ChIP-R uses an adaptation of the rank product statistic to assess the reproducibility of ChIP-seq peaks by incorporating information from multiple ChIP-seq replicates and "fragmenting" peak locations to better combine the information present across the replicates.
- Python3.x with the following packages:
- Numpy
- Scipy
Installation for ChIP-R has been made easy with a range of different install methods available. Installing via any of these options will handle all dependencies for you as well.
conda install chip-r
pip install ChIP-R
If you want to install from source:
git clone https://github.com/rhysnewell/ChIP-R.git
cd ChIP-R
python3 setup.py install
ChIP-R requires only a single input type: A set of any number of BED file regions. Typically the output of peak calling from ChIP-seq peak calling on transcription factor or histone mark samples. Alternatively, ChIP-R can also be used on ATAC-seq peaks to retrieve reproducible peaks across ATAC-seq experiments.
The input BED files must follow ENCODE narrowPeak or broadPeak format specifications. Typically, this format is the default for peak callers such as MACS2.
ChIP-R is compatible with the output peaks for any peak caller as long as the output is in the correct narrowPeak or broadPeak format. Additionally, there is no need to call peaks with relaxed thresholds when using your chosen peak caller as is the suggested by IDR.
ChIP-R is fairly light on parameters that need to be chosen by the user. A couple of options that users may want to play with is
minentries and size.
minentries determines the number of peak overlaps required to start calling a peak "reproducible".
The default of 2 typically provides the best results in our benchmarks but there may be a case where a user requires
ChIP-R to call peaks within a much stricter window.
size determines the minimum peak size during peak output. Transcription factors generally want more punctate peaks, and
so the default value of 20 may be sufficient. However, histone marks may require a much larger value be set for this depending
on how broad you expect the histone mark to be. Generally, if you find ChIP-R produces too many small noisy peaks then this
value can be increased to filter them out.
$ chipr -i sample1.bed sample2.bed sample3.bed sample4.bed -m 2 -o output_prefix
In the command line, type in 'chipr -h ' for detailed usage.
$ chipr -h
usage: chipr [-h] -i INPUT [INPUT ...] [-o OUTPUT] [-m MINENTRIES]
[--rankmethod RANKMETHOD] [--duphandling DUPHANDLING]
[--seed RANDOM_SEED] [-a ALPHA]
Combine multiple ChIP-seq files and return a union of all peak locations and a
set confident, reproducible peaks as determined by rank product analysis
optional arguments:
-h, --help show this help message and exit
-i INPUT [INPUT ...], --input INPUT [INPUT ...]
ChIP-seq input files. These files must be in either
narrowPeak, broadPeak, or regionPeak format. Multiple
inputs are separeted by a single space
-o OUTPUT, --output OUTPUT
ChIP-seq output filename prefix
-m MINENTRIES, --minentries MINENTRIES
The minimum peaks between replicates required to form
an intersection of the peaks Default: 1
--rankmethod RANKMETHOD
The ranking method used to rank peaks within
replicates. Options: 'signalvalue', 'pvalue',
'qvalue'. Default: pvalue
--duphandling DUPHANDLING
Specifies how to handle entries that are ranked
equally within a replicate Can either take the
'average' ranks or a 'random' rearrangement of the
ordinal ranks Options: 'average', 'random' Default:
'average'
--seed RANDOM_SEED Specify a seed to be used in conjunction with the
'random' option for -duphandling Must be between 0 and
1 Default: 0.5
-a ALPHA, --alpha ALPHA
Alpha specifies the user cut-off value for set of
reproducible peaks The analysis will still produce
results including peaks within the threshold
calculated using the binomial method Default: 0.05
-s SIZE, --size SIZE Sets the default minimum peak size when peaks are
reconnected after fragmentation. Usually the minimum
peak size is determined by the size of surrounding
peaks, but in the case that there are no surrounding
peaks this value will be used Default: 20
I get that the naming convention for ChIP-R kind of sucks, and have to remember the capital letters and the hyphen can be frustrating as such you can call ChIP-R from three different entrypoints:
# Easiest
chipr -h
# Lowercase
chip-r -h
# Hard mode
ChIP-R -h
I learned my lesson when naming programs and will never do this again. My apologies :P
Important result files:
- prefixname_ALL.bed: All intersected peaks, ordered from most significant to least (10 columns)
- prefixname_T2.bed: The tier 2 intersected peaks, the peaks that fall within the binomial threshold (10 columns)
- prefixname_T1.bed: The tier 1 intersected peaks, the peaks that fall within the user defined threshold (10 columns)
- prefixname_log.txt: A log containing the number of peaks appearing in each tier.
prefixname.bed file has 10 columns. The output follows the standard peak format for bed files, with the addition of a 10th column that specifies the ranks of the peaks that produced this possible peak. See the toy example below.
| chr | start | end | name | score | strand | signalValue | p-value | q-value |
|---|---|---|---|---|---|---|---|---|
| chr1 | 9118 | 10409 | T3_peak_87823 | 491 | . | 15.000000 | 0.113938 | 0.712353 |
The following is a short tutorial on how to generate a set of peaks that can be used as input for ChIP-R. Usually, an experiment begins with the generation of HTS reads which will require mapping against a reference genome. Here we will be using dummy values for the reference genome and reads. This example also makes use of BWA for the read mapping and MACS2 for the peak calling. Alternatives to these programs is perfectly acceptable when using ChIP-R.
bwa mem reference_genome.fasta sample_1.1.fastq sample_1.2.fastq | samtools view -h -b -S -F4 | samtools sort > aln_pe_rep1.bam
bwa mem reference_genome.fasta control_1.1.fastq control_1.2.fastq | samtools view -h -b -S -F4 | samtools sort > aln_pe_input1.bam
bwa mem reference_genome.fasta sample_2.1.fastq sample_2.2.fastq | samtools view -h -b -S -F4 | samtools sort > aln_pe_rep2.bam
bwa mem reference_genome.fasta control_2.1.fastq control_2.2.fastq | samtools view -h -b -S -F4 | samtools sort > aln_pe_input2.bam
This will produce four BAM files, two experimental replicate read mapping files aln_pe_rep1.bam & aln_pe_rep2.bam
and two control read mapping files used for the differential peak calling aln_pe_input1.bam & aln_pe_input2.bam
We keep experiment 1 with control 1, and experiment 2 with control 2.
macs callpeak -t aln_pe_rep1.bam -c aln_pe_input1.bam -n rep1
macs callpeak -t aln_pe_rep2.bam -c aln_pe_input2.bam -n rep2
This will result in multiple different output files from MACS2 but the files of interest will be
rep1_macs2_peaks.bed & rep2_macs2_peaks.bed. We can pass these peaks directly to ChIP-R.
You may want to change the m values for your specific experiment. Lower m values will produce finer peak boundaries
but there will be many fragmented peaks. However, m must always be less than or equal to the number of input files.
chipr -i rep1_macs_peaks.bed rep2_macs_peak.bed -m 2 -o output
ChIP-R will produce three output files including a log file and two BED files. The BED files consist of the set of ALL peak fragments and then the set of optimal peak fragments.
Preprint available on bioarxiv https://www.biorxiv.org/content/10.1101/2020.11.24.396960v1
Authors: Rhys Newell, Michael Piper, Mikael Boden, Alexandra Essebier
Contact: rhys.newell(AT)hdr.qut.edu.au