This package provides code to call peaks in ChIP-seq data with biological replicates using the BinQuasi algorithm of Goren, Liu, Wang, and Wang (2018) doi.org/10.1093/bioinformatics/bty227.
BinQuasi accepts sorted and indexed BAM files (note that it does not perform genome alignment of raw reads). If your BAM files are not indexed and sorted, we recommend using samtools.
Once installed, BinQuasi calls peaks with the function “BQ().” Below is code to run BinQuasi with all default settings, where the sorted and indexed BAM files are stored in the directory specified by “fpath” under the file names “C1.bam”, " C2.bam" and “I1.bam”, “I2.bam” for ChIP and input files, respectively.
library(BinQuasi)
fpath <- paste0(system.file(package = 'BinQuasi'), '/extdata/')
results <- BQ(fpath,
ChIP.files = c('C1.bam', 'C2.bam'),
control.files = c('I1.bam', 'I2.bam'))
#> Fragment length not provided. Estimating fragment length using cross correlation... please wait...
#> Bin size not provided. Estimating bin size... please wait...
#> Using bin size of 50 bp
#> Using estimated fragment length for C1.bam equal to 100 bp
#> Using estimated fragment length for C2.bam equal to 100 bp
#> Using estimated fragment length for I1.bam equal to 100 bp
#> Using estimated fragment length for I2.bam equal to 100 bp
#> [1] "Analyzing Window # 2"
#> [1] "Analyzing Window # 10"
#> [1] "Analyzing Window # 100"
#> [1] "Analyzing Window # 500"
#> [1] "Analyzing Window # 1000"
#> [1] "Analyzing Window # 2500"
#> [1] "Analyzing Window # 5000"
#> [1] "Analyzing Window # 10000"
#> [1] "Analyzing Window # 15000"
#> [1] "Analyzing Window # 2"
#> [1] "Analyzing Window # 10"
#> [1] "Analyzing Window # 100"
#> [1] "Analyzing Window # 500"
#> [1] "Analyzing Window # 1000"
#> [1] "Analyzing Window # 2500"
#> [1] "Analyzing Window # 5000"
#> [1] "Analyzing Window # 10000"
#> [1] "Analyzing Window # 15000"
#> [1] "Spline scaling factor: 1.7634298697216"
head(results$peaks)
#> start end width chr P.val Q.val
#> 1 18051 18200 150 chr4 1.344579e-08 2.834062e-08
#> 2 21951 22100 150 chr4 8.874951e-07 1.216727e-06
#> 3 25401 25550 150 chr4 8.999441e-09 1.961417e-08
#> 4 29851 29950 100 chr4 7.552531e-07 1.052402e-06
#> 5 39551 39650 100 chr4 3.514902e-06 4.268095e-06
#> 6 53001 53100 100 chr4 2.743101e-07 4.020062e-07See the package documentation for information on changing the default settings.
The code below saves the called peaks in BED format in the file “BinQuasiPeaks.bed”.
# Sort peaks by p-value
opeaks <- results$peaks[order(results$peaks$P.val),]
# Name the peaks by rank
opeaks$name <- paste0('BQ_Peak_', 1:nrow(opeaks))
# Save as .bed file, setting the scores to be -log10(p-value)
bedout <- data.frame(chrom = opeaks$chr,
chromStart = opeaks$start,
chromEnd = opeaks$end,
name = opeaks$name,
score = -log10(opeaks$P.val),
strand = c(rep(".", nrow(opeaks))))
head(bedout)
#> chrom chromStart chromEnd name score strand
#> 1 chr4 241451 241650 BQ_Peak_1 16.15782 .
#> 2 chr4 685551 685750 BQ_Peak_2 15.64186 .
#> 3 chr4 697051 697200 BQ_Peak_3 14.69503 .
#> 4 chr4 439301 439500 BQ_Peak_4 13.90893 .
#> 5 chr4 322051 322250 BQ_Peak_5 13.50279 .
#> 6 chr4 650851 651050 BQ_Peak_6 12.78873 .
write.table(bedout, file="BinQuasiPeaks.bed", quote = FALSE, sep = "\t", row.names = FALSE, col.names = FALSE)