Mercurial > repos > petr-novak > dante_ltr

annotate clean_ltr.R @ 7:c33d6583e548 draft

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"planemo upload commit 50884f7f0269a0bbde078f24fe5020975693bcd9"
author petr-novak
date Fri, 24 Jun 2022 14:19:48 +0000
parents b91ca438a1cb
children ff01d4263391
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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
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5 (file_arg_name,
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6 initial_options)]))
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7 script_dir <- dirname(script_name)
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8 library(optparse)
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9
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10 parser <- OptionParser()
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11 option_list <- list(
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12 make_option(c("-g", "--gff3"), action = "store", type = "character",
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13 help = "gff3 with LTR Transposable elements", default = NULL),
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14 make_option(c("-s", "--reference_sequence"), action = "store", type = "character",
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15 help = "reference sequence as fasta",
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16 default = NULL),
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17 make_option(c("-o", "--output"), action = "store", type = "character",
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18 help = "output file prefix", default = NULL),
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19 make_option(c("-c", "--cpu"), type =
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20 "integer", default = 5, help = "Number of cpu to use [default %default]",
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21 metavar = "number")
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22
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23 )
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24 description <- paste(strwrap(""))
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25
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26 epilogue <- ""
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27 parser <- OptionParser(option_list = option_list, epilogue = epilogue, description =
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28 description, usage = "usage: %prog COMMAND [OPTIONS]")
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29 opt <- parse_args(parser, args = commandArgs(TRUE))
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30
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31
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32 # load packages
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33 suppressPackageStartupMessages({ library(rtracklayer)
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34 library(Biostrings)
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35 library(BSgenome)
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36 library(parallel)
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37 })
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38
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39 # CONFIGURATION
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40 # load configuration files and functions:
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41 lineage_file <- paste0(script_dir, "/databases/lineage_domain_order.csv")
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42 ltr_utils_r <- paste0(script_dir, "/R/ltr_utils.R")
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43 if (file.exists(lineage_file)) {
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44 lineage_info <- read.table(lineage_file, sep = "\t",
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45 header = TRUE,
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46 as.is = TRUE)
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47 source(ltr_utils_r)
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48 }else {
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49 lineage_file <- paste0(script_dir, "/.
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50 ./share/dante_ltr/databases/lineage_domain_order.csv")
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51 ltr_utils_r <- paste0(script_dir, "/.
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52 ./share/dante_ltr/R/ltr_utils.R")
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53 if (file.exists(lineage_file)) {
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54 lineage_info <- read.table(lineage_file, sep = "\t",
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55 header = TRUE,
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56 as.is = TRUE)
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57 source(ltr_utils_r)
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58 }else {
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59 (stop("configuration files not found"))
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60 }
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61 }
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62
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63
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64 ncpus <- opt$cpu
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65
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66
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67 # load data
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68 cat("reading fasta...")
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69 s <- readDNAStringSet(opt$reference_sequence) # genome assembly
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70 cat("done\n")
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71 outfile <- opt$output
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72 # clean sequence names:
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73 names(s) <- gsub(" .+", "", names(s))
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74 cat("reading gff...")
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75 g <- rtracklayer::import(opt$gff3, format = 'gff3') # DANTE gff3
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76 cat("done\n")
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77 # testing
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78 if (FALSE) {
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79 g <- rtracklayer::import("./test_data/big_test_data/dante_ltr_unfiltered_t.cacao.gff3")
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80 s <- readDNAStringSet("./test_data/big_test_data/T_cacao_chromosomes.fasta")
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81
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82 g <- rtracklayer::import("./test_data/sample_ltr_annotation.gff3")
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83 g <- rtracklayer::import("./test_data/sample_DANTE_LTR_annotation.gff3")
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84 s <- readDNAStringSet("./test_data/sample_genome.fasta")
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85
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86 g <- rtracklayer::import("./test_data/DANTE_LTR_Vfaba_chr5.gff3")
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87 s <- readDNAStringSet("./test_data/211010_Vfaba_chr5.fasta")
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88 names(s) <- gsub(" .+", "", names(s))
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89 ncpus <- 10
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90 lineage_info <- read.table("databases/lineage_domain_order.csv", sep = "\t", header =
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91 TRUE, as.is = TRUE)
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92 source("./R/ltr_utils.R")
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93 }
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94
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95 ## ID in g must be unique - this could be a problem if gff is concatenated from multiple files!
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96 ## id ID is renamed - rename parent to!
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97 ## add chromosom index to disctinguish same IDs
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98 suffix <- as.numeric(seqnames(g))
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99 g$ID <- ifelse(is.na(g$ID), NA, paste0(g$ID,"_", suffix))
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100 g$Parent <- ifelse(is.na(g$Parent), NA, paste0(g$Parent,"_", suffix))
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101
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102 # get te sequence based on rank
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103
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104 # best quality - split by lineage
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105 s_te <- get_te_sequences(g, s) # split by 'element quality'
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106 # evaluate best TE - DLTP grou
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107
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108 # comparison parameters
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109 word_size <- 11
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110 task <- "blastn"
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111 perc_identity <- 80
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112
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113 # best TE
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114 TE_DLTP_info <- analyze_TE(s_te$DLTP,
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115 word_size = word_size,
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116 ncpus = ncpus,
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117 perc_identity = perc_identity,
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118 task = task)
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119
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120 # TE rank 2:
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121 TE_DLT_plus_DLP_info <- analyze_TE(c(s_te$DLP, s_te$DLT),
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122 word_size = word_size,
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123 ncpus = ncpus,
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124 perc_identity = perc_identity,
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125 task = task)
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126
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127 TE_D_plus_DL_info <- analyze_TE(c(s_te$DL, s_te$D),
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128 word_size = word_size,
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129 ncpus = ncpus,
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130 perc_identity = perc_identity,
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131 task = task)
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132
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133 TE_DLT_plus_DLP_info_DLTP_verified <- compare_TE_datasets(
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134 c(s_te$DLT, s_te$DLP),
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135 ncpus = ncpus,
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136 TE_DLTP_info$seqs_representative,
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137 word_size = word_size,
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138 perc_identity = perc_identity,
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139 task = task)
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140
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141 TE_DLT_plus_DLP_info_multiplicity <- verify_based_on_multiplicity(TE_DLT_plus_DLP_info)
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142 TE_D_plus_DL_info_multiplicity <- verify_based_on_multiplicity(TE_D_plus_DL_info)
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143
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144 # create additional library from rank 2 verified by multiplicity and DLTP
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145 id_for_additional_library <- union(
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146 TE_DLT_plus_DLP_info_multiplicity$id_ok_mp_verified,
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147 TE_DLT_plus_DLP_info_DLTP_verified$id_ok_verified)
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148
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149 if (length(id_for_additional_library) > 1) {
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150 seqs_for_additional_library <- c(s_te$DLP, s_te$DLT)[names(c(s_te$DLP, s_te$DLT)) %in%
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151 id_for_additional_library]
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152 seqs_additional_info <- analyze_TE(seqs_for_additional_library, word_size =
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153 word_size, ncpus = ncpus)
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154 seqs_representative <- c(
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155 TE_DLTP_info$seqs_representative,
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156 seqs_additional_info$seqs_representative
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157 )
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158 }else {
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159 if (length(id_for_additional_library) == 1) {
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160 seqs_representative <- c(
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161 TE_DLTP_info$seqs_representative,
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162 c(s_te$DLP, s_te$DLT)[names(c(s_te$DLP, s_te$DLT)) %in% id_for_additional_library]
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163 )
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164 }else {
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165 seqs_representative <- TE_DLTP_info$seqs_representative
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166 }
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167 }
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168
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169 # TE rank 3 - verify agains good DLTP
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170 TE_DL_info_DLTP_verified <- compare_TE_datasets(
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171 s_te$DL,
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172 seqs_representative, min_coverage = 0.90,
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173 ncpus = ncpus,
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174 word_size = word_size,
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175 task = task,
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176 perc_identity = perc_identity)
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177
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178 TE_D_info_DLTP_verified <- compare_TE_datasets(
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179 s_te$D,
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180 seqs_representative, min_coverage = 0.90,
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181 ncpus = ncpus,
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182 word_size = word_size,
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183 task = task,
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184 perc_identity = perc_identity)
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185
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186
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187
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188 R <- seq_diversity(seqs_representative)$richness
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189 SI <- seq_diversity(seqs_representative)$shannon_index
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190
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191 # final RM library:
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192 seqs_representative_no_ssr <- seqs_representative[R > 20]
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193
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194 ID <- g$ID[g$type == "transposable_element"]
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195 names(ID) <- paste0(seqnames(g), "_",
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196 start(g), "_",
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197 end(g), "#",
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198 g$Final_Classification
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199 )[g$type %in% "transposable_element"]
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200
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201 # create clean gff3
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202 id_of_good_te <- unique(c(
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203 TE_DLTP_info$te_conflict_info$ok,
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204 TE_DLT_plus_DLP_info_DLTP_verified$id_ok_verified,
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205 TE_DLT_plus_DLP_info_multiplicity$id_ok_mp_verified,
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206 TE_DL_info_DLTP_verified$id_ok_verified,
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207 TE_D_info_DLTP_verified$id_ok_verified,
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208 TE_D_plus_DL_info_multiplicity$id_ok_mp_verified),
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209 )
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210
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211 c1 <- g$ID %in% ID[id_of_good_te]
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212 c2 <- sapply(g$Parent, function(x)ifelse(length(x) == 0, "", x)) %in% ID[id_of_good_te]
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213
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214 gff_out <- g[c1 | c2]
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215
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216 gff_te <- gff_out[gff_out$type %in% "transposable_element"]
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217 # remove partial elements
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218 gff_te_with_ltr <- gff_out[gff_out$type %in% "transposable_element" & gff_out$Rank != "D"]
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219
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220
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221 gff_5ltr <- gff_out[gff_out$LTR %in% "5LTR"]
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222 gff_3ltr <- gff_out[gff_out$LTR %in% "3LTR"]
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223
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224 full_te <- getSeqNamed(s, gff_te)
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225 names(full_te) <- paste0(gff_te$ID,":",names(full_te))
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226 ltr5 <- getSeqNamed(s, gff_5ltr)
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227 names(ltr5) <- paste0(gff_5ltr$Parent,":",names(ltr5))
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228 ltr3 <- getSeqNamed(s, gff_3ltr)
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229 names(ltr3) <- paste0(gff_3ltr$Parent,":",names(ltr3))
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230 inc <- gff_te_with_ltr$Rank != "DL"
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231
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232 writeXStringSet(seqs_representative, paste0(opt$output, "_RM_lib_non_redundant.fasta"))
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233 writeXStringSet(full_te, paste0(opt$output, "_RM_lib_full_TE.fasta"))
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234 writeXStringSet(ltr5, paste0(opt$output, "_RM_lib_5LTR.fasta"))
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235 writeXStringSet(ltr3, paste0(opt$output, "_RM_lib_3LTR.fasta"))
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236
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237 export(gff_out, paste0(opt$output, "_clean.gff3"), format = "gff3")
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238
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239 lv <- sort(unique(gff_te_with_ltr$Final_Classification))
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240 te_count <- table(factor(gff_te_with_ltr$Final_Classification, levels=lv))
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241
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242 pdf(paste0(opt$output, "_summary.pdf"), width = 13, height=8, pointsize = 10)
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243 par(mfrow=c(1,2), mar=c(5,7,2,0), las=1)
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244 boxplot(width(gff_te_with_ltr) ~ factor(gff_te_with_ltr$Final_Classification, levels=lv),
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245 horizontal = TRUE, xlab="length[bp]", ylab="",
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246 names = paste0(gsub("^.+[|]", "", lv), " (", te_count, ")"),
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247 main = 'Full TE', at = seq_along(lv)*4
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248 )
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249 boxplot(width(gff_te_with_ltr[inc]) ~ factor(gff_te_with_ltr$Final_Classification[inc], levels=lv),
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250 horizontal = TRUE, xlab="length[bp]", ylab="",
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251 names = rep("", length(lv)),
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252 main = 'Full TE', at = seq_along(lv)*4-1, add=TRUE, col=2
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253 )
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254 par(mar=c(5,0,2,7))
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255 boxplot(width(gff_5ltr) ~ factor(gff_5ltr$Final_Classification, levels=lv),
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256 horizontal = TRUE, xlab="length[bp]", ylab="",
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257 names = rep("", length(lv)),
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258 main = "5'LTR", at = seq_along(lv)*4
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259 )
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260 boxplot(width(gff_5ltr[inc]) ~ factor(gff_5ltr$Final_Classification[inc], levels=lv),
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261 horizontal = TRUE, xlab="length[bp]", ylab="",
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262 names = rep("", length(lv)),
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263 main = "5'LTR", at = seq_along(lv)*4-1, add=TRUE, col=2
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264 )
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265 legend('bottomright', col=c("grey","2"), legend=c("All TE", "TE with PBS/TSD"), pch=15)
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266 dev.off()