Merge BAM files of replicates prior to peak calling

Merging BAM files of technical and/or biological replicates can improve the sensitivity of the peak calling by increasing the depth of read coverage. The mergeBamByFactor function merges BAM files based on grouping information specified by a factor, here the Factor column of the imported targets file. It also returns an updated SYSargs object containing the paths to the merged BAM files as well as to any unmerged files without replicates. This step can be skipped if merging of BAM files is not desired.

args <- systemArgs(sysma=NULL, mytargets="targets_bam.txt")
args_merge <- mergeBamByFactor(args, overwrite=TRUE)
writeTargetsout(x=args_merge, file="targets_mergeBamByFactor.txt", overwrite=TRUE)

Peak calling without input/reference sample

MACS2 can perform peak calling on ChIP-Seq data with and without input samples (Zhang et al., 2008). The following performs peak calling without input on all samples specified in the corresponding args object. Note, due to the small size of the sample data, MACS2 needs to be run here with the nomodel setting. For real data sets, users want to remove this parameter in the corresponding *.param file(s).

args <- systemArgs(sysma="param/macs2_noinput.param", mytargets="targets_mergeBamByFactor.txt")
sysargs(args)[1] # Command-line parameters for first FASTQ file
# moduleload(module="python", envir=c("PATH", "LD_LIBRARY_PATH", "PYTHONPATH")) # Temp solution due to Python path change
writeTargetsout(x=args, file="targets_macs.txt", overwrite=TRUE)

Peak calling with input/reference sample

To perform peak calling with input samples, they can be most conveniently specified in the SampleReference column of the initial targets file. The writeTargetsRef function uses this information to create a targets file intermediate for running MACS2 with the corresponding input samples.

writeTargetsRef(infile="targets_mergeBamByFactor.txt", outfile="targets_bam_ref.txt", silent=FALSE, overwrite=TRUE)
args_input <- systemArgs(sysma="param/macs2.param", mytargets="targets_bam_ref.txt")
sysargs(args_input)[1] # Command-line parameters for first FASTQ file
# unlink(outpaths(args_input)) # Note: if output exists then next line will not be run
writeTargetsout(x=args_input, file="targets_macs_input.txt", overwrite=TRUE)

The peak calling results from MACS2 are written for each sample to separate files in the results directory. They are named after the corresponding files with extensions used by MACS2.

Identify consensus peaks

The following example shows how one can identify consensus preaks among two peak sets sharing either a minimum absolute overlap and/or minimum relative overlap using the subsetByOverlaps or olRanges functions, respectively. Note, the latter is a custom function imported below by sourcing it.

peak_M1A <- outpaths(args)["M1A"]
peak_M1A <- as(read.delim(peak_M1A, comment="#")[,1:3], "GRanges")
peak_A1A <- outpaths(args)["A1A"]
peak_A1A <- as(read.delim(peak_A1A, comment="#")[,1:3], "GRanges")
(myol1 <- subsetByOverlaps(peak_M1A, peak_A1A, minoverlap=1)) # Returns any overlap
myol2 <- olRanges(query=peak_M1A, subject=peak_A1A, output="gr") # Returns any overlap with OL length information
myol2[values(myol2)["OLpercQ"][,1]>=50] # Returns only query peaks with a minimum overlap of 50%

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