Chromatin immunoprecipitation followed by high throughput sequencing (ChIP-seq) allows biologists to identify protein/DNA binding and histone modifications across the genome. Every experiment is prone to noise and bias, and ChIP-seq experiments are no exception. To alleviate bias, the incorporation of control datasets in ChIPseq analysis is an essential step. The controls are used to account for the background signal, while the remainder of the ChIP-seq signal captures true binding or histone modification. However, a recurrent issue is different types of bias in different ChIP-seq experiments. Depending on which controls are used, different aspects of ChIP-seq bias are better or worse accounted for, and peak calling can produce different results for the same ChIP-seq experiment.

We introduce WACS (Weighted Analysis of ChIP-seq). WACS is an extension of the well-known peak caller -- MACS2. It allows the use of “smart” controls to model the non-signal effect for a specific ChIP-seq experiment. WACS first generates the weights per control to model the background distribution per ChIP-seq experiment. This is then followed by peak calling.

• Bioinformatics:
  • SAMtools (http://www.htslib.org/download/)
  • bedtools (https://bedtools.readthedocs.io/en/latest/content/installation.html)
• Python 2.7:
  • pip install numpy
  • pip install pandas
  • pip install scipy

Make sure Part1.sh, getWeights.py, ManipulateData.py and getData.py are in the same directory. Your environment should also be set with Python, BEDtools and SAMtools.

./Part1.sh controlDir treatmentFile chromSize

Inputs:

(a) a path to a directory holding all the control BAM files -- required.

(b) full path to the treatment BAM file -- required.

(c) full path to file containing chromosome sizes corresponding to the species genome. -- optional. File format eg: chr1 248956422 If no chromosome size is provided, by default chromosome sizes corresponding to the human genome will be used.

The BAM files could either be (1) unsorted or unindexed bam files, or (2) sorted and indexed bam files.

For each chip, get control names and their corresponding weights and pass them to macs2 callpeak_wacs.

FILENAME=basename name.bam

BASE=echo $FILENAME | cut -d. -f1

coefFile=Coefficients/$BASE.coefficients.csv

ControlBamDir=BAM/Control

if [ -e $coefFile ]

then

controlNames="`cut -d, -f1 $coefFile`"

controlWeights="`cut -d, -f2 $coefFile`"

for i in $controlNames; do controlNames_full+="$ControlBamDir$i.bam "; done

macs2 callpeak_wacs -t name.bam -c $controlNames_full -w $controlWeights

fi

If you use this code for your research, please cite our paper:

Awdeh, Aseel, Marcel Turcotte, and Theodore J. Perkins. "WACS: improving ChIP-seq peak calling by optimally weighting controls." BMC bioinformatics 22.1 (2021): 1-21.