Read mapping with BWA-MEM

The NGS reads of this project are aligned against the reference genome sequence using the highly variant tolerant short read aligner BWA-MEM (Li , 2013; Li et al., 2009). The parameter settings of the aligner are defined in the bwa.param file.

args <- systemArgs(sysma="param/bwa.param", mytargets="targets.txt")
sysargs(args)[1] # Command-line parameters for first FASTQ file

Runs the alignments sequentially (e.g. on a single machine)

system("bwa index -a bwtsw ./data/tair10.fasta")
bampaths <- runCommandline(args=args)
writeTargetsout(x=args, file="targets_bam.txt", overwrite=TRUE)

Alternatively, the alignment jobs can be submitted to a compute cluster, here using 72 CPU cores (18 qsub processes each with 4 CPU cores).

system("bwa index -a bwtsw ./data/tair10.fasta")
resources <- list(walltime="1:00:00", ntasks=1, ncpus=cores(args), memory="10G")
reg <- clusterRun(args, conffile=".BatchJobs.R", template="slurm.tmpl", Njobs=18, runid="01",
writeTargetsout(x=args, file="targets_bam.txt", overwrite=TRUE)

Check whether all BAM files have been created


Read mapping with gsnap

An alternative variant tolerant aligner is gsnap from the gmapR package (Wu et al., 2010). The following code shows how to run this aligner on multiple nodes of a computer cluster that uses Torque as scheduler.

library(gmapR); library(BiocParallel); library(BatchJobs)
args <- systemArgs(sysma="param/gsnap.param", mytargets="targetsPE.txt")
gmapGenome <- GmapGenome(systemPipeR::reference(args), directory="data", name="gmap_tair10chr", create=TRUE)
f <- function(x) {
    library(gmapR); library(systemPipeR)
    args <- systemArgs(sysma="param/gsnap.param", mytargets="targetsPE.txt")
    gmapGenome <- GmapGenome(reference(args), directory="data", name="gmap_tair10chr", create=FALSE)
    p <- GsnapParam(genome=gmapGenome, unique_only=TRUE, molecule="DNA", max_mismatches=3)
    o <- gsnap(input_a=infile1(args)[x], input_b=infile2(args)[x], params=p, output=outfile1(args)[x])
funs <- makeClusterFunctionsSLURM("slurm.tmpl")
param <- BatchJobsParam(length(args), resources=list(walltime="00:20:00", ntasks=1, ncpus=1, memory="6G"), cluster.functions=funs)
d <- bplapply(seq(along=args), f)
writeTargetsout(x=args, file="targets_gsnap_bam.txt", overwrite=TRUE)

Read and alignment stats

The following generates a summary table of the number of reads in each sample and how many of them aligned to the reference.

read_statsDF <- alignStats(args=args) 
write.table(read_statsDF, "results/alignStats.xls", row.names=FALSE, quote=FALSE, sep="\t")

The symLink2bam function creates symbolic links to view the BAM alignment files in a genome browser such as IGV. The corresponding URLs are written to a file with a path specified under urlfile, here IGVurl.txt.

symLink2bam(sysargs=args, htmldir=c("~/.html/", "projects/gen242/"), 

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