Mercurial > repos > petr-novak > dante_ltr

annotate extract_putative_ltr.R @ 9:1aa578e6c8b3 draft

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"planemo upload commit 9488b982bae902f1868785ec4ad47134dac50ff3"
author petr-novak
date Wed, 29 Jun 2022 09:25:54 +0000
parents 9de392f2fc02
children
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1 #!/usr/bin/env Rscript
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2 initial_options <- commandArgs(trailingOnly = FALSE)
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3 file_arg_name <- "--file="
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4 script_name <- normalizePath(sub(file_arg_name, "", initial_options[grep(file_arg_name, initial_options)]))
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5 script_dir <- dirname(script_name)
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6 library(optparse)
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7
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8 parser <- OptionParser()
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9 option_list <- list(
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10 make_option(c("-g", "--gff3"), action = "store", type = "character",
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11 help = "gff3 with dante results", default = NULL),
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12 make_option(c("-s", "--reference_sequence"), action = "store", type = "character",
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13 help = "reference sequence as fasta", default = NULL),
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14 make_option(c("-o", "--output"), action = "store", type = "character",
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15 help = "output file path and prefix", default = NULL),
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16 make_option(c("-c", "--cpu"), type = "integer", default = 5,
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17 help = "Number of cpu to use [default %default]", metavar = "number"),
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18 make_option(c("-M", "--max_missing_domains"), type = "integer", default = 0,
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19 help = "Maximum number of missing domains is retrotransposon [default %default]",
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20 metavar = "number"),
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21 make_option(c("-L", "--min_relative_length"), type = "numeric", default = 0.6,
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22 help = "Minimum relative length of protein domain to be considered for retrostransposon detection [default %default]",
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23 metavar = "number")
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24
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25 )
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26 description <- paste(strwrap(""))
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27
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28 epilogue <- ""
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29 parser <- OptionParser(option_list = option_list, epilogue = epilogue, description = description,
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30 usage = "usage: %prog COMMAND [OPTIONS]")
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31 opt <- parse_args(parser, args = commandArgs(TRUE))
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33
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34 # load packages
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35 suppressPackageStartupMessages({
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36 library(rtracklayer)
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37 library(Biostrings)
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38 library(BSgenome)
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39 library(parallel)
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40 })
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42
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43 # CONFIGURATION
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44 OFFSET <- 15000
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45 # load configuration files and functions:
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46 lineage_file <- paste0(script_dir, "/databases/lineage_domain_order.csv")
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47 FDM_file <- paste0(script_dir, "/databases/feature_distances_model.RDS")
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48 trna_db <- paste0(script_dir, "/databases/tRNAscan-SE_ALL_spliced-no_plus-old-tRNAs_UC_unique-3ends.fasta")
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49 ltr_utils_r <- paste0(script_dir, "/R/ltr_utils.R")
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50 if (file.exists(lineage_file) & file.exists(trna_db)) {
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51 lineage_info <- read.table(lineage_file, sep = "\t", header = TRUE, as.is = TRUE)
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52 FDM <- readRDS(FDM_file)
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53 source(ltr_utils_r)
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54 }else {
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55 # this destination work is installed using conda
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56 lineage_file <- paste0(script_dir, "/../share/dante_ltr/databases/lineage_domain_order.csv")
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57 FDM_file <- paste0(script_dir, "/../share/dante_ltr/databases/feature_distances_model.RDS")
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58 trna_db <- paste0(script_dir, "/../share/dante_ltr/databases/tRNAscan-SE_ALL_spliced-no_plus-old-tRNAs_UC_unique-3ends.fasta")
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59 ltr_utils_r <- paste0(script_dir, "/../share/dante_ltr/R/ltr_utils.R")
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60 if (file.exists(lineage_file) & file.exists((trna_db))) {
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61 lineage_info <- read.table(lineage_file, sep = "\t", header = TRUE, as.is = TRUE)
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62 source(ltr_utils_r)
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63 FDM <- readRDS(FDM_file)
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64 }else(
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65 stop("configuration files not found")
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66 )
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67 }
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70 # for testing
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71 if (FALSE) {
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72 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")
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73 s <- readDNAStringSet("/mnt/raid/454_data/cuscuta/Ceuropea_assembly_v4/0_final_asm_hifiasm+longstitch/asm.bp.p.ctg_scaffolds.short_names.fa")
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74 lineage_info <- read.table("/mnt/raid/users/petr/workspace/ltr_finder_test/lineage_domain_order.csv", sep = "\t", header = TRUE, as.is = TRUE)
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75
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76 g <- rtracklayer::import("/mnt/raid/users/petr/workspace/dante_ltr/test_data/sample_DANTE_unfiltered.gff3")
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77 g <- rtracklayer::import("/mnt/raid/users/petr/workspace/ltr_finder_test/test_data/DANTE_filtered_part.gff3")
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78 s <- readDNAStringSet("/mnt/raid/users/petr/workspace/ltr_finder_test/test_data/Rbp_part.fa")
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79
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80 # oriza
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81 g <- rtracklayer::import("/mnt/raid/users/petr/workspace/dante_ltr/test_data/big_test_data/DANTE_full_oryza.gff3")
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82 s <- readDNAStringSet("/mnt/raid/users/petr/workspace/dante_ltr/test_data/big_test_data/o_sativa_msu7.0.fasta")
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83
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84 g <- rtracklayer::import("/mnt/raid/users/petr/workspace/dante_ltr/test_data
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85 /DANTE_Vfaba_chr5.gff3")
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86 s <- readDNAStringSet("/mnt/ceph/454_data/Vicia_faba_assembly/assembly/ver_210910
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87 /fasta_parts/211010_Vfaba_chr5.fasta")
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88
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89 g <- rtracklayer::import("/mnt/raid/users/petr/workspace/dante_ltr/test_data/big_test_data//Cocoa_theobroma_DANTE_filtered.gff3")
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90 s <- readDNAStringSet("/mnt/raid/users/petr/workspace/dante_ltr/test_data/big_test_data/Cocoa_theobroma_chr1.fasta.gz")
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91
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92 source("R/ltr_utils.R")
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93 ## feature distance model
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94 FDM <- readRDS("./databases/feature_distances_model.RDS")
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95 g <- rtracklayer::import("./test_data/sample_DANTE.gff3")
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96 s <- readDNAStringSet("./test_data/sample_genome.fasta")
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97 outfile <- "/mnt/raid/users/petr/workspace/ltr_finder_test/te_with_domains_2.gff3"
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98 lineage_info <- read.table("databases/lineage_domain_order.csv", sep = "\t", header =
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99 TRUE, as.is = TRUE)
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100 trna_db <- "./databases/tRNAscan-SE_ALL_spliced-no_plus-old-tRNAs_UC_unique-3ends.fasta"
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101
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102 }
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103
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104 # MAIN #############################################################
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105
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106 # load data:
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107
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108 cat("reading gff...")
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109 g <- rtracklayer::import(opt$gff3, format = 'gff3') # DANTE gff3
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110 cat("done\n")
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111 cat("reading fasta...")
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112 s <- readDNAStringSet(opt$reference_sequence) # genome assembly
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113 cat("done\n")
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114 outfile <- opt$output
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115 # clean sequence names:
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116 names(s) <- gsub(" .+", "", names(s))
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117 lineage_domain <- lineage_info$Domains.order
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118 lineage_domain_span <- lineage_info$domain_span
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119 lineage_ltr_mean_length <- lineage_info$ltr_length
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120 lineage_offset5prime <- lineage_info$offset5prime
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121 lineage_offset3prime <- lineage_info$offset3prime
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122 ln <- gsub("ss/I", "ss_I", gsub("_", "/", gsub("/", "|", lineage_info$Lineage)))
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123 names(lineage_offset3prime) <- ln
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124 names(lineage_offset5prime) <- ln
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125 names(lineage_domain) <- ln
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126 names(lineage_domain_span) <- ln
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127 names(lineage_ltr_mean_length) <- ln
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128 lineage_domains_sequence <- unlist(mapply(function(d,l) {
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129 paste(strsplit(d, " ")[[1]], ":", l, sep = "")
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130 }, d = lineage_domain, l = names(lineage_domain)))
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131
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132 # filter g gff3
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133 g <- dante_filtering(g, Relative_Length = opt$min_relative_length) # default
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134
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135 seqlengths(g) <- seqlengths(s)[names(seqlengths(g))]
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136 g <- add_coordinates_of_closest_neighbor(g)
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137
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138 # add info about domain order:
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139 g$domain_order <- as.numeric(factor(paste(g$Name, g$Final_Classification, sep = ":"), levels = lineage_domains_sequence))
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140 # set NA to 0 in g$domain_order ( some domains are not fromm ClassI elements
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141 g$domain_order[is.na(g$domain_order)] <- 0
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142
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143 # NOTE - some operation is faster of GrangesList but some on list of data.frames
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144 # this is primary clusteing
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145 cls <- get_domain_clusters(g)
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146 gcl <- split(as.data.frame(g), cls)
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147 # gcl_as_GRL <- split(g, cls) # delete?
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148
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149 cls_alt <- get_domain_clusters_alt(g, FDM)
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150 g$Cluster <- as.numeric(factor(cls_alt))
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151
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152 gcl_alt <- split(as.data.frame(g), cls_alt)
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153
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154 TE_partial <- GRanges(seqnames = sapply(gcl_alt, function(x) x$seqnames[1]),
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155 Name = sapply(gcl_alt, function(x) x$Final_Classification[1]),
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156 Final_Classification = sapply(gcl_alt, function(x) x$Final_Classification[1]),
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157 ID = sapply(gcl_alt, function(x) paste0("TE_partial_", x$Cluster[1])),
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158 strand = sapply(gcl_alt, function(x) x$strand[1]),
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159 Ndomains = sapply(gcl_alt, function(x) nrow(x)),
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160 type = "transposable_element",
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161 source = "dante_ltr",
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162 Rank="D",
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163 IRanges(start = sapply(gcl_alt, function(x) min(x$start)),
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164 end = sapply(gcl_alt, function(x) max(x$end)))
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165 )
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166 g$Ndomains_in_cluster <- count_occurences_for_each_element(g$Cluster)
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167 g$Parent <- paste0("TE_partial_", g$Cluster)
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168 g$Rank <- "D"
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169
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170 # keep only partial TE with more than one domain
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171 TE_partial_with_more_than_one_domain <- TE_partial[TE_partial$Ndomains > 1]
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172 g_with_more_than_one_domain <- g[as.vector(g$Ndomains_in_cluster > 1)]
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173
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174 # first filtering - remove TEs with low number of domains
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175 gcl_clean <- clean_domain_clusters(gcl, lineage_domain_span, min_domains = 5 - opt$max_missing_domains)
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176
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177 # glc annotation
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178 gcl_clean_lineage <- sapply(gcl_clean, function(x) x$Final_Classification[1])
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179 gcl_clean_domains <- sapply(gcl_clean, function(x) ifelse(x$strand[1] == "-",
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180 paste(rev(x$Name), collapse = " "),
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181 paste(x$Name, collapse = " "))
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182 )
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183
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184 # compare detected domains with domains in lineages from REXdb database
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185 dd <- mapply(domain_distance,
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186 d_query = gcl_clean_domains,
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187 d_reference = lineage_domain[gcl_clean_lineage])
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188
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189 # get lineages which has correct number and order of domains
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190 # gcl_clean2 <- gcl_clean[gcl_clean_domains == lineage_domain[gcl_clean_lineage]]
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191 gcl_clean2 <- gcl_clean[dd <= opt$max_missing_domains]
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192
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193 gcl_clean_with_domains <- gcl_clean2[check_ranges(gcl_clean2, s)]
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194 gr <- get_ranges(gcl_clean_with_domains)
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195
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196
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197 cat('Number of analyzed regions:\n')
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198 cat('Total number of domain clusters : ', length(gcl), '\n')
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199 cat('Number of clean clusters : ', length(gcl_clean), '\n')
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200 cat('Number of clusters with complete domain set : ', length(gcl_clean_with_domains), '\n')
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201
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202
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203 te_strand <- sapply(gcl_clean_with_domains, function(x)x$strand[1])
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204 te_lineage <- sapply(gcl_clean_with_domains, function(x)x$Final_Classification[1])
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205
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206 max_left_offset <- ifelse(te_strand == "+", lineage_offset5prime[te_lineage], lineage_offset3prime[te_lineage])
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207 max_right_offset <- ifelse(te_strand == "-", lineage_offset5prime[te_lineage], lineage_offset3prime[te_lineage])
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208
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209 grL <- get_ranges_left(gcl_clean_with_domains, max_left_offset)
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210 grR <- get_ranges_right(gcl_clean_with_domains, max_right_offset)
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211
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212 s_left <- getSeq(s, grL)
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213 s_right <- getSeq(s, grR)
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214
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215 expected_ltr_length <- lineage_ltr_mean_length[sapply(gcl_clean_with_domains, function (x)x$Final_Classification[1])]
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216 # for statistics
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217 RT <- g[g$Name == "RT" & substring(g$Final_Classification, 1, 11) == "Class_I|LTR"]
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218 # cleanup
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219 #rm(g)
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220 rm(gcl)
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221 rm(gcl_clean)
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222 rm(gcl_clean2)
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223
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224 names(te_strand) <- paste(seqnames(gr), start(gr), end(gr), sep = "_")
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225 names(s_left) <- paste(seqnames(grL), start(grL), end(grL), sep = "_")
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226 names(s_right) <- paste(seqnames(grR), start(grR), end(grR), sep = "_")
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227 cat('Identification of LTRs...')
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228 TE <- mclapply(seq_along(gr), function(x)get_TE(s_left[x],
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229 s_right[x],
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230 gcl_clean_with_domains[[x]],
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231 gr[x],
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232 grL[x],
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233 grR[x],
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234 expected_ltr_length[x]),
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235 mc.set.seed = TRUE, mc.cores = opt$cpu, mc.preschedule = FALSE
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236 )
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237
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238 cat('done.\n')
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239
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240 good_TE <- TE[!sapply(TE, is.null)]
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241 cat('Number of putative TE with identified LTR :', length(good_TE), '\n')
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242
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243 # TODO - extent TE region to cover also TSD
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244 ID <- paste0('TE_', sprintf("%08d", seq(good_TE)))
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245 gff3_list <- mcmapply(get_te_gff3, g = good_TE, ID = ID, mc.cores = opt$cpu)
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246
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247 cat('Identification of PBS ...')
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248 gff3_list2 <- mclapply(gff3_list, FUN = add_pbs, s = s, trna_db = trna_db, mc.set.seed = TRUE, mc.cores = opt$cpu, mc.preschedule = FALSE)
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249 cat('done\n')
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250 gff3_out <- do.call(c, gff3_list2)
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251
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252 # define new source
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253 src <- as.character(gff3_out$source)
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254 src[is.na(src)] <- "dante_ltr"
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255 gff3_out$source <- src
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256 gff3_out$Rank <- get_te_rank(gff3_out)
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257
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258 # add partial TEs but first remove all ovelaping
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259 TE_partial_parent_part <- TE_partial_with_more_than_one_domain[TE_partial_with_more_than_one_domain %outside% gff3_out]
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260 TE_partial_domain_part <- g[g$Parent %in% TE_partial_parent_part$ID]
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261
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262 gff3_out <- sort(c(gff3_out, TE_partial_domain_part, TE_partial_parent_part), by = ~ seqnames * start)
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263
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264 export(gff3_out, con = paste0(outfile, ".gff3"), format = 'gff3')
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265
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266 all_tbl <- get_te_statistics(gff3_out, RT)
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267 all_tbl <- cbind(Classification = rownames(all_tbl), all_tbl)
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268 write.table(all_tbl, file = paste0(outfile, "_statistics.csv"), sep = "\t", quote = FALSE, row.names = FALSE)
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269 # export fasta files:
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270 s_te <- get_te_sequences(gff3_out, s)
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271 for (i in seq_along(s_te)) {
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272 outname <- paste0(outfile, "_", names(s_te)[i], ".fasta")
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273 writeXStringSet(s_te[[i]], filepath = outname)
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274 }
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275