IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v9y2018i1d10.1038_s41467-018-03113-2.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-018-03113-2
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1038/s41467-018-03113-2?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. Ana Stanojevic & Stanisław Woźniak & Guillaume Bellec & Giovanni Cherubini & Angeliki Pantazi & Wulfram Gerstner, 2024. "High-performance deep spiking neural networks with 0.3 spikes per neuron," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. Yufan Zhou & Tian Li & Lavanya Choppavarapu & Kun Fang & Shili Lin & Victor X. Jin, 2024. "Integration of scHi-C and scRNA-seq data defines distinct 3D-regulated and biological-context dependent cell subpopulations," Nature Communications, Nature, vol. 15(1), pages 1-11, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:9:y:2018:i:1:d:10.1038_s41467-018-03113-2. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.