annotate rmsk_summary_table_multiple.r @ 9:c2c69c6090f0 draft

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author petr-novak
date Fri, 31 Jan 2020 06:55:23 -0500
parents a4cd8608ef6b
children 62fefa284036
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1 #! /usr/bin/env Rscript
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2 # repeat masker summary
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3 # analysis of *.out file
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4 # input arguments: <rmsk_result.out> <reads.fas.
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5 # calculates totoal proportion of matched repetetive families and averagee score and print the table
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6 suppressPackageStartupMessages(library(getopt))
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7
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8 # INPUT ARGUMENTS:
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9 opt = getopt(matrix(c(
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10 'fasta', 'f', 1, "character",
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11 'repeat_masker','r', 1, "character",
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12 'output', 'o', 1, "character",
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13 'help','h',0,'logical'),ncol=4,byrow=T))
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14
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15 if (!is.null(opt$help)) {
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16 cat("Usage:\n\n -f fasta file \n -r repeat masker output\n -o output files\n")
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17 stop()
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18 }
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19
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20 RMfiles=system(paste("ls ",opt$repeat_masker,sep=""),intern=T)
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21 fasta=system(paste("ls ",opt$fasta,sep=""),intern=T)
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24
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25 getsubdir=function(x){
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26 xx=gsub(".*/","",x)
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27 sdir=gsub(xx,"",x,fixed=T)
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28 sdir
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29 }
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30
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31 fasta.summary=function(reads){
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32 suppressPackageStartupMessages(library(Biostrings,quiet=T))
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33 if (exists("readDNAStringSet")){
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34 seqs=readDNAStringSet(reads)
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35 }else{
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36 seqs=read.DNAStringSet(reads)
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37 }
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38
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39 N=length(seqs)
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40 Ls=nchar(seqs)
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41 L=sum(Ls)
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42 M=median(Ls)
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43 A=mean(Ls)
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44 output=c(N,L,M,A)
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45 names(output)=c("N","length","median","mean")
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46 output
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47 }
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48
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49 repCont=function(repname){ # uses external variable!
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50 pos=repname==classfamily
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51 RC=unname(sum(lengths[pos])/totalLength)
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52 RC
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53 }
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54 repScore=function(repname){ # uses external variable!
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55 pos=repname==classfamily
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56 S=unname(mean(score[pos]))
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57 S
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58 }
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59
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60 N=length(RMfiles)
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61
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62 for (i in seq_along(RMfiles)){
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63 cat(paste((RMfiles[i]),"\n"))
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64
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65 sdir=getsubdir(RMfiles[i])
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66 seqs.summary=fasta.summary(fasta[i])
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67 totalLength=seqs.summary["length"]
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68
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69 total.counts=data.frame(c("All_Reads_Length","All_Reads_Number"),c(NA,NA),c(NA,NA),c(totalLength,seqs.summary['N']),c(totalLength,seqs.summary['N']),c(NA,NA))
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70 colnames(total.counts)=c("class/fammily","class","family","hits","content","Mean_Score")
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72
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73 # get RM data if not empty
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74 rmsk_empty=length(grep("There were no repetitive sequences detected",scan(RMfiles[i],what=character(),n=1,sep="\n",quiet=T),fixed=T))>0
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75 rmsk_notperformed = length(grep("No Repbase used with RepetMasker",scan(RMfiles[i],what=character(),n=1,sep="\n",quiet=T),fixed=T))>0
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76 if (!rmsk_empty & !rmsk_notperformed) {
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77 rmsk=read.table(RMfiles[i],header=F,as.is=T,skip=2,comment.char="*",fill=T)
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78 score=rmsk[,1]
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79 Ids=rmsk[,5]
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80 starts=rmsk[,6]
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81 ends=rmsk[,7]
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82 lengths=ends-starts
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83 classfamily=rmsk[,11]
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84 out.table=table(classfamily)
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85 out.table=data.frame(names(out.table),c(out.table),stringsAsFactors=F)
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86 contents=sapply(out.table[,1],repCont)*100
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87 meanScore=sapply(out.table[,1],repScore)
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88 class=gsub("/.*","",out.table[,1])
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89 families=gsub(".*/","",out.table[,1])
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90 out.table=data.frame(out.table[,1],class,families,out.table[,-1],contents,meanScore,stringsAsFactors=F)
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91 colnames(out.table)=c("class/fammily","class","family","hits","content","Mean_Score")
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92 out.table=out.table[order(out.table$content,decreasing=T),]
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93 out.table=rbind(out.table,total.counts)
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94 }else{
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95 out.table=total.counts
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96 }
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97 out.table=out.table[order(out.table$content,decreasing=T),]
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98
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99 write.table(out.table,file=paste(sdir,opt$output,sep=""),row.names=F,quote=F,sep="\t")
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100
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101
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102 # merge all tables:
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103
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104 colnames(out.table)=paste(colnames(out.table),c("",rep(sdir,5)))
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105
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106 if (i==1) {
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107 mergedTable=out.table[,c(1,4,5,6)]
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108 }else{
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109
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110 mergedTable=merge(mergedTable,out.table[,c(1,4,5,6)],by=1,all=T)
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111 }
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112
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113 }
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114 mergedTable=mergedTable[order(rowSums(mergedTable[,seq(2,N*3,by=3),drop=FALSE],na.rm=T),decreasing=T),]
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115 mergedTable[is.na(mergedTable)]<-0
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116
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117 write.table(t(mergedTable),file=paste(opt$output,"summary.csv",sep=""),col.names=F,row.names=T,quote=F,sep="\t")
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118
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119 #save.image("tmp.RData")
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