HaploCalder

Introduction

HaploCalder detects significant differences in chromatin compartment organization between the two haplotypes. It contains three main steps

1. Region selection for compartment inference
2. Bin size selection
3. Differential compartment calls

Usage

HaploC-tools/bin/downstreams.sh --help

Examples

conda run -n nHapCUT2 HaploC-tools/bin/downstreams.sh -d demo_data -k diffComp

Parameters:

Name	Description
-d	The working directory for phasing
-k	The module to run, should be one of diffIns, diffComp or HaploCNV

Output Structure

The output of the workflow is stored in the CALDER/diffComp sub-directory and will look like this:

CALDER/diffComp/
|-- diffComp.sig.bed
|-- diffComp.sig.wig
|-- phased.diff_rank.bedgraph
|-- random_k=1.diff_rank.bedgraph
|-- random_k=2.diff_rank.bedgraph
|-- random_k=3.diff_rank.bedgraph
|-- random_k=4.diff_rank.bedgraph
`-- random_k=5.diff_rank.bedgraph

File description:

Name	Description
diffComp.sig.bed	a .bed file containing the genomic regions showing significant compartment difference between the two haplotypes
diffComp.sig.wig	a .wig file containing the magnitude of compartment differences
phased.diff_rank.bedgraph	a .bedgraph file containing the difference of compartment rank at each 10kb bin between 'pat' and 'mat' Hi-C map
random_k=xx.diff_rank.bedgraph	a .bedgraph file containing the difference of compartment rank at each 10kb bin between two random (pseudo) 'pat' and 'mat' Hi-C map

All .bedgraph and .wig files can be viewed directly through IGV, as exemplified here:

Run time:

For the computational requirement, running HaploCalder on the xx Hi-C dataset at bin size of 25kb it took xx minutes (server information: 40 cores, 64GB Ram, Intel(R) Xeon(R) Silver 4210 CPU @ 2.20GHz). The evaluation was done using a single core although HaploCalder can be run in a parallel manner.