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Graph embedding and unsupervised learning predict genomic sub-compartments from HiC chromatin interaction data

Author

Listed:
  • Haitham Ashoor

    (The Jackson Laboratory for Genomic Medicine)

  • Xiaowen Chen

    (The Jackson Laboratory for Genomic Medicine)

  • Wojciech Rosikiewicz

    (The Jackson Laboratory for Genomic Medicine)

  • Jiahui Wang

    (The Jackson Laboratory for Genomic Medicine)

  • Albert Cheng

    (The Jackson Laboratory for Genomic Medicine)

  • Ping Wang

    (The Jackson Laboratory for Genomic Medicine)

  • Yijun Ruan

    (The Jackson Laboratory for Genomic Medicine
    The Jackson Laboratory Cancer Center
    University of Connecticut School of Medicine)

  • Sheng Li

    (The Jackson Laboratory for Genomic Medicine
    The Jackson Laboratory Cancer Center
    University of Connecticut School of Medicine
    University of Connecticut)

Abstract

Chromatin interaction studies can reveal how the genome is organized into spatially confined sub-compartments in the nucleus. However, accurately identifying sub-compartments from chromatin interaction data remains a challenge in computational biology. Here, we present Sub-Compartment Identifier (SCI), an algorithm that uses graph embedding followed by unsupervised learning to predict sub-compartments using Hi-C chromatin interaction data. We find that the network topological centrality and clustering performance of SCI sub-compartment predictions are superior to those of hidden Markov model (HMM) sub-compartment predictions. Moreover, using orthogonal Chromatin Interaction Analysis by in-situ Paired-End Tag Sequencing (ChIA-PET) data, we confirmed that SCI sub-compartment prediction outperforms HMM. We show that SCI-predicted sub-compartments have distinct epigenetic marks, transcriptional activities, and transcription factor enrichment. Moreover, we present a deep neural network to predict sub-compartments using epigenome, replication timing, and sequence data. Our neural network predicts more accurate sub-compartment predictions when SCI-determined sub-compartments are used as labels for training.

Suggested Citation

  • Haitham Ashoor & Xiaowen Chen & Wojciech Rosikiewicz & Jiahui Wang & Albert Cheng & Ping Wang & Yijun Ruan & Sheng Li, 2020. "Graph embedding and unsupervised learning predict genomic sub-compartments from HiC chromatin interaction data," Nature Communications, Nature, vol. 11(1), pages 1-11, December.
  • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-14974-x
    DOI: 10.1038/s41467-020-14974-x
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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. 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.

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