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Enhancing Hi-C data resolution with deep convolutional neural network HiCPlus

Author

Listed:
  • Yan Zhang

    (University of South Carolina)

  • Lin An

    (The Pennsylvania State University)

  • Jie Xu

    (The Pennsylvania State University)

  • Bo Zhang

    (The Pennsylvania State University)

  • W. Jim Zheng

    (University of Texas Health Science Center at Houston)

  • Ming Hu

    (Cleveland Clinic Foundation)

  • Jijun Tang

    (University of South Carolina
    Tianjin University
    Tianjin University)

  • Feng Yue

    (The Pennsylvania State University
    The Pennsylvania State University)

Abstract

Although Hi-C technology is one of the most popular tools for studying 3D genome organization, due to sequencing cost, the resolution of most Hi-C datasets are coarse and cannot be used to link distal regulatory elements to their target genes. Here we develop HiCPlus, a computational approach based on deep convolutional neural network, to infer high-resolution Hi-C interaction matrices from low-resolution Hi-C data. We demonstrate that HiCPlus can impute interaction matrices highly similar to the original ones, while only using 1/16 of the original sequencing reads. We show that the models learned from one cell type can be applied to make predictions in other cell or tissue types. Our work not only provides a computational framework to enhance Hi-C data resolution but also reveals features underlying the formation of 3D chromatin interactions.

Suggested Citation

  • Yan Zhang & Lin An & Jie Xu & Bo Zhang & W. Jim Zheng & Ming Hu & Jijun Tang & Feng Yue, 2018. "Enhancing Hi-C data resolution with deep convolutional neural network HiCPlus," Nature Communications, Nature, vol. 9(1), pages 1-9, December.
    • Handle: RePEc:nat:natcom:v:9:y:2018:i:1:d:10.1038_s41467-018-03113-2
    DOI: 10.1038/s41467-018-03113-2
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    Cited by:

    1. Lara Connolley & Lucas Schnabel & Martin Thanbichler & Seán M. Murray, 2023. "Partition complex structure can arise from sliding and bridging of ParB dimers," Nature Communications, Nature, vol. 14(1), pages 1-12, 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. Hao Wang & Jiaxin Yang & Yu Zhang & Jianliang Qian & Jianrong Wang, 2022. "Reconstruct high-resolution 3D genome structures for diverse cell-types using FLAMINGO," Nature Communications, Nature, vol. 13(1), pages 1-18, December.
    5. 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.
    6. Mochen Liao & Kai Lan & Yuan Yao, 2022. "Sustainability implications of artificial intelligence in the chemical industry: A conceptual framework," Journal of Industrial Ecology, Yale University, vol. 26(1), pages 164-182, February.
    7. Shaofu Xu & Jing Wang & Sicheng Yi & Weiwen Zou, 2022. "High-order tensor flow processing using integrated photonic circuits," Nature Communications, Nature, vol. 13(1), pages 1-10, December.

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