# Peaks with hichipper¶

To call peaks from HiChIP data directly, hichipper aggregates read density from either all samples or each sample individually. Additionally, users can specify whether all read density is used or if only self-ligation reads are used. To specify these options, put the appropriate string of the form {COMBINED,EACH},{ALL,SELF} in the peaks slot of the .yaml.

For example, to replicate the peak calling performed in Mumbach et al., one would use the following .yaml:

peaks:
- COMBINED,SELF
resfrags:
- hg19_MboI_resfrag.bed.gz
hicpro_output:
- hicpro


Alternatively, we can call peaks from the HiChIP data for each sample individually using all reads using this specification–

peaks:
- EACH,ALL
resfrags:
- hg19_MboI_resfrag.bed.gz
hicpro_output:
- hicpro


The figure below shows all options for peak specification in hichipper including every option for inferring peaks which are noted in the table.

Alternatively, users can pre-specify a set of peaks to used. In this case, a “connectome” will be inferred between the peaks specified in the .bed file. Of note, pre-specified peaks will still be padded either by fixed amounts or to the edges of the restriction fragment pads (or both) unless the user specifies these flags differently (see below).

peaks:
- predeterminedPeaks.bed
resfrags:
- hg19_MboI_resfrag.bed.gz
hicpro_output:
- hicpro


Note: the input of pre-determined peaks does not have to explicitly be a .bed file. Rather, any file name is acceptable so long as the first three columns indicate appropriate genomic loci as if it were a .bed file. For example, .narrowPeak files from macs2 should be fine.

# Multiple ChIP-Seq peaks as input¶

As raised in this issue, if you have multiple samples and multiple ChIP-Seq or related high-quality peak definitions to be used as an input, the way to do this is to create two or more .yaml files, each one specifying its own bed file of peaks. Then, execute hichipper such that you restrict the analysis to the sample you want per bed file using the --keep-samples or --ignore-samples flags. Thanks to user sb5169 for bringing this up.

# HiChIP-Specific Bias Correction¶

A key difference of HiChIP data compared to ChIA-PET, ChIP-Seq, and related immunoprecipitation assays is the a notable bias where a greater read density accumulates near the motif used in the restriction enzyme digestion. The image below shows the ratio of the treatment to the background (the statistic used in macs2 to call peaks) as a function of distance to the nearest restriction fragment locus. Note the plot below–

A more detailed description of this bias and our analysis is contained in this writeup.