +generate the transition table for all IGG3 sequences.</span></p> + +<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB +style='font-size:12.0pt;font-family:"Times New Roman","serif"'>The data for the +'IGG4' transition plot:</span></u><span lang=EN-GB style='font-size:12.0pt; +font-family:"Times New Roman","serif"'> Contains the information used to +generate the transition table for all IGG4 sequences.</span></p> + +<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB +style='font-size:12.0pt;font-family:"Times New Roman","serif"'>The data for the +'IGM' transition plot :</span></u><span lang=EN-GB style='font-size:12.0pt; +font-family:"Times New Roman","serif"'> Contains the information used to +generate the transition table for all IGM sequences.</span></p> + +<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB +style='font-size:12.0pt;font-family:"Times New Roman","serif"'>The data for the +'IGE' transition plot:</span></u><span lang=EN-GB style='font-size:12.0pt; +font-family:"Times New Roman","serif"'> Contains the +information used to generate the transition table for all IGE sequences.</span></p> + +<p class=MsoNoSpacing style='text-align:justify'><b><span lang=EN-GB +style='font-size:12.0pt;font-family:"Times New Roman","serif"'>Antigen +selection</span></b></p> + +<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB +style='font-size:12.0pt;font-family:"Times New Roman","serif"'>AA mutation data +per sequence ID:</span></u><span lang=EN-GB style='font-size:12.0pt;font-family: +"Times New Roman","serif"'> Provides for each transcript information on whether +there is replacement mutation at each amino acid location (as defined by IMGT). +For all amino acids outside of the analysed region the value 0 is given.</span></p> + +<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB +style='font-size:12.0pt;font-family:"Times New Roman","serif"'>Presence of AA +per sequence ID:</span></u><span lang=EN-GB style='font-size:12.0pt;font-family: +"Times New Roman","serif"'> Provides for each transcript information on which +amino acid location (as defined by IMGT) is present. </span><span lang=NL +style='font-size:12.0pt;font-family:"Times New Roman","serif"'>0 is absent, 1 +is present. </span></p> + +<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB +style='font-size:12.0pt;font-family:"Times New Roman","serif"'>The data used to +generate the aa mutation frequency plot:</span></u><span lang=EN-GB +style='font-size:12.0pt;font-family:"Times New Roman","serif"'> Provides the +data used to generate the aa mutation frequency plot for all sequences in the +antigen selection tab.</span></p> + +<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB +style='font-size:12.0pt;font-family:"Times New Roman","serif"'>The data used to +generate the aa mutation frequency plot for IGA:</span></u><span lang=EN-GB +style='font-size:12.0pt;font-family:"Times New Roman","serif"'> Provides the +data used to generate the aa mutation frequency plot for all IGA sequences in +the antigen selection tab.</span></p> + +<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB +style='font-size:12.0pt;font-family:"Times New Roman","serif"'>The data used to +generate the aa mutation frequency plot for IGG:</span></u><span lang=EN-GB +style='font-size:12.0pt;font-family:"Times New Roman","serif"'> Provides the +data used to generate the aa mutation frequency plot for all IGG sequences in +the antigen selection tab.</span></p> + +<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB +style='font-size:12.0pt;font-family:"Times New Roman","serif"'>The data used to +generate the aa mutation frequency plot for IGM:</span></u><span lang=EN-GB +style='font-size:12.0pt;font-family:"Times New Roman","serif"'> Provides the +data used to generate the aa mutation frequency plot for all IGM sequences in +the antigen selection tab.</span></p> + +<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB +style='font-size:12.0pt;font-family:"Times New Roman","serif"'>The data used to +generate the aa mutation frequency plot for IGE:</span></u><span lang=EN-GB +style='font-size:12.0pt;font-family:"Times New Roman","serif"'> Provides the +data used to generate the aa mutation frequency plot for all IGE sequences in +the antigen selection tab.</span></p> + +<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB +style='font-size:12.0pt;font-family:"Times New Roman","serif"'>Baseline PDF (</span></u><span +lang=EN-GB><a href="http://selection.med.yale.edu/baseline/"><span +style='font-size:12.0pt;font-family:"Times New Roman","serif"'>http://selection.med.yale.edu/baseline/</span></a></span><u><span +lang=EN-GB style='font-size:12.0pt;font-family:"Times New Roman","serif"'>):</span></u><span +lang=EN-GB style='font-size:12.0pt;font-family:"Times New Roman","serif"'> PDF +containing the </span><span lang=EN-GB style='font-size:12.0pt;font-family: +"Times New Roman","serif"'>Antigen selection (BASELINe) graph for all +sequences.</span></p> + +<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB +style='font-size:12.0pt;font-family:"Times New Roman","serif"'>Baseline data:</span></u><span +lang=EN-GB style='font-size:12.0pt;font-family:"Times New Roman","serif"'> +Table output of the BASELINe analysis. Calculation of antigen selection as +performed by BASELINe are shown for each individual sequence and the sum of all +sequences.</span></p> + +<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB +style='font-size:12.0pt;font-family:"Times New Roman","serif"'>Baseline IGA +PDF:</span></u><span lang=EN-GB style='font-size:12.0pt;font-family:"Times New Roman","serif"'> +PDF containing the </span><span lang=EN-GB style='font-size:12.0pt;font-family: +"Times New Roman","serif"'>Antigen selection (BASELINe) graph for all +sequences.</span></p> + +<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB +style='font-size:12.0pt;font-family:"Times New Roman","serif"'>Baseline IGA +data:</span></u><span lang=EN-GB style='font-size:12.0pt;font-family:"Times New Roman","serif"'> +Table output of the BASELINe analysis. Calculation of antigen selection as +performed by BASELINe are shown for each individual IGA sequence and the sum of +all IGA sequences.</span></p> + +<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB +style='font-size:12.0pt;font-family:"Times New Roman","serif"'>Baseline IGG +PDF:</span></u><span lang=EN-GB style='font-size:12.0pt;font-family:"Times New Roman","serif"'> +PDF containing the </span><span lang=EN-GB style='font-size:12.0pt;font-family: +"Times New Roman","serif"'>Antigen selection (BASELINe) graph for all IGG +sequences.</span></p> + +<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB +style='font-size:12.0pt;font-family:"Times New Roman","serif"'>Baseline IGG +data:</span></u><span lang=EN-GB style='font-size:12.0pt;font-family:"Times New Roman","serif"'> +Table output of the BASELINe analysis. Calculation of antigen selection as +performed by BASELINe are shown for each individual IGG sequence and the sum of +all IGG sequences. </span></p> + +<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB +style='font-size:12.0pt;font-family:"Times New Roman","serif"'>Baseline IGM PDF:</span></u><span +lang=EN-GB style='font-size:12.0pt;font-family:"Times New Roman","serif"'> PDF +containing the </span><span lang=EN-GB style='font-size:12.0pt;font-family: +"Times New Roman","serif"'>Antigen selection (BASELINe) graph for all IGM +sequences.</span></p> + +<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB +style='font-size:12.0pt;font-family:"Times New Roman","serif"'>Baseline IGM +data:</span></u><span lang=EN-GB style='font-size:12.0pt;font-family:"Times New Roman","serif"'> +Table output of the BASELINe analysis. Calculation of antigen selection as +performed by BASELINe are shown for each individual IGM sequence and the sum of +all IGM sequences.</span></p> + +<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB +style='font-size:12.0pt;font-family:"Times New Roman","serif"'>Baseline IGE +PDF:</span></u><span lang=EN-GB style='font-size:12.0pt;font-family:"Times New Roman","serif"'> +PDF containing the </span><span lang=EN-GB style='font-size:12.0pt;font-family: +"Times New Roman","serif"'>Antigen selection (BASELINe) graph for all IGE +sequences.</span><span lang=EN-GB style='font-size:12.0pt;font-family:"Times New Roman","serif"'> +</span></p> + +<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB +style='font-size:12.0pt;font-family:"Times New Roman","serif"'>Baseline IGE +data:</span></u><span lang=EN-GB style='font-size:12.0pt;font-family:"Times New Roman","serif"'> +Table output of the BASELINe analysis. Calculation of antigen selection as +performed by BASELINe are shown for each individual IGE sequence and the sum of +all IGE sequences.</span></p> + +<p class=MsoNoSpacing style='text-align:justify'><b><span lang=EN-GB +style='font-size:12.0pt;font-family:"Times New Roman","serif"'>CSR</span></b></p> + +<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB +style='font-size:12.0pt;font-family:"Times New Roman","serif"'>The data for the +</span></u><u><span lang=EN-GB style='font-size:12.0pt;font-family:"Times New Roman","serif"'>IGA +subclass distribution plot :</span></u><span lang=EN-GB style='font-size:12.0pt; +font-family:"Times New Roman","serif"'> </span><span lang=EN-GB +style='font-size:12.0pt;font-family:"Times New Roman","serif"'>Data used for +the generation of the </span><span lang=EN-GB style='font-size:12.0pt; +font-family:"Times New Roman","serif"'>IGA subclass distribution plot provided +in the CSR tab. </span></p> + +<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB +style='font-size:12.0pt;font-family:"Times New Roman","serif"'>The data for the +</span></u><u><span lang=EN-GB style='font-size:12.0pt;font-family:"Times New Roman","serif"'>IGA +subclass distribution plot :</span></u><span lang=EN-GB style='font-size:12.0pt; +font-family:"Times New Roman","serif"'> Data used for the generation of the </span><span +lang=EN-GB style='font-size:12.0pt;font-family:"Times New Roman","serif"'>IGG +subclass distribution plot provided in the CSR tab. </span></p> + +<p class=MsoNoSpacing style='text-align:justify'><b><span lang=NL +style='font-size:12.0pt;font-family:"Times New Roman","serif"'>Clonal relation</span></b></p> + +<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB +style='font-size:12.0pt;font-family:"Times New Roman","serif"'>Sequence overlap +between subclasses:</span></u><span lang=EN-GB style='font-size:12.0pt; +font-family:"Times New Roman","serif"'> Link to the overlap table as provided +under the clonality overlap tab. </span></p> + +<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB +style='font-size:12.0pt;font-family:"Times New Roman","serif"'>The Change-O DB +file with defined clones and subclass annotation:</span></u><span +lang=EN-GB style='font-size:12.0pt;font-family:"Times New Roman","serif"'> +Downloads a table with the calculation of clonal relation between all +sequences. For each individual transcript the results of the clonal assignment +as provided by Change-O are provided. Sequences with the same number in the CLONE +column are considered clonally related. </span></p> + +<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB +style='font-size:12.0pt;font-family:"Times New Roman","serif"'>The Change-O DB +defined clones summary file:</span></u><span lang=EN-GB style='font-size:12.0pt; +font-family:"Times New Roman","serif"'> Gives a summary of the total number of +clones in all sequences and their clone size. </span></p> + +<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB +style='font-size:12.0pt;font-family:"Times New Roman","serif"'>The Change-O DB +file with defined clones of IGA:</span></u><span lang=EN-GB style='font-size: +12.0pt;font-family:"Times New Roman","serif"'> Downloads a table with the +calculation of clonal relation between all IGA sequences. For each individual +transcript the results of the clonal assignment as provided by Change-O are +provided. Sequences with the same number in the CLONE column are considered +clonally related. </span></p> + +<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB +style='font-size:12.0pt;font-family:"Times New Roman","serif"'>The Change-O DB +defined clones summary file of IGA:</span></u><span lang=EN-GB +style='font-size:12.0pt;font-family:"Times New Roman","serif"'> Gives a summary +of the total number of clones in all IGA sequences and their clone size.</span></p> + +<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB +style='font-size:12.0pt;font-family:"Times New Roman","serif"'>The Change-O DB +file with defined clones of IGG:</span></u><span lang=EN-GB style='font-size: +12.0pt;font-family:"Times New Roman","serif"'> Downloads a table with the +calculation of clonal relation between all IGG sequences. For each individual +transcript the results of the clonal assignment as provided by Change-O are +provided. Sequences with the same number in the CLONE column are considered +clonally related. </span></p> + +<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB +style='font-size:12.0pt;font-family:"Times New Roman","serif"'>The Change-O DB +defined clones summary file of IGG:</span></u><span lang=EN-GB +style='font-size:12.0pt;font-family:"Times New Roman","serif"'> Gives a summary +of the total number of clones in all IGG sequences and their clone size.</span></p> + +<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB +style='font-size:12.0pt;font-family:"Times New Roman","serif"'>The Change-O DB +file with defined clones of IGM:</span></u><span lang=EN-GB style='font-size: +12.0pt;font-family:"Times New Roman","serif"'> Downloads a table +with the calculation of clonal relation between all IGM sequences. For each +individual transcript the results of the clonal assignment as provided by +Change-O are provided. Sequences with the same number in the CLONE column are +considered clonally related. </span></p> + +<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB +style='font-size:12.0pt;font-family:"Times New Roman","serif"'>The Change-O DB +defined clones summary file of IGM:</span></u><span lang=EN-GB +style='font-size:12.0pt;font-family:"Times New Roman","serif"'> Gives a summary +of the total number of clones in all IGM sequences and their clone size.</span></p> + +<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB +style='font-size:12.0pt;font-family:"Times New Roman","serif"'>The Change-O DB +file with defined clones of IGE:</span></u><span lang=EN-GB style='font-size: +12.0pt;font-family:"Times New Roman","serif"'> Downloads a table with the +calculation of clonal relation between all IGE sequences. For each individual +transcript the results of the clonal assignment as provided by Change-O are +provided. Sequences with the same number in the CLONE column are considered +clonally related. </span></p> + +<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB +style='font-size:12.0pt;font-family:"Times New Roman","serif"'>The Change-O DB +defined clones summary file of IGE:</span></u><span lang=EN-GB +style='font-size:12.0pt;font-family:"Times New Roman","serif"'> Gives a summary +of the total number of clones in all IGE sequences and their clone size.</span></p> + +<p class=MsoNoSpacing style='text-align:justify'><b><span lang=EN-GB +style='font-size:12.0pt;font-family:"Times New Roman","serif"'>Filtered IMGT +output files</span></b></p> + +<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB +style='font-size:12.0pt;font-family:"Times New Roman","serif"'>An IMGT archive +with just the matched and filtered sequences:</span></u><span lang=EN-GB +style='font-size:12.0pt;font-family:"Times New Roman","serif"'> Downloads a +.txz file with the same format as downloaded IMGT files that contains all +sequences that have passed the chosen filter settings.</span></p> + +<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB +style='font-size:12.0pt;font-family:"Times New Roman","serif"'>An IMGT archive +with just the matched and filtered IGA sequences:</span></u><span lang=EN-GB +style='font-size:12.0pt;font-family:"Times New Roman","serif"'> Downloads a +.txz file with the same format as downloaded IMGT files that contains all IGA +sequences that have passed the chosen filter settings.</span></p> + +<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB +style='font-size:12.0pt;font-family:"Times New Roman","serif"'>An IMGT archive +with just the matched and filtered IGA1 sequences:</span></u><span lang=EN-GB +style='font-size:12.0pt;font-family:"Times New Roman","serif"'> Downloads a +.txz file with the same format as downloaded IMGT files that contains all IGA1 +sequences that have passed the chosen filter settings.</span></p> + +<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB +style='font-size:12.0pt;font-family:"Times New Roman","serif"'>An IMGT archive +with just the matched and filtered IGA2 sequences:</span></u><span lang=EN-GB +style='font-size:12.0pt;font-family:"Times New Roman","serif"'> Downloads a .txz +file with the same format as downloaded IMGT files that contains all IGA2 +sequences that have passed the chosen filter settings.</span></p> + +<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB +style='font-size:12.0pt;font-family:"Times New Roman","serif"'>An IMGT archive +with just the matched and filtered IGG sequences:</span></u><span lang=EN-GB +style='font-size:12.0pt;font-family:"Times New Roman","serif"'> Downloads a .txz +file with the same format as downloaded IMGT files that contains all IGG +sequences that have passed the chosen filter settings.</span></p> + +<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB +style='font-size:12.0pt;font-family:"Times New Roman","serif"'>An IMGT archive +with just the matched and filtered IGG1 sequences:</span></u><span lang=EN-GB +style='font-size:12.0pt;font-family:"Times New Roman","serif"'> Downloads a +.txz file with the same format as downloaded IMGT files that contains all IGG1 +sequences that have passed the chosen filter settings.</span></p> + +<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB +style='font-size:12.0pt;font-family:"Times New Roman","serif"'>An IMGT archive +with just the matched and filtered IGG2 sequences:</span></u><span lang=EN-GB +style='font-size:12.0pt;font-family:"Times New Roman","serif"'> Downloads a +.txz file with the same format as downloaded IMGT files that contains all IGG2 +sequences that have passed the chosen filter settings.</span></p> + +<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB +style='font-size:12.0pt;font-family:"Times New Roman","serif"'>An IMGT archive +with just the matched and filtered IGG3 sequences:</span></u><span lang=EN-GB +style='font-size:12.0pt;font-family:"Times New Roman","serif"'> Downloads a .txz +file with the same format as downloaded IMGT files that contains all IGG3 +sequences that have passed the chosen filter settings.</span></p> + +<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB +style='font-size:12.0pt;font-family:"Times New Roman","serif"'>An IMGT archive +with just the matched and filtered IGG4 sequences:</span></u><span lang=EN-GB +style='font-size:12.0pt;font-family:"Times New Roman","serif"'> Downloads a +.txz file with the same format as downloaded IMGT files that contains all IGG4 +sequences that have passed the chosen filter settings.</span></p> + +<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB +style='font-size:12.0pt;font-family:"Times New Roman","serif"'>An IMGT archive +with just the matched and filtered IGM sequences:</span></u><span lang=EN-GB +style='font-size:12.0pt;font-family:"Times New Roman","serif"'> Downloads a .txz +file with the same format as downloaded IMGT files that contains all IGM +sequences that have passed the chosen filter settings.</span></p> + +<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB +style='font-size:12.0pt;font-family:"Times New Roman","serif"'>An IMGT archive +with just the matched and filtered IGE sequences:</span></u><span lang=EN-GB +style='font-size:12.0pt;font-family:"Times New Roman","serif"'> Downloads a +.txz file with the same format as downloaded IMGT files that contains all IGE +sequences that have passed the chosen filter settings.</span></p> + +</div> + +</body> + +</html>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/shm_csr/shm_first.htm Fri Feb 19 15:08:51 2021 +0000 @@ -0,0 +1,127 @@ +<html> + +<head> +<meta http-equiv=Content-Type content="text/html; charset=windows-1252"> +<meta name=Generator content="Microsoft Word 14 (filtered)"> +<style> +<!-- + /* Font Definitions */ + @font-face + {font-family:Calibri; + panose-1:2 15 5 2 2 2 4 3 2 4;} + /* Style Definitions */ + p.MsoNormal, li.MsoNormal, div.MsoNormal + {margin-top:0in; + margin-right:0in; + margin-bottom:10.0pt; + margin-left:0in; + line-height:115%; + font-size:11.0pt; + font-family:"Calibri","sans-serif";} +.MsoChpDefault + {font-family:"Calibri","sans-serif";} +.MsoPapDefault + {margin-bottom:10.0pt; + line-height:115%;} +@page WordSection1 + {size:8.5in 11.0in; + margin:1.0in 1.0in 1.0in 1.0in;} +div.WordSection1 + {page:WordSection1;} +--> +</style> + +</head> + +<body lang=EN-US> + +<div class=WordSection1> + +<p class=MsoNormalCxSpFirst style='margin-bottom:0in;margin-bottom:.0001pt; +text-align:justify;line-height:normal'><span lang=EN-GB style='font-size:12.0pt; +font-family:"Times New Roman","serif"'>Table showing the order of each +filtering step and the number and percentage of sequences after each filtering +step. </span></p> + +<p class=MsoNormalCxSpMiddle style='margin-bottom:0in;margin-bottom:.0001pt; +text-align:justify;line-height:normal'><u><span lang=EN-GB style='font-size: +12.0pt;font-family:"Times New Roman","serif"'>Input:</span></u><span +lang=EN-GB style='font-size:12.0pt;font-family:"Times New Roman","serif"'> The +number of sequences in the original IMGT file. This is always 100% of the +sequences.</span></p> + +<p class=MsoNormalCxSpMiddle style='margin-bottom:0in;margin-bottom:.0001pt; +text-align:justify;line-height:normal'><u><span lang=EN-GB style='font-size: +12.0pt;font-family:"Times New Roman","serif"'>After "no results" filter: </span></u><span +lang=EN-GB style='font-size:12.0pt;font-family:"Times New Roman","serif"'>IMGT +classifies sequences either as "productive", "unproductive", "unknown", or "no +results". Here, the number and percentages of sequences that are not classified +as "no results" are reported.</span></p> + +<p class=MsoNormalCxSpMiddle style='margin-bottom:0in;margin-bottom:.0001pt; +text-align:justify;line-height:normal'><u><span lang=EN-GB style='font-size: +12.0pt;font-family:"Times New Roman","serif"'>After functionality filter:</span></u><span +lang=EN-GB style='font-size:12.0pt;font-family:"Times New Roman","serif"'> The +number and percentages of sequences that have passed the functionality filter. The +filtering performed is dependent on the settings of the functionality filter. +Details on the functionality filter <a name="OLE_LINK12"></a><a +name="OLE_LINK11"></a><a name="OLE_LINK10">can be found on the start page of +the SHM&CSR pipeline</a>.</span></p> + +<p class=MsoNormalCxSpMiddle style='text-align:justify'><u><span lang=EN-GB +style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>After +removal sequences that are missing a gene region:</span></u><span lang=EN-GB +style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'> +In this step all sequences that are missing a gene region (FR1, CDR1, FR2, +CDR2, FR3) that should be present are removed from analysis. The sequence +regions that should be present are dependent on the settings of the sequence +starts at filter. <a name="OLE_LINK9"></a><a name="OLE_LINK8">The number and +percentage of sequences that pass this filter step are reported.</a> </span></p> + +<p class=MsoNormalCxSpMiddle style='text-align:justify'><u><span lang=EN-GB +style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>After +N filter:</span></u><span lang=EN-GB style='font-size:12.0pt;line-height:115%; +font-family:"Times New Roman","serif"'> In this step all sequences that contain +an ambiguous base (n) in the analysed region or the CDR3 are removed from the +analysis. The analysed region is determined by the setting of the sequence +starts at filter. The number and percentage of sequences that pass this filter +step are reported.</span></p> + +<p class=MsoNormalCxSpMiddle style='text-align:justify'><u><span lang=EN-GB +style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>After +filter unique sequences</span></u><span lang=EN-GB style='font-size:12.0pt; +line-height:115%;font-family:"Times New Roman","serif"'>: The number and +percentage of sequences that pass the "filter unique sequences" filter. Details +on this filter </span><span lang=EN-GB style='font-size:12.0pt;line-height: +115%;font-family:"Times New Roman","serif"'>can be found on the start page of +the SHM&CSR pipeline</span></p> + +<p class=MsoNormalCxSpMiddle style='text-align:justify'><u><span lang=EN-GB +style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>After +remove duplicate based on filter:</span></u><span lang=EN-GB style='font-size: +12.0pt;line-height:115%;font-family:"Times New Roman","serif"'> The number and +percentage of sequences that passed the remove duplicate filter. Details on the +"remove duplicate filter based on filter" can be found on the start page of the +SHM&CSR pipeline.</span></p> + +<p class=MsoNormalCxSpMiddle style='text-align:justify'><a name="OLE_LINK17"></a><a +name="OLE_LINK16"><u><span lang=EN-GB style='font-size:12.0pt;line-height:115%; +font-family:"Times New Roman","serif"'>Number of matches sequences:</span></u></a><span +lang=EN-GB style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'> +The number and percentage of sequences that passed all the filters described +above and have a (sub)class assigned.</span></p> + +<p class=MsoNormalCxSpMiddle style='text-align:justify'><u><span lang=EN-GB +style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>Number +of unmatched sequences</span></u><span lang=EN-GB style='font-size:12.0pt; +line-height:115%;font-family:"Times New Roman","serif"'>: The number and percentage +of sequences that passed all the filters described above and do not have +subclass assigned.</span></p> + +<p class=MsoNormal><span lang=EN-GB> </span></p> + +</div> + +</body> + +</html>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/shm_csr/shm_frequency.htm Fri Feb 19 15:08:51 2021 +0000 @@ -0,0 +1,87 @@ +<html> + +<head> +<meta http-equiv=Content-Type content="text/html; charset=windows-1252"> +<meta name=Generator content="Microsoft Word 14 (filtered)"> +<style> +<!-- + /* Style Definitions */ + p.MsoNormal, li.MsoNormal, div.MsoNormal + {margin-top:0in; + margin-right:0in; + margin-bottom:10.0pt; + margin-left:0in; + line-height:115%; + font-size:11.0pt; + font-family:"Calibri","sans-serif";} +.MsoChpDefault + {font-family:"Calibri","sans-serif";} +.MsoPapDefault + {margin-bottom:10.0pt; + line-height:115%;} +@page WordSection1 + {size:8.5in 11.0in; + margin:1.0in 1.0in 1.0in 1.0in;} +div.WordSection1 + {page:WordSection1;} +--> +</style> + +</head> + +<body lang=EN-US> + +<div class=WordSection1> + +<p class=MsoNormalCxSpFirst style='text-align:justify'><b><u><span lang=EN-GB +style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>SHM +frequency tab</span></u></b></p> + +<p class=MsoNormalCxSpMiddle style='text-align:justify'><b><span lang=EN-GB +style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>Graphs</span></b></p> + +<p class=MsoNormalCxSpMiddle style='text-align:justify'><span lang=EN-GB +style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>These +graphs give insight into the level of SHM. The data represented in these graphs +can be downloaded in the download tab. <a name="OLE_LINK24"></a><a +name="OLE_LINK23"></a><a name="OLE_LINK90"></a><a name="OLE_LINK89">More +information on the values found in healthy individuals of different ages can be +found in IJspeert and van Schouwenburg et al, PMID: 27799928. </a></span></p> + +<p class=MsoNormalCxSpMiddle style='text-align:justify'><u><span lang=EN-GB +style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>Frequency +scatter plot</span></u></p> + +<p class=MsoNormalCxSpMiddle style='text-align:justify'><span lang=EN-GB +style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>A +dot plot showing the percentage of SHM in each transcript divided into the +different (sub)classes. </span><span lang=NL style='font-size:12.0pt; +line-height:115%;font-family:"Times New Roman","serif"'>In the graph each dot +represents an individual transcript.</span></p> + +<p class=MsoNormalCxSpMiddle style='text-align:justify'><u><span lang=EN-GB +style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>Mutation +frequency by class</span></u></p> + +<p class=MsoNormalCxSpLast style='text-align:justify'><span lang=EN-GB +style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>A +bar graph showing the percentage of transcripts that contain 0%, 0-2%, 2-5%, +5-10% 10-15%, 15-20% or more than 20% SHM for each subclass. </span></p> + +<p class=MsoNormal><span lang=NL style='font-size:12.0pt;line-height:115%; +font-family:"Times New Roman","serif"'>Hanna IJspeert, Pauline A. van +Schouwenburg, David van Zessen, Ingrid Pico-Knijnenburg, Gertjan J. Driessen, +Andrew P. Stubbs, and Mirjam van der Burg (2016). </span><span +style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>Evaluation +of the Antigen-Experienced B-Cell Receptor Repertoire in Healthy Children and +Adults. In <i>Frontiers in Immunolog, 7, pp. e410-410. </i>[<a +href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5066086/"><span +style='color:windowtext'>doi:10.3389/fimmu.2016.00410</span></a>][<a +href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5066086/"><span +style='color:windowtext'>Link</span></a>]</span></p> + +</div> + +</body> + +</html>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/shm_csr/shm_overview.htm Fri Feb 19 15:08:51 2021 +0000 @@ -0,0 +1,332 @@ +<html> + +<head> +<meta http-equiv=Content-Type content="text/html; charset=windows-1252"> +<meta name=Generator content="Microsoft Word 14 (filtered)"> +<style> +<!-- + /* Font Definitions */ + @font-face + {font-family:Calibri; + panose-1:2 15 5 2 2 2 4 3 2 4;} + /* Style Definitions */ + p.MsoNormal, li.MsoNormal, div.MsoNormal + {margin-top:0in; + margin-right:0in; + margin-bottom:10.0pt; + margin-left:0in; + line-height:115%; + font-size:11.0pt; + font-family:"Calibri","sans-serif";} +.MsoChpDefault + {font-family:"Calibri","sans-serif";} +.MsoPapDefault + {margin-bottom:10.0pt; + line-height:115%;} +@page WordSection1 + {size:8.5in 11.0in; + margin:1.0in 1.0in 1.0in 1.0in;} +div.WordSection1 + {page:WordSection1;} +--> +</style> + +</head> + +<body lang=EN-US> + +<div class=WordSection1> + +<p class=MsoNormalCxSpFirst style='text-align:justify'><b><span lang=EN-GB +style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>Info +table</span></b></p> + +<p class=MsoNormalCxSpMiddle style='text-align:justify'><span lang=EN-GB +style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>This +table contains information on different characteristics of SHM. For all +characteristics information can be found for all sequences or only sequences of +a certain (sub)class. All results are based on the sequences that passed the filter +settings chosen on the start page of the SHM & CSR pipeline and only +include details on the analysed region as determined by the setting of the +sequence starts at filter. All data in this table can be downloaded via the +“downloads” tab.</span></p> + +<p class=MsoNormalCxSpMiddle style='text-align:justify'><u><span lang=EN-GB +style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>Mutation +frequency:</span></u></p> + +<p class=MsoNormalCxSpMiddle style='text-align:justify'><a name="OLE_LINK83"></a><a +name="OLE_LINK82"></a><a name="OLE_LINK81"><span lang=EN-GB style='font-size: +12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>These values +give information on the level of SHM. </span></a><a name="OLE_LINK22"></a><a +name="OLE_LINK21"></a><a name="OLE_LINK20"><span lang=EN-GB style='font-size: +12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>More information +on the values found in healthy individuals of different ages can be found in </span></a><a +name="OLE_LINK15"></a><a name="OLE_LINK14"></a><a name="OLE_LINK13"><span +lang=EN-GB style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>IJspeert +and van Schouwenburg et al, PMID: 27799928</span></a></p> + +<p class=MsoNormalCxSpMiddle style='text-align:justify'><i><span lang=EN-GB +style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>Number +of mutations:</span></i><span lang=EN-GB style='font-size:12.0pt;line-height: +115%;font-family:"Times New Roman","serif"'> Shows the number of total +mutations / the number of sequenced bases (the % of mutated bases).</span></p> + +<p class=MsoNormalCxSpMiddle style='text-align:justify'><i><span lang=EN-GB +style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>Median +number of mutations:</span></i><span lang=EN-GB style='font-size:12.0pt; +line-height:115%;font-family:"Times New Roman","serif"'> Shows the median % of +SHM of all sequences.</span></p> + +<p class=MsoNormalCxSpMiddle style='text-align:justify'><u><span lang=EN-GB +style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>Patterns +of SHM:</span></u></p> + +<p class=MsoNormalCxSpMiddle style='text-align:justify'><a name="OLE_LINK72"></a><a +name="OLE_LINK71"></a><a name="OLE_LINK70"><span lang=EN-GB style='font-size: +12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>These values +give insights into the targeting and patterns of SHM. These values can give +insight into the repair pathways used to repair the U:G mismatches introduced +by AID. </span></a><a name="OLE_LINK40"></a><a name="OLE_LINK39"></a><a +name="OLE_LINK38"></a><a name="OLE_LINK60"><span lang=EN-GB style='font-size: +12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>More information +on the values found in healthy individuals of different ages can be found in +IJspeert and van Schouwenburg et al, PMID: 27799928</span></a></p> + +<p class=MsoNormalCxSpMiddle style='text-align:justify'><i><span lang=EN-GB +style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>Transitions:</span></i><span +lang=EN-GB style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'> +Shows the number of transition mutations / the number of total mutations (the +percentage of mutations that are transitions). Transition mutations are C>T, +T>C, A>G, G>A. </span></p> + +<p class=MsoNormalCxSpMiddle style='text-align:justify'><i><span lang=EN-GB +style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>Transversions:</span></i><span +lang=EN-GB style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'> +Shows the number of transversion mutations / the number of total mutations (the +percentage of mutations that are transitions). Transversion mutations are +C>A, C>G, T>A, T>G, A>T, A>C, G>T, G>C.</span></p> + +<p class=MsoNormalCxSpMiddle style='text-align:justify'><i><span lang=EN-GB +style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>Transitions +at GC:</span></i><span lang=EN-GB style='font-size:12.0pt;line-height:115%; +font-family:"Times New Roman","serif"'> <a name="OLE_LINK2"></a><a +name="OLE_LINK1">Shows the number of transitions at GC locations (C>T, +G>A) / the total number of mutations at GC locations (the percentage of +mutations at GC locations that are transitions).</a></span></p> + +<p class=MsoNormalCxSpMiddle style='text-align:justify'><i><span lang=EN-GB +style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>Targeting +of GC:</span></i><span lang=EN-GB style='font-size:12.0pt;line-height:115%; +font-family:"Times New Roman","serif"'> <a name="OLE_LINK7"></a><a +name="OLE_LINK6"></a><a name="OLE_LINK3">Shows the number of mutations at GC +locations / the total number of mutations (the percentage of total mutations +that are at GC locations).</a> </span></p> + +<p class=MsoNormalCxSpMiddle style='text-align:justify'><i><span lang=EN-GB +style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>Transitions +at AT:</span></i><span lang=EN-GB style='font-size:12.0pt;line-height:115%; +font-family:"Times New Roman","serif"'> Shows the number of transitions at AT +locations (T>C, A>G) / the total number of mutations at AT locations (the +percentage of mutations at AT locations that are transitions).</span></p> + +<p class=MsoNormalCxSpMiddle style='text-align:justify'><i><span lang=EN-GB +style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>Targeting +of AT:</span></i><span lang=EN-GB style='font-size:12.0pt;line-height:115%; +font-family:"Times New Roman","serif"'> Shows the number of mutations at AT +locations / the total number of mutations (the percentage of total mutations +that are at AT locations).</span></p> + +<p class=MsoNormalCxSpMiddle style='text-align:justify'><i><span lang=EN-GB +style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>RGYW:</span></i><span +lang=EN-GB style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'> +<a name="OLE_LINK28"></a><a name="OLE_LINK27"></a><a name="OLE_LINK26">Shows +the number of mutations that are in a RGYW motive / The number of total mutations +(the percentage of mutations that are in a RGYW motive). </a><a +name="OLE_LINK62"></a><a name="OLE_LINK61">RGYW motives are known to be +preferentially targeted by AID </a></span><span lang=EN-GB style='font-size: +12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>(R=Purine, +Y=pyrimidine, W = A or T).</span></p> + +<p class=MsoNormalCxSpMiddle style='text-align:justify'><i><span lang=EN-GB +style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>WRCY:</span></i><span +lang=EN-GB style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'> +<a name="OLE_LINK34"></a><a name="OLE_LINK33">Shows the number of mutations +that are in a </a><a name="OLE_LINK32"></a><a name="OLE_LINK31"></a><a +name="OLE_LINK30"></a><a name="OLE_LINK29">WRCY</a> motive / The number of +total mutations (the percentage of mutations that are in a WRCY motive). WRCY +motives are known to be preferentially targeted by AID </span><span lang=EN-GB +style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>(R=Purine, +Y=pyrimidine, W = A or T).</span></p> + +<p class=MsoNormalCxSpMiddle style='text-align:justify'><i><span lang=EN-GB +style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>WA:</span></i><span +lang=EN-GB style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'> +<a name="OLE_LINK37"></a><a name="OLE_LINK36"></a><a name="OLE_LINK35">Shows +the number of mutations that are in a WA motive / The number of total mutations +(the percentage of mutations that are in a WA motive). It is described that +polymerase eta preferentially makes errors at WA motives </a></span><span +lang=EN-GB style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>(W += A or T).</span></p> + +<p class=MsoNormalCxSpMiddle style='text-align:justify'><i><span lang=EN-GB +style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>TW:</span></i><span +lang=EN-GB style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'> +Shows the number of mutations that are in a TW motive / The number of total mutations +(the percentage of mutations that are in a TW motive). It is described that +polymerase eta preferentially makes errors at TW motives </span><span +lang=EN-GB style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>(W += A or T).</span></p> + +<p class=MsoNormalCxSpMiddle style='text-align:justify'><u><span lang=EN-GB +style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>Antigen +selection:</span></u></p> + +<p class=MsoNormalCxSpMiddle style='text-align:justify'><span lang=EN-GB +style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>These +values give insight into antigen selection. It has been described that during +antigen selection, there is selection against replacement mutations in the FR +regions as these can cause instability of the B-cell receptor. In contrast +replacement mutations in the CDR regions are important for changing the +affinity of the B-cell receptor and therefore there is selection for this type +of mutations. Silent mutations do not alter the amino acid sequence and +therefore do not play a role in selection. More information on the values found +in healthy individuals of different ages can be found in IJspeert and van +Schouwenburg et al, PMID: 27799928</span></p> + +<p class=MsoNormalCxSpMiddle style='text-align:justify'><i><span lang=EN-GB +style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>FR +R/S:</span></i><span lang=EN-GB style='font-size:12.0pt;line-height:115%; +font-family:"Times New Roman","serif"'> <a name="OLE_LINK43"></a><a +name="OLE_LINK42"></a><a name="OLE_LINK41">Shows the number of replacement +mutations in the FR regions / The number of silent mutations in the FR regions +(the number of replacement mutations in the FR regions divided by the number of +silent mutations in the FR regions)</a></span></p> + +<p class=MsoNormalCxSpMiddle style='text-align:justify'><i><span lang=EN-GB +style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>CDR +R/S:</span></i><span lang=EN-GB style='font-size:12.0pt;line-height:115%; +font-family:"Times New Roman","serif"'> Shows the number of replacement +mutations in the CDR regions / The number of silent mutations in the CDR +regions (the number of replacement mutations in the CDR regions divided by the +number of silent mutations in the CDR regions)</span></p> + +<p class=MsoNormalCxSpMiddle style='text-align:justify'><u><span lang=EN-GB +style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>Number +of sequences nucleotides:</span></u></p> + +<p class=MsoNormalCxSpMiddle style='text-align:justify'><span lang=EN-GB +style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>These +values give information on the number of sequenced nucleotides.</span></p> + +<p class=MsoNormalCxSpMiddle style='text-align:justify'><i><span lang=EN-GB +style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>Nt +in FR:</span></i><span lang=EN-GB style='font-size:12.0pt;line-height:115%; +font-family:"Times New Roman","serif"'> <a name="OLE_LINK46"></a><a +name="OLE_LINK45"></a><a name="OLE_LINK44">Shows the number of sequences bases +that are located in the FR regions / The total number of sequenced bases (the +percentage of sequenced bases that are present in the FR regions).</a></span></p> + +<p class=MsoNormalCxSpMiddle style='text-align:justify'><i><span lang=EN-GB +style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>Nt +in CDR:</span></i><span lang=EN-GB style='font-size:12.0pt;line-height:115%; +font-family:"Times New Roman","serif"'> Shows the number of sequenced bases +that are located in the CDR regions / <a name="OLE_LINK48"></a><a +name="OLE_LINK47">The total number of sequenced bases (the percentage of +sequenced bases that are present in the CDR regions).</a></span></p> + +<p class=MsoNormalCxSpMiddle style='text-align:justify'><i><span lang=EN-GB +style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>A: +</span></i><a name="OLE_LINK51"></a><a name="OLE_LINK50"></a><a +name="OLE_LINK49"><span lang=EN-GB style='font-size:12.0pt;line-height:115%; +font-family:"Times New Roman","serif"'>Shows the total number of sequenced +adenines / The total number of sequenced bases (the percentage of sequenced +bases that were adenines).</span></a></p> + +<p class=MsoNormalCxSpMiddle style='text-align:justify'><i><span lang=EN-GB +style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>C: +</span></i><a name="OLE_LINK53"></a><a name="OLE_LINK52"><span lang=EN-GB +style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>Shows +the total number of sequenced cytosines / The total number of sequenced bases +(the percentage of sequenced bases that were cytosines).</span></a></p> + +<p class=MsoNormalCxSpMiddle style='text-align:justify'><i><span lang=EN-GB +style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>T: +</span></i><a name="OLE_LINK57"></a><a name="OLE_LINK56"><span lang=EN-GB +style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>Shows +the total number of sequenced </span></a><a name="OLE_LINK55"></a><a +name="OLE_LINK54"><span lang=EN-GB style='font-size:12.0pt;line-height:115%; +font-family:"Times New Roman","serif"'>thymines</span></a><span lang=EN-GB +style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'> +/ The total number of sequenced bases (the percentage of sequenced bases that +were thymines).</span></p> + +<p class=MsoNormalCxSpMiddle style='text-align:justify'><i><span lang=EN-GB +style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>G: +</span></i><span lang=EN-GB style='font-size:12.0pt;line-height:115%; +font-family:"Times New Roman","serif"'>Shows the total number of sequenced <a +name="OLE_LINK59"></a><a name="OLE_LINK58">guanine</a>s / The total number of +sequenced bases (the percentage of sequenced bases that were guanines).</span></p> + +<p class=MsoNormalCxSpMiddle style='text-align:justify'><a name="OLE_LINK69"><b><span +lang=EN-GB style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>Graphs</span></b></a></p> + +<p class=MsoNormalCxSpMiddle style='text-align:justify'><a name="OLE_LINK75"></a><a +name="OLE_LINK74"></a><a name="OLE_LINK73"><span lang=EN-GB style='font-size: +12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>These graphs visualize +information on the patterns and targeting of SHM and thereby give information +into the repair pathways used to repair the U:G mismatches introduced by AID. The +data represented in these graphs can be downloaded in the download tab. More +information on the values found in healthy individuals of different ages can be +found in IJspeert and van Schouwenburg et al, PMID: 27799928</span></a><span +lang=EN-GB style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>. +<a name="OLE_LINK85"></a><a name="OLE_LINK84"></a></span></p> + +<p class=MsoNormalCxSpMiddle style='text-align:justify'><u><span lang=EN-GB +style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>Percentage +of mutations in AID and pol eta motives</span></u></p> + +<p class=MsoNormalCxSpMiddle style='text-align:justify'><span lang=EN-GB +style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>Visualizes +<a name="OLE_LINK80"></a><a name="OLE_LINK79"></a><a name="OLE_LINK78">for each +(sub)class </a>the percentage of mutations that are present in AID (RGYW or +WRCY) or polymerase eta motives (WA or TW) in the different subclasses </span><span +lang=EN-GB style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>(R=Purine, +Y=pyrimidine, W = A or T).</span></p> + +<p class=MsoNormalCxSpMiddle style='text-align:justify'><u><span lang=NL +style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>Relative +mutation patterns</span></u></p> + +<p class=MsoNormalCxSpMiddle style='text-align:justify'><span lang=EN-GB +style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>Visualizes +for each (sub)class the distribution of mutations between mutations at AT +locations and transitions or transversions at GC locations. </span></p> + +<p class=MsoNormalCxSpMiddle style='text-align:justify'><u><span lang=NL +style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>Absolute +mutation patterns</span></u></p> + +<p class=MsoNormalCxSpLast style='text-align:justify'><span lang=EN-GB +style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>Visualized +for each (sub)class the percentage of sequenced AT and GC bases that are +mutated. The mutations at GC bases are divided into transition and transversion +mutations<a name="OLE_LINK77"></a><a name="OLE_LINK76">. </a></span></p> + +<p class=MsoNormal><span lang=NL style='font-size:12.0pt;line-height:115%; +font-family:"Times New Roman","serif"'>Hanna IJspeert, Pauline A. van +Schouwenburg, David van Zessen, Ingrid Pico-Knijnenburg, Gertjan J. Driessen, +Andrew P. Stubbs, and Mirjam van der Burg (2016). </span><span +style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>Evaluation +of the Antigen-Experienced B-Cell Receptor Repertoire in Healthy Children and +Adults. In <i>Frontiers in Immunolog, 7, pp. e410-410. </i>[<a +href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5066086/"><span +style='color:windowtext'>doi:10.3389/fimmu.2016.00410</span></a>][<a +href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5066086/"><span +style='color:windowtext'>Link</span></a>]</span></p> + +</div> + +</body> + +</html>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/shm_csr/shm_selection.htm Fri Feb 19 15:08:51 2021 +0000 @@ -0,0 +1,128 @@ +<html> + +<head> +<meta http-equiv=Content-Type content="text/html; charset=windows-1252"> +<meta name=Generator content="Microsoft Word 14 (filtered)"> +<style> +<!-- + /* Font Definitions */ + @font-face + {font-family:Calibri; + panose-1:2 15 5 2 2 2 4 3 2 4;} +@font-face + {font-family:UICTFontTextStyleBody;} + /* Style Definitions */ + p.MsoNormal, li.MsoNormal, div.MsoNormal + {margin-top:0in; + margin-right:0in; + margin-bottom:10.0pt; + margin-left:0in; + line-height:115%; + font-size:11.0pt; + font-family:"Calibri","sans-serif";} +a:link, span.MsoHyperlink + {color:blue; + text-decoration:underline;} +a:visited, span.MsoHyperlinkFollowed + {color:purple; + text-decoration:underline;} +span.apple-converted-space + {mso-style-name:apple-converted-space;} +.MsoChpDefault + {font-family:"Calibri","sans-serif";} +.MsoPapDefault + {margin-bottom:10.0pt; + line-height:115%;} +@page WordSection1 + {size:8.5in 11.0in; + margin:1.0in 1.0in 1.0in 1.0in;} +div.WordSection1 + {page:WordSection1;} +--> +</style> + +</head> + +<body lang=EN-US link=blue vlink=purple> + +<div class=WordSection1> + +<p class=MsoNormalCxSpFirst style='text-align:justify'><b><span lang=EN-GB +style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>References</span></b></p> + +<p class=MsoNormalCxSpMiddle style='text-align:justify'><span lang=EN-GB +style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"; +color:black'>Yaari, G. and Uduman, M. and Kleinstein, S. H. (2012). Quantifying +selection in high-throughput Immunoglobulin sequencing data sets. In<span +class=apple-converted-space> </span><em>Nucleic Acids Research, 40 (17), +pp. e134–e134.</em><span class=apple-converted-space><i> </i></span>[</span><span +lang=EN-GB><a href="http://dx.doi.org/10.1093/nar/gks457" target="_blank"><span +style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"; +color:#303030'>doi:10.1093/nar/gks457</span></a></span><span lang=EN-GB +style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"; +color:black'>][</span><span lang=EN-GB><a +href="http://dx.doi.org/10.1093/nar/gks457" target="_blank"><span +style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"; +color:#303030'>Link</span></a></span><span lang=EN-GB style='font-size:12.0pt; +line-height:115%;font-family:"Times New Roman","serif";color:black'>]</span></p> + +<p class=MsoNormalCxSpMiddle style='text-align:justify'><b><span lang=EN-GB +style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>Graphs</span></b></p> + +<p class=MsoNormalCxSpMiddle style='text-align:justify'><u><span lang=EN-GB +style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>AA +mutation frequency</span></u></p> + +<p class=MsoNormalCxSpMiddle style='text-align:justify'><span lang=EN-GB +style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>For +each class, the frequency of replacement mutations at each amino acid position +is shown, which is calculated by dividing the number of replacement mutations +at a particular amino acid position/the number sequences that have an amino +acid at that particular position. Since the length of the CDR1 and CDR2 region +is not the same for every VH gene, some amino acids positions are absent. +Therefore we calculate the frequency using the number of amino acids present at +that that particular location. </span></p> + +<p class=MsoNormalCxSpMiddle style='text-align:justify'><u><span lang=EN-GB +style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>Antigen +selection (BASELINe)</span></u></p> + +<p class=MsoNormalCxSpMiddle style='text-align:justify'><span lang=EN-GB +style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>Shows +the results of the analysis of antigen selection as performed using BASELINe. +Details on the analysis performed by BASELINe can be found in Yaari et al, +PMID: 22641856. The settings used for the analysis are</span><span lang=EN-GB +style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>: +focused, SHM targeting model: human Tri-nucleotide, custom bounderies. The +custom boundries are dependent on the ‘sequence starts at filter’. </span></p> + +<p class=MsoNormalCxSpMiddle style='line-height:normal'><span lang=NL +style='font-family:UICTFontTextStyleBody;color:black'>Leader: +1:26:38:55:65:104:-</span></p> + +<p class=MsoNormalCxSpMiddle style='line-height:normal'><span lang=NL +style='font-family:UICTFontTextStyleBody;color:black'>FR1: 27:27:38:55:65:104:-</span></p> + +<p class=MsoNormalCxSpMiddle style='line-height:normal'><span lang=NL +style='font-family:UICTFontTextStyleBody;color:black'>CDR1: 27:27:38:55:65:104:-</span></p> + +<p class=MsoNormalCxSpLast style='line-height:normal'><span lang=NL +style='font-family:UICTFontTextStyleBody;color:black'>FR2: 27:27:38:55:65:104:-</span></p> + +<p class=MsoNormal><span lang=NL style='font-size:12.0pt;line-height:115%; +font-family:"Times New Roman","serif"'>Hanna IJspeert, Pauline A. van +Schouwenburg, David van Zessen, Ingrid Pico-Knijnenburg, Gertjan J. Driessen, +Andrew P. Stubbs, and Mirjam van der Burg (2016). </span><span +style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>Evaluation +of the Antigen-Experienced B-Cell Receptor Repertoire in Healthy Children and +Adults. In <i>Frontiers in Immunolog, 7, pp. e410-410. </i>[<a +href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5066086/"><span +style='color:windowtext'>doi:10.3389/fimmu.2016.00410</span></a>][<a +href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5066086/"><span +style='color:windowtext'>Link</span></a>]</span></p> + +</div> + +</body> + +</html>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/shm_csr/shm_transition.htm Fri Feb 19 15:08:51 2021 +0000 @@ -0,0 +1,120 @@ +<html> + +<head> +<meta http-equiv=Content-Type content="text/html; charset=windows-1252"> +<meta name=Generator content="Microsoft Word 14 (filtered)"> +<style> +<!-- + /* Font Definitions */ + @font-face + {font-family:Calibri; + panose-1:2 15 5 2 2 2 4 3 2 4;} + /* Style Definitions */ + p.MsoNormal, li.MsoNormal, div.MsoNormal + {margin-top:0in; + margin-right:0in; + margin-bottom:10.0pt; + margin-left:0in; + line-height:115%; + font-size:11.0pt; + font-family:"Calibri","sans-serif";} +a:link, span.MsoHyperlink + {color:blue; + text-decoration:underline;} +a:visited, span.MsoHyperlinkFollowed + {color:purple; + text-decoration:underline;} +p.msochpdefault, li.msochpdefault, div.msochpdefault + {mso-style-name:msochpdefault; + margin-right:0in; + margin-left:0in; + font-size:12.0pt; + font-family:"Calibri","sans-serif";} +p.msopapdefault, li.msopapdefault, div.msopapdefault + {mso-style-name:msopapdefault; + margin-right:0in; + margin-bottom:10.0pt; + margin-left:0in; + line-height:115%; + font-size:12.0pt; + font-family:"Times New Roman","serif";} +span.apple-converted-space + {mso-style-name:apple-converted-space;} +.MsoChpDefault + {font-size:10.0pt; + font-family:"Calibri","sans-serif";} +.MsoPapDefault + {margin-bottom:10.0pt; + line-height:115%;} +@page WordSection1 + {size:8.5in 11.0in; + margin:1.0in 1.0in 1.0in 1.0in;} +div.WordSection1 + {page:WordSection1;} +--> +</style> + +</head> + +<body lang=EN-US link=blue vlink=purple> + +<div class=WordSection1> + +<p class=MsoNormalCxSpFirst style='text-align:justify'><span style='font-size: +12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>These graphs and +tables give insight into the targeting and patterns of SHM. This can give +insight into the DNA repair pathways used to solve the U:G mismatches +introduced by AID. More information on the values found in healthy individuals +of different ages can be found in IJspeert and van Schouwenburg et al, PMID: +27799928.</span></p> + +<p class=MsoNormalCxSpMiddle style='text-align:justify'><b><span +style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>Graphs +</span></b></p> + +<p class=MsoNormalCxSpMiddle style='text-align:justify'><a name="OLE_LINK93"></a><a +name="OLE_LINK92"></a><a name="OLE_LINK91"><u><span style='font-size:12.0pt; +line-height:115%;font-family:"Times New Roman","serif"'>Heatmap transition +information</span></u></a></p> + +<p class=MsoNormalCxSpMiddle style='text-align:justify'><a name="OLE_LINK98"></a><a +name="OLE_LINK97"><span style='font-size:12.0pt;line-height:115%;font-family: +"Times New Roman","serif"'>Heatmaps visualizing for each subclass the frequency +of all possible substitutions. On the x-axes the original base is shown, while +the y-axes shows the new base. The darker the shade of blue, the more frequent +this type of substitution is occurring. </span></a></p> + +<p class=MsoNormalCxSpMiddle style='text-align:justify'><u><span +style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>Bargraph +transition information</span></u></p> + +<p class=MsoNormalCxSpMiddle style='text-align:justify'><span style='font-size: +12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>Bar graph +visualizing for each original base the distribution of substitutions into the other +bases. A graph is included for each (sub)class. </span></p> + +<p class=MsoNormalCxSpMiddle style='text-align:justify'><b><span +style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>Tables</span></b></p> + +<p class=MsoNormalCxSpMiddle style='text-align:justify'><span style='font-size: +12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>Transition +tables are shown for each (sub)class. All the original bases are listed +horizontally, while the new bases are listed vertically. </span></p> + +<p class=MsoNormal><span lang=NL style='font-size:12.0pt;line-height:115%; +font-family:"Times New Roman","serif"'>Hanna IJspeert, Pauline A. van +Schouwenburg, David van Zessen, Ingrid Pico-Knijnenburg, Gertjan J. Driessen, +Andrew P. Stubbs, and Mirjam van der Burg (2016). </span><span +style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>Evaluation +of the Antigen-Experienced B-Cell Receptor Repertoire in Healthy Children and +Adults. In <i>Frontiers in Immunolog, 7, pp. e410-410. </i>[<a +href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5066086/"><span +style='color:windowtext'>doi:10.3389/fimmu.2016.00410</span></a>][<a +href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5066086/"><span +style='color:windowtext'>Link</span></a>]</span></p> + +</div> + +</body> + +</html>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/shm_csr/summary_to_fasta.py Fri Feb 19 15:08:51 2021 +0000 @@ -0,0 +1,42 @@ +import argparse + +parser = argparse.ArgumentParser() +parser.add_argument("--input", help="The 1_Summary file of an IMGT zip file") +parser.add_argument("--fasta", help="The output fasta file") + +args = parser.parse_args() + +infile = args.input +fasta = args.fasta + +with open(infile, 'r') as i, open(fasta, 'w') as o: + first = True + id_col = 0 + seq_col = 0 + no_results = 0 + no_seqs = 0 + passed = 0 + for line in i: + splt = line.split("\t") + if first: + id_col = splt.index("Sequence ID") + seq_col = splt.index("Sequence") + first = False + continue + if len(splt) < 5: + no_results += 1 + continue + + ID = splt[id_col] + seq = splt[seq_col] + + if not len(seq) > 0: + no_seqs += 1 + continue + + o.write(">" + ID + "\n" + seq + "\n") + passed += 1 + + print "No results:", no_results + print "No sequences:", no_seqs + print "Written to fasta file:", passed
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/shm_csr/wrapper.sh Fri Feb 19 15:08:51 2021 +0000 @@ -0,0 +1,913 @@ +#!/bin/bash +#set -e +dir="$(cd "$(dirname "$0")" && pwd)" +input=$1 +method=$2 +log=$3 #becomes the main html page at the end +outdir=$4 +output="$outdir/index.html" #copied to $log location at the end +title="$5" +include_fr1=$6 +functionality=$7 +unique=$8 +naive_output=$9 +naive_output_ca=${10} +naive_output_cg=${11} +naive_output_cm=${12} +naive_output_ce=${13} +naive_output_all=${14} +filter_unique=${15} +filter_unique_count=${16} +class_filter=${17} +empty_region_filter=${18} +fast=${19} + +mkdir $outdir + +tar -xzf $dir/style.tar.gz -C $outdir + +echo "---------------- read parameters ----------------" +echo "---------------- read parameters ----------------<br />" > $log + +echo "unpacking IMGT file" + +type="`file $input`" +if [[ "$type" == *"Zip archive"* ]] ; then + echo "Zip archive" + echo "unzip $input -d $PWD/files/" + unzip $input -d $PWD/files/ +elif [[ "$type" == *"XZ compressed data"* ]] ; then + echo "ZX archive" + echo "tar -xJf $input -C $PWD/files/" + mkdir -p "$PWD/files/$title" + tar -xJf $input -C "$PWD/files/$title" +else + echo "Unrecognized format $type" + echo "Unrecognized format $type" > $log + exit 1 +fi + +cat "`find $PWD/files/ -name "1_*"`" > $PWD/summary.txt +cat "`find $PWD/files/ -name "2_*"`" > $PWD/gapped_nt.txt +cat "`find $PWD/files/ -name "3_*"`" > $PWD/sequences.txt +cat "`find $PWD/files/ -name "4_*"`" > $PWD/gapped_aa.txt +cat "`find $PWD/files/ -name "5_*"`" > $PWD/aa.txt +cat "`find $PWD/files/ -name "6_*"`" > $PWD/junction.txt +cat "`find $PWD/files/ -name "7_*"`" > $PWD/mutationanalysis.txt +cat "`find $PWD/files/ -name "8_*"`" > $PWD/mutationstats.txt +cat "`find $PWD/files/ -name "9_*"`" > $PWD/aa_change_stats.txt +cat "`find $PWD/files/ -name "10_*"`" > $PWD/hotspots.txt + +echo "---------------- unique id check ----------------" + +Rscript $dir/check_unique_id.r $PWD/summary.txt $PWD/gapped_nt.txt $PWD/sequences.txt $PWD/gapped_aa.txt $PWD/aa.txt $PWD/junction.txt $PWD/mutationanalysis.txt $PWD/mutationstats.txt $PWD/aa_change_stats.txt $PWD/hotspots.txt + +if [[ ${#BLASTN_DIR} -ge 5 ]] ; then + echo "On server, using BLASTN_DIR env: ${BLASTN_DIR}" +else + BLASTN_DIR="/home/galaxy/Downloads/ncbi-blast-2.4.0+/bin" + echo "Dev Galaxy set BLASTN_DIR to: ${BLASTN_DIR}" +fi + +echo "---------------- class identification ----------------" +echo "---------------- class identification ----------------<br />" >> $log + +python $dir/gene_identification.py --input $PWD/summary.txt --output $outdir/identified_genes.txt + +echo "---------------- merge_and_filter.r ----------------" +echo "---------------- merge_and_filter.r ----------------<br />" >> $log + +Rscript $dir/merge_and_filter.r $PWD/summary.txt $PWD/sequences.txt $PWD/mutationanalysis.txt $PWD/mutationstats.txt $PWD/hotspots.txt "$PWD/gapped_aa.txt" $outdir/identified_genes.txt $outdir/merged.txt $outdir/before_unique_filter.txt $outdir/unmatched.txt $method $functionality $unique ${filter_unique} ${filter_unique_count} ${class_filter} ${empty_region_filter} 2>&1 + +if [[ "${naive_output}" == "yes" ]] || [[ "$fast" == "no" ]] ; then + + echo "---------------- creating new IMGT zips ----------------" + echo "---------------- creating new IMGT zips ----------------<br />" >> $log + + mkdir $outdir/new_IMGT + + cp $PWD/summary.txt "$outdir/new_IMGT/1_Summary.txt" + cp $PWD/gapped_nt.txt "$outdir/new_IMGT/2_IMGT-gapped-nt-sequences.txt" + cp $PWD/sequences.txt "$outdir/new_IMGT/3_Nt-sequences.txt" + cp $PWD/gapped_aa.txt "$outdir/new_IMGT/4_IMGT-gapped-AA-sequences.txt" + cp $PWD/aa.txt "$outdir/new_IMGT/5_AA-sequences.txt" + cp $PWD/junction.txt "$outdir/new_IMGT/6_Junction.txt" + cp $PWD/mutationanalysis.txt "$outdir/new_IMGT/7_V-REGION-mutation-and-AA-change-table.txt" + cp $PWD/mutationstats.txt "$outdir/new_IMGT/8_V-REGION-nt-mutation-statistics.txt" + cp $PWD/aa_change_stats.txt "$outdir/new_IMGT/9_V-REGION-AA-change-statistics.txt" + cp $PWD/hotspots.txt "$outdir/new_IMGT/10_V-REGION-mutation-hotspots.txt" + + mkdir $outdir/new_IMGT_IGA + cp $outdir/new_IMGT/* $outdir/new_IMGT_IGA + + mkdir $outdir/new_IMGT_IGA1 + cp $outdir/new_IMGT/* $outdir/new_IMGT_IGA1 + + mkdir $outdir/new_IMGT_IGA2 + cp $outdir/new_IMGT/* $outdir/new_IMGT_IGA2 + + mkdir $outdir/new_IMGT_IGG + cp $outdir/new_IMGT/* $outdir/new_IMGT_IGG + + mkdir $outdir/new_IMGT_IGG1 + cp $outdir/new_IMGT/* $outdir/new_IMGT_IGG1 + + mkdir $outdir/new_IMGT_IGG2 + cp $outdir/new_IMGT/* $outdir/new_IMGT_IGG2 + + mkdir $outdir/new_IMGT_IGG3 + cp $outdir/new_IMGT/* $outdir/new_IMGT_IGG3 + + mkdir $outdir/new_IMGT_IGG4 + cp $outdir/new_IMGT/* $outdir/new_IMGT_IGG4 + + mkdir $outdir/new_IMGT_IGM + cp $outdir/new_IMGT/* $outdir/new_IMGT_IGM + + mkdir $outdir/new_IMGT_IGE + cp $outdir/new_IMGT/* $outdir/new_IMGT_IGE + + Rscript $dir/new_imgt.r $outdir/new_IMGT/ $outdir/merged.txt "-" 2>&1 + + Rscript $dir/new_imgt.r $outdir/new_IMGT_IGA/ $outdir/merged.txt "IGA" 2>&1 + Rscript $dir/new_imgt.r $outdir/new_IMGT_IGA1/ $outdir/merged.txt "IGA1" 2>&1 + Rscript $dir/new_imgt.r $outdir/new_IMGT_IGA2/ $outdir/merged.txt "IGA2" 2>&1 + + Rscript $dir/new_imgt.r $outdir/new_IMGT_IGG/ $outdir/merged.txt "IGG" 2>&1 + Rscript $dir/new_imgt.r $outdir/new_IMGT_IGG1/ $outdir/merged.txt "IGG1" 2>&1 + Rscript $dir/new_imgt.r $outdir/new_IMGT_IGG2/ $outdir/merged.txt "IGG2" 2>&1 + Rscript $dir/new_imgt.r $outdir/new_IMGT_IGG3/ $outdir/merged.txt "IGG3" 2>&1 + Rscript $dir/new_imgt.r $outdir/new_IMGT_IGG4/ $outdir/merged.txt "IGG4" 2>&1 + + Rscript $dir/new_imgt.r $outdir/new_IMGT_IGM/ $outdir/merged.txt "IGM" 2>&1 + + Rscript $dir/new_imgt.r $outdir/new_IMGT_IGE/ $outdir/merged.txt "IGE" 2>&1 + + + tmp="$PWD" + cd $outdir/new_IMGT/ #tar weirdness... + tar -cJf ../new_IMGT.txz * + + cd $outdir/new_IMGT_IGA/ + tar -cJf ../new_IMGT_IGA.txz * + + cd $outdir/new_IMGT_IGA1/ + tar -cJf ../new_IMGT_IGA1.txz * + + cd $outdir/new_IMGT_IGA2/ + tar -cJf ../new_IMGT_IGA2.txz * + + cd $outdir/new_IMGT_IGG/ + tar -cJf ../new_IMGT_IGG.txz * + + cd $outdir/new_IMGT_IGG1/ + tar -cJf ../new_IMGT_IGG1.txz * + + cd $outdir/new_IMGT_IGG2/ + tar -cJf ../new_IMGT_IGG2.txz * + + cd $outdir/new_IMGT_IGG3/ + tar -cJf ../new_IMGT_IGG3.txz * + + cd $outdir/new_IMGT_IGG4/ + tar -cJf ../new_IMGT_IGG4.txz * + + cd $outdir/new_IMGT_IGM/ + tar -cJf ../new_IMGT_IGM.txz * + + cd $outdir/new_IMGT_IGE/ + tar -cJf ../new_IMGT_IGE.txz * + + cd $tmp +fi + +echo "---------------- shm_csr.r ----------------" +echo "---------------- shm_csr.r ----------------<br />" >> $log + +classes="IGA,IGA1,IGA2,IGG,IGG1,IGG2,IGG3,IGG4,IGM,IGE,unmatched" +echo "R mutation analysis" +Rscript $dir/shm_csr.r $outdir/merged.txt $classes $outdir ${empty_region_filter} 2>&1 + +echo "---------------- plot_pdfs.r ----------------" +echo "---------------- plot_pdfs.r ----------------<br />" >> $log + +echo "Rscript $dir/shm_csr.r $outdir/pdfplots.RData $outdir 2>&1" + +Rscript $dir/plot_pdf.r "$outdir/pdfplots.RData" "$outdir" 2>&1 + +echo "---------------- shm_csr.py ----------------" +echo "---------------- shm_csr.py ----------------<br />" >> $log + +python $dir/shm_csr.py --input $outdir/merged.txt --genes $classes --empty_region_filter "${empty_region_filter}" --output $outdir/hotspot_analysis.txt + +echo "---------------- aa_histogram.r ----------------" +echo "---------------- aa_histogram.r ----------------<br />" >> $log + +Rscript $dir/aa_histogram.r $outdir/aa_id_mutations.txt $outdir/absent_aa_id.txt "IGA,IGG,IGM,IGE" $outdir/ 2>&1 +if [ -e "$outdir/aa_histogram_.png" ]; then + mv $outdir/aa_histogram_.png $outdir/aa_histogram.png + mv $outdir/aa_histogram_.pdf $outdir/aa_histogram.pdf + mv $outdir/aa_histogram_.txt $outdir/aa_histogram.txt + mv $outdir/aa_histogram_absent_.txt $outdir/aa_histogram_absent.txt + mv $outdir/aa_histogram_count_.txt $outdir/aa_histogram_count.txt + mv $outdir/aa_histogram_sum_.txt $outdir/aa_histogram_sum.txt +fi + +genes=(IGA IGA1 IGA2 IGG IGG1 IGG2 IGG3 IGG4 IGM IGE) + +funcs=(sum mean median) +funcs=(sum) + +echo "---------------- sequence_overview.r ----------------" +echo "---------------- sequence_overview.r ----------------<br />" >> $log + +mkdir $outdir/sequence_overview + +Rscript $dir/sequence_overview.r $outdir/before_unique_filter.txt $outdir/merged.txt $outdir/sequence_overview $classes $outdir/hotspot_analysis_sum.txt ${empty_region_filter} 2>&1 + +echo "<table border='1'>" > $outdir/base_overview.html + +while IFS=$'\t' read ID class seq A C G T +do + echo "<tr><td>$ID</td><td>$seq</td><td>$class</td><td>$A</td><td>$C</td><td>$G</td><td>$T</td></tr>" >> $outdir/base_overview.html +done < $outdir/sequence_overview/ntoverview.txt + +echo "<html><center><h1>$title</h1></center>" > $output +echo "<meta name='viewport' content='width=device-width, initial-scale=1'>" >> $output +echo "<script type='text/javascript' src='jquery-1.11.0.min.js'></script>" >> $output +echo "<script type='text/javascript' src='tabber.js'></script>" >> $output +echo "<script type='text/javascript' src='script.js'></script>" >> $output +echo "<link rel='stylesheet' type='text/css' href='style.css'>" >> $output +echo "<link rel='stylesheet' type='text/css' href='pure-min.css'>" >> $output + +matched_count="`cat $outdir/merged.txt | grep -v 'unmatched' | tail -n +2 | wc -l`" +unmatched_count="`cat $outdir/unmatched.txt | tail -n +2 | wc -l`" +total_count=$((matched_count + unmatched_count)) +perc_count=$((unmatched_count / total_count * 100)) +perc_count=`bc -l <<< "scale=2; ${unmatched_count} / ${total_count} * 100"` +perc_count=`bc -l <<< "scale=2; (${unmatched_count} / ${total_count} * 100 ) / 1"` + +echo "<center><h2>Total: ${total_count}</h2></center>" >> $output +echo "<center><h2>Matched: ${matched_count} Unmatched: ${unmatched_count}</h2></center>" >> $output +echo "<center><h2>Percentage unmatched: ${perc_count}</h2></center>" >> $output + +echo "---------------- main tables ----------------" +echo "---------------- main tables ----------------<br />" >> $log + +echo "<div class='tabber'>" >> $output +echo "<div class='tabbertab' title='SHM Overview' style='width: 3000px;'>" >> $output + +for func in ${funcs[@]} +do + + echo "---------------- $func table ----------------" + echo "---------------- $func table ----------------<br />" >> $log + + cat $outdir/mutations_${func}.txt $outdir/shm_overview_tandem_row.txt $outdir/hotspot_analysis_${func}.txt > $outdir/data_${func}.txt + + echo "---------------- pattern_plots.r ----------------" + echo "---------------- pattern_plots.r ----------------<br />" >> $log + + Rscript $dir/pattern_plots.r $outdir/data_${func}.txt $outdir/aid_motives $outdir/relative_mutations $outdir/absolute_mutations $outdir/shm_overview.txt 2>&1 + + echo "<table class='pure-table pure-table-striped'>" >> $output + echo "<thead><tr><th>info</th>" >> $output + + if [ "${class_filter}" != "101_101" ] ; then + + for gene in ${genes[@]} + do + tmp=`cat $outdir/${gene}_${func}_n.txt` + echo "<th><a href='matched_${gene}_${func}.txt'>${gene} (N = $tmp)</a></th>" >> $output + done + + tmp=`cat $outdir/all_${func}_n.txt` + echo "<th><a href='matched_all_${func}.txt'>all (N = $tmp)</a></th>" >> $output + tmp=`cat $outdir/unmatched_${func}_n.txt` + echo "<th><a href='unmatched.txt'>unmatched (N = ${unmatched_count})</a></th><tr></thead>" >> $output + + while IFS=, read name cax cay caz ca1x ca1y ca1z ca2x ca2y ca2z cgx cgy cgz cg1x cg1y cg1z cg2x cg2y cg2z cg3x cg3y cg3z cg4x cg4y cg4z cmx cmy cmz cex cey cez unx uny unz allx ally allz + do + if [ "$name" == "FR R/S (ratio)" ] || [ "$name" == "CDR R/S (ratio)" ] || [ "$name" == "Tandems/Expected (ratio)" ] ; then #meh + echo "<tr><td>$name</td><td>${cax}/${cay} (${caz})</td><td>${ca1x}/${ca1y} (${ca1z})</td><td>${ca2x}/${ca2y} (${ca2z})</td><td>${cgx}/${cgy} (${cgz})</td><td>${cg1x}/${cg1y} (${cg1z})</td><td>${cg2x}/${cg2y} (${cg2z})</td><td>${cg3x}/${cg3y} (${cg3z})</td><td>${cg4x}/${cg4y} (${cg4z})</td><td>${cmx}/${cmy} (${cmz})</td><td>${cex}/${cey} (${cez})</td><td>${allx}/${ally} (${allz})</td><td>${unx}/${uny} (${unz})</td></tr>" >> $output + elif [ "$name" == "Median of Number of Mutations (%)" ] ; then + echo "<tr><td>$name</td><td>${caz}%</td><td>${ca1z}%</td><td>${ca2z}%</td><td>${cgz}%</td><td>${cg1z}%</td><td>${cg2z}%</td><td>${cg3z}%</td><td>${cg4z}%</td><td>${cmz}%</td><td>${cez}%</td><td>${allz}%</td><td>${unz}%</td></tr>" >> $output + else + echo "<tr><td>$name</td><td>${cax}/${cay} (${caz}%)</td><td>${ca1x}/${ca1y} (${ca1z}%)</td><td>${ca2x}/${ca2y} (${ca2z}%)</td><td>${cgx}/${cgy} (${cgz}%)</td><td>${cg1x}/${cg1y} (${cg1z}%)</td><td>${cg2x}/${cg2y} (${cg2z}%)</td><td>${cg3x}/${cg3y} (${cg3z}%)</td><td>${cg4x}/${cg4y} (${cg4z}%)</td><td>${cmx}/${cmy} (${cmz}%)</td><td>${cex}/${cey} (${cez}%)</td><td>${allx}/${ally} (${allz}%)</td><td>${unx}/${uny} (${unz}%)</td></tr>" >> $output + fi + done < $outdir/data_${func}.txt + + else + tmp=`cat $outdir/all_${func}_n.txt` + echo "<th><a href='matched_all_${func}.txt'>all (N = $tmp)</a></th>" >> $output + + while IFS=, read name cax cay caz ca1x ca1y ca1z ca2x ca2y ca2z cgx cgy cgz cg1x cg1y cg1z cg2x cg2y cg2z cg3x cg3y cg3z cg4x cg4y cg4z cmx cmy cmz cex cey cez unx uny unz allx ally allz + do + if [ "$name" == "FR R/S (ratio)" ] || [ "$name" == "CDR R/S (ratio)" ] ; then #meh + echo "<tr><td>$name</td><td>${allx}/${ally}</td></tr>" >> $output + elif [ "$name" == "Median of Number of Mutations (%)" ] ; then + echo "<tr><td>$name</td><td>${allz}%</td></tr>" >> $output + else + echo "<tr><td>$name</td><td>${allx}/${ally} (${allz}%)</td></tr>" >> $output + fi + done < $outdir/data_${func}.txt + + fi + echo "</table>" >> $output + #echo "<a href='data_${func}.txt'>Download data</a>" >> $output +done + +echo "<a href='aid_motives.pdf'><img src='aid_motives.png' /></a><br />" >> $output +echo "<a href='relative_mutations.pdf'><img src='relative_mutations.png' /></a><br />" >> $output +echo "<a href='absolute_mutations.pdf'><img src='absolute_mutations.png' /></a><br />" >> $output +echo "<br />" >> $output +cat $dir/shm_overview.htm >> $output +echo "</div>" >> $output #SHM overview tab end + +echo "---------------- images ----------------" +echo "---------------- images ----------------<br />" >> $log + +echo "<div class='tabbertab' title='SHM Frequency' style='width: 3000px;'></a>" >> $output + +if [ -a $outdir/scatter.png ] +then + echo "<a href='scatter.pdf'><img src='scatter.png'/><br />" >> $output +fi +if [ -a $outdir/frequency_ranges.png ] +then + echo "<a href='frequency_ranges.pdf'><img src='frequency_ranges.png'/></a><br />" >> $output +fi + +echo "<br />" >> $output +cat $dir/shm_frequency.htm >> $output + +echo "</div>" >> $output #SHM frequency tab end + +echo "<div class='tabbertab' title='Transition tables' style='width: 3000px;'>" >> $output + +echo "<table border='0'>" >> $output + +for gene in ${genes[@]} +do + echo "<tr>" >> $output + echo "<td><h1>${gene}</h1></td>" >> $output + + if [ -e $outdir/transitions_heatmap_${gene}.png ] + then + echo "<td><a href='transitions_heatmap_${gene}.pdf'><img src='transitions_heatmap_${gene}.png' /></a></td>" >> $output + else + echo "<td></td>" >> $output + fi + + if [ -e $outdir/transitions_stacked_${gene}.png ] + then + echo "<td><a href='transitions_stacked_${gene}.pdf'><img src='transitions_stacked_${gene}.png' /></a></td>" >> $output + else + echo "<td></td>" >> $output + fi + + echo "<td><table style='border-left-width: 1;' class='pure-table transition-table pure-table-bordered'>" >> $output + echo "<tr><td></td><td colspan="5"><center>To</center></td></tr>" >> $output + first="true" + while IFS=, read from a c g t + do + if [ "$first" == "true" ] ; then + echo "<tr><td rowspan='5'>From</td><td>$from</td><td>$a</td><td>$c</td><td>$g</td><td>$t</td></tr>" >> $output + first="false" + else + echo "<tr><td>$from</td><td>$a</td><td>$c</td><td>$g</td><td>$t</td></tr>" >> $output + fi + done < $outdir/transitions_${gene}_sum.txt + echo "</table></td>" >> $output + + echo "</tr>" >> $output +done + +echo "<tr>" >> $output +echo "<td><h1>All</h1></td>" >> $output +echo "<td><a href='transitions_heatmap_all.pdf'><img src='transitions_heatmap_all.png' /></a></td>" >> $output +echo "<td><a href='transitions_stacked_all.pdf'><img src='transitions_stacked_all.png' /></a></td>" >> $output +echo "<td><table style='border-left-width: 1;' class='pure-table transition-table pure-table-bordered'>" >> $output +echo "<tr><td></td><td colspan="5"><center>To</center></td></tr>" >> $output +first="true" +while IFS=, read from a c g t + do + if [ "$first" == "true" ] ; then + echo "<tr><td rowspan='5'>From</td><td>$from</td><td>$a</td><td>$c</td><td>$g</td><td>$t</td></tr>" >> $output + first="false" + else + echo "<tr><td>$from</td><td>$a</td><td>$c</td><td>$g</td><td>$t</td></tr>" >> $output + fi +done < $outdir/transitions_all_sum.txt +echo "</table></td>" >> $output + +echo "</tr>" >> $output + +echo "</table>" >> $output + +echo "<br />" >> $output +cat $dir/shm_transition.htm >> $output + +echo "</div>" >> $output #transition tables tab end + +echo "<div class='tabbertab' title='Antigen Selection'>" >> $output + +if [ -e $outdir/aa_histogram.png ] +then + echo "<a href='aa_histogram.pdf'><img src='aa_histogram.png'/></a><br />" >> $output +fi + +if [ -e $outdir/aa_histogram_IGA.png ] +then + echo "<a href='aa_histogram_IGA.pdf'><img src='aa_histogram_IGA.png'/></a><br />" >> $output +fi + +if [ -e $outdir/aa_histogram_IGG.png ] +then + echo "<a href='aa_histogram_IGG.pdf'><img src='aa_histogram_IGG.png'/></a><br />" >> $output +fi + +if [ -e $outdir/aa_histogram_IGM.png ] +then + echo "<a href='aa_histogram_IGM.pdf'><img src='aa_histogram_IGM.png'/></a><br />" >> $output +fi + +if [ -e $outdir/aa_histogram_IGE.png ] +then + echo "<a href='aa_histogram_IGE.pdf'><img src='aa_histogram_IGE.png'/></a><br />" >> $output +fi + + + +if [[ "$fast" == "no" ]] ; then + + + + echo "---------------- baseline ----------------" + echo "---------------- baseline ----------------<br />" >> $log + tmp="$PWD" + + mkdir $outdir/baseline + + echo "<center><h1>BASELINe</h1>" >> $output + header_substring="Based on CDR1, FR2, CDR2, FR3 (27:27:38:55:65:104:-)" + + baseline_boundaries="27:27:38:55:65:104:-" + + if [[ "${empty_region_filter}" == "leader" ]] ; then + baseline_boundaries="1:26:38:55:65:104:-" + header_substring="Based on FR1, CDR1, FR2, CDR2, FR3 (1:26:38:55:65:104,-)" + fi + + echo "<p>${header_substring}</p></center>" >> $output + + mkdir $outdir/baseline/IGA_IGG_IGM + if [[ $(wc -l < $outdir/new_IMGT/1_Summary.txt) -gt "1" ]]; then + cd $outdir/baseline/IGA_IGG_IGM + bash $dir/baseline/wrapper.sh 1 1 1 1 0 0 "${baseline_boundaries}" $outdir/new_IMGT.txz "IGA_IGG_IGM_IGE" "$dir/baseline/IMGTVHreferencedataset20161215.fa" "$outdir/baseline.pdf" "Sequence.ID" "$outdir/baseline.txt" + else + echo "No sequences" > "$outdir/baseline.txt" + fi + + mkdir $outdir/baseline/IGA + if [[ $(wc -l < $outdir/new_IMGT_IGA/1_Summary.txt) -gt "1" ]]; then + cd $outdir/baseline/IGA + bash $dir/baseline/wrapper.sh 1 1 1 1 0 0 "${baseline_boundaries}" $outdir/new_IMGT_IGA.txz "IGA" "$dir/baseline/IMGTVHreferencedataset20161215.fa" "$outdir/baseline_IGA.pdf" "Sequence.ID" "$outdir/baseline_IGA.txt" + else + echo "No IGA sequences" > "$outdir/baseline_IGA.txt" + fi + + mkdir $outdir/baseline/IGG + if [[ $(wc -l < $outdir/new_IMGT_IGG/1_Summary.txt) -gt "1" ]]; then + cd $outdir/baseline/IGG + bash $dir/baseline/wrapper.sh 1 1 1 1 0 0 "${baseline_boundaries}" $outdir/new_IMGT_IGG.txz "IGG" "$dir/baseline/IMGTVHreferencedataset20161215.fa" "$outdir/baseline_IGG.pdf" "Sequence.ID" "$outdir/baseline_IGG.txt" + else + echo "No IGG sequences" > "$outdir/baseline_IGG.txt" + fi + + mkdir $outdir/baseline/IGM + if [[ $(wc -l < $outdir/new_IMGT_IGM/1_Summary.txt) -gt "1" ]]; then + cd $outdir/baseline/IGM + bash $dir/baseline/wrapper.sh 1 1 1 1 0 0 "${baseline_boundaries}" $outdir/new_IMGT_IGM.txz "IGM" "$dir/baseline/IMGTVHreferencedataset20161215.fa" "$outdir/baseline_IGM.pdf" "Sequence.ID" "$outdir/baseline_IGM.txt" + else + echo "No IGM sequences" > "$outdir/baseline_IGM.txt" + fi + + mkdir $outdir/baseline/IGE + if [[ $(wc -l < $outdir/new_IMGT_IGE/1_Summary.txt) -gt "1" ]]; then + cd $outdir/baseline/IGE + bash $dir/baseline/wrapper.sh 1 1 1 1 0 0 "${baseline_boundaries}" $outdir/new_IMGT_IGE.txz "IGE" "$dir/baseline/IMGTVHreferencedataset20161215.fa" "$outdir/baseline_IGE.pdf" "Sequence.ID" "$outdir/baseline_IGE.txt" + else + echo "No IGE sequences" > "$outdir/baseline_IGE.txt" + fi + + cd $tmp + + echo "Cleaning up *.RData files" + find $outdir/baseline -name "*.RData" -type f -delete + + if [ -e $outdir/baseline.pdf ] + then + echo "<embed src='baseline.pdf' width='700px' height='1000px'>" >> $output + fi + + if [ -e $outdir/baseline_IGA.pdf ] + then + echo "<embed src='baseline_IGA.pdf' width='700px' height='1000px'>" >> $output + fi + + if [ -e $outdir/baseline_IGG.pdf ] + then + echo "<embed src='baseline_IGG.pdf' width='700px' height='1000px'>" >> $output + fi + + if [ -e $outdir/baseline_IGM.pdf ] + then + echo "<embed src='baseline_IGM.pdf' width='700px' height='1000px'>" >> $output + fi + + if [ -e $outdir/baseline_IGE.pdf ] + then + echo "<embed src='baseline_IGE.pdf' width='700px' height='1000px'>" >> $output + fi +fi + +echo "<br />" >> $output +cat $dir/shm_selection.htm >> $output + +echo "</div>" >> $output #antigen selection tab end + +echo "<div class='tabbertab' title='CSR'>" >> $output #CSR tab + +if [ -e $outdir/IGA.png ] +then + echo "<a href='IGA.pdf'><img src='IGA.png'/></a><br />" >> $output +fi +if [ -e $outdir/IGG.png ] +then + echo "<a href='IGG.pdf'><img src='IGG.png'/></a><br />" >> $output +fi + +echo "<br />" >> $output +cat $dir/shm_csr.htm >> $output + +echo "</div>" >> $output #CSR tab end + +if [[ "$fast" == "no" ]] ; then + + echo "---------------- change-o MakeDB ----------------" + + mkdir $outdir/change_o + + tmp="$PWD" + + cd $outdir/change_o + + bash $dir/change_o/makedb.sh $outdir/new_IMGT.txz false false false $outdir/change_o/change-o-db.txt + bash $dir/change_o/define_clones.sh bygroup $outdir/change_o/change-o-db.txt gene first ham none min complete 3.0 $outdir/change_o/change-o-db-defined_clones.txt $outdir/change_o/change-o-defined_clones-summary.txt + Rscript $dir/change_o/select_first_in_clone.r $outdir/change_o/change-o-db-defined_clones.txt $outdir/change_o/change-o-db-defined_first_clones.txt 2>&1 + + mkdir $outdir/new_IMGT_changeo + cp $outdir/new_IMGT/* $outdir/new_IMGT_changeo + + Rscript $dir/new_imgt.r $outdir/new_IMGT_changeo $outdir/change_o/change-o-db-defined_first_clones.txt "-" 2>&1 + + cd $outdir/new_IMGT_changeo + tar -cJf ../new_IMGT_first_seq_of_clone.txz * + cd $outdir/change_o + + rm -rf $outdir/new_IMGT_changeo + + Rscript $dir/merge.r $outdir/change_o/change-o-db-defined_clones.txt $outdir/merged.txt "all" "Sequence.ID,best_match" "SEQUENCE_ID" "Sequence.ID" $outdir/change_o/change-o-db-defined_clones.txt 2>&1 + echo "Rscript $dir/merge.r $outdir/change_o/change-o-db-defined_clones.txt $outdir/$outdir/merged.txt 'all' 'Sequence.ID,best_match' 'Sequence.ID' 'Sequence.ID' '\t' $outdir/change_o/change-o-db-defined_clones.txt 2>&1" + + if [[ $(wc -l < $outdir/new_IMGT_IGA/1_Summary.txt) -gt "1" ]]; then + bash $dir/change_o/makedb.sh $outdir/new_IMGT_IGA.txz false false false $outdir/change_o/change-o-db-IGA.txt + bash $dir/change_o/define_clones.sh bygroup $outdir/change_o/change-o-db-IGA.txt gene first ham none min complete 3.0 $outdir/change_o/change-o-db-defined_clones-IGA.txt $outdir/change_o/change-o-defined_clones-summary-IGA.txt + Rscript $dir/change_o/select_first_in_clone.r $outdir/change_o/change-o-db-defined_clones-IGA.txt $outdir/change_o/change-o-db-defined_first_clones-IGA.txt 2>&1 + + mkdir $outdir/new_IMGT_IGA_changeo + cp $outdir/new_IMGT/* $outdir/new_IMGT_IGA_changeo + + Rscript $dir/new_imgt.r $outdir/new_IMGT_IGA_changeo $outdir/change_o/change-o-db-defined_first_clones-IGA.txt "-" 2>&1 + + cd $outdir/new_IMGT_IGA_changeo + tar -cJf ../new_IMGT_IGA_first_seq_of_clone.txz * + + rm -rf $outdir/new_IMGT_IGA_changeo + + cd $outdir/change_o + else + echo "No IGA sequences" > "$outdir/change_o/change-o-db-defined_clones-IGA.txt" + echo "No IGA sequences" > "$outdir/change_o/change-o-defined_clones-summary-IGA.txt" + fi + + if [[ $(wc -l < $outdir/new_IMGT_IGG/1_Summary.txt) -gt "1" ]]; then + bash $dir/change_o/makedb.sh $outdir/new_IMGT_IGG.txz false false false $outdir/change_o/change-o-db-IGG.txt + bash $dir/change_o/define_clones.sh bygroup $outdir/change_o/change-o-db-IGG.txt gene first ham none min complete 3.0 $outdir/change_o/change-o-db-defined_clones-IGG.txt $outdir/change_o/change-o-defined_clones-summary-IGG.txt + Rscript $dir/change_o/select_first_in_clone.r $outdir/change_o/change-o-db-defined_clones-IGG.txt $outdir/change_o/change-o-db-defined_first_clones-IGG.txt 2>&1 + + mkdir $outdir/new_IMGT_IGG_changeo + cp $outdir/new_IMGT/* $outdir/new_IMGT_IGG_changeo + + Rscript $dir/new_imgt.r $outdir/new_IMGT_IGG_changeo $outdir/change_o/change-o-db-defined_first_clones-IGG.txt "-" 2>&1 + + cd $outdir/new_IMGT_IGG_changeo + tar -cJf ../new_IMGT_IGG_first_seq_of_clone.txz * + rm -rf $outdir/new_IMGT_IGG_changeo + + cd $outdir/change_o + else + echo "No IGG sequences" > "$outdir/change_o/change-o-db-defined_clones-IGG.txt" + echo "No IGG sequences" > "$outdir/change_o/change-o-defined_clones-summary-IGG.txt" + fi + + if [[ $(wc -l < $outdir/new_IMGT_IGM/1_Summary.txt) -gt "1" ]]; then + bash $dir/change_o/makedb.sh $outdir/new_IMGT_IGM.txz false false false $outdir/change_o/change-o-db-IGM.txt + bash $dir/change_o/define_clones.sh bygroup $outdir/change_o/change-o-db-IGM.txt gene first ham none min complete 3.0 $outdir/change_o/change-o-db-defined_clones-IGM.txt $outdir/change_o/change-o-defined_clones-summary-IGM.txt + Rscript $dir/change_o/select_first_in_clone.r $outdir/change_o/change-o-db-defined_clones-IGM.txt $outdir/change_o/change-o-db-defined_first_clones-IGM.txt 2>&1 + + mkdir $outdir/new_IMGT_IGM_changeo + cp $outdir/new_IMGT/* $outdir/new_IMGT_IGM_changeo + + Rscript $dir/new_imgt.r $outdir/new_IMGT_IGM_changeo $outdir/change_o/change-o-db-defined_first_clones-IGM.txt "-" 2>&1 + + cd $outdir/new_IMGT_IGM_changeo + tar -cJf ../new_IMGT_IGM_first_seq_of_clone.txz * + + rm -rf $outdir/new_IMGT_IGM_changeo + + cd $outdir/change_o + else + echo "No IGM sequences" > "$outdir/change_o/change-o-db-defined_clones-IGM.txt" + echo "No IGM sequences" > "$outdir/change_o/change-o-defined_clones-summary-IGM.txt" + fi + + if [[ $(wc -l < $outdir/new_IMGT_IGE/1_Summary.txt) -gt "1" ]]; then + bash $dir/change_o/makedb.sh $outdir/new_IMGT_IGE.txz false false false $outdir/change_o/change-o-db-IGE.txt + bash $dir/change_o/define_clones.sh bygroup $outdir/change_o/change-o-db-IGE.txt gene first ham none min complete 3.0 $outdir/change_o/change-o-db-defined_clones-IGE.txt $outdir/change_o/change-o-defined_clones-summary-IGE.txt + Rscript $dir/change_o/select_first_in_clone.r $outdir/change_o/change-o-db-defined_clones-IGE.txt $outdir/change_o/change-o-db-defined_first_clones-IGE.txt 2>&1 + + mkdir $outdir/new_IMGT_IGE_changeo + cp $outdir/new_IMGT/* $outdir/new_IMGT_IGE_changeo + + Rscript $dir/new_imgt.r $outdir/new_IMGT_IGE_changeo $outdir/change_o/change-o-db-defined_first_clones-IGE.txt "-" 2>&1 + + cd $outdir/new_IMGT_IGE_changeo + tar -cJf ../new_IMGT_IGE_first_seq_of_clone.txz * + + rm -rf $outdir/new_IMGT_IGE_changeo + + cd $outdir/change_o + else + echo "No IGE sequences" > "$outdir/change_o/change-o-db-defined_clones-IGE.txt" + echo "No IGE sequences" > "$outdir/change_o/change-o-defined_clones-summary-IGE.txt" + fi + + cd "$tmp" + + rm -rf $outdir/new_IMGT + rm -rf $outdir/new_IMGT_IGA/ + rm -rf $outdir/new_IMGT_IGA1/ + rm -rf $outdir/new_IMGT_IGA2/ + rm -rf $outdir/new_IMGT_IGG/ + rm -rf $outdir/new_IMGT_IGG1/ + rm -rf $outdir/new_IMGT_IGG2/ + rm -rf $outdir/new_IMGT_IGG3/ + rm -rf $outdir/new_IMGT_IGG4/ + rm -rf $outdir/new_IMGT_IGM/ + rm -rf $outdir/new_IMGT_IGE/ + + echo "<div class='tabbertab' title='Clonal Relation' style='width: 7000px;'>" >> $output #clonality tab + + function clonality_table { + local infile=$1 + local outfile=$2 + + echo "<table class='pure-table pure-table-striped'>" >> $outfile + echo "<thead><tr><th>Clone size</th><th>Nr of clones</th><th>Nr of sequences</th></tr></thead>" >> $outfile + + first='true' + + while read size clones seqs + do + if [[ "$first" == "true" ]]; then + first="false" + continue + fi + echo "<tr><td>$size</td><td>$clones</td><td>$seqs</td></tr>" >> $outfile + done < $infile + + echo "</table>" >> $outfile + } + echo "<div class='tabber'>" >> $output + + echo "<div class='tabbertab' title='All'>" >> $output + clonality_table $outdir/change_o/change-o-defined_clones-summary.txt $output + echo "</div>" >> $output + + echo "<div class='tabbertab' title='IGA'>" >> $output + clonality_table $outdir/change_o/change-o-defined_clones-summary-IGA.txt $output + echo "</div>" >> $output + + echo "<div class='tabbertab' title='IGG'>" >> $output + clonality_table $outdir/change_o/change-o-defined_clones-summary-IGG.txt $output + echo "</div>" >> $output + + echo "<div class='tabbertab' title='IGM'>" >> $output + clonality_table $outdir/change_o/change-o-defined_clones-summary-IGM.txt $output + echo "</div>" >> $output + + echo "<div class='tabbertab' title='IGE'>" >> $output + clonality_table $outdir/change_o/change-o-defined_clones-summary-IGM.txt $output + echo "</div>" >> $output + + echo "<div class='tabbertab' title='Overlap' style='width: 7000px;'>" >> $output + cat "$outdir/sequence_overview/index.html" | sed -e 's:</td>:</td>\n:g' | sed "s:href='\(.*\).html:href='sequence_overview/\1.html:g" >> $output # rewrite href to 'sequence_overview/..." + echo "</div>" >> $output + + echo "</div>" >> $output #clonality tabber end + + echo "<br />" >> $output + cat $dir/shm_clonality.htm >> $output + + echo "</div>" >> $output #clonality tab end + +fi + +echo "<div class='tabbertab' title='Downloads'>" >> $output + +echo "<table class='pure-table pure-table-striped'>" >> $output +echo "<thead><tr><th>info</th><th>link</th></tr></thead>" >> $output +echo "<tr><td>The complete dataset</td><td><a href='merged.txt' download='merged.txt' >Download</a></td></tr>" >> $output +echo "<tr><td>The filtered dataset</td><td><a href='filtered.txt' download='filtered.txt' >Download</a></td></tr>" >> $output +echo "<tr><td>The alignment info on the unmatched sequences</td><td><a href='unmatched.txt' download='unmatched.txt' >Download</a></td></tr>" >> $output + +echo "<tr><td colspan='2' style='background-color:#E0E0E0;'>SHM Overview</td></tr>" >> $output +echo "<tr><td>The SHM Overview table as a dataset</td><td><a href='shm_overview.txt' download='shm_overview.txt' >Download</a></td></tr>" >> $output +echo "<tr><td>Motif data per sequence ID</td><td><a href='motif_per_seq.txt' download='motif_per_seq.txt' >Download</a></td></tr>" >> $output +echo "<tr><td>Mutation data per sequence ID</td><td><a href='mutation_by_id.txt' download='mutation_by_id.txt' >Download</a></td></tr>" >> $output +echo "<tr><td>Base count for every sequence</td><td><a href='base_overview.html'>View</a></td></tr>" >> $output +echo "<tr><td>The data used to generate the percentage of mutations in AID and pol eta motives plot</td><td><a href='aid_motives.txt' download='aid_motives.txt' >Download</a></td></tr>" >> $output +echo "<tr><td>The data used to generate the relative mutation patterns plot</td><td><a href='relative_mutations.txt' download='relative_mutations.txt' >Download</a></td></tr>" >> $output +echo "<tr><td>The data used to generate the absolute mutation patterns plot</td><td><a href='absolute_mutations.txt' download='absolute_mutations.txt' >Download</a></td></tr>" >> $output +echo "<tr><td>Data about tandem mutations by ID</td><td><a href='tandems_by_id.txt' download='tandems_by_id.txt' >Download</a></td></tr>" >> $output + +echo "<tr><td colspan='2' style='background-color:#E0E0E0;'>SHM Frequency</td></tr>" >> $output +echo "<tr><td>The data generate the frequency scatter plot</td><td><a href='scatter.txt' download='scatter.txt' >Download</a></td></tr>" >> $output +echo "<tr><td>The data used to generate the frequency by class plot</td><td><a href='frequency_ranges_classes.txt' download='frequency_ranges_classes.txt' >Download</a></td></tr>" >> $output +echo "<tr><td>The data for frequency by subclass</td><td><a href='frequency_ranges_subclasses.txt' download='frequency_ranges_subclasses.txt' >Download</a></td></tr>" >> $output + +echo "<tr><td colspan='2' style='background-color:#E0E0E0;'>Transition Tables</td></tr>" >> $output +echo "<tr><td>The data for the 'all' transition plot</td><td><a href='transitions_all_sum.txt' download='transitions_all_sum.txt' >Download</a></td></tr>" >> $output +echo "<tr><td>The data for the 'IGA' transition plot</td><td><a href='transitions_IGA_sum.txt' download='transitions_IGA_sum.txt' >Download</a></td></tr>" >> $output +echo "<tr><td>The data for the 'IGA1' transition plot</td><td><a href='transitions_IGA1_sum.txt' download='transitions_IGA1_sum.txt' >Download</a></td></tr>" >> $output +echo "<tr><td>The data for the 'IGA2' transition plot</td><td><a href='transitions_IGA2_sum.txt' download='transitions_IGA2_sum.txt' >Download</a></td></tr>" >> $output +echo "<tr><td>The data for the 'IGG' transition plot</td><td><a href='transitions_IGG_sum.txt' download='transitions_IGG_sum.txt' >Download</a></td></tr>" >> $output +echo "<tr><td>The data for the 'IGG1' transition plot</td><td><a href='transitions_IGG1_sum.txt' download='transitions_IGG1_sum.txt' >Download</a></td></tr>" >> $output +echo "<tr><td>The data for the 'IGG2' transition plot</td><td><a href='transitions_IGG2_sum.txt' download='transitions_IGG2_sum.txt' >Download</a></td></tr>" >> $output +echo "<tr><td>The data for the 'IGG3' transition plot</td><td><a href='transitions_IGG3_sum.txt' download='transitions_IGG3_sum.txt' >Download</a></td></tr>" >> $output +echo "<tr><td>The data for the 'IGG4' transition plot</td><td><a href='transitions_IGG4_sum.txt' download='transitions_IGG4_sum.txt' >Download</a></td></tr>" >> $output +echo "<tr><td>The data for the 'IGM' transition plot</td><td><a href='transitions_IGM_sum.txt' download='transitions_IGM_sum.txt' >Download</a></td></tr>" >> $output +echo "<tr><td>The data for the 'IGE' transition plot</td><td><a href='transitions_IGE_sum.txt' download='transitions_IGE_sum.txt' >Download</a></td></tr>" >> $output + +echo "<tr><td colspan='2' style='background-color:#E0E0E0;'>Antigen Selection</td></tr>" >> $output +echo "<tr><td>AA mutation data per sequence ID</td><td><a href='aa_id_mutations.txt' download='aa_id_mutations.txt' >Download</a></td></tr>" >> $output +echo "<tr><td>Presence of AA per sequence ID</td><td><a href='absent_aa_id.txt' download='absent_aa_id.txt' >Download</a></td></tr>" >> $output + +echo "<tr><td>The data used to generate the aa mutation frequency plot</td><td><a href='aa_histogram_sum.txt' download='aa_histogram_sum.txt' >Download</a></td></tr>" >> $output +echo "<tr><td>The data used to generate the aa mutation frequency plot for IGA</td><td><a href='aa_histogram_sum_IGA.txt' download='aa_histogram_sum_IGA.txt' >Download</a></td></tr>" >> $output +echo "<tr><td>The data used to generate the aa mutation frequency plot for IGG</td><td><a href='aa_histogram_sum_IGG.txt' download='aa_histogram_sum_IGG.txt' >Download</a></td></tr>" >> $output +echo "<tr><td>The data used to generate the aa mutation frequency plot for IGM</td><td><a href='aa_histogram_sum_IGM.txt' download='aa_histogram_sum_IGM.txt' >Download</a></td></tr>" >> $output +echo "<tr><td>The data used to generate the aa mutation frequency plot for IGE</td><td><a href='aa_histogram_sum_IGE.txt' download='aa_histogram_sum_IGE.txt' >Download</a></td></tr>" >> $output + +echo "<tr><td>Baseline PDF (<a href='http://selection.med.yale.edu/baseline/'>http://selection.med.yale.edu/baseline/</a>)</td><td><a href='baseline.pdf' download='baseline.pdf' >Download</a></td></tr>" >> $output +echo "<tr><td>Baseline data</td><td><a href='baseline.txt' download='baseline.txt' >Download</a></td></tr>" >> $output +echo "<tr><td>Baseline IGA PDF</td><td><a href='baseline_IGA.pdf' download='baseline_IGA.pdf' >Download</a></td></tr>" >> $output +echo "<tr><td>Baseline IGA data</td><td><a href='baseline_IGA.txt' download='baseline_IGA.txt' >Download</a></td></tr>" >> $output +echo "<tr><td>Baseline IGG PDF</td><td><a href='baseline_IGG.pdf' download='baseline_IGG.pdf' >Download</a></td></tr>" >> $output +echo "<tr><td>Baseline IGG data</td><td><a href='baseline_IGG.txt' download='baseline_IGG.txt' >Download</a></td></tr>" >> $output +echo "<tr><td>Baseline IGM PDF</td><td><a href='baseline_IGM.pdf' download='baseline_IGM.pdf' >Download</a></td></tr>" >> $output +echo "<tr><td>Baseline IGM data</td><td><a href='baseline_IGM.txt' download='baseline_IGM.txt' >Download</a></td></tr>" >> $output +echo "<tr><td>Baseline IGE PDF</td><td><a href='baseline_IGE.pdf' download='baseline_IGE.pdf' >Download</a></td></tr>" >> $output +echo "<tr><td>Baseline IGE data</td><td><a href='baseline_IGE.txt' download='baseline_IGE.txt' >Download</a></td></tr>" >> $output + +echo "<tr><td colspan='2' style='background-color:#E0E0E0;'>CSR</td></tr>" >> $output +echo "<tr><td>The data for the IGA subclass distribution plot</td><td><a href='IGA_pie.txt' download='IGA_pie.txt' >Download</a></td></tr>" >> $output +echo "<tr><td>The data for the IGG subclass distribution plot</td><td><a href='IGG_pie.txt' download='IGG_pie.txt' >Download</a></td></tr>" >> $output + + +echo "<tr><td colspan='2' style='background-color:#E0E0E0;'>Clonal Relation</td></tr>" >> $output +echo "<tr><td>Sequence overlap between subclasses</td><td><a href='sequence_overview/index.html'>View</a></td></tr>" >> $output +echo "<tr><td>The Change-O DB file with defined clones and subclass annotation</td><td><a href='change_o/change-o-db-defined_clones.txt' download='change_o/change-o-db-defined_clones.txt' >Download</a></td></tr>" >> $output +echo "<tr><td>The Change-O DB defined clones summary file</td><td><a href='change_o/change-o-defined_clones-summary.txt' download='change_o/change-o-defined_clones-summary.txt' >Download</a></td></tr>" >> $output +echo "<tr><td>An IMGT archive with just just the first sequence of a clone</td><td><a href='new_IMGT_first_seq_of_clone.txz' download='new_IMGT_first_seq_of_clone.txz' >Download</a></td></tr>" >> $output + +echo "<tr><td>The Change-O DB file with defined clones of IGA</td><td><a href='change_o/change-o-db-defined_clones-IGA.txt' download='change_o/change-o-db-defined_clones-IGA.txt' >Download</a></td></tr>" >> $output +echo "<tr><td>The Change-O DB defined clones summary file of IGA</td><td><a href='change_o/change-o-defined_clones-summary-IGA.txt' download='change_o/change-o-defined_clones-summary-IGA.txt' >Download</a></td></tr>" >> $output +echo "<tr><td>An IMGT archive with just just the first sequence of a clone (IGA)</td><td><a href='new_IMGT_IGA_first_seq_of_clone.txz' download='new_IMGT_IGA_first_seq_of_clone.txz' >Download</a></td></tr>" >> $output + +echo "<tr><td>The Change-O DB file with defined clones of IGG</td><td><a href='change_o/change-o-db-defined_clones-IGG.txt' download='change_o/change-o-db-defined_clones-IGG.txt' >Download</a></td></tr>" >> $output +echo "<tr><td>The Change-O DB defined clones summary file of IGG</td><td><a href='change_o/change-o-defined_clones-summary-IGG.txt' download='change_o/change-o-defined_clones-summary-IGG.txt' >Download</a></td></tr>" >> $output +echo "<tr><td>An IMGT archive with just just the first sequence of a clone (IGG)</td><td><a href='new_IMGT_IGG_first_seq_of_clone.txz' download='new_IMGT_IGG_first_seq_of_clone.txz' >Download</a></td></tr>" >> $output + +echo "<tr><td>The Change-O DB file with defined clones of IGM</td><td><a href='change_o/change-o-db-defined_clones-IGM.txt' download='change_o/change-o-db-defined_clones-IGM.txt' >Download</a></td></tr>" >> $output +echo "<tr><td>The Change-O DB defined clones summary file of IGM</td><td><a href='change_o/change-o-defined_clones-summary-IGM.txt' download='change_o/change-o-defined_clones-summary-IGM.txt' >Download</a></td></tr>" >> $output +echo "<tr><td>An IMGT archive with just just the first sequence of a clone (IGM)</td><td><a href='new_IMGT_IGM_first_seq_of_clone.txz' download='new_IMGT_IGM_first_seq_of_clone.txz' >Download</a></td></tr>" >> $output + +echo "<tr><td>The Change-O DB file with defined clones of IGE</td><td><a href='change_o/change-o-db-defined_clones-IGE.txt' download='change_o/change-o-db-defined_clones-IGE.txt' >Download</a></td></tr>" >> $output +echo "<tr><td>The Change-O DB defined clones summary file of IGE</td><td><a href='change_o/change-o-defined_clones-summary-IGE.txt' download='change_o/change-o-defined_clones-summary-IGE.txt' >Download</a></td></tr>" >> $output +echo "<tr><td>An IMGT archive with just just the first sequence of a clone (IGE)</td><td><a href='new_IMGT_IGE_first_seq_of_clone.txz' download='new_IMGT_IGE_first_seq_of_clone.txz' >Download</a></td></tr>" >> $output + +echo "<tr><td colspan='2' style='background-color:#E0E0E0;'>Filtered IMGT output files</td></tr>" >> $output +echo "<tr><td>An IMGT archive with just the matched and filtered sequences</td><td><a href='new_IMGT.txz' download='new_IMGT.txz' >Download</a></td></tr>" >> $output +echo "<tr><td>An IMGT archive with just the matched and filtered IGA sequences</td><td><a href='new_IMGT_IGA.txz' download='new_IMGT_IGA.txz' >Download</a></td></tr>" >> $output +echo "<tr><td>An IMGT archive with just the matched and filtered IGA1 sequences</td><td><a href='new_IMGT_IGA1.txz' download='new_IMGT_IGA1.txz' >Download</a></td></tr>" >> $output +echo "<tr><td>An IMGT archive with just the matched and filtered IGA2 sequences</td><td><a href='new_IMGT_IGA2.txz' download='new_IMGT_IGA2.txz' >Download</a></td></tr>" >> $output +echo "<tr><td>An IMGT archive with just the matched and filtered IGG sequences</td><td><a href='new_IMGT_IGG.txz' download='new_IMGT_IGG.txz' >Download</a></td></tr>" >> $output +echo "<tr><td>An IMGT archive with just the matched and filtered IGG1 sequences</td><td><a href='new_IMGT_IGG1.txz' download='new_IMGT_IGG1.txz' >Download</a></td></tr>" >> $output +echo "<tr><td>An IMGT archive with just the matched and filtered IGG2 sequences</td><td><a href='new_IMGT_IGG2.txz' download='new_IMGT_IGG2.txz' >Download</a></td></tr>" >> $output +echo "<tr><td>An IMGT archive with just the matched and filtered IGG3 sequences</td><td><a href='new_IMGT_IGG3.txz' download='new_IMGT_IGG3.txz' >Download</a></td></tr>" >> $output +echo "<tr><td>An IMGT archive with just the matched and filtered IGG4 sequences</td><td><a href='new_IMGT_IGG4.txz' download='new_IMGT_IGG4.txz' >Download</a></td></tr>" >> $output +echo "<tr><td>An IMGT archive with just the matched and filtered IGM sequences</td><td><a href='new_IMGT_IGM.txz' download='new_IMGT_IGM.txz' >Download</a></td></tr>" >> $output +echo "<tr><td>An IMGT archive with just the matched and filtered IGE sequences</td><td><a href='new_IMGT_IGE.txz' download='new_IMGT_IGE.txz' >Download</a></td></tr>" >> $output + +echo "</table>" >> $output + +echo "<br />" >> $output +cat $dir/shm_downloads.htm >> $output + +echo "</div>" >> $output #downloads tab end + +echo "</div>" >> $output #tabs end + +echo "</html>" >> $output + + +echo "---------------- naive_output.r ----------------" +echo "---------------- naive_output.r ----------------<br />" >> $log + +if [[ "$naive_output" == "yes" ]] +then + echo "output naive output" + if [[ "${class_filter}" == "101_101" ]] + then + echo "copy new_IMGT.txz to ${naive_output_all}" + cp $outdir/new_IMGT.txz ${naive_output_all} + else + echo "copy for classes" + cp $outdir/new_IMGT_IGA.txz ${naive_output_ca} + cp $outdir/new_IMGT_IGG.txz ${naive_output_cg} + cp $outdir/new_IMGT_IGM.txz ${naive_output_cm} + cp $outdir/new_IMGT_IGE.txz ${naive_output_ce} + fi +fi + +echo "</table>" >> $outdir/base_overview.html + +mv $log $outdir/log.html + +echo "<html><center><h1><a href='index.html'>Click here for the results</a></h1>Tip: Open it in a new tab (middle mouse button or right mouse button -> 'open in new tab' on the link above)<br />" > $log +echo "<table border = 1>" >> $log +echo "<thead><tr><th>Info</th><th>Sequences</th><th>Percentage</th></tr></thead>" >> $log +tIFS="$TMP" +IFS=$'\t' +while read step seq perc + do + echo "<tr>" >> $log + echo "<td>$step</td>" >> $log + echo "<td>$seq</td>" >> $log + echo "<td>${perc}%</td>" >> $log + echo "</tr>" >> $log +done < $outdir/filtering_steps.txt +echo "</table>" >> $log +echo "<br />" >> $log +cat $dir/shm_first.htm >> $log +echo "</center></html>" >> $log + +IFS="$tIFS" + + +echo "---------------- Done! ----------------" +echo "---------------- Done! ----------------<br />" >> $outdir/log.html + + + + + + + + + + + + + + + + + + + + +
--- a/shm_downloads.htm Tue Sep 01 16:03:44 2020 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,538 +0,0 @@ -<html> - -<head> -<meta http-equiv=Content-Type content="text/html; charset=windows-1252"> -<meta name=Generator content="Microsoft Word 14 (filtered)"> -<style> -<!-- - /* Font Definitions */ - @font-face - {font-family:Calibri; - panose-1:2 15 5 2 2 2 4 3 2 4;} - /* Style Definitions */ - p.MsoNormal, li.MsoNormal, div.MsoNormal - {margin-top:0in; - margin-right:0in; - margin-bottom:10.0pt; - margin-left:0in; - line-height:115%; - font-size:11.0pt; - font-family:"Calibri","sans-serif";} -a:link, span.MsoHyperlink - {color:blue; - text-decoration:underline;} -a:visited, span.MsoHyperlinkFollowed - {color:purple; - text-decoration:underline;} -p.MsoNoSpacing, li.MsoNoSpacing, div.MsoNoSpacing - {margin:0in; - margin-bottom:.0001pt; - font-size:11.0pt; - font-family:"Calibri","sans-serif";} -.MsoChpDefault - {font-family:"Calibri","sans-serif";} -.MsoPapDefault - {margin-bottom:10.0pt; - line-height:115%;} -@page WordSection1 - {size:8.5in 11.0in; - margin:1.0in 1.0in 1.0in 1.0in;} -div.WordSection1 - {page:WordSection1;} ---> -</style> - -</head> - -<body lang=EN-US link=blue vlink=purple> - -<div class=WordSection1> - -<p class=MsoNoSpacing style='text-align:justify'><b><span lang=EN-GB -style='font-size:12.0pt;font-family:"Times New Roman","serif"'>Info</span></b></p> - -<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB -style='font-size:12.0pt;font-family:"Times New Roman","serif"'>The complete -dataset:</span></u><span lang=EN-GB style='font-size:12.0pt;font-family:"Times New Roman","serif"'> -Allows downloading of the complete parsed data set.</span></p> - -<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB -style='font-size:12.0pt;font-family:"Times New Roman","serif"'>The filtered -dataset:</span></u><span lang=EN-GB style='font-size:12.0pt;font-family:"Times New Roman","serif"'> -Allows downloading of all parsed IMGT information of all transcripts that -passed the chosen filter settings.</span></p> - -<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB -style='font-size:12.0pt;font-family:"Times New Roman","serif"'>The alignment -info on the unmatched sequences:</span></u><span lang=EN-GB style='font-size: -12.0pt;font-family:"Times New Roman","serif"'> Provides information of the subclass -alignment of all unmatched sequences. For each sequence the chunck hit -percentage and the nt hit percentage is shown together with the best matched -subclass.</span></p> - -<p class=MsoNoSpacing style='text-align:justify'><b><span lang=EN-GB -style='font-size:12.0pt;font-family:"Times New Roman","serif"'>SHM Overview</span></b></p> - -<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB -style='font-size:12.0pt;font-family:"Times New Roman","serif"'>The SHM Overview -table as a dataset:</span></u><span lang=EN-GB style='font-size:12.0pt; -font-family:"Times New Roman","serif"'> Allows downloading of the SHM Overview -table as a data set. </span></p> - -<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB -style='font-size:12.0pt;font-family:"Times New Roman","serif"'>Motif data per -sequence ID:</span></u><span lang=EN-GB style='font-size:12.0pt;font-family: -"Times New Roman","serif"'> Provides a file that contains information for each -transcript on the number of mutations present in WA/TW and RGYW/WRCY motives.</span></p> - -<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB -style='font-size:12.0pt;font-family:"Times New Roman","serif"'>Mutation data -per sequence ID: </span></u><span lang=EN-GB style='font-size:12.0pt; -font-family:"Times New Roman","serif"'>Provides a file containing information -on the number of sequences bases, the number and location of mutations and the -type of mutations found in each transcript. </span></p> - -<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB -style='font-size:12.0pt;font-family:"Times New Roman","serif"'>Base count for -every sequence:</span></u><span lang=EN-GB style='font-size:12.0pt;font-family: -"Times New Roman","serif"'> links to a page showing for each transcript the -sequence of the analysed region (as dependent on the sequence starts at filter), -the assigned subclass and the number of sequenced A,C,G and T’s.</span></p> - -<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB -style='font-size:12.0pt;font-family:"Times New Roman","serif"'>The data used to -generate the percentage of mutations in AID and pol eta motives plot:</span></u><span -lang=EN-GB style='font-size:12.0pt;font-family:"Times New Roman","serif"'> -Provides a file containing the values used to generate the percentage of -mutations in AID and pol eta motives plot in the SHM overview tab.</span></p> - -<p class=MsoNormalCxSpFirst style='text-align:justify'><u><span lang=EN-GB -style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>The -data used to generate the relative mutation patterns plot:</span></u><span -lang=EN-GB style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'> -Provides a download with the data used to generate the relative mutation -patterns plot in the SHM overview tab.</span></p> - -<p class=MsoNormalCxSpLast style='text-align:justify'><u><span lang=EN-GB -style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>The -data used to generate the absolute mutation patterns plot:</span></u><span -lang=EN-GB style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'> -Provides a download with the data used to generate the absolute mutation -patterns plot in the SHM overview tab. </span></p> - -<p class=MsoNoSpacing style='text-align:justify'><b><span lang=EN-GB -style='font-size:12.0pt;font-family:"Times New Roman","serif"'>SHM Frequency</span></b></p> - -<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB -style='font-size:12.0pt;font-family:"Times New Roman","serif"'>The data -generate the frequency scatter plot:</span></u><span lang=EN-GB -style='font-size:12.0pt;font-family:"Times New Roman","serif"'> Allows -downloading the data used to generate the frequency scatter plot in the SHM -frequency tab. </span></p> - -<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB -style='font-size:12.0pt;font-family:"Times New Roman","serif"'>The data used to -generate the frequency by class plot:</span></u><span lang=EN-GB -style='font-size:12.0pt;font-family:"Times New Roman","serif"'> Allows -downloading the data used to generate frequency by class plot included in the -SHM frequency tab. </span></p> - -<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB -style='font-size:12.0pt;font-family:"Times New Roman","serif"'>The data for -frequency by subclass:</span></u><span lang=EN-GB style='font-size:12.0pt; -font-family:"Times New Roman","serif"'> Provides information of the number and -percentage of sequences that have 0%, 0-2%, 2-5%, 5-10%, 10-15%, 15-20%, ->20% SHM. Information is provided for each subclass.</span></p> - -<p class=MsoNoSpacing style='text-align:justify'><span lang=EN-GB -style='font-size:12.0pt;font-family:"Times New Roman","serif"'> </span></p> - -<p class=MsoNoSpacing style='text-align:justify'><b><span lang=EN-GB -style='font-size:12.0pt;font-family:"Times New Roman","serif"'>Transition -Tables</span></b></p> - -<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB -style='font-size:12.0pt;font-family:"Times New Roman","serif"'>The data for the -'all' transition plot:</span></u><span lang=EN-GB style='font-size:12.0pt; -font-family:"Times New Roman","serif"'> Contains the information used to -generate the transition table for all sequences.</span></p> - -<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB -style='font-size:12.0pt;font-family:"Times New Roman","serif"'>The data for the -'IGA' transition plot:</span></u><span lang=EN-GB style='font-size:12.0pt; -font-family:"Times New Roman","serif"'> Contains the information used to -generate the transition table for all IGA sequences.</span></p> - -<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB -style='font-size:12.0pt;font-family:"Times New Roman","serif"'>The data for the -'IGA1' transition plot:</span></u><span lang=EN-GB style='font-size:12.0pt; -font-family:"Times New Roman","serif"'> Contains the information used to -generate the transition table for all IGA1 sequences.</span></p> - -<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB -style='font-size:12.0pt;font-family:"Times New Roman","serif"'>The data for the -'IGA2' transition plot:</span></u><span lang=EN-GB style='font-size:12.0pt; -font-family:"Times New Roman","serif"'> Contains the information used to -generate the transition table for all IGA2 sequences.</span></p> - -<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB -style='font-size:12.0pt;font-family:"Times New Roman","serif"'>The data for the -'IGG' transition plot :</span></u><span lang=EN-GB style='font-size:12.0pt; -font-family:"Times New Roman","serif"'> Contains the information used to -generate the transition table for all IGG sequences.</span></p> - -<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB -style='font-size:12.0pt;font-family:"Times New Roman","serif"'>The data for the -'IGG1' transition plot:</span></u><span lang=EN-GB style='font-size:12.0pt; -font-family:"Times New Roman","serif"'> Contains the information used to -generate the transition table for all IGG1 sequences.</span></p> - -<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB -style='font-size:12.0pt;font-family:"Times New Roman","serif"'>The data for the -'IGG2' transition plot:</span></u><span lang=EN-GB style='font-size:12.0pt; -font-family:"Times New Roman","serif"'> Contains the information used to -generate the transition table for all IGG2 sequences.</span></p> - -<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB -style='font-size:12.0pt;font-family:"Times New Roman","serif"'>The data for the -'IGG3' transition plot:</span></u><span lang=EN-GB style='font-size:12.0pt; -font-family:"Times New Roman","serif"'> Contains the information used to -generate the transition table for all IGG3 sequences.</span></p> - -<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB -style='font-size:12.0pt;font-family:"Times New Roman","serif"'>The data for the -'IGG4' transition plot:</span></u><span lang=EN-GB style='font-size:12.0pt; -font-family:"Times New Roman","serif"'> Contains the information used to -generate the transition table for all IGG4 sequences.</span></p> - -<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB -style='font-size:12.0pt;font-family:"Times New Roman","serif"'>The data for the -'IGM' transition plot :</span></u><span lang=EN-GB style='font-size:12.0pt; -font-family:"Times New Roman","serif"'> Contains the information used to -generate the transition table for all IGM sequences.</span></p> - -<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB -style='font-size:12.0pt;font-family:"Times New Roman","serif"'>The data for the -'IGE' transition plot:</span></u><span lang=EN-GB style='font-size:12.0pt; -font-family:"Times New Roman","serif"'> Contains the -information used to generate the transition table for all IGE sequences.</span></p> - -<p class=MsoNoSpacing style='text-align:justify'><b><span lang=EN-GB -style='font-size:12.0pt;font-family:"Times New Roman","serif"'>Antigen -selection</span></b></p> - -<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB -style='font-size:12.0pt;font-family:"Times New Roman","serif"'>AA mutation data -per sequence ID:</span></u><span lang=EN-GB style='font-size:12.0pt;font-family: -"Times New Roman","serif"'> Provides for each transcript information on whether -there is replacement mutation at each amino acid location (as defined by IMGT). -For all amino acids outside of the analysed region the value 0 is given.</span></p> - -<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB -style='font-size:12.0pt;font-family:"Times New Roman","serif"'>Presence of AA -per sequence ID:</span></u><span lang=EN-GB style='font-size:12.0pt;font-family: -"Times New Roman","serif"'> Provides for each transcript information on which -amino acid location (as defined by IMGT) is present. </span><span lang=NL -style='font-size:12.0pt;font-family:"Times New Roman","serif"'>0 is absent, 1 -is present. </span></p> - -<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB -style='font-size:12.0pt;font-family:"Times New Roman","serif"'>The data used to -generate the aa mutation frequency plot:</span></u><span lang=EN-GB -style='font-size:12.0pt;font-family:"Times New Roman","serif"'> Provides the -data used to generate the aa mutation frequency plot for all sequences in the -antigen selection tab.</span></p> - -<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB -style='font-size:12.0pt;font-family:"Times New Roman","serif"'>The data used to -generate the aa mutation frequency plot for IGA:</span></u><span lang=EN-GB -style='font-size:12.0pt;font-family:"Times New Roman","serif"'> Provides the -data used to generate the aa mutation frequency plot for all IGA sequences in -the antigen selection tab.</span></p> - -<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB -style='font-size:12.0pt;font-family:"Times New Roman","serif"'>The data used to -generate the aa mutation frequency plot for IGG:</span></u><span lang=EN-GB -style='font-size:12.0pt;font-family:"Times New Roman","serif"'> Provides the -data used to generate the aa mutation frequency plot for all IGG sequences in -the antigen selection tab.</span></p> - -<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB -style='font-size:12.0pt;font-family:"Times New Roman","serif"'>The data used to -generate the aa mutation frequency plot for IGM:</span></u><span lang=EN-GB -style='font-size:12.0pt;font-family:"Times New Roman","serif"'> Provides the -data used to generate the aa mutation frequency plot for all IGM sequences in -the antigen selection tab.</span></p> - -<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB -style='font-size:12.0pt;font-family:"Times New Roman","serif"'>The data used to -generate the aa mutation frequency plot for IGE:</span></u><span lang=EN-GB -style='font-size:12.0pt;font-family:"Times New Roman","serif"'> Provides the -data used to generate the aa mutation frequency plot for all IGE sequences in -the antigen selection tab.</span></p> - -<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB -style='font-size:12.0pt;font-family:"Times New Roman","serif"'>Baseline PDF (</span></u><span -lang=EN-GB><a href="http://selection.med.yale.edu/baseline/"><span -style='font-size:12.0pt;font-family:"Times New Roman","serif"'>http://selection.med.yale.edu/baseline/</span></a></span><u><span -lang=EN-GB style='font-size:12.0pt;font-family:"Times New Roman","serif"'>):</span></u><span -lang=EN-GB style='font-size:12.0pt;font-family:"Times New Roman","serif"'> PDF -containing the </span><span lang=EN-GB style='font-size:12.0pt;font-family: -"Times New Roman","serif"'>Antigen selection (BASELINe) graph for all -sequences.</span></p> - -<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB -style='font-size:12.0pt;font-family:"Times New Roman","serif"'>Baseline data:</span></u><span -lang=EN-GB style='font-size:12.0pt;font-family:"Times New Roman","serif"'> -Table output of the BASELINe analysis. Calculation of antigen selection as -performed by BASELINe are shown for each individual sequence and the sum of all -sequences.</span></p> - -<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB -style='font-size:12.0pt;font-family:"Times New Roman","serif"'>Baseline IGA -PDF:</span></u><span lang=EN-GB style='font-size:12.0pt;font-family:"Times New Roman","serif"'> -PDF containing the </span><span lang=EN-GB style='font-size:12.0pt;font-family: -"Times New Roman","serif"'>Antigen selection (BASELINe) graph for all -sequences.</span></p> - -<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB -style='font-size:12.0pt;font-family:"Times New Roman","serif"'>Baseline IGA -data:</span></u><span lang=EN-GB style='font-size:12.0pt;font-family:"Times New Roman","serif"'> -Table output of the BASELINe analysis. Calculation of antigen selection as -performed by BASELINe are shown for each individual IGA sequence and the sum of -all IGA sequences.</span></p> - -<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB -style='font-size:12.0pt;font-family:"Times New Roman","serif"'>Baseline IGG -PDF:</span></u><span lang=EN-GB style='font-size:12.0pt;font-family:"Times New Roman","serif"'> -PDF containing the </span><span lang=EN-GB style='font-size:12.0pt;font-family: -"Times New Roman","serif"'>Antigen selection (BASELINe) graph for all IGG -sequences.</span></p> - -<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB -style='font-size:12.0pt;font-family:"Times New Roman","serif"'>Baseline IGG -data:</span></u><span lang=EN-GB style='font-size:12.0pt;font-family:"Times New Roman","serif"'> -Table output of the BASELINe analysis. Calculation of antigen selection as -performed by BASELINe are shown for each individual IGG sequence and the sum of -all IGG sequences. </span></p> - -<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB -style='font-size:12.0pt;font-family:"Times New Roman","serif"'>Baseline IGM PDF:</span></u><span -lang=EN-GB style='font-size:12.0pt;font-family:"Times New Roman","serif"'> PDF -containing the </span><span lang=EN-GB style='font-size:12.0pt;font-family: -"Times New Roman","serif"'>Antigen selection (BASELINe) graph for all IGM -sequences.</span></p> - -<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB -style='font-size:12.0pt;font-family:"Times New Roman","serif"'>Baseline IGM -data:</span></u><span lang=EN-GB style='font-size:12.0pt;font-family:"Times New Roman","serif"'> -Table output of the BASELINe analysis. Calculation of antigen selection as -performed by BASELINe are shown for each individual IGM sequence and the sum of -all IGM sequences.</span></p> - -<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB -style='font-size:12.0pt;font-family:"Times New Roman","serif"'>Baseline IGE -PDF:</span></u><span lang=EN-GB style='font-size:12.0pt;font-family:"Times New Roman","serif"'> -PDF containing the </span><span lang=EN-GB style='font-size:12.0pt;font-family: -"Times New Roman","serif"'>Antigen selection (BASELINe) graph for all IGE -sequences.</span><span lang=EN-GB style='font-size:12.0pt;font-family:"Times New Roman","serif"'> -</span></p> - -<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB -style='font-size:12.0pt;font-family:"Times New Roman","serif"'>Baseline IGE -data:</span></u><span lang=EN-GB style='font-size:12.0pt;font-family:"Times New Roman","serif"'> -Table output of the BASELINe analysis. Calculation of antigen selection as -performed by BASELINe are shown for each individual IGE sequence and the sum of -all IGE sequences.</span></p> - -<p class=MsoNoSpacing style='text-align:justify'><b><span lang=EN-GB -style='font-size:12.0pt;font-family:"Times New Roman","serif"'>CSR</span></b></p> - -<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB -style='font-size:12.0pt;font-family:"Times New Roman","serif"'>The data for the -</span></u><u><span lang=EN-GB style='font-size:12.0pt;font-family:"Times New Roman","serif"'>IGA -subclass distribution plot :</span></u><span lang=EN-GB style='font-size:12.0pt; -font-family:"Times New Roman","serif"'> </span><span lang=EN-GB -style='font-size:12.0pt;font-family:"Times New Roman","serif"'>Data used for -the generation of the </span><span lang=EN-GB style='font-size:12.0pt; -font-family:"Times New Roman","serif"'>IGA subclass distribution plot provided -in the CSR tab. </span></p> - -<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB -style='font-size:12.0pt;font-family:"Times New Roman","serif"'>The data for the -</span></u><u><span lang=EN-GB style='font-size:12.0pt;font-family:"Times New Roman","serif"'>IGA -subclass distribution plot :</span></u><span lang=EN-GB style='font-size:12.0pt; -font-family:"Times New Roman","serif"'> Data used for the generation of the </span><span -lang=EN-GB style='font-size:12.0pt;font-family:"Times New Roman","serif"'>IGG -subclass distribution plot provided in the CSR tab. </span></p> - -<p class=MsoNoSpacing style='text-align:justify'><b><span lang=NL -style='font-size:12.0pt;font-family:"Times New Roman","serif"'>Clonal relation</span></b></p> - -<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB -style='font-size:12.0pt;font-family:"Times New Roman","serif"'>Sequence overlap -between subclasses:</span></u><span lang=EN-GB style='font-size:12.0pt; -font-family:"Times New Roman","serif"'> Link to the overlap table as provided -under the clonality overlap tab. </span></p> - -<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB -style='font-size:12.0pt;font-family:"Times New Roman","serif"'>The Change-O DB -file with defined clones and subclass annotation:</span></u><span -lang=EN-GB style='font-size:12.0pt;font-family:"Times New Roman","serif"'> -Downloads a table with the calculation of clonal relation between all -sequences. For each individual transcript the results of the clonal assignment -as provided by Change-O are provided. Sequences with the same number in the CLONE -column are considered clonally related. </span></p> - -<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB -style='font-size:12.0pt;font-family:"Times New Roman","serif"'>The Change-O DB -defined clones summary file:</span></u><span lang=EN-GB style='font-size:12.0pt; -font-family:"Times New Roman","serif"'> Gives a summary of the total number of -clones in all sequences and their clone size. </span></p> - -<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB -style='font-size:12.0pt;font-family:"Times New Roman","serif"'>The Change-O DB -file with defined clones of IGA:</span></u><span lang=EN-GB style='font-size: -12.0pt;font-family:"Times New Roman","serif"'> Downloads a table with the -calculation of clonal relation between all IGA sequences. For each individual -transcript the results of the clonal assignment as provided by Change-O are -provided. Sequences with the same number in the CLONE column are considered -clonally related. </span></p> - -<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB -style='font-size:12.0pt;font-family:"Times New Roman","serif"'>The Change-O DB -defined clones summary file of IGA:</span></u><span lang=EN-GB -style='font-size:12.0pt;font-family:"Times New Roman","serif"'> Gives a summary -of the total number of clones in all IGA sequences and their clone size.</span></p> - -<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB -style='font-size:12.0pt;font-family:"Times New Roman","serif"'>The Change-O DB -file with defined clones of IGG:</span></u><span lang=EN-GB style='font-size: -12.0pt;font-family:"Times New Roman","serif"'> Downloads a table with the -calculation of clonal relation between all IGG sequences. For each individual -transcript the results of the clonal assignment as provided by Change-O are -provided. Sequences with the same number in the CLONE column are considered -clonally related. </span></p> - -<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB -style='font-size:12.0pt;font-family:"Times New Roman","serif"'>The Change-O DB -defined clones summary file of IGG:</span></u><span lang=EN-GB -style='font-size:12.0pt;font-family:"Times New Roman","serif"'> Gives a summary -of the total number of clones in all IGG sequences and their clone size.</span></p> - -<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB -style='font-size:12.0pt;font-family:"Times New Roman","serif"'>The Change-O DB -file with defined clones of IGM:</span></u><span lang=EN-GB style='font-size: -12.0pt;font-family:"Times New Roman","serif"'> Downloads a table -with the calculation of clonal relation between all IGM sequences. For each -individual transcript the results of the clonal assignment as provided by -Change-O are provided. Sequences with the same number in the CLONE column are -considered clonally related. </span></p> - -<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB -style='font-size:12.0pt;font-family:"Times New Roman","serif"'>The Change-O DB -defined clones summary file of IGM:</span></u><span lang=EN-GB -style='font-size:12.0pt;font-family:"Times New Roman","serif"'> Gives a summary -of the total number of clones in all IGM sequences and their clone size.</span></p> - -<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB -style='font-size:12.0pt;font-family:"Times New Roman","serif"'>The Change-O DB -file with defined clones of IGE:</span></u><span lang=EN-GB style='font-size: -12.0pt;font-family:"Times New Roman","serif"'> Downloads a table with the -calculation of clonal relation between all IGE sequences. For each individual -transcript the results of the clonal assignment as provided by Change-O are -provided. Sequences with the same number in the CLONE column are considered -clonally related. </span></p> - -<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB -style='font-size:12.0pt;font-family:"Times New Roman","serif"'>The Change-O DB -defined clones summary file of IGE:</span></u><span lang=EN-GB -style='font-size:12.0pt;font-family:"Times New Roman","serif"'> Gives a summary -of the total number of clones in all IGE sequences and their clone size.</span></p> - -<p class=MsoNoSpacing style='text-align:justify'><b><span lang=EN-GB -style='font-size:12.0pt;font-family:"Times New Roman","serif"'>Filtered IMGT -output files</span></b></p> - -<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB -style='font-size:12.0pt;font-family:"Times New Roman","serif"'>An IMGT archive -with just the matched and filtered sequences:</span></u><span lang=EN-GB -style='font-size:12.0pt;font-family:"Times New Roman","serif"'> Downloads a -.txz file with the same format as downloaded IMGT files that contains all -sequences that have passed the chosen filter settings.</span></p> - -<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB -style='font-size:12.0pt;font-family:"Times New Roman","serif"'>An IMGT archive -with just the matched and filtered IGA sequences:</span></u><span lang=EN-GB -style='font-size:12.0pt;font-family:"Times New Roman","serif"'> Downloads a -.txz file with the same format as downloaded IMGT files that contains all IGA -sequences that have passed the chosen filter settings.</span></p> - -<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB -style='font-size:12.0pt;font-family:"Times New Roman","serif"'>An IMGT archive -with just the matched and filtered IGA1 sequences:</span></u><span lang=EN-GB -style='font-size:12.0pt;font-family:"Times New Roman","serif"'> Downloads a -.txz file with the same format as downloaded IMGT files that contains all IGA1 -sequences that have passed the chosen filter settings.</span></p> - -<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB -style='font-size:12.0pt;font-family:"Times New Roman","serif"'>An IMGT archive -with just the matched and filtered IGA2 sequences:</span></u><span lang=EN-GB -style='font-size:12.0pt;font-family:"Times New Roman","serif"'> Downloads a .txz -file with the same format as downloaded IMGT files that contains all IGA2 -sequences that have passed the chosen filter settings.</span></p> - -<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB -style='font-size:12.0pt;font-family:"Times New Roman","serif"'>An IMGT archive -with just the matched and filtered IGG sequences:</span></u><span lang=EN-GB -style='font-size:12.0pt;font-family:"Times New Roman","serif"'> Downloads a .txz -file with the same format as downloaded IMGT files that contains all IGG -sequences that have passed the chosen filter settings.</span></p> - -<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB -style='font-size:12.0pt;font-family:"Times New Roman","serif"'>An IMGT archive -with just the matched and filtered IGG1 sequences:</span></u><span lang=EN-GB -style='font-size:12.0pt;font-family:"Times New Roman","serif"'> Downloads a -.txz file with the same format as downloaded IMGT files that contains all IGG1 -sequences that have passed the chosen filter settings.</span></p> - -<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB -style='font-size:12.0pt;font-family:"Times New Roman","serif"'>An IMGT archive -with just the matched and filtered IGG2 sequences:</span></u><span lang=EN-GB -style='font-size:12.0pt;font-family:"Times New Roman","serif"'> Downloads a -.txz file with the same format as downloaded IMGT files that contains all IGG2 -sequences that have passed the chosen filter settings.</span></p> - -<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB -style='font-size:12.0pt;font-family:"Times New Roman","serif"'>An IMGT archive -with just the matched and filtered IGG3 sequences:</span></u><span lang=EN-GB -style='font-size:12.0pt;font-family:"Times New Roman","serif"'> Downloads a .txz -file with the same format as downloaded IMGT files that contains all IGG3 -sequences that have passed the chosen filter settings.</span></p> - -<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB -style='font-size:12.0pt;font-family:"Times New Roman","serif"'>An IMGT archive -with just the matched and filtered IGG4 sequences:</span></u><span lang=EN-GB -style='font-size:12.0pt;font-family:"Times New Roman","serif"'> Downloads a -.txz file with the same format as downloaded IMGT files that contains all IGG4 -sequences that have passed the chosen filter settings.</span></p> - -<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB -style='font-size:12.0pt;font-family:"Times New Roman","serif"'>An IMGT archive -with just the matched and filtered IGM sequences:</span></u><span lang=EN-GB -style='font-size:12.0pt;font-family:"Times New Roman","serif"'> Downloads a .txz -file with the same format as downloaded IMGT files that contains all IGM -sequences that have passed the chosen filter settings.</span></p> - -<p class=MsoNoSpacing style='text-align:justify'><u><span lang=EN-GB -style='font-size:12.0pt;font-family:"Times New Roman","serif"'>An IMGT archive -with just the matched and filtered IGE sequences:</span></u><span lang=EN-GB -style='font-size:12.0pt;font-family:"Times New Roman","serif"'> Downloads a -.txz file with the same format as downloaded IMGT files that contains all IGE -sequences that have passed the chosen filter settings.</span></p> - -</div> - -</body> - -</html>
--- a/shm_first.htm Tue Sep 01 16:03:44 2020 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,127 +0,0 @@ -<html> - -<head> -<meta http-equiv=Content-Type content="text/html; charset=windows-1252"> -<meta name=Generator content="Microsoft Word 14 (filtered)"> -<style> -<!-- - /* Font Definitions */ - @font-face - {font-family:Calibri; - panose-1:2 15 5 2 2 2 4 3 2 4;} - /* Style Definitions */ - p.MsoNormal, li.MsoNormal, div.MsoNormal - {margin-top:0in; - margin-right:0in; - margin-bottom:10.0pt; - margin-left:0in; - line-height:115%; - font-size:11.0pt; - font-family:"Calibri","sans-serif";} -.MsoChpDefault - {font-family:"Calibri","sans-serif";} -.MsoPapDefault - {margin-bottom:10.0pt; - line-height:115%;} -@page WordSection1 - {size:8.5in 11.0in; - margin:1.0in 1.0in 1.0in 1.0in;} -div.WordSection1 - {page:WordSection1;} ---> -</style> - -</head> - -<body lang=EN-US> - -<div class=WordSection1> - -<p class=MsoNormalCxSpFirst style='margin-bottom:0in;margin-bottom:.0001pt; -text-align:justify;line-height:normal'><span lang=EN-GB style='font-size:12.0pt; -font-family:"Times New Roman","serif"'>Table showing the order of each -filtering step and the number and percentage of sequences after each filtering -step. </span></p> - -<p class=MsoNormalCxSpMiddle style='margin-bottom:0in;margin-bottom:.0001pt; -text-align:justify;line-height:normal'><u><span lang=EN-GB style='font-size: -12.0pt;font-family:"Times New Roman","serif"'>Input:</span></u><span -lang=EN-GB style='font-size:12.0pt;font-family:"Times New Roman","serif"'> The -number of sequences in the original IMGT file. This is always 100% of the -sequences.</span></p> - -<p class=MsoNormalCxSpMiddle style='margin-bottom:0in;margin-bottom:.0001pt; -text-align:justify;line-height:normal'><u><span lang=EN-GB style='font-size: -12.0pt;font-family:"Times New Roman","serif"'>After "no results" filter: </span></u><span -lang=EN-GB style='font-size:12.0pt;font-family:"Times New Roman","serif"'>IMGT -classifies sequences either as "productive", "unproductive", "unknown", or "no -results". Here, the number and percentages of sequences that are not classified -as "no results" are reported.</span></p> - -<p class=MsoNormalCxSpMiddle style='margin-bottom:0in;margin-bottom:.0001pt; -text-align:justify;line-height:normal'><u><span lang=EN-GB style='font-size: -12.0pt;font-family:"Times New Roman","serif"'>After functionality filter:</span></u><span -lang=EN-GB style='font-size:12.0pt;font-family:"Times New Roman","serif"'> The -number and percentages of sequences that have passed the functionality filter. The -filtering performed is dependent on the settings of the functionality filter. -Details on the functionality filter <a name="OLE_LINK12"></a><a -name="OLE_LINK11"></a><a name="OLE_LINK10">can be found on the start page of -the SHM&CSR pipeline</a>.</span></p> - -<p class=MsoNormalCxSpMiddle style='text-align:justify'><u><span lang=EN-GB -style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>After -removal sequences that are missing a gene region:</span></u><span lang=EN-GB -style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'> -In this step all sequences that are missing a gene region (FR1, CDR1, FR2, -CDR2, FR3) that should be present are removed from analysis. The sequence -regions that should be present are dependent on the settings of the sequence -starts at filter. <a name="OLE_LINK9"></a><a name="OLE_LINK8">The number and -percentage of sequences that pass this filter step are reported.</a> </span></p> - -<p class=MsoNormalCxSpMiddle style='text-align:justify'><u><span lang=EN-GB -style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>After -N filter:</span></u><span lang=EN-GB style='font-size:12.0pt;line-height:115%; -font-family:"Times New Roman","serif"'> In this step all sequences that contain -an ambiguous base (n) in the analysed region or the CDR3 are removed from the -analysis. The analysed region is determined by the setting of the sequence -starts at filter. The number and percentage of sequences that pass this filter -step are reported.</span></p> - -<p class=MsoNormalCxSpMiddle style='text-align:justify'><u><span lang=EN-GB -style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>After -filter unique sequences</span></u><span lang=EN-GB style='font-size:12.0pt; -line-height:115%;font-family:"Times New Roman","serif"'>: The number and -percentage of sequences that pass the "filter unique sequences" filter. Details -on this filter </span><span lang=EN-GB style='font-size:12.0pt;line-height: -115%;font-family:"Times New Roman","serif"'>can be found on the start page of -the SHM&CSR pipeline</span></p> - -<p class=MsoNormalCxSpMiddle style='text-align:justify'><u><span lang=EN-GB -style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>After -remove duplicate based on filter:</span></u><span lang=EN-GB style='font-size: -12.0pt;line-height:115%;font-family:"Times New Roman","serif"'> The number and -percentage of sequences that passed the remove duplicate filter. Details on the -"remove duplicate filter based on filter" can be found on the start page of the -SHM&CSR pipeline.</span></p> - -<p class=MsoNormalCxSpMiddle style='text-align:justify'><a name="OLE_LINK17"></a><a -name="OLE_LINK16"><u><span lang=EN-GB style='font-size:12.0pt;line-height:115%; -font-family:"Times New Roman","serif"'>Number of matches sequences:</span></u></a><span -lang=EN-GB style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'> -The number and percentage of sequences that passed all the filters described -above and have a (sub)class assigned.</span></p> - -<p class=MsoNormalCxSpMiddle style='text-align:justify'><u><span lang=EN-GB -style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>Number -of unmatched sequences</span></u><span lang=EN-GB style='font-size:12.0pt; -line-height:115%;font-family:"Times New Roman","serif"'>: The number and percentage -of sequences that passed all the filters described above and do not have -subclass assigned.</span></p> - -<p class=MsoNormal><span lang=EN-GB> </span></p> - -</div> - -</body> - -</html>
--- a/shm_frequency.htm Tue Sep 01 16:03:44 2020 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,87 +0,0 @@ -<html> - -<head> -<meta http-equiv=Content-Type content="text/html; charset=windows-1252"> -<meta name=Generator content="Microsoft Word 14 (filtered)"> -<style> -<!-- - /* Style Definitions */ - p.MsoNormal, li.MsoNormal, div.MsoNormal - {margin-top:0in; - margin-right:0in; - margin-bottom:10.0pt; - margin-left:0in; - line-height:115%; - font-size:11.0pt; - font-family:"Calibri","sans-serif";} -.MsoChpDefault - {font-family:"Calibri","sans-serif";} -.MsoPapDefault - {margin-bottom:10.0pt; - line-height:115%;} -@page WordSection1 - {size:8.5in 11.0in; - margin:1.0in 1.0in 1.0in 1.0in;} -div.WordSection1 - {page:WordSection1;} ---> -</style> - -</head> - -<body lang=EN-US> - -<div class=WordSection1> - -<p class=MsoNormalCxSpFirst style='text-align:justify'><b><u><span lang=EN-GB -style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>SHM -frequency tab</span></u></b></p> - -<p class=MsoNormalCxSpMiddle style='text-align:justify'><b><span lang=EN-GB -style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>Graphs</span></b></p> - -<p class=MsoNormalCxSpMiddle style='text-align:justify'><span lang=EN-GB -style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>These -graphs give insight into the level of SHM. The data represented in these graphs -can be downloaded in the download tab. <a name="OLE_LINK24"></a><a -name="OLE_LINK23"></a><a name="OLE_LINK90"></a><a name="OLE_LINK89">More -information on the values found in healthy individuals of different ages can be -found in IJspeert and van Schouwenburg et al, PMID: 27799928. </a></span></p> - -<p class=MsoNormalCxSpMiddle style='text-align:justify'><u><span lang=EN-GB -style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>Frequency -scatter plot</span></u></p> - -<p class=MsoNormalCxSpMiddle style='text-align:justify'><span lang=EN-GB -style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>A -dot plot showing the percentage of SHM in each transcript divided into the -different (sub)classes. </span><span lang=NL style='font-size:12.0pt; -line-height:115%;font-family:"Times New Roman","serif"'>In the graph each dot -represents an individual transcript.</span></p> - -<p class=MsoNormalCxSpMiddle style='text-align:justify'><u><span lang=EN-GB -style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>Mutation -frequency by class</span></u></p> - -<p class=MsoNormalCxSpLast style='text-align:justify'><span lang=EN-GB -style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>A -bar graph showing the percentage of transcripts that contain 0%, 0-2%, 2-5%, -5-10% 10-15%, 15-20% or more than 20% SHM for each subclass. </span></p> - -<p class=MsoNormal><span lang=NL style='font-size:12.0pt;line-height:115%; -font-family:"Times New Roman","serif"'>Hanna IJspeert, Pauline A. van -Schouwenburg, David van Zessen, Ingrid Pico-Knijnenburg, Gertjan J. Driessen, -Andrew P. Stubbs, and Mirjam van der Burg (2016). </span><span -style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>Evaluation -of the Antigen-Experienced B-Cell Receptor Repertoire in Healthy Children and -Adults. In <i>Frontiers in Immunolog, 7, pp. e410-410. </i>[<a -href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5066086/"><span -style='color:windowtext'>doi:10.3389/fimmu.2016.00410</span></a>][<a -href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5066086/"><span -style='color:windowtext'>Link</span></a>]</span></p> - -</div> - -</body> - -</html>
--- a/shm_overview.htm Tue Sep 01 16:03:44 2020 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,332 +0,0 @@ -<html> - -<head> -<meta http-equiv=Content-Type content="text/html; charset=windows-1252"> -<meta name=Generator content="Microsoft Word 14 (filtered)"> -<style> -<!-- - /* Font Definitions */ - @font-face - {font-family:Calibri; - panose-1:2 15 5 2 2 2 4 3 2 4;} - /* Style Definitions */ - p.MsoNormal, li.MsoNormal, div.MsoNormal - {margin-top:0in; - margin-right:0in; - margin-bottom:10.0pt; - margin-left:0in; - line-height:115%; - font-size:11.0pt; - font-family:"Calibri","sans-serif";} -.MsoChpDefault - {font-family:"Calibri","sans-serif";} -.MsoPapDefault - {margin-bottom:10.0pt; - line-height:115%;} -@page WordSection1 - {size:8.5in 11.0in; - margin:1.0in 1.0in 1.0in 1.0in;} -div.WordSection1 - {page:WordSection1;} ---> -</style> - -</head> - -<body lang=EN-US> - -<div class=WordSection1> - -<p class=MsoNormalCxSpFirst style='text-align:justify'><b><span lang=EN-GB -style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>Info -table</span></b></p> - -<p class=MsoNormalCxSpMiddle style='text-align:justify'><span lang=EN-GB -style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>This -table contains information on different characteristics of SHM. For all -characteristics information can be found for all sequences or only sequences of -a certain (sub)class. All results are based on the sequences that passed the filter -settings chosen on the start page of the SHM & CSR pipeline and only -include details on the analysed region as determined by the setting of the -sequence starts at filter. All data in this table can be downloaded via the -“downloads” tab.</span></p> - -<p class=MsoNormalCxSpMiddle style='text-align:justify'><u><span lang=EN-GB -style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>Mutation -frequency:</span></u></p> - -<p class=MsoNormalCxSpMiddle style='text-align:justify'><a name="OLE_LINK83"></a><a -name="OLE_LINK82"></a><a name="OLE_LINK81"><span lang=EN-GB style='font-size: -12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>These values -give information on the level of SHM. </span></a><a name="OLE_LINK22"></a><a -name="OLE_LINK21"></a><a name="OLE_LINK20"><span lang=EN-GB style='font-size: -12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>More information -on the values found in healthy individuals of different ages can be found in </span></a><a -name="OLE_LINK15"></a><a name="OLE_LINK14"></a><a name="OLE_LINK13"><span -lang=EN-GB style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>IJspeert -and van Schouwenburg et al, PMID: 27799928</span></a></p> - -<p class=MsoNormalCxSpMiddle style='text-align:justify'><i><span lang=EN-GB -style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>Number -of mutations:</span></i><span lang=EN-GB style='font-size:12.0pt;line-height: -115%;font-family:"Times New Roman","serif"'> Shows the number of total -mutations / the number of sequenced bases (the % of mutated bases).</span></p> - -<p class=MsoNormalCxSpMiddle style='text-align:justify'><i><span lang=EN-GB -style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>Median -number of mutations:</span></i><span lang=EN-GB style='font-size:12.0pt; -line-height:115%;font-family:"Times New Roman","serif"'> Shows the median % of -SHM of all sequences.</span></p> - -<p class=MsoNormalCxSpMiddle style='text-align:justify'><u><span lang=EN-GB -style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>Patterns -of SHM:</span></u></p> - -<p class=MsoNormalCxSpMiddle style='text-align:justify'><a name="OLE_LINK72"></a><a -name="OLE_LINK71"></a><a name="OLE_LINK70"><span lang=EN-GB style='font-size: -12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>These values -give insights into the targeting and patterns of SHM. These values can give -insight into the repair pathways used to repair the U:G mismatches introduced -by AID. </span></a><a name="OLE_LINK40"></a><a name="OLE_LINK39"></a><a -name="OLE_LINK38"></a><a name="OLE_LINK60"><span lang=EN-GB style='font-size: -12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>More information -on the values found in healthy individuals of different ages can be found in -IJspeert and van Schouwenburg et al, PMID: 27799928</span></a></p> - -<p class=MsoNormalCxSpMiddle style='text-align:justify'><i><span lang=EN-GB -style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>Transitions:</span></i><span -lang=EN-GB style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'> -Shows the number of transition mutations / the number of total mutations (the -percentage of mutations that are transitions). Transition mutations are C>T, -T>C, A>G, G>A. </span></p> - -<p class=MsoNormalCxSpMiddle style='text-align:justify'><i><span lang=EN-GB -style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>Transversions:</span></i><span -lang=EN-GB style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'> -Shows the number of transversion mutations / the number of total mutations (the -percentage of mutations that are transitions). Transversion mutations are -C>A, C>G, T>A, T>G, A>T, A>C, G>T, G>C.</span></p> - -<p class=MsoNormalCxSpMiddle style='text-align:justify'><i><span lang=EN-GB -style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>Transitions -at GC:</span></i><span lang=EN-GB style='font-size:12.0pt;line-height:115%; -font-family:"Times New Roman","serif"'> <a name="OLE_LINK2"></a><a -name="OLE_LINK1">Shows the number of transitions at GC locations (C>T, -G>A) / the total number of mutations at GC locations (the percentage of -mutations at GC locations that are transitions).</a></span></p> - -<p class=MsoNormalCxSpMiddle style='text-align:justify'><i><span lang=EN-GB -style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>Targeting -of GC:</span></i><span lang=EN-GB style='font-size:12.0pt;line-height:115%; -font-family:"Times New Roman","serif"'> <a name="OLE_LINK7"></a><a -name="OLE_LINK6"></a><a name="OLE_LINK3">Shows the number of mutations at GC -locations / the total number of mutations (the percentage of total mutations -that are at GC locations).</a> </span></p> - -<p class=MsoNormalCxSpMiddle style='text-align:justify'><i><span lang=EN-GB -style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>Transitions -at AT:</span></i><span lang=EN-GB style='font-size:12.0pt;line-height:115%; -font-family:"Times New Roman","serif"'> Shows the number of transitions at AT -locations (T>C, A>G) / the total number of mutations at AT locations (the -percentage of mutations at AT locations that are transitions).</span></p> - -<p class=MsoNormalCxSpMiddle style='text-align:justify'><i><span lang=EN-GB -style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>Targeting -of AT:</span></i><span lang=EN-GB style='font-size:12.0pt;line-height:115%; -font-family:"Times New Roman","serif"'> Shows the number of mutations at AT -locations / the total number of mutations (the percentage of total mutations -that are at AT locations).</span></p> - -<p class=MsoNormalCxSpMiddle style='text-align:justify'><i><span lang=EN-GB -style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>RGYW:</span></i><span -lang=EN-GB style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'> -<a name="OLE_LINK28"></a><a name="OLE_LINK27"></a><a name="OLE_LINK26">Shows -the number of mutations that are in a RGYW motive / The number of total mutations -(the percentage of mutations that are in a RGYW motive). </a><a -name="OLE_LINK62"></a><a name="OLE_LINK61">RGYW motives are known to be -preferentially targeted by AID </a></span><span lang=EN-GB style='font-size: -12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>(R=Purine, -Y=pyrimidine, W = A or T).</span></p> - -<p class=MsoNormalCxSpMiddle style='text-align:justify'><i><span lang=EN-GB -style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>WRCY:</span></i><span -lang=EN-GB style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'> -<a name="OLE_LINK34"></a><a name="OLE_LINK33">Shows the number of mutations -that are in a </a><a name="OLE_LINK32"></a><a name="OLE_LINK31"></a><a -name="OLE_LINK30"></a><a name="OLE_LINK29">WRCY</a> motive / The number of -total mutations (the percentage of mutations that are in a WRCY motive). WRCY -motives are known to be preferentially targeted by AID </span><span lang=EN-GB -style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>(R=Purine, -Y=pyrimidine, W = A or T).</span></p> - -<p class=MsoNormalCxSpMiddle style='text-align:justify'><i><span lang=EN-GB -style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>WA:</span></i><span -lang=EN-GB style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'> -<a name="OLE_LINK37"></a><a name="OLE_LINK36"></a><a name="OLE_LINK35">Shows -the number of mutations that are in a WA motive / The number of total mutations -(the percentage of mutations that are in a WA motive). It is described that -polymerase eta preferentially makes errors at WA motives </a></span><span -lang=EN-GB style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>(W -= A or T).</span></p> - -<p class=MsoNormalCxSpMiddle style='text-align:justify'><i><span lang=EN-GB -style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>TW:</span></i><span -lang=EN-GB style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'> -Shows the number of mutations that are in a TW motive / The number of total mutations -(the percentage of mutations that are in a TW motive). It is described that -polymerase eta preferentially makes errors at TW motives </span><span -lang=EN-GB style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>(W -= A or T).</span></p> - -<p class=MsoNormalCxSpMiddle style='text-align:justify'><u><span lang=EN-GB -style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>Antigen -selection:</span></u></p> - -<p class=MsoNormalCxSpMiddle style='text-align:justify'><span lang=EN-GB -style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>These -values give insight into antigen selection. It has been described that during -antigen selection, there is selection against replacement mutations in the FR -regions as these can cause instability of the B-cell receptor. In contrast -replacement mutations in the CDR regions are important for changing the -affinity of the B-cell receptor and therefore there is selection for this type -of mutations. Silent mutations do not alter the amino acid sequence and -therefore do not play a role in selection. More information on the values found -in healthy individuals of different ages can be found in IJspeert and van -Schouwenburg et al, PMID: 27799928</span></p> - -<p class=MsoNormalCxSpMiddle style='text-align:justify'><i><span lang=EN-GB -style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>FR -R/S:</span></i><span lang=EN-GB style='font-size:12.0pt;line-height:115%; -font-family:"Times New Roman","serif"'> <a name="OLE_LINK43"></a><a -name="OLE_LINK42"></a><a name="OLE_LINK41">Shows the number of replacement -mutations in the FR regions / The number of silent mutations in the FR regions -(the number of replacement mutations in the FR regions divided by the number of -silent mutations in the FR regions)</a></span></p> - -<p class=MsoNormalCxSpMiddle style='text-align:justify'><i><span lang=EN-GB -style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>CDR -R/S:</span></i><span lang=EN-GB style='font-size:12.0pt;line-height:115%; -font-family:"Times New Roman","serif"'> Shows the number of replacement -mutations in the CDR regions / The number of silent mutations in the CDR -regions (the number of replacement mutations in the CDR regions divided by the -number of silent mutations in the CDR regions)</span></p> - -<p class=MsoNormalCxSpMiddle style='text-align:justify'><u><span lang=EN-GB -style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>Number -of sequences nucleotides:</span></u></p> - -<p class=MsoNormalCxSpMiddle style='text-align:justify'><span lang=EN-GB -style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>These -values give information on the number of sequenced nucleotides.</span></p> - -<p class=MsoNormalCxSpMiddle style='text-align:justify'><i><span lang=EN-GB -style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>Nt -in FR:</span></i><span lang=EN-GB style='font-size:12.0pt;line-height:115%; -font-family:"Times New Roman","serif"'> <a name="OLE_LINK46"></a><a -name="OLE_LINK45"></a><a name="OLE_LINK44">Shows the number of sequences bases -that are located in the FR regions / The total number of sequenced bases (the -percentage of sequenced bases that are present in the FR regions).</a></span></p> - -<p class=MsoNormalCxSpMiddle style='text-align:justify'><i><span lang=EN-GB -style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>Nt -in CDR:</span></i><span lang=EN-GB style='font-size:12.0pt;line-height:115%; -font-family:"Times New Roman","serif"'> Shows the number of sequenced bases -that are located in the CDR regions / <a name="OLE_LINK48"></a><a -name="OLE_LINK47">The total number of sequenced bases (the percentage of -sequenced bases that are present in the CDR regions).</a></span></p> - -<p class=MsoNormalCxSpMiddle style='text-align:justify'><i><span lang=EN-GB -style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>A: -</span></i><a name="OLE_LINK51"></a><a name="OLE_LINK50"></a><a -name="OLE_LINK49"><span lang=EN-GB style='font-size:12.0pt;line-height:115%; -font-family:"Times New Roman","serif"'>Shows the total number of sequenced -adenines / The total number of sequenced bases (the percentage of sequenced -bases that were adenines).</span></a></p> - -<p class=MsoNormalCxSpMiddle style='text-align:justify'><i><span lang=EN-GB -style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>C: -</span></i><a name="OLE_LINK53"></a><a name="OLE_LINK52"><span lang=EN-GB -style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>Shows -the total number of sequenced cytosines / The total number of sequenced bases -(the percentage of sequenced bases that were cytosines).</span></a></p> - -<p class=MsoNormalCxSpMiddle style='text-align:justify'><i><span lang=EN-GB -style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>T: -</span></i><a name="OLE_LINK57"></a><a name="OLE_LINK56"><span lang=EN-GB -style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>Shows -the total number of sequenced </span></a><a name="OLE_LINK55"></a><a -name="OLE_LINK54"><span lang=EN-GB style='font-size:12.0pt;line-height:115%; -font-family:"Times New Roman","serif"'>thymines</span></a><span lang=EN-GB -style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'> -/ The total number of sequenced bases (the percentage of sequenced bases that -were thymines).</span></p> - -<p class=MsoNormalCxSpMiddle style='text-align:justify'><i><span lang=EN-GB -style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>G: -</span></i><span lang=EN-GB style='font-size:12.0pt;line-height:115%; -font-family:"Times New Roman","serif"'>Shows the total number of sequenced <a -name="OLE_LINK59"></a><a name="OLE_LINK58">guanine</a>s / The total number of -sequenced bases (the percentage of sequenced bases that were guanines).</span></p> - -<p class=MsoNormalCxSpMiddle style='text-align:justify'><a name="OLE_LINK69"><b><span -lang=EN-GB style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>Graphs</span></b></a></p> - -<p class=MsoNormalCxSpMiddle style='text-align:justify'><a name="OLE_LINK75"></a><a -name="OLE_LINK74"></a><a name="OLE_LINK73"><span lang=EN-GB style='font-size: -12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>These graphs visualize -information on the patterns and targeting of SHM and thereby give information -into the repair pathways used to repair the U:G mismatches introduced by AID. The -data represented in these graphs can be downloaded in the download tab. More -information on the values found in healthy individuals of different ages can be -found in IJspeert and van Schouwenburg et al, PMID: 27799928</span></a><span -lang=EN-GB style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>. -<a name="OLE_LINK85"></a><a name="OLE_LINK84"></a></span></p> - -<p class=MsoNormalCxSpMiddle style='text-align:justify'><u><span lang=EN-GB -style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>Percentage -of mutations in AID and pol eta motives</span></u></p> - -<p class=MsoNormalCxSpMiddle style='text-align:justify'><span lang=EN-GB -style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>Visualizes -<a name="OLE_LINK80"></a><a name="OLE_LINK79"></a><a name="OLE_LINK78">for each -(sub)class </a>the percentage of mutations that are present in AID (RGYW or -WRCY) or polymerase eta motives (WA or TW) in the different subclasses </span><span -lang=EN-GB style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>(R=Purine, -Y=pyrimidine, W = A or T).</span></p> - -<p class=MsoNormalCxSpMiddle style='text-align:justify'><u><span lang=NL -style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>Relative -mutation patterns</span></u></p> - -<p class=MsoNormalCxSpMiddle style='text-align:justify'><span lang=EN-GB -style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>Visualizes -for each (sub)class the distribution of mutations between mutations at AT -locations and transitions or transversions at GC locations. </span></p> - -<p class=MsoNormalCxSpMiddle style='text-align:justify'><u><span lang=NL -style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>Absolute -mutation patterns</span></u></p> - -<p class=MsoNormalCxSpLast style='text-align:justify'><span lang=EN-GB -style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>Visualized -for each (sub)class the percentage of sequenced AT and GC bases that are -mutated. The mutations at GC bases are divided into transition and transversion -mutations<a name="OLE_LINK77"></a><a name="OLE_LINK76">. </a></span></p> - -<p class=MsoNormal><span lang=NL style='font-size:12.0pt;line-height:115%; -font-family:"Times New Roman","serif"'>Hanna IJspeert, Pauline A. van -Schouwenburg, David van Zessen, Ingrid Pico-Knijnenburg, Gertjan J. Driessen, -Andrew P. Stubbs, and Mirjam van der Burg (2016). </span><span -style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>Evaluation -of the Antigen-Experienced B-Cell Receptor Repertoire in Healthy Children and -Adults. In <i>Frontiers in Immunolog, 7, pp. e410-410. </i>[<a -href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5066086/"><span -style='color:windowtext'>doi:10.3389/fimmu.2016.00410</span></a>][<a -href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5066086/"><span -style='color:windowtext'>Link</span></a>]</span></p> - -</div> - -</body> - -</html>
--- a/shm_selection.htm Tue Sep 01 16:03:44 2020 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,128 +0,0 @@ -<html> - -<head> -<meta http-equiv=Content-Type content="text/html; charset=windows-1252"> -<meta name=Generator content="Microsoft Word 14 (filtered)"> -<style> -<!-- - /* Font Definitions */ - @font-face - {font-family:Calibri; - panose-1:2 15 5 2 2 2 4 3 2 4;} -@font-face - {font-family:UICTFontTextStyleBody;} - /* Style Definitions */ - p.MsoNormal, li.MsoNormal, div.MsoNormal - {margin-top:0in; - margin-right:0in; - margin-bottom:10.0pt; - margin-left:0in; - line-height:115%; - font-size:11.0pt; - font-family:"Calibri","sans-serif";} -a:link, span.MsoHyperlink - {color:blue; - text-decoration:underline;} -a:visited, span.MsoHyperlinkFollowed - {color:purple; - text-decoration:underline;} -span.apple-converted-space - {mso-style-name:apple-converted-space;} -.MsoChpDefault - {font-family:"Calibri","sans-serif";} -.MsoPapDefault - {margin-bottom:10.0pt; - line-height:115%;} -@page WordSection1 - {size:8.5in 11.0in; - margin:1.0in 1.0in 1.0in 1.0in;} -div.WordSection1 - {page:WordSection1;} ---> -</style> - -</head> - -<body lang=EN-US link=blue vlink=purple> - -<div class=WordSection1> - -<p class=MsoNormalCxSpFirst style='text-align:justify'><b><span lang=EN-GB -style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>References</span></b></p> - -<p class=MsoNormalCxSpMiddle style='text-align:justify'><span lang=EN-GB -style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"; -color:black'>Yaari, G. and Uduman, M. and Kleinstein, S. H. (2012). Quantifying -selection in high-throughput Immunoglobulin sequencing data sets. In<span -class=apple-converted-space> </span><em>Nucleic Acids Research, 40 (17), -pp. e134–e134.</em><span class=apple-converted-space><i> </i></span>[</span><span -lang=EN-GB><a href="http://dx.doi.org/10.1093/nar/gks457" target="_blank"><span -style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"; -color:#303030'>doi:10.1093/nar/gks457</span></a></span><span lang=EN-GB -style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"; -color:black'>][</span><span lang=EN-GB><a -href="http://dx.doi.org/10.1093/nar/gks457" target="_blank"><span -style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"; -color:#303030'>Link</span></a></span><span lang=EN-GB style='font-size:12.0pt; -line-height:115%;font-family:"Times New Roman","serif";color:black'>]</span></p> - -<p class=MsoNormalCxSpMiddle style='text-align:justify'><b><span lang=EN-GB -style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>Graphs</span></b></p> - -<p class=MsoNormalCxSpMiddle style='text-align:justify'><u><span lang=EN-GB -style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>AA -mutation frequency</span></u></p> - -<p class=MsoNormalCxSpMiddle style='text-align:justify'><span lang=EN-GB -style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>For -each class, the frequency of replacement mutations at each amino acid position -is shown, which is calculated by dividing the number of replacement mutations -at a particular amino acid position/the number sequences that have an amino -acid at that particular position. Since the length of the CDR1 and CDR2 region -is not the same for every VH gene, some amino acids positions are absent. -Therefore we calculate the frequency using the number of amino acids present at -that that particular location. </span></p> - -<p class=MsoNormalCxSpMiddle style='text-align:justify'><u><span lang=EN-GB -style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>Antigen -selection (BASELINe)</span></u></p> - -<p class=MsoNormalCxSpMiddle style='text-align:justify'><span lang=EN-GB -style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>Shows -the results of the analysis of antigen selection as performed using BASELINe. -Details on the analysis performed by BASELINe can be found in Yaari et al, -PMID: 22641856. The settings used for the analysis are</span><span lang=EN-GB -style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>: -focused, SHM targeting model: human Tri-nucleotide, custom bounderies. The -custom boundries are dependent on the ‘sequence starts at filter’. </span></p> - -<p class=MsoNormalCxSpMiddle style='line-height:normal'><span lang=NL -style='font-family:UICTFontTextStyleBody;color:black'>Leader: -1:26:38:55:65:104:-</span></p> - -<p class=MsoNormalCxSpMiddle style='line-height:normal'><span lang=NL -style='font-family:UICTFontTextStyleBody;color:black'>FR1: 27:27:38:55:65:104:-</span></p> - -<p class=MsoNormalCxSpMiddle style='line-height:normal'><span lang=NL -style='font-family:UICTFontTextStyleBody;color:black'>CDR1: 27:27:38:55:65:104:-</span></p> - -<p class=MsoNormalCxSpLast style='line-height:normal'><span lang=NL -style='font-family:UICTFontTextStyleBody;color:black'>FR2: 27:27:38:55:65:104:-</span></p> - -<p class=MsoNormal><span lang=NL style='font-size:12.0pt;line-height:115%; -font-family:"Times New Roman","serif"'>Hanna IJspeert, Pauline A. van -Schouwenburg, David van Zessen, Ingrid Pico-Knijnenburg, Gertjan J. Driessen, -Andrew P. Stubbs, and Mirjam van der Burg (2016). </span><span -style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>Evaluation -of the Antigen-Experienced B-Cell Receptor Repertoire in Healthy Children and -Adults. In <i>Frontiers in Immunolog, 7, pp. e410-410. </i>[<a -href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5066086/"><span -style='color:windowtext'>doi:10.3389/fimmu.2016.00410</span></a>][<a -href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5066086/"><span -style='color:windowtext'>Link</span></a>]</span></p> - -</div> - -</body> - -</html>
--- a/shm_transition.htm Tue Sep 01 16:03:44 2020 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,120 +0,0 @@ -<html> - -<head> -<meta http-equiv=Content-Type content="text/html; charset=windows-1252"> -<meta name=Generator content="Microsoft Word 14 (filtered)"> -<style> -<!-- - /* Font Definitions */ - @font-face - {font-family:Calibri; - panose-1:2 15 5 2 2 2 4 3 2 4;} - /* Style Definitions */ - p.MsoNormal, li.MsoNormal, div.MsoNormal - {margin-top:0in; - margin-right:0in; - margin-bottom:10.0pt; - margin-left:0in; - line-height:115%; - font-size:11.0pt; - font-family:"Calibri","sans-serif";} -a:link, span.MsoHyperlink - {color:blue; - text-decoration:underline;} -a:visited, span.MsoHyperlinkFollowed - {color:purple; - text-decoration:underline;} -p.msochpdefault, li.msochpdefault, div.msochpdefault - {mso-style-name:msochpdefault; - margin-right:0in; - margin-left:0in; - font-size:12.0pt; - font-family:"Calibri","sans-serif";} -p.msopapdefault, li.msopapdefault, div.msopapdefault - {mso-style-name:msopapdefault; - margin-right:0in; - margin-bottom:10.0pt; - margin-left:0in; - line-height:115%; - font-size:12.0pt; - font-family:"Times New Roman","serif";} -span.apple-converted-space - {mso-style-name:apple-converted-space;} -.MsoChpDefault - {font-size:10.0pt; - font-family:"Calibri","sans-serif";} -.MsoPapDefault - {margin-bottom:10.0pt; - line-height:115%;} -@page WordSection1 - {size:8.5in 11.0in; - margin:1.0in 1.0in 1.0in 1.0in;} -div.WordSection1 - {page:WordSection1;} ---> -</style> - -</head> - -<body lang=EN-US link=blue vlink=purple> - -<div class=WordSection1> - -<p class=MsoNormalCxSpFirst style='text-align:justify'><span style='font-size: -12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>These graphs and -tables give insight into the targeting and patterns of SHM. This can give -insight into the DNA repair pathways used to solve the U:G mismatches -introduced by AID. More information on the values found in healthy individuals -of different ages can be found in IJspeert and van Schouwenburg et al, PMID: -27799928.</span></p> - -<p class=MsoNormalCxSpMiddle style='text-align:justify'><b><span -style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>Graphs -</span></b></p> - -<p class=MsoNormalCxSpMiddle style='text-align:justify'><a name="OLE_LINK93"></a><a -name="OLE_LINK92"></a><a name="OLE_LINK91"><u><span style='font-size:12.0pt; -line-height:115%;font-family:"Times New Roman","serif"'>Heatmap transition -information</span></u></a></p> - -<p class=MsoNormalCxSpMiddle style='text-align:justify'><a name="OLE_LINK98"></a><a -name="OLE_LINK97"><span style='font-size:12.0pt;line-height:115%;font-family: -"Times New Roman","serif"'>Heatmaps visualizing for each subclass the frequency -of all possible substitutions. On the x-axes the original base is shown, while -the y-axes shows the new base. The darker the shade of blue, the more frequent -this type of substitution is occurring. </span></a></p> - -<p class=MsoNormalCxSpMiddle style='text-align:justify'><u><span -style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>Bargraph -transition information</span></u></p> - -<p class=MsoNormalCxSpMiddle style='text-align:justify'><span style='font-size: -12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>Bar graph -visualizing for each original base the distribution of substitutions into the other -bases. A graph is included for each (sub)class. </span></p> - -<p class=MsoNormalCxSpMiddle style='text-align:justify'><b><span -style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>Tables</span></b></p> - -<p class=MsoNormalCxSpMiddle style='text-align:justify'><span style='font-size: -12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>Transition -tables are shown for each (sub)class. All the original bases are listed -horizontally, while the new bases are listed vertically. </span></p> - -<p class=MsoNormal><span lang=NL style='font-size:12.0pt;line-height:115%; -font-family:"Times New Roman","serif"'>Hanna IJspeert, Pauline A. van -Schouwenburg, David van Zessen, Ingrid Pico-Knijnenburg, Gertjan J. Driessen, -Andrew P. Stubbs, and Mirjam van der Burg (2016). </span><span -style='font-size:12.0pt;line-height:115%;font-family:"Times New Roman","serif"'>Evaluation -of the Antigen-Experienced B-Cell Receptor Repertoire in Healthy Children and -Adults. In <i>Frontiers in Immunolog, 7, pp. e410-410. </i>[<a -href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5066086/"><span -style='color:windowtext'>doi:10.3389/fimmu.2016.00410</span></a>][<a -href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5066086/"><span -style='color:windowtext'>Link</span></a>]</span></p> - -</div> - -</body> - -</html>
--- a/summary_to_fasta.py Tue Sep 01 16:03:44 2020 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,42 +0,0 @@ -import argparse - -parser = argparse.ArgumentParser() -parser.add_argument("--input", help="The 1_Summary file of an IMGT zip file") -parser.add_argument("--fasta", help="The output fasta file") - -args = parser.parse_args() - -infile = args.input -fasta = args.fasta - -with open(infile, 'r') as i, open(fasta, 'w') as o: - first = True - id_col = 0 - seq_col = 0 - no_results = 0 - no_seqs = 0 - passed = 0 - for line in i: - splt = line.split("\t") - if first: - id_col = splt.index("Sequence ID") - seq_col = splt.index("Sequence") - first = False - continue - if len(splt) < 5: - no_results += 1 - continue - - ID = splt[id_col] - seq = splt[seq_col] - - if not len(seq) > 0: - no_seqs += 1 - continue - - o.write(">" + ID + "\n" + seq + "\n") - passed += 1 - - print "No results:", no_results - print "No sequences:", no_seqs - print "Written to fasta file:", passed
--- a/wrapper.sh Tue Sep 01 16:03:44 2020 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,913 +0,0 @@ -#!/bin/bash -#set -e -dir="$(cd "$(dirname "$0")" && pwd)" -input=$1 -method=$2 -log=$3 #becomes the main html page at the end -outdir=$4 -output="$outdir/index.html" #copied to $log location at the end -title="$5" -include_fr1=$6 -functionality=$7 -unique=$8 -naive_output=$9 -naive_output_ca=${10} -naive_output_cg=${11} -naive_output_cm=${12} -naive_output_ce=${13} -naive_output_all=${14} -filter_unique=${15} -filter_unique_count=${16} -class_filter=${17} -empty_region_filter=${18} -fast=${19} - -mkdir $outdir - -tar -xzf $dir/style.tar.gz -C $outdir - -echo "---------------- read parameters ----------------" -echo "---------------- read parameters ----------------<br />" > $log - -echo "unpacking IMGT file" - -type="`file $input`" -if [[ "$type" == *"Zip archive"* ]] ; then - echo "Zip archive" - echo "unzip $input -d $PWD/files/" - unzip $input -d $PWD/files/ -elif [[ "$type" == *"XZ compressed data"* ]] ; then - echo "ZX archive" - echo "tar -xJf $input -C $PWD/files/" - mkdir -p "$PWD/files/$title" - tar -xJf $input -C "$PWD/files/$title" -else - echo "Unrecognized format $type" - echo "Unrecognized format $type" > $log - exit 1 -fi - -cat "`find $PWD/files/ -name "1_*"`" > $PWD/summary.txt -cat "`find $PWD/files/ -name "2_*"`" > $PWD/gapped_nt.txt -cat "`find $PWD/files/ -name "3_*"`" > $PWD/sequences.txt -cat "`find $PWD/files/ -name "4_*"`" > $PWD/gapped_aa.txt -cat "`find $PWD/files/ -name "5_*"`" > $PWD/aa.txt -cat "`find $PWD/files/ -name "6_*"`" > $PWD/junction.txt -cat "`find $PWD/files/ -name "7_*"`" > $PWD/mutationanalysis.txt -cat "`find $PWD/files/ -name "8_*"`" > $PWD/mutationstats.txt -cat "`find $PWD/files/ -name "9_*"`" > $PWD/aa_change_stats.txt -cat "`find $PWD/files/ -name "10_*"`" > $PWD/hotspots.txt - -echo "---------------- unique id check ----------------" - -Rscript $dir/check_unique_id.r $PWD/summary.txt $PWD/gapped_nt.txt $PWD/sequences.txt $PWD/gapped_aa.txt $PWD/aa.txt $PWD/junction.txt $PWD/mutationanalysis.txt $PWD/mutationstats.txt $PWD/aa_change_stats.txt $PWD/hotspots.txt - -if [[ ${#BLASTN_DIR} -ge 5 ]] ; then - echo "On server, using BLASTN_DIR env: ${BLASTN_DIR}" -else - BLASTN_DIR="/home/galaxy/Downloads/ncbi-blast-2.4.0+/bin" - echo "Dev Galaxy set BLASTN_DIR to: ${BLASTN_DIR}" -fi - -echo "---------------- class identification ----------------" -echo "---------------- class identification ----------------<br />" >> $log - -python $dir/gene_identification.py --input $PWD/summary.txt --output $outdir/identified_genes.txt - -echo "---------------- merge_and_filter.r ----------------" -echo "---------------- merge_and_filter.r ----------------<br />" >> $log - -Rscript $dir/merge_and_filter.r $PWD/summary.txt $PWD/sequences.txt $PWD/mutationanalysis.txt $PWD/mutationstats.txt $PWD/hotspots.txt "$PWD/gapped_aa.txt" $outdir/identified_genes.txt $outdir/merged.txt $outdir/before_unique_filter.txt $outdir/unmatched.txt $method $functionality $unique ${filter_unique} ${filter_unique_count} ${class_filter} ${empty_region_filter} 2>&1 - -if [[ "${naive_output}" == "yes" ]] || [[ "$fast" == "no" ]] ; then - - echo "---------------- creating new IMGT zips ----------------" - echo "---------------- creating new IMGT zips ----------------<br />" >> $log - - mkdir $outdir/new_IMGT - - cp $PWD/summary.txt "$outdir/new_IMGT/1_Summary.txt" - cp $PWD/gapped_nt.txt "$outdir/new_IMGT/2_IMGT-gapped-nt-sequences.txt" - cp $PWD/sequences.txt "$outdir/new_IMGT/3_Nt-sequences.txt" - cp $PWD/gapped_aa.txt "$outdir/new_IMGT/4_IMGT-gapped-AA-sequences.txt" - cp $PWD/aa.txt "$outdir/new_IMGT/5_AA-sequences.txt" - cp $PWD/junction.txt "$outdir/new_IMGT/6_Junction.txt" - cp $PWD/mutationanalysis.txt "$outdir/new_IMGT/7_V-REGION-mutation-and-AA-change-table.txt" - cp $PWD/mutationstats.txt "$outdir/new_IMGT/8_V-REGION-nt-mutation-statistics.txt" - cp $PWD/aa_change_stats.txt "$outdir/new_IMGT/9_V-REGION-AA-change-statistics.txt" - cp $PWD/hotspots.txt "$outdir/new_IMGT/10_V-REGION-mutation-hotspots.txt" - - mkdir $outdir/new_IMGT_IGA - cp $outdir/new_IMGT/* $outdir/new_IMGT_IGA - - mkdir $outdir/new_IMGT_IGA1 - cp $outdir/new_IMGT/* $outdir/new_IMGT_IGA1 - - mkdir $outdir/new_IMGT_IGA2 - cp $outdir/new_IMGT/* $outdir/new_IMGT_IGA2 - - mkdir $outdir/new_IMGT_IGG - cp $outdir/new_IMGT/* $outdir/new_IMGT_IGG - - mkdir $outdir/new_IMGT_IGG1 - cp $outdir/new_IMGT/* $outdir/new_IMGT_IGG1 - - mkdir $outdir/new_IMGT_IGG2 - cp $outdir/new_IMGT/* $outdir/new_IMGT_IGG2 - - mkdir $outdir/new_IMGT_IGG3 - cp $outdir/new_IMGT/* $outdir/new_IMGT_IGG3 - - mkdir $outdir/new_IMGT_IGG4 - cp $outdir/new_IMGT/* $outdir/new_IMGT_IGG4 - - mkdir $outdir/new_IMGT_IGM - cp $outdir/new_IMGT/* $outdir/new_IMGT_IGM - - mkdir $outdir/new_IMGT_IGE - cp $outdir/new_IMGT/* $outdir/new_IMGT_IGE - - Rscript $dir/new_imgt.r $outdir/new_IMGT/ $outdir/merged.txt "-" 2>&1 - - Rscript $dir/new_imgt.r $outdir/new_IMGT_IGA/ $outdir/merged.txt "IGA" 2>&1 - Rscript $dir/new_imgt.r $outdir/new_IMGT_IGA1/ $outdir/merged.txt "IGA1" 2>&1 - Rscript $dir/new_imgt.r $outdir/new_IMGT_IGA2/ $outdir/merged.txt "IGA2" 2>&1 - - Rscript $dir/new_imgt.r $outdir/new_IMGT_IGG/ $outdir/merged.txt "IGG" 2>&1 - Rscript $dir/new_imgt.r $outdir/new_IMGT_IGG1/ $outdir/merged.txt "IGG1" 2>&1 - Rscript $dir/new_imgt.r $outdir/new_IMGT_IGG2/ $outdir/merged.txt "IGG2" 2>&1 - Rscript $dir/new_imgt.r $outdir/new_IMGT_IGG3/ $outdir/merged.txt "IGG3" 2>&1 - Rscript $dir/new_imgt.r $outdir/new_IMGT_IGG4/ $outdir/merged.txt "IGG4" 2>&1 - - Rscript $dir/new_imgt.r $outdir/new_IMGT_IGM/ $outdir/merged.txt "IGM" 2>&1 - - Rscript $dir/new_imgt.r $outdir/new_IMGT_IGE/ $outdir/merged.txt "IGE" 2>&1 - - - tmp="$PWD" - cd $outdir/new_IMGT/ #tar weirdness... - tar -cJf ../new_IMGT.txz * - - cd $outdir/new_IMGT_IGA/ - tar -cJf ../new_IMGT_IGA.txz * - - cd $outdir/new_IMGT_IGA1/ - tar -cJf ../new_IMGT_IGA1.txz * - - cd $outdir/new_IMGT_IGA2/ - tar -cJf ../new_IMGT_IGA2.txz * - - cd $outdir/new_IMGT_IGG/ - tar -cJf ../new_IMGT_IGG.txz * - - cd $outdir/new_IMGT_IGG1/ - tar -cJf ../new_IMGT_IGG1.txz * - - cd $outdir/new_IMGT_IGG2/ - tar -cJf ../new_IMGT_IGG2.txz * - - cd $outdir/new_IMGT_IGG3/ - tar -cJf ../new_IMGT_IGG3.txz * - - cd $outdir/new_IMGT_IGG4/ - tar -cJf ../new_IMGT_IGG4.txz * - - cd $outdir/new_IMGT_IGM/ - tar -cJf ../new_IMGT_IGM.txz * - - cd $outdir/new_IMGT_IGE/ - tar -cJf ../new_IMGT_IGE.txz * - - cd $tmp -fi - -echo "---------------- shm_csr.r ----------------" -echo "---------------- shm_csr.r ----------------<br />" >> $log - -classes="IGA,IGA1,IGA2,IGG,IGG1,IGG2,IGG3,IGG4,IGM,IGE,unmatched" -echo "R mutation analysis" -Rscript $dir/shm_csr.r $outdir/merged.txt $classes $outdir ${empty_region_filter} 2>&1 - -echo "---------------- plot_pdfs.r ----------------" -echo "---------------- plot_pdfs.r ----------------<br />" >> $log - -echo "Rscript $dir/shm_csr.r $outdir/pdfplots.RData $outdir 2>&1" - -Rscript $dir/plot_pdf.r "$outdir/pdfplots.RData" "$outdir" 2>&1 - -echo "---------------- shm_csr.py ----------------" -echo "---------------- shm_csr.py ----------------<br />" >> $log - -python $dir/shm_csr.py --input $outdir/merged.txt --genes $classes --empty_region_filter "${empty_region_filter}" --output $outdir/hotspot_analysis.txt - -echo "---------------- aa_histogram.r ----------------" -echo "---------------- aa_histogram.r ----------------<br />" >> $log - -Rscript $dir/aa_histogram.r $outdir/aa_id_mutations.txt $outdir/absent_aa_id.txt "IGA,IGG,IGM,IGE" $outdir/ 2>&1 -if [ -e "$outdir/aa_histogram_.png" ]; then - mv $outdir/aa_histogram_.png $outdir/aa_histogram.png - mv $outdir/aa_histogram_.pdf $outdir/aa_histogram.pdf - mv $outdir/aa_histogram_.txt $outdir/aa_histogram.txt - mv $outdir/aa_histogram_absent_.txt $outdir/aa_histogram_absent.txt - mv $outdir/aa_histogram_count_.txt $outdir/aa_histogram_count.txt - mv $outdir/aa_histogram_sum_.txt $outdir/aa_histogram_sum.txt -fi - -genes=(IGA IGA1 IGA2 IGG IGG1 IGG2 IGG3 IGG4 IGM IGE) - -funcs=(sum mean median) -funcs=(sum) - -echo "---------------- sequence_overview.r ----------------" -echo "---------------- sequence_overview.r ----------------<br />" >> $log - -mkdir $outdir/sequence_overview - -Rscript $dir/sequence_overview.r $outdir/before_unique_filter.txt $outdir/merged.txt $outdir/sequence_overview $classes $outdir/hotspot_analysis_sum.txt ${empty_region_filter} 2>&1 - -echo "<table border='1'>" > $outdir/base_overview.html - -while IFS=$'\t' read ID class seq A C G T -do - echo "<tr><td>$ID</td><td>$seq</td><td>$class</td><td>$A</td><td>$C</td><td>$G</td><td>$T</td></tr>" >> $outdir/base_overview.html -done < $outdir/sequence_overview/ntoverview.txt - -echo "<html><center><h1>$title</h1></center>" > $output -echo "<meta name='viewport' content='width=device-width, initial-scale=1'>" >> $output -echo "<script type='text/javascript' src='jquery-1.11.0.min.js'></script>" >> $output -echo "<script type='text/javascript' src='tabber.js'></script>" >> $output -echo "<script type='text/javascript' src='script.js'></script>" >> $output -echo "<link rel='stylesheet' type='text/css' href='style.css'>" >> $output -echo "<link rel='stylesheet' type='text/css' href='pure-min.css'>" >> $output - -matched_count="`cat $outdir/merged.txt | grep -v 'unmatched' | tail -n +2 | wc -l`" -unmatched_count="`cat $outdir/unmatched.txt | tail -n +2 | wc -l`" -total_count=$((matched_count + unmatched_count)) -perc_count=$((unmatched_count / total_count * 100)) -perc_count=`bc -l <<< "scale=2; ${unmatched_count} / ${total_count} * 100"` -perc_count=`bc -l <<< "scale=2; (${unmatched_count} / ${total_count} * 100 ) / 1"` - -echo "<center><h2>Total: ${total_count}</h2></center>" >> $output -echo "<center><h2>Matched: ${matched_count} Unmatched: ${unmatched_count}</h2></center>" >> $output -echo "<center><h2>Percentage unmatched: ${perc_count}</h2></center>" >> $output - -echo "---------------- main tables ----------------" -echo "---------------- main tables ----------------<br />" >> $log - -echo "<div class='tabber'>" >> $output -echo "<div class='tabbertab' title='SHM Overview' style='width: 3000px;'>" >> $output - -for func in ${funcs[@]} -do - - echo "---------------- $func table ----------------" - echo "---------------- $func table ----------------<br />" >> $log - - cat $outdir/mutations_${func}.txt $outdir/shm_overview_tandem_row.txt $outdir/hotspot_analysis_${func}.txt > $outdir/data_${func}.txt - - echo "---------------- pattern_plots.r ----------------" - echo "---------------- pattern_plots.r ----------------<br />" >> $log - - Rscript $dir/pattern_plots.r $outdir/data_${func}.txt $outdir/aid_motives $outdir/relative_mutations $outdir/absolute_mutations $outdir/shm_overview.txt 2>&1 - - echo "<table class='pure-table pure-table-striped'>" >> $output - echo "<thead><tr><th>info</th>" >> $output - - if [ "${class_filter}" != "101_101" ] ; then - - for gene in ${genes[@]} - do - tmp=`cat $outdir/${gene}_${func}_n.txt` - echo "<th><a href='matched_${gene}_${func}.txt'>${gene} (N = $tmp)</a></th>" >> $output - done - - tmp=`cat $outdir/all_${func}_n.txt` - echo "<th><a href='matched_all_${func}.txt'>all (N = $tmp)</a></th>" >> $output - tmp=`cat $outdir/unmatched_${func}_n.txt` - echo "<th><a href='unmatched.txt'>unmatched (N = ${unmatched_count})</a></th><tr></thead>" >> $output - - while IFS=, read name cax cay caz ca1x ca1y ca1z ca2x ca2y ca2z cgx cgy cgz cg1x cg1y cg1z cg2x cg2y cg2z cg3x cg3y cg3z cg4x cg4y cg4z cmx cmy cmz cex cey cez unx uny unz allx ally allz - do - if [ "$name" == "FR R/S (ratio)" ] || [ "$name" == "CDR R/S (ratio)" ] || [ "$name" == "Tandems/Expected (ratio)" ] ; then #meh - echo "<tr><td>$name</td><td>${cax}/${cay} (${caz})</td><td>${ca1x}/${ca1y} (${ca1z})</td><td>${ca2x}/${ca2y} (${ca2z})</td><td>${cgx}/${cgy} (${cgz})</td><td>${cg1x}/${cg1y} (${cg1z})</td><td>${cg2x}/${cg2y} (${cg2z})</td><td>${cg3x}/${cg3y} (${cg3z})</td><td>${cg4x}/${cg4y} (${cg4z})</td><td>${cmx}/${cmy} (${cmz})</td><td>${cex}/${cey} (${cez})</td><td>${allx}/${ally} (${allz})</td><td>${unx}/${uny} (${unz})</td></tr>" >> $output - elif [ "$name" == "Median of Number of Mutations (%)" ] ; then - echo "<tr><td>$name</td><td>${caz}%</td><td>${ca1z}%</td><td>${ca2z}%</td><td>${cgz}%</td><td>${cg1z}%</td><td>${cg2z}%</td><td>${cg3z}%</td><td>${cg4z}%</td><td>${cmz}%</td><td>${cez}%</td><td>${allz}%</td><td>${unz}%</td></tr>" >> $output - else - echo "<tr><td>$name</td><td>${cax}/${cay} (${caz}%)</td><td>${ca1x}/${ca1y} (${ca1z}%)</td><td>${ca2x}/${ca2y} (${ca2z}%)</td><td>${cgx}/${cgy} (${cgz}%)</td><td>${cg1x}/${cg1y} (${cg1z}%)</td><td>${cg2x}/${cg2y} (${cg2z}%)</td><td>${cg3x}/${cg3y} (${cg3z}%)</td><td>${cg4x}/${cg4y} (${cg4z}%)</td><td>${cmx}/${cmy} (${cmz}%)</td><td>${cex}/${cey} (${cez}%)</td><td>${allx}/${ally} (${allz}%)</td><td>${unx}/${uny} (${unz}%)</td></tr>" >> $output - fi - done < $outdir/data_${func}.txt - - else - tmp=`cat $outdir/all_${func}_n.txt` - echo "<th><a href='matched_all_${func}.txt'>all (N = $tmp)</a></th>" >> $output - - while IFS=, read name cax cay caz ca1x ca1y ca1z ca2x ca2y ca2z cgx cgy cgz cg1x cg1y cg1z cg2x cg2y cg2z cg3x cg3y cg3z cg4x cg4y cg4z cmx cmy cmz cex cey cez unx uny unz allx ally allz - do - if [ "$name" == "FR R/S (ratio)" ] || [ "$name" == "CDR R/S (ratio)" ] ; then #meh - echo "<tr><td>$name</td><td>${allx}/${ally}</td></tr>" >> $output - elif [ "$name" == "Median of Number of Mutations (%)" ] ; then - echo "<tr><td>$name</td><td>${allz}%</td></tr>" >> $output - else - echo "<tr><td>$name</td><td>${allx}/${ally} (${allz}%)</td></tr>" >> $output - fi - done < $outdir/data_${func}.txt - - fi - echo "</table>" >> $output - #echo "<a href='data_${func}.txt'>Download data</a>" >> $output -done - -echo "<a href='aid_motives.pdf'><img src='aid_motives.png' /></a><br />" >> $output -echo "<a href='relative_mutations.pdf'><img src='relative_mutations.png' /></a><br />" >> $output -echo "<a href='absolute_mutations.pdf'><img src='absolute_mutations.png' /></a><br />" >> $output -echo "<br />" >> $output -cat $dir/shm_overview.htm >> $output -echo "</div>" >> $output #SHM overview tab end - -echo "---------------- images ----------------" -echo "---------------- images ----------------<br />" >> $log - -echo "<div class='tabbertab' title='SHM Frequency' style='width: 3000px;'></a>" >> $output - -if [ -a $outdir/scatter.png ] -then - echo "<a href='scatter.pdf'><img src='scatter.png'/><br />" >> $output -fi -if [ -a $outdir/frequency_ranges.png ] -then - echo "<a href='frequency_ranges.pdf'><img src='frequency_ranges.png'/></a><br />" >> $output -fi - -echo "<br />" >> $output -cat $dir/shm_frequency.htm >> $output - -echo "</div>" >> $output #SHM frequency tab end - -echo "<div class='tabbertab' title='Transition tables' style='width: 3000px;'>" >> $output - -echo "<table border='0'>" >> $output - -for gene in ${genes[@]} -do - echo "<tr>" >> $output - echo "<td><h1>${gene}</h1></td>" >> $output - - if [ -e $outdir/transitions_heatmap_${gene}.png ] - then - echo "<td><a href='transitions_heatmap_${gene}.pdf'><img src='transitions_heatmap_${gene}.png' /></a></td>" >> $output - else - echo "<td></td>" >> $output - fi - - if [ -e $outdir/transitions_stacked_${gene}.png ] - then - echo "<td><a href='transitions_stacked_${gene}.pdf'><img src='transitions_stacked_${gene}.png' /></a></td>" >> $output - else - echo "<td></td>" >> $output - fi - - echo "<td><table style='border-left-width: 1;' class='pure-table transition-table pure-table-bordered'>" >> $output - echo "<tr><td></td><td colspan="5"><center>To</center></td></tr>" >> $output - first="true" - while IFS=, read from a c g t - do - if [ "$first" == "true" ] ; then - echo "<tr><td rowspan='5'>From</td><td>$from</td><td>$a</td><td>$c</td><td>$g</td><td>$t</td></tr>" >> $output - first="false" - else - echo "<tr><td>$from</td><td>$a</td><td>$c</td><td>$g</td><td>$t</td></tr>" >> $output - fi - done < $outdir/transitions_${gene}_sum.txt - echo "</table></td>" >> $output - - echo "</tr>" >> $output -done - -echo "<tr>" >> $output -echo "<td><h1>All</h1></td>" >> $output -echo "<td><a href='transitions_heatmap_all.pdf'><img src='transitions_heatmap_all.png' /></a></td>" >> $output -echo "<td><a href='transitions_stacked_all.pdf'><img src='transitions_stacked_all.png' /></a></td>" >> $output -echo "<td><table style='border-left-width: 1;' class='pure-table transition-table pure-table-bordered'>" >> $output -echo "<tr><td></td><td colspan="5"><center>To</center></td></tr>" >> $output -first="true" -while IFS=, read from a c g t - do - if [ "$first" == "true" ] ; then - echo "<tr><td rowspan='5'>From</td><td>$from</td><td>$a</td><td>$c</td><td>$g</td><td>$t</td></tr>" >> $output - first="false" - else - echo "<tr><td>$from</td><td>$a</td><td>$c</td><td>$g</td><td>$t</td></tr>" >> $output - fi -done < $outdir/transitions_all_sum.txt -echo "</table></td>" >> $output - -echo "</tr>" >> $output - -echo "</table>" >> $output - -echo "<br />" >> $output -cat $dir/shm_transition.htm >> $output - -echo "</div>" >> $output #transition tables tab end - -echo "<div class='tabbertab' title='Antigen Selection'>" >> $output - -if [ -e $outdir/aa_histogram.png ] -then - echo "<a href='aa_histogram.pdf'><img src='aa_histogram.png'/></a><br />" >> $output -fi - -if [ -e $outdir/aa_histogram_IGA.png ] -then - echo "<a href='aa_histogram_IGA.pdf'><img src='aa_histogram_IGA.png'/></a><br />" >> $output -fi - -if [ -e $outdir/aa_histogram_IGG.png ] -then - echo "<a href='aa_histogram_IGG.pdf'><img src='aa_histogram_IGG.png'/></a><br />" >> $output -fi - -if [ -e $outdir/aa_histogram_IGM.png ] -then - echo "<a href='aa_histogram_IGM.pdf'><img src='aa_histogram_IGM.png'/></a><br />" >> $output -fi - -if [ -e $outdir/aa_histogram_IGE.png ] -then - echo "<a href='aa_histogram_IGE.pdf'><img src='aa_histogram_IGE.png'/></a><br />" >> $output -fi - - - -if [[ "$fast" == "no" ]] ; then - - - - echo "---------------- baseline ----------------" - echo "---------------- baseline ----------------<br />" >> $log - tmp="$PWD" - - mkdir $outdir/baseline - - echo "<center><h1>BASELINe</h1>" >> $output - header_substring="Based on CDR1, FR2, CDR2, FR3 (27:27:38:55:65:104:-)" - - baseline_boundaries="27:27:38:55:65:104:-" - - if [[ "${empty_region_filter}" == "leader" ]] ; then - baseline_boundaries="1:26:38:55:65:104:-" - header_substring="Based on FR1, CDR1, FR2, CDR2, FR3 (1:26:38:55:65:104,-)" - fi - - echo "<p>${header_substring}</p></center>" >> $output - - mkdir $outdir/baseline/IGA_IGG_IGM - if [[ $(wc -l < $outdir/new_IMGT/1_Summary.txt) -gt "1" ]]; then - cd $outdir/baseline/IGA_IGG_IGM - bash $dir/baseline/wrapper.sh 1 1 1 1 0 0 "${baseline_boundaries}" $outdir/new_IMGT.txz "IGA_IGG_IGM_IGE" "$dir/baseline/IMGTVHreferencedataset20161215.fa" "$outdir/baseline.pdf" "Sequence.ID" "$outdir/baseline.txt" - else - echo "No sequences" > "$outdir/baseline.txt" - fi - - mkdir $outdir/baseline/IGA - if [[ $(wc -l < $outdir/new_IMGT_IGA/1_Summary.txt) -gt "1" ]]; then - cd $outdir/baseline/IGA - bash $dir/baseline/wrapper.sh 1 1 1 1 0 0 "${baseline_boundaries}" $outdir/new_IMGT_IGA.txz "IGA" "$dir/baseline/IMGTVHreferencedataset20161215.fa" "$outdir/baseline_IGA.pdf" "Sequence.ID" "$outdir/baseline_IGA.txt" - else - echo "No IGA sequences" > "$outdir/baseline_IGA.txt" - fi - - mkdir $outdir/baseline/IGG - if [[ $(wc -l < $outdir/new_IMGT_IGG/1_Summary.txt) -gt "1" ]]; then - cd $outdir/baseline/IGG - bash $dir/baseline/wrapper.sh 1 1 1 1 0 0 "${baseline_boundaries}" $outdir/new_IMGT_IGG.txz "IGG" "$dir/baseline/IMGTVHreferencedataset20161215.fa" "$outdir/baseline_IGG.pdf" "Sequence.ID" "$outdir/baseline_IGG.txt" - else - echo "No IGG sequences" > "$outdir/baseline_IGG.txt" - fi - - mkdir $outdir/baseline/IGM - if [[ $(wc -l < $outdir/new_IMGT_IGM/1_Summary.txt) -gt "1" ]]; then - cd $outdir/baseline/IGM - bash $dir/baseline/wrapper.sh 1 1 1 1 0 0 "${baseline_boundaries}" $outdir/new_IMGT_IGM.txz "IGM" "$dir/baseline/IMGTVHreferencedataset20161215.fa" "$outdir/baseline_IGM.pdf" "Sequence.ID" "$outdir/baseline_IGM.txt" - else - echo "No IGM sequences" > "$outdir/baseline_IGM.txt" - fi - - mkdir $outdir/baseline/IGE - if [[ $(wc -l < $outdir/new_IMGT_IGE/1_Summary.txt) -gt "1" ]]; then - cd $outdir/baseline/IGE - bash $dir/baseline/wrapper.sh 1 1 1 1 0 0 "${baseline_boundaries}" $outdir/new_IMGT_IGE.txz "IGE" "$dir/baseline/IMGTVHreferencedataset20161215.fa" "$outdir/baseline_IGE.pdf" "Sequence.ID" "$outdir/baseline_IGE.txt" - else - echo "No IGE sequences" > "$outdir/baseline_IGE.txt" - fi - - cd $tmp - - echo "Cleaning up *.RData files" - find $outdir/baseline -name "*.RData" -type f -delete - - if [ -e $outdir/baseline.pdf ] - then - echo "<embed src='baseline.pdf' width='700px' height='1000px'>" >> $output - fi - - if [ -e $outdir/baseline_IGA.pdf ] - then - echo "<embed src='baseline_IGA.pdf' width='700px' height='1000px'>" >> $output - fi - - if [ -e $outdir/baseline_IGG.pdf ] - then - echo "<embed src='baseline_IGG.pdf' width='700px' height='1000px'>" >> $output - fi - - if [ -e $outdir/baseline_IGM.pdf ] - then - echo "<embed src='baseline_IGM.pdf' width='700px' height='1000px'>" >> $output - fi - - if [ -e $outdir/baseline_IGE.pdf ] - then - echo "<embed src='baseline_IGE.pdf' width='700px' height='1000px'>" >> $output - fi -fi - -echo "<br />" >> $output -cat $dir/shm_selection.htm >> $output - -echo "</div>" >> $output #antigen selection tab end - -echo "<div class='tabbertab' title='CSR'>" >> $output #CSR tab - -if [ -e $outdir/IGA.png ] -then - echo "<a href='IGA.pdf'><img src='IGA.png'/></a><br />" >> $output -fi -if [ -e $outdir/IGG.png ] -then - echo "<a href='IGG.pdf'><img src='IGG.png'/></a><br />" >> $output -fi - -echo "<br />" >> $output -cat $dir/shm_csr.htm >> $output - -echo "</div>" >> $output #CSR tab end - -if [[ "$fast" == "no" ]] ; then - - echo "---------------- change-o MakeDB ----------------" - - mkdir $outdir/change_o - - tmp="$PWD" - - cd $outdir/change_o - - bash $dir/change_o/makedb.sh $outdir/new_IMGT.txz false false false $outdir/change_o/change-o-db.txt - bash $dir/change_o/define_clones.sh bygroup $outdir/change_o/change-o-db.txt gene first ham none min complete 3.0 $outdir/change_o/change-o-db-defined_clones.txt $outdir/change_o/change-o-defined_clones-summary.txt - Rscript $dir/change_o/select_first_in_clone.r $outdir/change_o/change-o-db-defined_clones.txt $outdir/change_o/change-o-db-defined_first_clones.txt 2>&1 - - mkdir $outdir/new_IMGT_changeo - cp $outdir/new_IMGT/* $outdir/new_IMGT_changeo - - Rscript $dir/new_imgt.r $outdir/new_IMGT_changeo $outdir/change_o/change-o-db-defined_first_clones.txt "-" 2>&1 - - cd $outdir/new_IMGT_changeo - tar -cJf ../new_IMGT_first_seq_of_clone.txz * - cd $outdir/change_o - - rm -rf $outdir/new_IMGT_changeo - - Rscript $dir/merge.r $outdir/change_o/change-o-db-defined_clones.txt $outdir/merged.txt "all" "Sequence.ID,best_match" "SEQUENCE_ID" "Sequence.ID" $outdir/change_o/change-o-db-defined_clones.txt 2>&1 - echo "Rscript $dir/merge.r $outdir/change_o/change-o-db-defined_clones.txt $outdir/$outdir/merged.txt 'all' 'Sequence.ID,best_match' 'Sequence.ID' 'Sequence.ID' '\t' $outdir/change_o/change-o-db-defined_clones.txt 2>&1" - - if [[ $(wc -l < $outdir/new_IMGT_IGA/1_Summary.txt) -gt "1" ]]; then - bash $dir/change_o/makedb.sh $outdir/new_IMGT_IGA.txz false false false $outdir/change_o/change-o-db-IGA.txt - bash $dir/change_o/define_clones.sh bygroup $outdir/change_o/change-o-db-IGA.txt gene first ham none min complete 3.0 $outdir/change_o/change-o-db-defined_clones-IGA.txt $outdir/change_o/change-o-defined_clones-summary-IGA.txt - Rscript $dir/change_o/select_first_in_clone.r $outdir/change_o/change-o-db-defined_clones-IGA.txt $outdir/change_o/change-o-db-defined_first_clones-IGA.txt 2>&1 - - mkdir $outdir/new_IMGT_IGA_changeo - cp $outdir/new_IMGT/* $outdir/new_IMGT_IGA_changeo - - Rscript $dir/new_imgt.r $outdir/new_IMGT_IGA_changeo $outdir/change_o/change-o-db-defined_first_clones-IGA.txt "-" 2>&1 - - cd $outdir/new_IMGT_IGA_changeo - tar -cJf ../new_IMGT_IGA_first_seq_of_clone.txz * - - rm -rf $outdir/new_IMGT_IGA_changeo - - cd $outdir/change_o - else - echo "No IGA sequences" > "$outdir/change_o/change-o-db-defined_clones-IGA.txt" - echo "No IGA sequences" > "$outdir/change_o/change-o-defined_clones-summary-IGA.txt" - fi - - if [[ $(wc -l < $outdir/new_IMGT_IGG/1_Summary.txt) -gt "1" ]]; then - bash $dir/change_o/makedb.sh $outdir/new_IMGT_IGG.txz false false false $outdir/change_o/change-o-db-IGG.txt - bash $dir/change_o/define_clones.sh bygroup $outdir/change_o/change-o-db-IGG.txt gene first ham none min complete 3.0 $outdir/change_o/change-o-db-defined_clones-IGG.txt $outdir/change_o/change-o-defined_clones-summary-IGG.txt - Rscript $dir/change_o/select_first_in_clone.r $outdir/change_o/change-o-db-defined_clones-IGG.txt $outdir/change_o/change-o-db-defined_first_clones-IGG.txt 2>&1 - - mkdir $outdir/new_IMGT_IGG_changeo - cp $outdir/new_IMGT/* $outdir/new_IMGT_IGG_changeo - - Rscript $dir/new_imgt.r $outdir/new_IMGT_IGG_changeo $outdir/change_o/change-o-db-defined_first_clones-IGG.txt "-" 2>&1 - - cd $outdir/new_IMGT_IGG_changeo - tar -cJf ../new_IMGT_IGG_first_seq_of_clone.txz * - rm -rf $outdir/new_IMGT_IGG_changeo - - cd $outdir/change_o - else - echo "No IGG sequences" > "$outdir/change_o/change-o-db-defined_clones-IGG.txt" - echo "No IGG sequences" > "$outdir/change_o/change-o-defined_clones-summary-IGG.txt" - fi - - if [[ $(wc -l < $outdir/new_IMGT_IGM/1_Summary.txt) -gt "1" ]]; then - bash $dir/change_o/makedb.sh $outdir/new_IMGT_IGM.txz false false false $outdir/change_o/change-o-db-IGM.txt - bash $dir/change_o/define_clones.sh bygroup $outdir/change_o/change-o-db-IGM.txt gene first ham none min complete 3.0 $outdir/change_o/change-o-db-defined_clones-IGM.txt $outdir/change_o/change-o-defined_clones-summary-IGM.txt - Rscript $dir/change_o/select_first_in_clone.r $outdir/change_o/change-o-db-defined_clones-IGM.txt $outdir/change_o/change-o-db-defined_first_clones-IGM.txt 2>&1 - - mkdir $outdir/new_IMGT_IGM_changeo - cp $outdir/new_IMGT/* $outdir/new_IMGT_IGM_changeo - - Rscript $dir/new_imgt.r $outdir/new_IMGT_IGM_changeo $outdir/change_o/change-o-db-defined_first_clones-IGM.txt "-" 2>&1 - - cd $outdir/new_IMGT_IGM_changeo - tar -cJf ../new_IMGT_IGM_first_seq_of_clone.txz * - - rm -rf $outdir/new_IMGT_IGM_changeo - - cd $outdir/change_o - else - echo "No IGM sequences" > "$outdir/change_o/change-o-db-defined_clones-IGM.txt" - echo "No IGM sequences" > "$outdir/change_o/change-o-defined_clones-summary-IGM.txt" - fi - - if [[ $(wc -l < $outdir/new_IMGT_IGE/1_Summary.txt) -gt "1" ]]; then - bash $dir/change_o/makedb.sh $outdir/new_IMGT_IGE.txz false false false $outdir/change_o/change-o-db-IGE.txt - bash $dir/change_o/define_clones.sh bygroup $outdir/change_o/change-o-db-IGE.txt gene first ham none min complete 3.0 $outdir/change_o/change-o-db-defined_clones-IGE.txt $outdir/change_o/change-o-defined_clones-summary-IGE.txt - Rscript $dir/change_o/select_first_in_clone.r $outdir/change_o/change-o-db-defined_clones-IGE.txt $outdir/change_o/change-o-db-defined_first_clones-IGE.txt 2>&1 - - mkdir $outdir/new_IMGT_IGE_changeo - cp $outdir/new_IMGT/* $outdir/new_IMGT_IGE_changeo - - Rscript $dir/new_imgt.r $outdir/new_IMGT_IGE_changeo $outdir/change_o/change-o-db-defined_first_clones-IGE.txt "-" 2>&1 - - cd $outdir/new_IMGT_IGE_changeo - tar -cJf ../new_IMGT_IGE_first_seq_of_clone.txz * - - rm -rf $outdir/new_IMGT_IGE_changeo - - cd $outdir/change_o - else - echo "No IGE sequences" > "$outdir/change_o/change-o-db-defined_clones-IGE.txt" - echo "No IGE sequences" > "$outdir/change_o/change-o-defined_clones-summary-IGE.txt" - fi - - cd "$tmp" - - rm -rf $outdir/new_IMGT - rm -rf $outdir/new_IMGT_IGA/ - rm -rf $outdir/new_IMGT_IGA1/ - rm -rf $outdir/new_IMGT_IGA2/ - rm -rf $outdir/new_IMGT_IGG/ - rm -rf $outdir/new_IMGT_IGG1/ - rm -rf $outdir/new_IMGT_IGG2/ - rm -rf $outdir/new_IMGT_IGG3/ - rm -rf $outdir/new_IMGT_IGG4/ - rm -rf $outdir/new_IMGT_IGM/ - rm -rf $outdir/new_IMGT_IGE/ - - echo "<div class='tabbertab' title='Clonal Relation' style='width: 7000px;'>" >> $output #clonality tab - - function clonality_table { - local infile=$1 - local outfile=$2 - - echo "<table class='pure-table pure-table-striped'>" >> $outfile - echo "<thead><tr><th>Clone size</th><th>Nr of clones</th><th>Nr of sequences</th></tr></thead>" >> $outfile - - first='true' - - while read size clones seqs - do - if [[ "$first" == "true" ]]; then - first="false" - continue - fi - echo "<tr><td>$size</td><td>$clones</td><td>$seqs</td></tr>" >> $outfile - done < $infile - - echo "</table>" >> $outfile - } - echo "<div class='tabber'>" >> $output - - echo "<div class='tabbertab' title='All'>" >> $output - clonality_table $outdir/change_o/change-o-defined_clones-summary.txt $output - echo "</div>" >> $output - - echo "<div class='tabbertab' title='IGA'>" >> $output - clonality_table $outdir/change_o/change-o-defined_clones-summary-IGA.txt $output - echo "</div>" >> $output - - echo "<div class='tabbertab' title='IGG'>" >> $output - clonality_table $outdir/change_o/change-o-defined_clones-summary-IGG.txt $output - echo "</div>" >> $output - - echo "<div class='tabbertab' title='IGM'>" >> $output - clonality_table $outdir/change_o/change-o-defined_clones-summary-IGM.txt $output - echo "</div>" >> $output - - echo "<div class='tabbertab' title='IGE'>" >> $output - clonality_table $outdir/change_o/change-o-defined_clones-summary-IGM.txt $output - echo "</div>" >> $output - - echo "<div class='tabbertab' title='Overlap' style='width: 7000px;'>" >> $output - cat "$outdir/sequence_overview/index.html" | sed -e 's:</td>:</td>\n:g' | sed "s:href='\(.*\).html:href='sequence_overview/\1.html:g" >> $output # rewrite href to 'sequence_overview/..." - echo "</div>" >> $output - - echo "</div>" >> $output #clonality tabber end - - echo "<br />" >> $output - cat $dir/shm_clonality.htm >> $output - - echo "</div>" >> $output #clonality tab end - -fi - -echo "<div class='tabbertab' title='Downloads'>" >> $output - -echo "<table class='pure-table pure-table-striped'>" >> $output -echo "<thead><tr><th>info</th><th>link</th></tr></thead>" >> $output -echo "<tr><td>The complete dataset</td><td><a href='merged.txt' download='merged.txt' >Download</a></td></tr>" >> $output -echo "<tr><td>The filtered dataset</td><td><a href='filtered.txt' download='filtered.txt' >Download</a></td></tr>" >> $output -echo "<tr><td>The alignment info on the unmatched sequences</td><td><a href='unmatched.txt' download='unmatched.txt' >Download</a></td></tr>" >> $output - -echo "<tr><td colspan='2' style='background-color:#E0E0E0;'>SHM Overview</td></tr>" >> $output -echo "<tr><td>The SHM Overview table as a dataset</td><td><a href='shm_overview.txt' download='shm_overview.txt' >Download</a></td></tr>" >> $output -echo "<tr><td>Motif data per sequence ID</td><td><a href='motif_per_seq.txt' download='motif_per_seq.txt' >Download</a></td></tr>" >> $output -echo "<tr><td>Mutation data per sequence ID</td><td><a href='mutation_by_id.txt' download='mutation_by_id.txt' >Download</a></td></tr>" >> $output -echo "<tr><td>Base count for every sequence</td><td><a href='base_overview.html'>View</a></td></tr>" >> $output -echo "<tr><td>The data used to generate the percentage of mutations in AID and pol eta motives plot</td><td><a href='aid_motives.txt' download='aid_motives.txt' >Download</a></td></tr>" >> $output -echo "<tr><td>The data used to generate the relative mutation patterns plot</td><td><a href='relative_mutations.txt' download='relative_mutations.txt' >Download</a></td></tr>" >> $output -echo "<tr><td>The data used to generate the absolute mutation patterns plot</td><td><a href='absolute_mutations.txt' download='absolute_mutations.txt' >Download</a></td></tr>" >> $output -echo "<tr><td>Data about tandem mutations by ID</td><td><a href='tandems_by_id.txt' download='tandems_by_id.txt' >Download</a></td></tr>" >> $output - -echo "<tr><td colspan='2' style='background-color:#E0E0E0;'>SHM Frequency</td></tr>" >> $output -echo "<tr><td>The data generate the frequency scatter plot</td><td><a href='scatter.txt' download='scatter.txt' >Download</a></td></tr>" >> $output -echo "<tr><td>The data used to generate the frequency by class plot</td><td><a href='frequency_ranges_classes.txt' download='frequency_ranges_classes.txt' >Download</a></td></tr>" >> $output -echo "<tr><td>The data for frequency by subclass</td><td><a href='frequency_ranges_subclasses.txt' download='frequency_ranges_subclasses.txt' >Download</a></td></tr>" >> $output - -echo "<tr><td colspan='2' style='background-color:#E0E0E0;'>Transition Tables</td></tr>" >> $output -echo "<tr><td>The data for the 'all' transition plot</td><td><a href='transitions_all_sum.txt' download='transitions_all_sum.txt' >Download</a></td></tr>" >> $output -echo "<tr><td>The data for the 'IGA' transition plot</td><td><a href='transitions_IGA_sum.txt' download='transitions_IGA_sum.txt' >Download</a></td></tr>" >> $output -echo "<tr><td>The data for the 'IGA1' transition plot</td><td><a href='transitions_IGA1_sum.txt' download='transitions_IGA1_sum.txt' >Download</a></td></tr>" >> $output -echo "<tr><td>The data for the 'IGA2' transition plot</td><td><a href='transitions_IGA2_sum.txt' download='transitions_IGA2_sum.txt' >Download</a></td></tr>" >> $output -echo "<tr><td>The data for the 'IGG' transition plot</td><td><a href='transitions_IGG_sum.txt' download='transitions_IGG_sum.txt' >Download</a></td></tr>" >> $output -echo "<tr><td>The data for the 'IGG1' transition plot</td><td><a href='transitions_IGG1_sum.txt' download='transitions_IGG1_sum.txt' >Download</a></td></tr>" >> $output -echo "<tr><td>The data for the 'IGG2' transition plot</td><td><a href='transitions_IGG2_sum.txt' download='transitions_IGG2_sum.txt' >Download</a></td></tr>" >> $output -echo "<tr><td>The data for the 'IGG3' transition plot</td><td><a href='transitions_IGG3_sum.txt' download='transitions_IGG3_sum.txt' >Download</a></td></tr>" >> $output -echo "<tr><td>The data for the 'IGG4' transition plot</td><td><a href='transitions_IGG4_sum.txt' download='transitions_IGG4_sum.txt' >Download</a></td></tr>" >> $output -echo "<tr><td>The data for the 'IGM' transition plot</td><td><a href='transitions_IGM_sum.txt' download='transitions_IGM_sum.txt' >Download</a></td></tr>" >> $output -echo "<tr><td>The data for the 'IGE' transition plot</td><td><a href='transitions_IGE_sum.txt' download='transitions_IGE_sum.txt' >Download</a></td></tr>" >> $output - -echo "<tr><td colspan='2' style='background-color:#E0E0E0;'>Antigen Selection</td></tr>" >> $output -echo "<tr><td>AA mutation data per sequence ID</td><td><a href='aa_id_mutations.txt' download='aa_id_mutations.txt' >Download</a></td></tr>" >> $output -echo "<tr><td>Presence of AA per sequence ID</td><td><a href='absent_aa_id.txt' download='absent_aa_id.txt' >Download</a></td></tr>" >> $output - -echo "<tr><td>The data used to generate the aa mutation frequency plot</td><td><a href='aa_histogram_sum.txt' download='aa_histogram_sum.txt' >Download</a></td></tr>" >> $output -echo "<tr><td>The data used to generate the aa mutation frequency plot for IGA</td><td><a href='aa_histogram_sum_IGA.txt' download='aa_histogram_sum_IGA.txt' >Download</a></td></tr>" >> $output -echo "<tr><td>The data used to generate the aa mutation frequency plot for IGG</td><td><a href='aa_histogram_sum_IGG.txt' download='aa_histogram_sum_IGG.txt' >Download</a></td></tr>" >> $output -echo "<tr><td>The data used to generate the aa mutation frequency plot for IGM</td><td><a href='aa_histogram_sum_IGM.txt' download='aa_histogram_sum_IGM.txt' >Download</a></td></tr>" >> $output -echo "<tr><td>The data used to generate the aa mutation frequency plot for IGE</td><td><a href='aa_histogram_sum_IGE.txt' download='aa_histogram_sum_IGE.txt' >Download</a></td></tr>" >> $output - -echo "<tr><td>Baseline PDF (<a href='http://selection.med.yale.edu/baseline/'>http://selection.med.yale.edu/baseline/</a>)</td><td><a href='baseline.pdf' download='baseline.pdf' >Download</a></td></tr>" >> $output -echo "<tr><td>Baseline data</td><td><a href='baseline.txt' download='baseline.txt' >Download</a></td></tr>" >> $output -echo "<tr><td>Baseline IGA PDF</td><td><a href='baseline_IGA.pdf' download='baseline_IGA.pdf' >Download</a></td></tr>" >> $output -echo "<tr><td>Baseline IGA data</td><td><a href='baseline_IGA.txt' download='baseline_IGA.txt' >Download</a></td></tr>" >> $output -echo "<tr><td>Baseline IGG PDF</td><td><a href='baseline_IGG.pdf' download='baseline_IGG.pdf' >Download</a></td></tr>" >> $output -echo "<tr><td>Baseline IGG data</td><td><a href='baseline_IGG.txt' download='baseline_IGG.txt' >Download</a></td></tr>" >> $output -echo "<tr><td>Baseline IGM PDF</td><td><a href='baseline_IGM.pdf' download='baseline_IGM.pdf' >Download</a></td></tr>" >> $output -echo "<tr><td>Baseline IGM data</td><td><a href='baseline_IGM.txt' download='baseline_IGM.txt' >Download</a></td></tr>" >> $output -echo "<tr><td>Baseline IGE PDF</td><td><a href='baseline_IGE.pdf' download='baseline_IGE.pdf' >Download</a></td></tr>" >> $output -echo "<tr><td>Baseline IGE data</td><td><a href='baseline_IGE.txt' download='baseline_IGE.txt' >Download</a></td></tr>" >> $output - -echo "<tr><td colspan='2' style='background-color:#E0E0E0;'>CSR</td></tr>" >> $output -echo "<tr><td>The data for the IGA subclass distribution plot</td><td><a href='IGA_pie.txt' download='IGA_pie.txt' >Download</a></td></tr>" >> $output -echo "<tr><td>The data for the IGG subclass distribution plot</td><td><a href='IGG_pie.txt' download='IGG_pie.txt' >Download</a></td></tr>" >> $output - - -echo "<tr><td colspan='2' style='background-color:#E0E0E0;'>Clonal Relation</td></tr>" >> $output -echo "<tr><td>Sequence overlap between subclasses</td><td><a href='sequence_overview/index.html'>View</a></td></tr>" >> $output -echo "<tr><td>The Change-O DB file with defined clones and subclass annotation</td><td><a href='change_o/change-o-db-defined_clones.txt' download='change_o/change-o-db-defined_clones.txt' >Download</a></td></tr>" >> $output -echo "<tr><td>The Change-O DB defined clones summary file</td><td><a href='change_o/change-o-defined_clones-summary.txt' download='change_o/change-o-defined_clones-summary.txt' >Download</a></td></tr>" >> $output -echo "<tr><td>An IMGT archive with just just the first sequence of a clone</td><td><a href='new_IMGT_first_seq_of_clone.txz' download='new_IMGT_first_seq_of_clone.txz' >Download</a></td></tr>" >> $output - -echo "<tr><td>The Change-O DB file with defined clones of IGA</td><td><a href='change_o/change-o-db-defined_clones-IGA.txt' download='change_o/change-o-db-defined_clones-IGA.txt' >Download</a></td></tr>" >> $output -echo "<tr><td>The Change-O DB defined clones summary file of IGA</td><td><a href='change_o/change-o-defined_clones-summary-IGA.txt' download='change_o/change-o-defined_clones-summary-IGA.txt' >Download</a></td></tr>" >> $output -echo "<tr><td>An IMGT archive with just just the first sequence of a clone (IGA)</td><td><a href='new_IMGT_IGA_first_seq_of_clone.txz' download='new_IMGT_IGA_first_seq_of_clone.txz' >Download</a></td></tr>" >> $output - -echo "<tr><td>The Change-O DB file with defined clones of IGG</td><td><a href='change_o/change-o-db-defined_clones-IGG.txt' download='change_o/change-o-db-defined_clones-IGG.txt' >Download</a></td></tr>" >> $output -echo "<tr><td>The Change-O DB defined clones summary file of IGG</td><td><a href='change_o/change-o-defined_clones-summary-IGG.txt' download='change_o/change-o-defined_clones-summary-IGG.txt' >Download</a></td></tr>" >> $output -echo "<tr><td>An IMGT archive with just just the first sequence of a clone (IGG)</td><td><a href='new_IMGT_IGG_first_seq_of_clone.txz' download='new_IMGT_IGG_first_seq_of_clone.txz' >Download</a></td></tr>" >> $output - -echo "<tr><td>The Change-O DB file with defined clones of IGM</td><td><a href='change_o/change-o-db-defined_clones-IGM.txt' download='change_o/change-o-db-defined_clones-IGM.txt' >Download</a></td></tr>" >> $output -echo "<tr><td>The Change-O DB defined clones summary file of IGM</td><td><a href='change_o/change-o-defined_clones-summary-IGM.txt' download='change_o/change-o-defined_clones-summary-IGM.txt' >Download</a></td></tr>" >> $output -echo "<tr><td>An IMGT archive with just just the first sequence of a clone (IGM)</td><td><a href='new_IMGT_IGM_first_seq_of_clone.txz' download='new_IMGT_IGM_first_seq_of_clone.txz' >Download</a></td></tr>" >> $output - -echo "<tr><td>The Change-O DB file with defined clones of IGE</td><td><a href='change_o/change-o-db-defined_clones-IGE.txt' download='change_o/change-o-db-defined_clones-IGE.txt' >Download</a></td></tr>" >> $output -echo "<tr><td>The Change-O DB defined clones summary file of IGE</td><td><a href='change_o/change-o-defined_clones-summary-IGE.txt' download='change_o/change-o-defined_clones-summary-IGE.txt' >Download</a></td></tr>" >> $output -echo "<tr><td>An IMGT archive with just just the first sequence of a clone (IGE)</td><td><a href='new_IMGT_IGE_first_seq_of_clone.txz' download='new_IMGT_IGE_first_seq_of_clone.txz' >Download</a></td></tr>" >> $output - -echo "<tr><td colspan='2' style='background-color:#E0E0E0;'>Filtered IMGT output files</td></tr>" >> $output -echo "<tr><td>An IMGT archive with just the matched and filtered sequences</td><td><a href='new_IMGT.txz' download='new_IMGT.txz' >Download</a></td></tr>" >> $output -echo "<tr><td>An IMGT archive with just the matched and filtered IGA sequences</td><td><a href='new_IMGT_IGA.txz' download='new_IMGT_IGA.txz' >Download</a></td></tr>" >> $output -echo "<tr><td>An IMGT archive with just the matched and filtered IGA1 sequences</td><td><a href='new_IMGT_IGA1.txz' download='new_IMGT_IGA1.txz' >Download</a></td></tr>" >> $output -echo "<tr><td>An IMGT archive with just the matched and filtered IGA2 sequences</td><td><a href='new_IMGT_IGA2.txz' download='new_IMGT_IGA2.txz' >Download</a></td></tr>" >> $output -echo "<tr><td>An IMGT archive with just the matched and filtered IGG sequences</td><td><a href='new_IMGT_IGG.txz' download='new_IMGT_IGG.txz' >Download</a></td></tr>" >> $output -echo "<tr><td>An IMGT archive with just the matched and filtered IGG1 sequences</td><td><a href='new_IMGT_IGG1.txz' download='new_IMGT_IGG1.txz' >Download</a></td></tr>" >> $output -echo "<tr><td>An IMGT archive with just the matched and filtered IGG2 sequences</td><td><a href='new_IMGT_IGG2.txz' download='new_IMGT_IGG2.txz' >Download</a></td></tr>" >> $output -echo "<tr><td>An IMGT archive with just the matched and filtered IGG3 sequences</td><td><a href='new_IMGT_IGG3.txz' download='new_IMGT_IGG3.txz' >Download</a></td></tr>" >> $output -echo "<tr><td>An IMGT archive with just the matched and filtered IGG4 sequences</td><td><a href='new_IMGT_IGG4.txz' download='new_IMGT_IGG4.txz' >Download</a></td></tr>" >> $output -echo "<tr><td>An IMGT archive with just the matched and filtered IGM sequences</td><td><a href='new_IMGT_IGM.txz' download='new_IMGT_IGM.txz' >Download</a></td></tr>" >> $output -echo "<tr><td>An IMGT archive with just the matched and filtered IGE sequences</td><td><a href='new_IMGT_IGE.txz' download='new_IMGT_IGE.txz' >Download</a></td></tr>" >> $output - -echo "</table>" >> $output - -echo "<br />" >> $output -cat $dir/shm_downloads.htm >> $output - -echo "</div>" >> $output #downloads tab end - -echo "</div>" >> $output #tabs end - -echo "</html>" >> $output - - -echo "---------------- naive_output.r ----------------" -echo "---------------- naive_output.r ----------------<br />" >> $log - -if [[ "$naive_output" == "yes" ]] -then - echo "output naive output" - if [[ "${class_filter}" == "101_101" ]] - then - echo "copy new_IMGT.txz to ${naive_output_all}" - cp $outdir/new_IMGT.txz ${naive_output_all} - else - echo "copy for classes" - cp $outdir/new_IMGT_IGA.txz ${naive_output_ca} - cp $outdir/new_IMGT_IGG.txz ${naive_output_cg} - cp $outdir/new_IMGT_IGM.txz ${naive_output_cm} - cp $outdir/new_IMGT_IGE.txz ${naive_output_ce} - fi -fi - -echo "</table>" >> $outdir/base_overview.html - -mv $log $outdir/log.html - -echo "<html><center><h1><a href='index.html'>Click here for the results</a></h1>Tip: Open it in a new tab (middle mouse button or right mouse button -> 'open in new tab' on the link above)<br />" > $log -echo "<table border = 1>" >> $log -echo "<thead><tr><th>Info</th><th>Sequences</th><th>Percentage</th></tr></thead>" >> $log -tIFS="$TMP" -IFS=$'\t' -while read step seq perc - do - echo "<tr>" >> $log - echo "<td>$step</td>" >> $log - echo "<td>$seq</td>" >> $log - echo "<td>${perc}%</td>" >> $log - echo "</tr>" >> $log -done < $outdir/filtering_steps.txt -echo "</table>" >> $log -echo "<br />" >> $log -cat $dir/shm_first.htm >> $log -echo "</center></html>" >> $log - -IFS="$tIFS" - - -echo "---------------- Done! ----------------" -echo "---------------- Done! ----------------<br />" >> $outdir/log.html - - - - - - - - - - - - - - - - - - - - -