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Practical Analysis of Hi-C Data: Generating A/B Compartment Profiles.

Hisashi Miura1, Rawin Poonperm1, Saori Takahashi1

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Methods in Molecular Biology (Clifton, N.J.)
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PubMed
Summary

This chapter details calculating A/B compartment profiles from processed Hi-C data. It simplifies genome-wide chromosome organization analysis for researchers using readily available datasets.

Keywords:
3D genome organizationA/B compartmentsBioinformaticsEpigeneticsHi-C contact mapInactive X chromosome

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Area of Science:

  • Genomics
  • Molecular Biology
  • Epigenetics

Background:

  • Next-generation sequencing (NGS) and chromosome conformation capture (3C) have enabled Hi-C for genome-wide analysis.
  • Hi-C reveals chromosome organization levels like A/B compartments, TADs, and chromatin loops.
  • Hi-C data analysis is complex, posing a barrier to laboratory implementation.

Purpose of the Study:

  • To provide a detailed protocol for calculating A/B compartment profiles from processed Hi-C data.
  • To enable researchers to analyze genome-wide chromosome organization without extensive computational resources.
  • To facilitate the use of publicly available Hi-C datasets for further research.

Main Methods:

  • Utilizing processed Hi-C data from genomics repositories.
  • Calculating A/B compartment profiles for autosomes.
  • Analyzing A/B compartment profiles for active and inactive X chromosomes.

Main Results:

  • Demonstration of a method to derive A/B compartment profiles from processed Hi-C data.
  • Characterization of compartment profiles across autosomes and sex chromosomes.
  • Accessibility of complex Hi-C analysis through processed data.

Conclusions:

  • The described method simplifies the analysis of A/B compartments from Hi-C data.
  • Researchers can leverage processed Hi-C datasets for insights into chromosome organization.
  • This approach lowers the barrier for implementing Hi-C analysis in diverse research settings.