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Constructing 3D interaction maps from 1D epigenomes

Author

Listed:
  • Yun Zhu

    (University of California at San Diego
    Present address: Thermo Fisher Scientific 5781 Van Allen Way, Carlsbad, California 92008, USA)

  • Zhao Chen

    (University of California at San Diego)

  • Kai Zhang

    (University of California at San Diego)

  • Mengchi Wang

    (University of California at San Diego)

  • David Medovoy

    (University of California at San Diego)

  • John W. Whitaker

    (University of California at San Diego
    Present address: Discovery Sciences, Janssen Research and Development, LLC. 3210 Merryfield Row, San Diego, California 92101, USA)

  • Bo Ding

    (University of California at San Diego)

  • Nan Li

    (University of California at San Diego)

  • Lina Zheng

    (University of California at San Diego)

  • Wei Wang

    (University of California at San Diego
    University of California at San Diego)

Abstract

The human genome is tightly packaged into chromatin whose functional output depends on both one-dimensional (1D) local chromatin states and three-dimensional (3D) genome organization. Currently, chromatin modifications and 3D genome organization are measured by distinct assays. An emerging question is whether it is possible to deduce 3D interactions by integrative analysis of 1D epigenomic data and associate 3D contacts to functionality of the interacting loci. Here we present EpiTensor, an algorithm to identify 3D spatial associations within topologically associating domains (TADs) from 1D maps of histone modifications, chromatin accessibility and RNA-seq. We demonstrate that active promoter–promoter, promoter–enhancer and enhancer–enhancer associations identified by EpiTensor are highly concordant with those detected by Hi-C, ChIA-PET and eQTL analyses at 200 bp resolution. Moreover, EpiTensor has identified a set of interaction hotspots, characterized by higher chromatin and transcriptional activity as well as enriched TF and ncRNA binding across diverse cell types, which may be critical for stabilizing the local 3D interactions.

Suggested Citation

  • Yun Zhu & Zhao Chen & Kai Zhang & Mengchi Wang & David Medovoy & John W. Whitaker & Bo Ding & Nan Li & Lina Zheng & Wei Wang, 2016. "Constructing 3D interaction maps from 1D epigenomes," Nature Communications, Nature, vol. 7(1), pages 1-11, April.
    • Handle: RePEc:nat:natcom:v:7:y:2016:i:1:d:10.1038_ncomms10812
    DOI: 10.1038/ncomms10812
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    Cited by:

    1. Lina Zheng & Wei Wang, 2022. "Regulation associated modules reflect 3D genome modularity associated with chromatin activity," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    2. Fan Feng & Yuan Yao & Xue Qing David Wang & Xiaotian Zhang & Jie Liu, 2022. "Connecting high-resolution 3D chromatin organization with epigenomics," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    3. Kevin B. Dsouza & Alexandra Maslova & Ediem Al-Jibury & Matthias Merkenschlager & Vijay K. Bhargava & Maxwell W. Libbrecht, 2022. "Learning representations of chromatin contacts using a recurrent neural network identifies genomic drivers of conformation," Nature Communications, Nature, vol. 13(1), pages 1-19, December.
    4. Surya K Ghosh & Daniel Jost, 2018. "How epigenome drives chromatin folding and dynamics, insights from efficient coarse-grained models of chromosomes," PLOS Computational Biology, Public Library of Science, vol. 14(5), pages 1-26, May.

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