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

Journal: Nature communications
PMID: 29467363

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.

Authors

  • Yan Zhang
    Affiliated Hospital of Liaoning University of Traditional Chinese Medicine, Shenyang, 110032, China.
  • Lin An
    Bioinformatics and Genomics Program, Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, PA, 16802, USA.
  • Jie Xu
    Second Affiliated Hospital Zhejiang University School of Medicine, Hangzhou, Zhejiang Province 310000, China.
  • Bo Zhang
    Department of Clinical Pharmacology, Key Laboratory of Clinical Cancer Pharmacology and Toxicology Research of Zhejiang Province, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310006, PR China.
  • W Jim Zheng
    McWilliams School of Biomedical Informatics, University of Texas Health Science at Houston, Houston, TX, USA.
  • Ming Hu
    Department of Civil and Environmental Engineering and Earth Sciences, College of Engineering, University of Notre Dame, Notre Dame, IN, 46556, USA.
  • Jijun Tang
    School of Computer Science and Engineering, Tianjin University, Tianjin, 300072, China. jtang@cse.sc.edu.
  • Feng Yue
    Bioinformatics and Genomics Program, Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, PA, 16802, USA. fyue@hmc.psu.edu.

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