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wgs_functions.R
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wgs_functions.R
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#Collection of functions for working with WGS data
#Pavitra Roychoudhury
#Aug 2017
#Return the number of mapped reads in a bam file
n_mapped_reads<-function(bamfname){
require(Rsamtools)
indexBam(bamfname)
if(file.exists(bamfname)&class(try(scanBamHeader(bamfname),silent=T))!='try-error'){
return(idxstatsBam(bamfname)$mapped)
}else{
return(NA)
}
}
#Make a new reference from scaffolds
make_ref_from_assembly<-function(bamfname,reffname){
require(Rsamtools);
require(GenomicAlignments);
require(parallel)
ncores<-detectCores();
#Read reference sequence
ref_seq<-readDNAStringSet(reffname);
if(!is.na(bamfname)&class(try(scanBamHeader(bamfname),silent=T))!='try-error'){
#Index bam
baifname<-indexBam(bamfname);
#Import bam file
params<-ScanBamParam(flag=scanBamFlag(isUnmappedQuery=FALSE),
what=c('qname','rname','strand','pos','qwidth','mapq','cigar','seq'));
gal<-readGAlignments(bamfname,index=baifname,param=params);
#Remove any contigs with width <200 bp
gal<-gal[width(gal)>200];
#First lay contigs on reference space--this removes insertions and produces a seq of the same length as ref
qseq_on_ref<-sequenceLayer(mcols(gal)$seq,cigar(gal),from="query",to="reference");
qseq_on_ref_aligned<-stackStrings(qseq_on_ref,1,max(mcols(gal)$pos+width(gal)-1,width(ref_seq)),
shift=mcols(gal)$pos-1,Lpadding.letter='N',Rpadding.letter='N');
#Make a consensus matrix and get a consensus sequence from the aligned scaffolds
cm<-consensusMatrix(qseq_on_ref_aligned,as.prob=T,shift=0)[c('A','C','G','T','N','-'),];
# cm[c('N','-'),]<-0;
cm['N',]<-0;
cm<-apply(cm,2,function(x)if(all(x==0))return(x) else return(x/sum(x)));
cm['N',colSums(cm)==0]<-1;
con_seq<-DNAStringSet(gsub('\\?','N',consensusString(cm,threshold=0.25)));
con_seq<-DNAStringSet(gsub('\\+','N',con_seq));
#Now fill in the Ns with the reference
temp<-as.matrix(con_seq);
temp[temp=='N']<-as.matrix(ref_seq)[temp=='N'];
con_seq<-DNAStringSet(paste0(temp,collapse=''));
names(con_seq)<-sub('.bam','_consensus',basename(bamfname));
#Look for insertions in bam cigar string
cigs_ref<-cigarRangesAlongReferenceSpace(cigar(gal),with.ops=F,ops='I',
reduce.ranges=T,drop.empty.ranges=F,
pos=mcols(gal)$pos);
cigs_query<-cigarRangesAlongQuerySpace(cigar(gal),ops='I',with.ops=F,
reduce.ranges=T,drop.empty.ranges=F);
all_ins<-mclapply(c(1:length(cigs_query)),function(i)
extractAt(mcols(gal)$seq[i],cigs_query[[i]])[[1]]);
#Merge all insertions
all_ins_merged<-do.call('rbind',mclapply(c(1:length(cigs_ref)),function(i)
return(data.frame(
start_ref=start(cigs_ref[[i]]),end_ref=end(cigs_ref[[i]]),
start_query=start(cigs_query[[i]]),end_query=end(cigs_query[[i]]),
ins_seq=all_ins[[i]],width_ins=width(all_ins[[i]]))),
mc.cores=ncores));
all_ins_merged<-all_ins_merged[order(all_ins_merged$end_ref),];
# write.csv(all_ins_merged,'./testing_all_ins.csv',row.names=F);
#TO DO: Check for overlaps--should be minimal since scaffolds don't usually overlap that much
if(any(table(all_ins_merged$start_ref)>1)){
print('Overlapping insertions')
#not the best way, but just pick the first for now
all_ins_merged<-all_ins_merged[!duplicated(all_ins_merged[,c('start_ref','end_ref')]),];
}
#Now the beauty part of inserting the strings back in
#Split ref seq by the insert positions
if(nrow(all_ins_merged)!=0){
new_strs<-DNAStringSet(rep('',nrow(all_ins_merged)+1))
for(i in 1:nrow(all_ins_merged)){
if(i==1){
new_strs[i]<-paste0(extractAt(con_seq,IRanges(start=1,end=all_ins_merged$end_ref[i]))[[1]],
all_ins_merged$ins_seq[i]);
}else{
new_strs[i]<-paste0(extractAt(con_seq,IRanges(start=all_ins_merged$start_ref[i-1],
end=all_ins_merged$end_ref[i]))[[1]],
all_ins_merged$ins_seq[i]);
}
}
#Last bit
new_strs[i+1]<-paste0(extractAt(con_seq,IRanges(start=all_ins_merged$start_ref[i],
end=width(con_seq)))[[1]])
temp_str<-paste0(as.character(new_strs),collapse='');
#Remove gaps to get final sequence
con_seq_final<-DNAStringSet(gsub('-','',temp_str));
#No insertions
}else{
con_seq_final<-con_seq;
}
names(con_seq_final)<-sub('.bam','_consensus',basename(bamfname));
if(!dir.exists('./ref_for_remapping')) dir.create('./ref_for_remapping');
writeXStringSet(con_seq_final,
paste0('./ref_for_remapping/',names(con_seq_final),'.fasta'));
#Delete bai file
file.remove(baifname);
}else{
print('Bam file could not be opened.')
return(NA)
}
}
#Takes in a bam file, produces consensus sequence
generate_consensus<-function(bamfname){
require(Rsamtools)
require(GenomicAlignments)
require(Biostrings)
#for testing this function--comment out or remove
# bamfname<-'./testing/ABI-HHV6A_S385_L001_A.sorted.bam'
if(!is.na(bamfname)&class(try(scanBamHeader(bamfname),silent=T))!='try-error'){
#Index bam if required
if(!file.exists(paste(bamfname,'.bai',sep=''))){
baifname<-indexBam(bamfname);
}else{
baifname<-paste(bamfname,'.bai',sep='');
}
#Import bam file
params<-ScanBamParam(flag=scanBamFlag(isUnmappedQuery=FALSE),
what=c('qname','rname','strand','pos','qwidth','mapq','cigar','seq'));
gal<-readGAlignments(bamfname,index=baifname,param=params);
# summary(gal);
#Remove any contigs with mapq <2 -- this leads to a loss of a lot of the DR seqs even though there are reads there
# gal<-gal[mcols(gal)$mapq>2];
#First lay reads on reference space--this doesn't include insertions
qseq_on_ref<-sequenceLayer(mcols(gal)$seq,cigar(gal),from="query",to="reference");
#Make a consensus matrix and get a consensus sequence from the aligned scaffolds
# cm<-consensusMatrix(qseq_on_ref,as.prob=T,shift=start(gal)-1,width=seqlengths(gal))[c('A','C','G','T','N','-'),];
# cm['N',colSums(cm)==0]<-1;
#Edit to include a coverage threshold
cm<-consensusMatrix(qseq_on_ref,as.prob=F,shift=start(gal)-1,width=seqlengths(gal))[c('A','C','G','T','N','-'),];
poor_cov<-which(colSums(cm)<10);
cm<-apply(cm,2,function(x)x/sum(x));
cm[,poor_cov]<-0;
cm['N',poor_cov]<-1;
tmp_str<-strsplit(consensusString(cm,ambiguityMap='?',threshold=0.25),'')[[1]];
ambig_sites<-which(tmp_str=='?');
ambig_bases<-unlist(lapply(ambig_sites,function(i){mixedbase<-paste(names(cm[,i])[cm[,i]>0],collapse='');
if(mixedbase%in%IUPAC_CODE_MAP) return(names(IUPAC_CODE_MAP)[IUPAC_CODE_MAP==mixedbase])
else return('N')}));
tmp_str[ambig_sites]<-ambig_bases
con_seq<-DNAStringSet(paste0(tmp_str,collapse=''));
names(con_seq)<-sub('.bam','_consensus',basename(bamfname));
rm(tmp_str);
#Remove gaps and leading and trailing Ns to get final sequence
con_seq_trimmed<-DNAStringSet(gsub("N*N$",'',gsub("^N*",'',as.character(con_seq))));
con_seq_final<-DNAStringSet(gsub('-','',as.character(con_seq_trimmed)));
names(con_seq_final)<-sub('.bam','_consensus',basename(bamfname));
#Delete bai file
file.remove(baifname);
return(con_seq_final);
}else{
return(NA)
}
}
clean_consensus_hsv<-function(sampname,merged_bam_folder,mapped_reads_folder){
require(Rsamtools);
require(GenomicAlignments);
require(Biostrings);
sampname<-paste0(sampname,'_');
mapping_stats<-data.frame(ref=c('hsv1_ref','hsv2_sd90e','hsv2_ref_hg52'),
bamfname_merged=c(grep(sampname,list.files(merged_bam_folder,'_hsv1_ref.*bam$',full.names=T),value=T),
grep(sampname,list.files(merged_bam_folder,'_hsv2_sd90e.*bam$',full.names=T),value=T),
grep(sampname,list.files(merged_bam_folder,'_hsv2_ref_hg52.*bam$',full.names=T),value=T)),
bamfname_mapped=c(grep(sampname,list.files(mapped_reads_folder,'_hsv1_ref.*bam$',full.names=T),value=T),
grep(sampname,list.files(mapped_reads_folder,'_hsv2_sd90e.*bam$',full.names=T),value=T),
grep(sampname,list.files(mapped_reads_folder,'_hsv2_ref_hg52.*bam$',full.names=T),value=T)),
mapped_reads_ref=0,mapped_reads_assemblyref=0,perc_Ns=0,num_Ns=0,width=0,
stringsAsFactors=F);
#Import mapped reads + assembly and generate consensus
con_seqs<-lapply(mapping_stats$bamfname_merged,generate_consensus);
if(!dir.exists('./consensus_seqs_all')) dir.create('./consensus_seqs_all');
dummyvar<-lapply(con_seqs,function(x)
writeXStringSet(x,file=paste('./consensus_seqs_all/',names(x),'.fasta',sep=''),format='fasta'));
rm(dummyvar)
#Compute #mapped reads and %Ns
mapping_stats$mapped_reads_ref<-unlist(lapply(mapping_stats$bamfname_mapped,n_mapped_reads));
mapping_stats$mapped_reads_assemblyref<-unlist(lapply(mapping_stats$bamfname_merged,n_mapped_reads));
mapping_stats$num_Ns<-unlist(lapply(con_seqs,function(x)sum(letterFrequency(x,c('N','+')))));
mapping_stats$width<-unlist(lapply(con_seqs,width));
mapping_stats$perc_Ns<-100*mapping_stats$num_Ns/mapping_stats$width;
if(!dir.exists('./stats/')) dir.create('./stats/');
write.csv(mapping_stats,file=paste('./stats/',sampname,'_mappingstats.csv',sep=''),row.names=F);
return(TRUE)
}
clean_consensus_hhv6<-function(sampname,merged_bam_folder,mapped_reads_folder){
require(Rsamtools);
require(GenomicAlignments);
require(Biostrings);
mapping_stats<-data.frame(
ref=c('hhv6A_ref_U1102','hhv6B_ref_z29'),
bamfname_merged=c(grep(paste0('\\/',sampname,'_'),list.files(merged_bam_folder,"_hhv6A_ref_U1102.*bam$",full.names=T),value=T),
grep(paste0('\\/',sampname,'_'),list.files(merged_bam_folder,'_hhv6B_ref_z29.*bam$',full.names=T),value=T)),
bamfname_mapped=c(grep(paste0('\\/',sampname,'_'),list.files(mapped_reads_folder,'_hhv6A_ref_U1102.*bam$',full.names=T),value=T),
grep(paste0('\\/',sampname,'_'),list.files(mapped_reads_folder,'_hhv6B_ref_z29.*bam$',full.names=T),value=T)),
mapped_reads_ref=0,mapped_reads_assemblyref=0,perc_Ns=0,num_Ns=0,width=0,
stringsAsFactors=F);
#Import mapped reads + assembly and generate consensus
con_seqs<-lapply(mapping_stats$bamfname_merged,generate_consensus);
if(!dir.exists('./consensus_seqs_all')) dir.create('./consensus_seqs_all');
dummyvar<-lapply(con_seqs,function(x)
writeXStringSet(x,file=paste('./consensus_seqs_all/',names(x),'.fasta',sep=''),format='fasta'));
rm(dummyvar)
#Compute #mapped reads and %Ns
mapping_stats$mapped_reads_ref<-unlist(lapply(mapping_stats$bamfname_mapped,n_mapped_reads));
mapping_stats$mapped_reads_assemblyref<-unlist(lapply(mapping_stats$bamfname_merged,n_mapped_reads));
mapping_stats$num_Ns<-unlist(lapply(con_seqs,function(x)sum(letterFrequency(x,c('N','+')))));
mapping_stats$width<-unlist(lapply(con_seqs,width));
mapping_stats$perc_Ns<-100*mapping_stats$num_Ns/mapping_stats$width;
if(!dir.exists('./stats/')) dir.create('./stats/');
write.csv(mapping_stats,file=paste('./stats/',sampname,'_mappingstats.csv',sep=''),row.names=F);
return(TRUE)
}
clean_consensus_hiv<-function(sampname,merged_bam_folder,mapped_reads_folder){
require(Rsamtools);
require(GenomicAlignments);
require(Biostrings);
mapping_stats<-data.frame(
ref=c('hiv_hxb2_ref'),
bamfname_merged=c(grep(sampname,list.files(merged_bam_folder,"_hiv_hxb2_ref.*bam$",full.names=T),value=T)),
bamfname_mapped=c(grep(sampname,list.files(mapped_reads_folder,'_hiv_hxb2_ref.*bam$',full.names=T),value=T)),
mapped_reads_ref=0,mapped_reads_assemblyref=0,perc_Ns=0,num_Ns=0,width=0,
stringsAsFactors=F);
#Import mapped reads + assembly and generate consensus
con_seqs<-lapply(mapping_stats$bamfname_merged,generate_consensus);
if(!dir.exists('./consensus_seqs_all')) dir.create('./consensus_seqs_all');
dummyvar<-lapply(con_seqs,function(x)
writeXStringSet(x,file=paste('./consensus_seqs_all/',names(x),'.fasta',sep=''),format='fasta'));
rm(dummyvar)
#Compute #mapped reads and %Ns
mapping_stats$mapped_reads_ref<-unlist(lapply(mapping_stats$bamfname_mapped,n_mapped_reads));
mapping_stats$mapped_reads_assemblyref<-unlist(lapply(mapping_stats$bamfname_merged,n_mapped_reads));
mapping_stats$num_Ns<-unlist(lapply(con_seqs,function(x)sum(letterFrequency(x,c('N','+')))));
mapping_stats$width<-unlist(lapply(con_seqs,width));
mapping_stats$perc_Ns<-100*mapping_stats$num_Ns/mapping_stats$width;
if(!dir.exists('./stats/')) dir.create('./stats/');
write.csv(mapping_stats,file=paste('./stats/',sampname,'_mappingstats.csv',sep=''),row.names=F);
return(TRUE)
}
clean_consensus_hhv8<-function(sampname,merged_bam_folder,mapped_reads_folder){
require(Rsamtools);
require(GenomicAlignments);
require(Biostrings);
sampname<-paste0(sampname,'_');
mapping_stats<-data.frame(ref='hhv8_ref',
bamfname_merged=grep(sampname,list.files(merged_bam_folder,'_hhv8.*bam$',full.names=T),value=T),
bamfname_mapped=grep(sampname,list.files(mapped_reads_folder,'_hhv8.*bam$',full.names=T),value=T),
mapped_reads_ref=0,mapped_reads_assemblyref=0,perc_Ns=0,num_Ns=0,width=0,
stringsAsFactors=F);
#Import mapped reads + assembly and generate consensus
con_seqs<-lapply(mapping_stats$bamfname_merged,generate_consensus);
if(!dir.exists('./consensus_seqs_all')) dir.create('./consensus_seqs_all');
dummyvar<-lapply(con_seqs,function(x)
writeXStringSet(x,file=paste('./consensus_seqs_all/',names(x),'.fasta',sep=''),format='fasta'));
rm(dummyvar)
#Compute #mapped reads and %Ns
mapping_stats$mapped_reads_ref<-unlist(lapply(mapping_stats$bamfname_mapped,n_mapped_reads));
mapping_stats$mapped_reads_assemblyref<-unlist(lapply(mapping_stats$bamfname_merged,n_mapped_reads));
mapping_stats$num_Ns<-unlist(lapply(con_seqs,function(x)sum(letterFrequency(x,c('N','+')))));
mapping_stats$width<-unlist(lapply(con_seqs,width));
mapping_stats$perc_Ns<-100*mapping_stats$num_Ns/mapping_stats$width;
if(!dir.exists('./stats/')) dir.create('./stats/');
write.csv(mapping_stats,file=paste('./stats/',sampname,'_mappingstats.csv',sep=''),row.names=F);
return(TRUE)
}
clean_consensus_rsv<-function(sampname,merged_bam_folder,mapped_reads_folder){
require(Rsamtools);
require(GenomicAlignments);
require(Biostrings);
sampname<-paste0(sampname,'_');
mapping_stats<-data.frame(ref=c('rsvA_ref','rsvB_ref'),
bamfname_merged=c(grep(sampname,list.files(merged_bam_folder,'_rsvA_ref.*bam$',full.names=T),value=T),
grep(sampname,list.files(merged_bam_folder,'_rsvB_ref.*bam$',full.names=T),value=T)),
bamfname_mapped=c(grep(sampname,list.files(mapped_reads_folder,'_rsvA_ref.*bam$',full.names=T),value=T),
grep(sampname,list.files(mapped_reads_folder,'_rsvB_ref.*bam$',full.names=T),value=T)),
mapped_reads_ref=0,mapped_reads_assemblyref=0,perc_Ns=0,num_Ns=0,width=0,
stringsAsFactors=F);
#Import mapped reads + assembly and generate consensus
con_seqs<-lapply(mapping_stats$bamfname_merged,generate_consensus);
if(!dir.exists('./consensus_seqs_all')) dir.create('./consensus_seqs_all');
dummyvar<-lapply(con_seqs,function(x)
writeXStringSet(x,file=paste('./consensus_seqs_all/',names(x),'.fasta',sep=''),format='fasta'));
rm(dummyvar)
#Compute #mapped reads and %Ns
mapping_stats$mapped_reads_ref<-unlist(lapply(mapping_stats$bamfname_mapped,n_mapped_reads));
mapping_stats$mapped_reads_assemblyref<-unlist(lapply(mapping_stats$bamfname_merged,n_mapped_reads));
mapping_stats$num_Ns<-unlist(lapply(con_seqs,function(x)sum(letterFrequency(x,c('N','+')))));
mapping_stats$width<-unlist(lapply(con_seqs,width));
mapping_stats$perc_Ns<-100*mapping_stats$num_Ns/mapping_stats$width;
if(!dir.exists('./stats/')) dir.create('./stats/');
write.csv(mapping_stats,file=paste('./stats/',sampname,'_mappingstats.csv',sep=''),row.names=F);
return(TRUE)
}
#Measles (added Aug 2019)
clean_consensus_measles<-function(sampname,merged_bam_folder,mapped_reads_folder){
require(Rsamtools);
require(GenomicAlignments);
require(Biostrings);
sampname<-paste0(sampname,'_');
mapping_stats<-data.frame(ref='measles_ref',
bamfname_merged=grep(sampname,list.files(merged_bam_folder,'_measles_ref.*bam$',full.names=T),value=T),
bamfname_mapped=grep(sampname,list.files(mapped_reads_folder,'_measles_ref.*bam$',full.names=T),value=T),
mapped_reads_ref=0,mapped_reads_assemblyref=0,perc_Ns=0,num_Ns=0,width=0,
stringsAsFactors=F);
#Import mapped reads + assembly and generate consensus
con_seqs<-lapply(mapping_stats$bamfname_merged,generate_consensus);
if(!dir.exists('./consensus_seqs_all')) dir.create('./consensus_seqs_all');
dummyvar<-lapply(con_seqs,function(x)
writeXStringSet(x,file=paste('./consensus_seqs_all/',names(x),'.fasta',sep=''),format='fasta'));
rm(dummyvar)
#Compute #mapped reads and %Ns
mapping_stats$mapped_reads_ref<-unlist(lapply(mapping_stats$bamfname_mapped,n_mapped_reads));
mapping_stats$mapped_reads_assemblyref<-unlist(lapply(mapping_stats$bamfname_merged,n_mapped_reads));
mapping_stats$num_Ns<-unlist(lapply(con_seqs,function(x)sum(letterFrequency(x,c('N','+')))));
mapping_stats$width<-unlist(lapply(con_seqs,width));
mapping_stats$perc_Ns<-100*mapping_stats$num_Ns/mapping_stats$width;
if(!dir.exists('./stats/')) dir.create('./stats/');
write.csv(mapping_stats,file=paste('./stats/',sampname,'_mappingstats.csv',sep=''),row.names=F);
return(TRUE)
}
#Treponema (added Dec 2019)
clean_consensus_tp<-function(sampname,merged_bam_folder,mapped_reads_folder,ref){
require(Rsamtools);
require(GenomicAlignments);
require(Biostrings);
mapping_stats<-data.frame(ref=ref,
bamfname_merged=grep(sampname,list.files(merged_bam_folder,'*.bam$',full.names=T),value=T),
bamfname_mapped=grep(sampname,list.files(mapped_reads_folder,'*.bam$',full.names=T),value=T),
mapped_reads_ref=0,mapped_reads_assemblyref=0,perc_Ns=0,num_Ns=0,width=0,
stringsAsFactors=F);
#Import mapped reads + assembly and generate consensus
con_seqs<-lapply(mapping_stats$bamfname_merged,generate_consensus);
if(!dir.exists('./consensus_seqs_all')) dir.create('./consensus_seqs_all');
dummyvar<-lapply(con_seqs,function(x)
writeXStringSet(x,file=paste('./consensus_seqs_all/',names(x),'.fasta',sep=''),format='fasta'));
rm(dummyvar)
#Compute #mapped reads and %Ns
mapping_stats$mapped_reads_ref<-unlist(lapply(mapping_stats$bamfname_mapped,n_mapped_reads));
mapping_stats$mapped_reads_assemblyref<-unlist(lapply(mapping_stats$bamfname_merged,n_mapped_reads));
mapping_stats$num_Ns<-unlist(lapply(con_seqs,function(x)sum(letterFrequency(x,c('N','+')))));
mapping_stats$width<-unlist(lapply(con_seqs,width));
mapping_stats$perc_Ns<-100*mapping_stats$num_Ns/mapping_stats$width;
if(!dir.exists('./stats/')) dir.create('./stats/');
write.csv(mapping_stats,file=paste('./stats/',sampname,'_mappingstats.csv',sep=''),row.names=F);
return(TRUE)
}
#Monkeypox (added 11-Jul-22)
clean_consensus_mpx<-function(sampname,merged_bam_folder,mapped_reads_folder,ref){
require(Rsamtools);
require(GenomicAlignments);
require(Biostrings);
mapping_stats<-data.frame(ref=ref,
bamfname_merged=grep(sampname,list.files(merged_bam_folder,'*.bam$',full.names=T),value=T),
bamfname_mapped=grep(sampname,list.files(mapped_reads_folder,'*.bam$',full.names=T),value=T),
mapped_reads_ref=0,mapped_reads_assemblyref=0,perc_Ns=0,num_Ns=0,width=0,
stringsAsFactors=F);
#Import mapped reads + assembly and generate consensus
con_seqs<-lapply(mapping_stats$bamfname_merged,generate_consensus);
if(!dir.exists('./consensus_seqs_all')) dir.create('./consensus_seqs_all');
dummyvar<-lapply(con_seqs,function(x)
writeXStringSet(x,file=paste('./consensus_seqs_all/',names(x),'.fasta',sep=''),format='fasta'));
rm(dummyvar)
#Compute #mapped reads and %Ns
mapping_stats$mapped_reads_ref<-unlist(lapply(mapping_stats$bamfname_mapped,n_mapped_reads));
mapping_stats$mapped_reads_assemblyref<-unlist(lapply(mapping_stats$bamfname_merged,n_mapped_reads));
mapping_stats$num_Ns<-unlist(lapply(con_seqs,function(x)sum(letterFrequency(x,c('N','+')))));
mapping_stats$width<-unlist(lapply(con_seqs,width));
mapping_stats$perc_Ns<-100*mapping_stats$num_Ns/mapping_stats$width;
if(!dir.exists('./stats/')) dir.create('./stats/');
write.csv(mapping_stats,file=paste('./stats/',sampname,'_mappingstats.csv',sep=''),row.names=F);
return(TRUE)
}
#hCoV (added Mar 2020)
clean_consensus_hcov<-function(sampname,remapped_bamfname,mappedtoref_bamfname,ref){
require(Rsamtools);
require(GenomicAlignments);
require(Biostrings);
mapping_stats<-data.frame(ref=ref,
remapped_bam=remapped_bamfname,
mappedtoref_bam=mappedtoref_bamfname,
mapped_reads_ref=0,mapped_reads_assemblyref=0,perc_Ns=0,num_Ns=0,width=0,
stringsAsFactors=F);
#Import mapped reads + assembly and generate consensus
con_seq<-generate_consensus(mapping_stats$remapped_bam);
if(!dir.exists('./consensus_seqs')) dir.create('./consensus_seqs');
writeXStringSet(con_seq,file=paste('./consensus_seqs/',sampname,'.fasta',sep=''),format='fasta');
#Compute #mapped reads and %Ns
mapping_stats$mapped_reads_ref<-unlist(lapply(mapping_stats$mappedtoref_bam,n_mapped_reads));
mapping_stats$mapped_reads_assemblyref<-unlist(lapply(mapping_stats$remapped_bam,n_mapped_reads));
mapping_stats$num_Ns<-sum(letterFrequency(con_seq,c('N','+')));
mapping_stats$width<-width(con_seq);
mapping_stats$perc_Ns<-100*mapping_stats$num_Ns/mapping_stats$width;
if(!dir.exists('./stats/')) dir.create('./stats/');
write.csv(mapping_stats,file=paste('./stats/',sampname,'_mappingstats.csv',sep=''),row.names=F);
return(TRUE)
}
#Find coverage at each position in the alignment
cov_by_pos<-function(bamfname){
require(Rsamtools);
require(GenomicAlignments);
if(file.exists(bamfname)&class(try(scanBamHeader(bamfname),silent=T))!='try-error'){
#Import alignment
if(file.exists(paste(bamfname,'.bai',sep='')))
file.remove(paste(bamfname,'.bai',sep='')); #remove any old index files
baifname<-indexBam(bamfname); #Make an index file
params<-ScanBamParam(flag=scanBamFlag(isUnmappedQuery=FALSE),
what=c('qname','rname','strand','pos','qwidth','mapq','cigar','seq'));
gal<-readGAlignments(bamfname,index=baifname,param=params);
cov<-coverage(gal);
file.remove(baifname);
return(cov)
}else{
return(NA)
}
}
#Compute coverage stats
get_coverage<-function(bamfname){
if(length(bamfname)==0){
mapped<-NA; avg_cov<-NA;
sd_cov<-NA; min_cov<-NA; max_cov<-NA;
}else if(file.exists(bamfname)&class(try(scanBamHeader(bamfname),silent=T))!='try-error'){
require(Rsamtools);
require(GenomicAlignments);
#Import alignment
if(file.exists(paste(bamfname,'.bai',sep='')))
file.remove(paste(bamfname,'.bai',sep='')); #remove any old index files
baifname<-indexBam(bamfname); #Make an index file
params<-ScanBamParam(flag=scanBamFlag(isUnmappedQuery=FALSE),
what=c('qname','rname','strand','pos','qwidth','mapq','cigar','seq'));
gal<-readGAlignments(bamfname,index=baifname,param=params);
# summary(gal);
cov<-coverage(gal);
mapped<-length(gal);
avg_cov<-mean(cov);
sd_cov<-sd(cov);
min_cov<-min(cov);
max_cov<-max(cov);
file.remove(baifname);
}else{
mapped<-NA; avg_cov<-NA;
sd_cov<-NA; min_cov<-NA; max_cov<-NA;
}
return(data.frame(mapped,avg_cov,sd_cov,min_cov,max_cov))
}
#Extracts number of reads and read widths from html report generated by fastqc
fastqc_readstats<-function(fname){
require(rvest)
if(file.exists(fname)){
tmp_fastqc<-read_html(fname);
tmp_table<-html_table(tmp_fastqc)[[1]];
fastq_reads<-as.numeric(tmp_table[tmp_table$Measure=='Total Sequences','Value']);
fastq_width<-tmp_table[tmp_table$Measure=='Sequence length','Value']; #returns single number for raw reads and range for trimmed
gc<-as.numeric(tmp_table[tmp_table$Measure=='%GC','Value']);
}else{
fastq_reads<-NA;
fastq_width<-NA;
gc<-NA;
}
return(data.frame(fastq_reads,fastq_width,gc,stringsAsFactors=F));
}
#Compute stats on a consensus seq (or really any fasta file)
conseq_stats<-function(fname){
require(Biostrings)
if(length(fname)==0){
width<-NA; Ns<-NA; percNs<-NA;
}else if(file.exists(fname)){
conseq<-readDNAStringSet(fname,format='fasta');
if(length(conseq)>0){
width<-width(conseq);
Ns<-sum(letterFrequency(conseq,c('N','+')));
percNs<-100*Ns/width;
}else{
width<-NA; Ns<-NA; percNs<-NA;
}
}else{
width<-NA; Ns<-NA; percNs<-NA;
}
return(data.frame(width,Ns,percNs));
}
#VCF to data frame for a vcf generated by Lofreq
vcf_to_df<-function(vcf_fname,sampid){
require(VariantAnnotation);
vcf<-readVcf(vcf_fname);
results<-data.frame(samp_id=sampid,pos=start(rowRanges(vcf)),af=info(vcf)$AF,dp=info(vcf)$DP,ref=ref(vcf),
alt=unlist(alt(vcf)),stringsAsFactors=F);
results$snpid<-paste(results$ref,'_',results$pos,'_',results$alt,sep='');
results$major_af<-unlist(lapply(results$af,function(x)max(x,1-x)));
results$minor_af<-unlist(lapply(results$af,function(x)min(x,1-x)));
return(results)
}
#Extract VRC samp year and ID from the fastq file name
get_year<-function(in_string){
yr<-strsplit(in_string,"-")[[1]][1];
if(grepl("^[0-9]{1,2}_(19[0-9][0-9]|20[0,1][0-9])",yr)){
return(strsplit(yr,'_')[[1]][2]);
}else if(!grepl("19[0-9][0-9]|20[0,1][0-9]",yr)){
return(NA)
}else{
return(yr)
}
}
get_sampid<-function(in_string){
if(!is.na(get_year(in_string))){
return(strsplit(strsplit(in_string,'-')[[1]][2],'_')[[1]][1]);
}else{
return(NA);
}
}