HiCHap: a package to correct and analyze the diploid Hi-C data

Area of Science:

• Genomics
• Molecular Biology
• Bioinformatics

Background:

• Diploid cells require distinct maternal and paternal Hi-C maps due to differing 3D chromatin organization.
• Existing software for diploid Hi-C maps overlooks biases from variable genetic variant density.
• Limited tools offer quantitative analysis of allele-specific 3D chromatin structures like compartments, domains, and loops.

Purpose of the Study:

• To identify and correct systematic allele-assignment biases in diploid Hi-C contact maps.
• To develop an integrated tool for comprehensive diploid Hi-C data analysis.
• To enable accurate, whole-genome identification of allele-specific 3D chromatin organization.

Main Methods:

• Developed a novel strategy to correct allele-assignment bias in Hi-C data.
• Integrated read mapping, contact map construction, and 3D structure identification into a single tool, HiCHap.
• Implemented allele-specific testing for diploid Hi-C data.

Main Results:

• Revealed allele-assignment bias linked to variable genetic variant density.
• Demonstrated that HiCHap's bias correction significantly enhances diploid Hi-C map quality.
• Facilitated whole-genome identification of diploid chromatin compartments, topological domains, and chromatin loops.
• Showcased HiCHap's capability for haploid Hi-C data analysis.

Conclusions:

• Introduced HiCHap, an integrated package for diploid Hi-C data processing, bias correction, and structural analysis.
• HiCHap provides a robust solution for analyzing allele-specific 3D chromatin organization.
• Source code and tutorials for HiCHap are publicly available.