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dcHiC detects differential compartments across multiple Hi-C datasets.

Abhijit Chakraborty1, Jeffrey G Wang2,3,4, Ferhat Ay5,6,7

  • 1Centers for Autoimmunity, Inflammation and Cancer Immunotherapy, La Jolla Institute for Immunology, La Jolla, CA, 92037, USA. abhijit@lji.org.

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|November 11, 2022
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Summary
This summary is machine-generated.

dcHiC identifies significant changes in mammalian genome compartmentalization using a novel multivariate distance measure. This tool enhances the analysis of Hi-C contact maps for biological insights.

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Area of Science:

  • Genomics
  • Epigenetics
  • Computational Biology

Background:

  • Mammalian genome compartmentalization is crucial for biological processes.
  • Understanding dynamic changes in genome organization is essential.

Purpose of the Study:

  • Introduce dcHiC, a tool for identifying significant changes in genome compartmentalization across multiple Hi-C contact maps.
  • Evaluate dcHiC's effectiveness and sensitivity in detecting biologically relevant changes.

Main Methods:

  • Utilizes a multivariate distance measure for analyzing Hi-C contact maps.
  • Applied to diverse datasets including in vitro neural differentiation, hematopoiesis, human LCLs, and mouse brain development.
  • Includes bulk and single-cell contact map analysis.

Main Results:

  • dcHiC effectively detects biologically relevant changes in genome compartmentalization.
  • Identified dynamic gene regulation linked to cell identity and correlated chromatin state changes.
  • Demonstrated sensitivity in detecting changes in subcompartments, replication timing, and lamin association.

Conclusions:

  • dcHiC is a sensitive and effective tool for high-resolution genome compartment analysis.
  • Enables differential interaction identification and time-series clustering of Hi-C data.
  • An essential addition to the Hi-C analysis toolbox for bulk and single-cell data.