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

Yan Zhang, Lin An, Jie Xu, Bo Zhang, W. Jim Zheng, Ming Hu, Jijun Tang () and Feng Yue ()
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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
Feng Yue: The Pennsylvania State University

Nature Communications, 2018, vol. 9, issue 1, 1-9

Abstract: 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.

Date: 2018
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DOI: 10.1038/s41467-018-03113-2

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