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Decoding topologically associating domains with ultra-low resolution Hi-C data by graph structural entropy

Author

Listed:
  • Angsheng Li

    (Beihang University
    Chinese Academy of Sciences
    University of Chinese Academy of Sciences)

  • Xianchen Yin

    (Chinese Academy of Sciences
    University of Chinese Academy of Sciences)

  • Bingxiang Xu

    (Chinese Academy of Sciences
    University of Chinese Academy of Sciences)

  • Danyang Wang

    (Chinese Academy of Sciences
    University of Chinese Academy of Sciences)

  • Jimin Han

    (University of Chinese Academy of Sciences)

  • Yi Wei

    (University of Chinese Academy of Sciences)

  • Yun Deng

    (University of Chinese Academy of Sciences)

  • Ying Xiong

    (University of Chinese Academy of Sciences)

  • Zhihua Zhang

    (Chinese Academy of Sciences
    University of Chinese Academy of Sciences)

Abstract

Submegabase-size topologically associating domains (TAD) have been observed in high-throughput chromatin interaction data (Hi-C). However, accurate detection of TADs depends on ultra-deep sequencing and sophisticated normalization procedures. Here we propose a fast and normalization-free method to decode the domains of chromosomes (deDoc) that utilizes structural information theory. By treating Hi-C contact matrix as a representation of a graph, deDoc partitions the graph into segments with minimal structural entropy. We show that structural entropy can also be used to determine the proper bin size of the Hi-C data. By applying deDoc to pooled Hi-C data from 10 single cells, we detect megabase-size TAD-like domains. This result implies that the modular structure of the genome spatial organization may be fundamental to even a small cohort of single cells. Our algorithms may facilitate systematic investigations of chromosomal domains on a larger scale than hitherto have been possible.

Suggested Citation

  • Angsheng Li & Xianchen Yin & Bingxiang Xu & Danyang Wang & Jimin Han & Yi Wei & Yun Deng & Ying Xiong & Zhihua Zhang, 2018. "Decoding topologically associating domains with ultra-low resolution Hi-C data by graph structural entropy," Nature Communications, Nature, vol. 9(1), pages 1-12, December.
    • Handle: RePEc:nat:natcom:v:9:y:2018:i:1:d:10.1038_s41467-018-05691-7
    DOI: 10.1038/s41467-018-05691-7
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    Cited by:

    1. Jingxuan Xu & Xiang Xu & Dandan Huang & Yawen Luo & Lin Lin & Xuemei Bai & Yang Zheng & Qian Yang & Yu Cheng & An Huang & Jingyi Shi & Xiaochen Bo & Jin Gu & Hebing Chen, 2024. "A comprehensive benchmarking with interpretation and operational guidance for the hierarchy of topologically associating domains," Nature Communications, Nature, vol. 15(1), pages 1-19, December.
    2. Long Jin & Danyang Wang & Jiaman Zhang & Pengliang Liu & Yujie Wang & Yu Lin & Can Liu & Ziyin Han & Keren Long & Diyan Li & Yu Jiang & Guisen Li & Yu Zhang & Jingyi Bai & Xiaokai Li & Jing Li & Lu Lu, 2023. "Dynamic chromatin architecture of the porcine adipose tissues with weight gain and loss," Nature Communications, Nature, vol. 14(1), pages 1-19, December.
    3. 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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