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HiC-DC+ enables systematic 3D interaction calls and differential analysis for Hi-C and HiChIP

Author

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  • Merve Sahin

    (Memorial Sloan Kettering Cancer Center
    Tri-Institutional Training Program in Computational Biology and Medicine)

  • Wilfred Wong

    (Memorial Sloan Kettering Cancer Center
    Tri-Institutional Training Program in Computational Biology and Medicine)

  • Yingqian Zhan

    (Memorial Sloan Kettering Cancer Center)

  • Kinsey Van Deynze

    (University of California at San Diego)

  • Richard Koche

    (Memorial Sloan Kettering Cancer Center)

  • Christina S. Leslie

    (Memorial Sloan Kettering Cancer Center)

Abstract

Recent genome-wide chromosome conformation capture assays such as Hi-C and HiChIP have vastly expanded the resolution and throughput with which we can study 3D genomic architecture and function. Here, we present HiC-DC+, a software tool for Hi-C/HiChIP interaction calling and differential analysis using an efficient implementation of the HiC-DC statistical framework. HiC-DC+ integrates with popular preprocessing and visualization tools and includes topologically associating domain (TAD) and A/B compartment callers. We found that HiC-DC+ can more accurately identify enhancer-promoter interactions in H3K27ac HiChIP, as validated by CRISPRi-FlowFISH experiments, compared to existing methods. Differential HiC-DC+ analyses of published HiChIP and Hi-C data sets in settings of cellular differentiation and cohesin perturbation systematically and quantitatively recovers biological findings, including enhancer hubs, TAD aggregation, and the relationship between promoter-enhancer loop dynamics and gene expression changes. HiC-DC+ therefore provides a principled statistical analysis tool to empower genome-wide studies of 3D chromatin architecture and function.

Suggested Citation

  • Merve Sahin & Wilfred Wong & Yingqian Zhan & Kinsey Van Deynze & Richard Koche & Christina S. Leslie, 2021. "HiC-DC+ enables systematic 3D interaction calls and differential analysis for Hi-C and HiChIP," Nature Communications, Nature, vol. 12(1), pages 1-11, December.
    • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-23749-x
    DOI: 10.1038/s41467-021-23749-x
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    Cited by:

    1. Dunming Hua & Ming Gu & Xiao Zhang & Yanyi Du & Hangcheng Xie & Li Qi & Xiangjun Du & Zhidong Bai & Xiaopeng Zhu & Dechao Tian, 2024. "DiffDomain enables identification of structurally reorganized topologically associating domains," Nature Communications, Nature, vol. 15(1), pages 1-14, December.

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