ChIP-R 1.0

ChIP-R is a method for assessing the reproducibility of replicated ChIP-seq type experiments. It incorporates the rank product method, a novel thresholding methods, and the use of peak fragmentation return the most reproducible peaks.

ChIP-R ("chipper")

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.

Install

• Python3.x with the following packages:
• Numpy
• Scipy

To install ChIP-R:

git clone https://github.com/rhysnewell/ChIP-R.git
cd ChIP-R
python3 setup.py install

Usage

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
                        calculatedusing the binomial method Default: 0.05

Example

$ chipr -i input_prefix1.bed input_prefix2.bed input_prefix3.bed input_prefix4.bed -m 2 -o output_prefix   

Output

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

Citation

Contact

Authors: Rhys Newell, Michael Piper, Mikael Boden, Alexandra Essebier

Contact: rhys.newell(AT)uq.edu.au