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An integrated model for detecting significant chromatin interactions from high-resolution Hi-C data

Author

Listed:
  • Mark Carty

    (Computational Biology Program, Memorial Sloan Kettering Cancer Center
    Institute for Computational Biomedicine, Weill Cornell Medical College
    Tri-Institutional Training Program in Computational Biology and Medicine)

  • Lee Zamparo

    (Computational Biology Program, Memorial Sloan Kettering Cancer Center)

  • Merve Sahin

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

  • Alvaro González

    (Computational Biology Program, Memorial Sloan Kettering Cancer Center)

  • Raphael Pelossof

    (Computational Biology Program, Memorial Sloan Kettering Cancer Center)

  • Olivier Elemento

    (Institute for Computational Biomedicine, Weill Cornell Medical College)

  • Christina S. Leslie

    (Computational Biology Program, Memorial Sloan Kettering Cancer Center)

Abstract

Here we present HiC-DC, a principled method to estimate the statistical significance (P values) of chromatin interactions from Hi-C experiments. HiC-DC uses hurdle negative binomial regression account for systematic sources of variation in Hi-C read counts—for example, distance-dependent random polymer ligation and GC content and mappability bias—and model zero inflation and overdispersion. Applied to high-resolution Hi-C data in a lymphoblastoid cell line, HiC-DC detects significant interactions at the sub-topologically associating domain level, identifying potential structural and regulatory interactions supported by CTCF binding sites, DNase accessibility, and/or active histone marks. CTCF-associated interactions are most strongly enriched in the middle genomic distance range (∼700 kb–1.5 Mb), while interactions involving actively marked DNase accessible elements are enriched both at short ( 1.5 Mb) genomic distances. There is a striking enrichment of longer-range interactions connecting replication-dependent histone genes on chromosome 6, potentially representing the chromatin architecture at the histone locus body.

Suggested Citation

  • Mark Carty & Lee Zamparo & Merve Sahin & Alvaro González & Raphael Pelossof & Olivier Elemento & Christina S. Leslie, 2017. "An integrated model for detecting significant chromatin interactions from high-resolution Hi-C data," Nature Communications, Nature, vol. 8(1), pages 1-10, August.
    • Handle: RePEc:nat:natcom:v:8:y:2017:i:1:d:10.1038_ncomms15454
    DOI: 10.1038/ncomms15454
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    Cited by:

    1. Zhang Qi & Xu Zheng & Lai Yutong, 2021. "An Empirical Bayes approach for the identification of long-range chromosomal interaction from Hi-C data," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 20(1), pages 1-15, February.
    2. Yanlin Zhang & Mathieu Blanchette, 2022. "Reference panel guided topological structure annotation of Hi-C data," Nature Communications, Nature, vol. 13(1), pages 1-12, December.

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