--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/detect_putative_ltr.R	Thu Jul 21 08:23:15 2022 +0000
@@ -0,0 +1,283 @@
+#!/usr/bin/env Rscript
+initial_options <- commandArgs(trailingOnly = FALSE)
+file_arg_name <- "--file="
+script_name <- normalizePath(sub(file_arg_name, "", initial_options[grep(file_arg_name, initial_options)]))
+script_dir <- dirname(script_name)
+library(optparse)
+
+parser <- OptionParser()
+option_list <- list(
+  make_option(c("-g", "--gff3"), action = "store", type = "character",
+              help = "gff3 with dante results", default = NULL),
+  make_option(c("-s", "--reference_sequence"), action = "store", type = "character",
+              help = "reference sequence as fasta", default = NULL),
+  make_option(c("-o", "--output"), action = "store", type = "character",
+              help = "output file path and prefix", default = NULL),
+  make_option(c("-c", "--cpu"), type = "integer", default = 5,
+              help = "Number of cpu to use [default %default]", metavar = "number"),
+  make_option(c("-M", "--max_missing_domains"), type = "integer", default = 0,
+              help = "Maximum number of missing domains is retrotransposon [default %default]",
+              metavar = "number"),
+  make_option(c("-L", "--min_relative_length"), type = "numeric", default = 0.6,
+              help = "Minimum relative length of protein domain to be considered for retrostransposon detection [default %default]",
+              metavar = "number")
+
+)
+description <- paste(strwrap(""))
+
+epilogue <- ""
+parser <- OptionParser(option_list = option_list, epilogue = epilogue, description = description,
+                       usage = "usage: %prog COMMAND [OPTIONS]")
+opt <- parse_args(parser, args = commandArgs(TRUE))
+
+
+# load packages
+suppressPackageStartupMessages({
+  library(rtracklayer)
+  library(Biostrings)
+  library(BSgenome)
+  library(parallel)
+})
+
+
+# CONFIGURATION
+OFFSET <- 15000
+# load configuration files and functions:
+lineage_file <- paste0(script_dir, "/databases/lineage_domain_order.csv")
+FDM_file <- paste0(script_dir, "/databases/feature_distances_model.RDS")
+trna_db <- paste0(script_dir, "/databases/tRNAscan-SE_ALL_spliced-no_plus-old-tRNAs_UC_unique-3ends.fasta")
+ltr_utils_r <- paste0(script_dir, "/R/ltr_utils.R")
+if (file.exists(lineage_file) & file.exists(trna_db)) {
+  lineage_info <- read.table(lineage_file, sep = "\t", header = TRUE, as.is = TRUE)
+  FDM <- readRDS(FDM_file)
+  source(ltr_utils_r)
+}else {
+  # this destination work is installed using conda
+  lineage_file <- paste0(script_dir, "/../share/dante_ltr/databases/lineage_domain_order.csv")
+  FDM_file <- paste0(script_dir, "/../share/dante_ltr/databases/feature_distances_model.RDS")
+  trna_db <- paste0(script_dir, "/../share/dante_ltr/databases/tRNAscan-SE_ALL_spliced-no_plus-old-tRNAs_UC_unique-3ends.fasta")
+  ltr_utils_r <- paste0(script_dir, "/../share/dante_ltr/R/ltr_utils.R")
+  if (file.exists(lineage_file) & file.exists((trna_db))) {
+    lineage_info <- read.table(lineage_file, sep = "\t", header = TRUE, as.is = TRUE)
+    source(ltr_utils_r)
+    FDM <- readRDS(FDM_file)
+  }else(
+    stop("configuration files not found")
+  )
+}
+
+
+# for testing
+if (FALSE) {
+  g <- rtracklayer::import("/mnt/raid/454_data/cuscuta/Ceuropea_assembly_v4/0_final_asm_hifiasm+longstitch/repeat_annotation/DANTE_on_CEUR_filtered_short_names.gff3")
+  s <- readDNAStringSet("/mnt/raid/454_data/cuscuta/Ceuropea_assembly_v4/0_final_asm_hifiasm+longstitch/asm.bp.p.ctg_scaffolds.short_names.fa")
+  lineage_info <- read.table("/mnt/raid/users/petr/workspace/ltr_finder_test/lineage_domain_order.csv", sep = "\t", header = TRUE, as.is = TRUE)
+
+  g <- rtracklayer::import("/mnt/raid/users/petr/workspace/dante_ltr/test_data/sample_DANTE_unfiltered.gff3")
+  g <- rtracklayer::import("/mnt/raid/users/petr/workspace/ltr_finder_test/test_data/DANTE_filtered_part.gff3")
+  s <- readDNAStringSet("/mnt/raid/users/petr/workspace/ltr_finder_test/test_data/Rbp_part.fa")
+
+  # oriza
+  g <- rtracklayer::import("/mnt/raid/users/petr/workspace/dante_ltr/test_data/big_test_data/DANTE_full_oryza.gff3")
+  s <- readDNAStringSet("/mnt/raid/users/petr/workspace/dante_ltr/test_data/big_test_data/o_sativa_msu7.0.fasta")
+
+  g <- rtracklayer::import("/mnt/raid/users/petr/workspace/dante_ltr/test_data
+  /DANTE_Vfaba_chr5.gff3")
+  s <- readDNAStringSet("/mnt/ceph/454_data/Vicia_faba_assembly/assembly/ver_210910
+  /fasta_parts/211010_Vfaba_chr5.fasta")
+
+  g <- rtracklayer::import("/mnt/raid/users/petr/workspace/dante_ltr/test_data/big_test_data//Cocoa_theobroma_DANTE_filtered.gff3")
+  s <- readDNAStringSet("/mnt/raid/users/petr/workspace/dante_ltr/test_data/big_test_data/Cocoa_theobroma_chr1.fasta.gz")
+  # test on bigger data:
+
+  g <- rtracklayer::import("/mnt/raid/users/petr/workspace/dante_ltr/test_data/tmp/DANTE_unfiltered/1.gff3")
+  s <- readDNAStringSet("/mnt/raid/users/petr/workspace/dante_ltr/test_data/tmp/fasta_parts/1.fasta")
+
+  source("R/ltr_utils.R")
+  ## feature distance model
+  FDM <- readRDS("./databases/feature_distances_model.RDS")
+  g <- rtracklayer::import("./test_data/sample_DANTE.gff3")
+  s <- readDNAStringSet("./test_data/sample_genome.fasta")
+  outfile <- "/mnt/raid/users/petr/workspace/ltr_finder_test/te_with_domains_2.gff3"
+  lineage_info <- read.table("databases/lineage_domain_order.csv", sep = "\t", header =
+    TRUE, as.is = TRUE)
+  trna_db <- "./databases/tRNAscan-SE_ALL_spliced-no_plus-old-tRNAs_UC_unique-3ends.fasta"
+  opt <- list(min_relative_length=0.6, cpu = 8)
+
+}
+
+# MAIN #############################################################
+
+# load data:
+
+cat("reading gff...")
+g <- rtracklayer::import(opt$gff3, format = 'gff3')  # DANTE gff3
+cat("done\n")
+cat("reading fasta...")
+s <- readDNAStringSet(opt$reference_sequence)  # genome assembly
+cat("done\n")
+outfile <- opt$output
+# clean sequence names:
+names(s) <- gsub(" .+", "", names(s))
+lineage_domain <- lineage_info$Domains.order
+lineage_domain_span <- lineage_info$domain_span
+lineage_ltr_mean_length <- lineage_info$ltr_length
+lineage_offset5prime <- lineage_info$offset5prime
+lineage_offset3prime <- lineage_info$offset3prime
+ln <- gsub("ss/I", "ss_I", gsub("_", "/", gsub("/", "|", lineage_info$Lineage)))
+names(lineage_offset3prime) <-  ln
+names(lineage_offset5prime) <-  ln
+names(lineage_domain) <- ln
+names(lineage_domain_span) <- ln
+names(lineage_ltr_mean_length) <- ln
+lineage_domains_sequence <- unlist(mapply(function(d,l) {
+  paste(strsplit(d, " ")[[1]], ":", l, sep = "")
+}, d = lineage_domain, l = names(lineage_domain)))
+
+# filter g gff3
+g <- dante_filtering(g, Relative_Length = opt$min_relative_length) # default
+
+seqlengths(g) <- seqlengths(s)[names(seqlengths(g))]
+g <- add_coordinates_of_closest_neighbor(g)
+
+# add info about domain order:
+g$domain_order <- as.numeric(factor(paste(g$Name, g$Final_Classification, sep = ":"), levels = lineage_domains_sequence))
+# set NA to 0 in  g$domain_order ( some domains are not fromm ClassI elements
+g$domain_order[is.na(g$domain_order)] <- 0
+
+# NOTE - some operation is faster of GrangesList but some on list of data.frames
+# this is primary clusteing
+cls <- get_domain_clusters(g)
+gcl <- split(as.data.frame(g), cls)
+# gcl_as_GRL <- split(g, cls)  # delete?
+
+cls_alt <- get_domain_clusters_alt(g, FDM)
+g$Cluster <- as.numeric(factor(cls_alt))
+
+gcl_alt <- split(as.data.frame(g), cls_alt)
+
+TE_partial <-  GRanges(seqnames =  sapply(gcl_alt, function(x) x$seqnames[1]),
+                       Name = sapply(gcl_alt, function(x) x$Final_Classification[1]),
+                       Final_Classification = sapply(gcl_alt, function(x) x$Final_Classification[1]),
+                       ID = sapply(gcl_alt, function(x) paste0("TE_partial_", sprintf("%08d", x$Cluster[1]))),
+                       strand = sapply(gcl_alt, function(x) x$strand[1]),
+                       Ndomains = sapply(gcl_alt, function(x) nrow(x)),
+                       type = "transposable_element",
+                       source = "dante_ltr",
+                       Rank="D",
+                       IRanges(start = sapply(gcl_alt, function(x) min(x$start)),
+                               end = sapply(gcl_alt, function(x) max(x$end)))
+)
+g$Ndomains_in_cluster <- count_occurences_for_each_element(g$Cluster)
+g$Parent <- paste0("TE_partial_", sprintf("%08d", g$Cluster))
+g$Rank <- "D"
+
+# keep only partial TE with more than one domain
+TE_partial_with_more_than_one_domain <- TE_partial[TE_partial$Ndomains > 1]
+g_with_more_than_one_domain <- g[as.vector(g$Ndomains_in_cluster > 1)]
+
+# first filtering  - remove TEs with low number of domains
+gcl_clean <- clean_domain_clusters(gcl, lineage_domain_span, min_domains = 5 - opt$max_missing_domains)
+
+# glc annotation
+gcl_clean_lineage <- sapply(gcl_clean, function(x)  x$Final_Classification[1])
+gcl_clean_domains <- sapply(gcl_clean, function(x) ifelse(x$strand[1] == "-",
+                                                paste(rev(x$Name), collapse = " "),
+                                                paste(x$Name, collapse = " "))
+)
+
+# compare detected domains with domains in lineages from REXdb database
+dd <- mapply(domain_distance,
+             d_query = gcl_clean_domains,
+             d_reference = lineage_domain[gcl_clean_lineage])
+
+# get lineages which has correct number and order of domains
+# gcl_clean2 <- gcl_clean[gcl_clean_domains == lineage_domain[gcl_clean_lineage]]
+gcl_clean2 <- gcl_clean[dd <= opt$max_missing_domains]
+
+gcl_clean_with_domains <- gcl_clean2[check_ranges(gcl_clean2, s)]
+gr <- get_ranges(gcl_clean_with_domains)
+
+
+cat('Number of analyzed regions:\n')
+cat('Total number of domain clusters             : ', length(gcl), '\n')
+cat('Number of clean clusters                    : ', length(gcl_clean), '\n')
+cat('Number of clusters with complete domain set : ', length(gcl_clean_with_domains), '\n')
+
+
+te_strand <- sapply(gcl_clean_with_domains, function(x)x$strand[1])
+te_lineage <- sapply(gcl_clean_with_domains, function(x)x$Final_Classification[1])
+
+max_left_offset <- ifelse(te_strand == "+", lineage_offset5prime[te_lineage], lineage_offset3prime[te_lineage])
+max_right_offset <- ifelse(te_strand == "-", lineage_offset5prime[te_lineage], lineage_offset3prime[te_lineage])
+
+grL <- get_ranges_left(gcl_clean_with_domains, max_left_offset)
+grR <- get_ranges_right(gcl_clean_with_domains, max_right_offset)
+
+s_left <- getSeq(s, grL)
+s_right <- getSeq(s, grR)
+
+expected_ltr_length <- lineage_ltr_mean_length[sapply(gcl_clean_with_domains, function (x)x$Final_Classification[1])]
+# for statistics
+RT <- g[g$Name == "RT" & substring(g$Final_Classification, 1, 11) == "Class_I|LTR"]
+# cleanup
+#rm(g)
+rm(gcl)
+rm(gcl_clean)
+rm(gcl_clean2)
+
+names(te_strand) <- paste(seqnames(gr), start(gr), end(gr), sep = "_")
+names(s_left) <- paste(seqnames(grL), start(grL), end(grL), sep = "_")
+names(s_right) <- paste(seqnames(grR), start(grR), end(grR), sep = "_")
+cat('Identification of LTRs...')
+TE <- mclapply(seq_along(gr), function(x)get_TE(s_left[x],
+                                                s_right[x],
+                                                gcl_clean_with_domains[[x]],
+                                                gr[x],
+                                                grL[x],
+                                                grR[x],
+                                                expected_ltr_length[x]),
+               mc.set.seed = TRUE, mc.cores = opt$cpu, mc.preschedule = FALSE
+)
+
+cat('done.\n')
+
+good_TE <- TE[!sapply(TE, is.null)]
+cat('Number of putative TE with identified LTR   :', length(good_TE), '\n')
+
+# TODO - extent TE region to cover also TSD
+ID <- paste0('TE_', sprintf("%08d", seq(good_TE)))
+gff3_list <- mcmapply(get_te_gff3, g = good_TE, ID = ID, mc.cores = opt$cpu)
+
+cat('Identification of PBS ...')
+gff3_list2 <- mclapply(gff3_list, FUN = add_pbs, s = s, trna_db = trna_db, mc.set.seed = TRUE, mc.cores = opt$cpu, mc.preschedule = FALSE)
+cat('done\n')
+gff3_out <- do.call(c, gff3_list2)
+
+# define new source
+src <- as.character(gff3_out$source)
+src[is.na(src)] <- "dante_ltr"
+gff3_out$source <- src
+gff3_out$Rank <- get_te_rank(gff3_out)
+
+# add partial TEs but first remove all ovelaping
+TE_partial_parent_part <- TE_partial_with_more_than_one_domain[TE_partial_with_more_than_one_domain %outside% gff3_out]
+TE_partial_domain_part <-  g[g$Parent %in% TE_partial_parent_part$ID]
+
+gff3_out <- sort(c(gff3_out, TE_partial_domain_part, TE_partial_parent_part), by = ~ seqnames * start)
+# modify ID and Parent - add seqname - this will ensure it is unique is done on chunk level
+gff3_out$ID[!is.na(gff3_out$ID)] <- paste0(gff3_out$ID[!is.na(gff3_out$ID)], "_", seqnames(gff3_out)[!is.na(gff3_out$ID)])
+gff3_out$Parent[!is.na(gff3_out$Parent)] <- paste0(gff3_out$Parent[!is.na(gff3_out$Parent)], "_", seqnames(gff3_out)[!is.na(gff3_out$Parent)])
+
+export(gff3_out, con = paste0(outfile, ".gff3"), format = 'gff3')
+
+all_tbl <- get_te_statistics(gff3_out, RT)
+all_tbl <- cbind(Classification = rownames(all_tbl), all_tbl)
+write.table(all_tbl, file = paste0(outfile, "_statistics.csv"), sep = "\t", quote = FALSE, row.names = FALSE)
+# export fasta files:
+s_te <- get_te_sequences(gff3_out, s)
+for (i in seq_along(s_te)) {
+  outname <- paste0(outfile, "_", names(s_te)[i], ".fasta")
+  writeXStringSet(s_te[[i]], filepath = outname)
+}
+