%0 Journal Article
%T DeepHiC: A generative adversarial network for enhancing Hi-C data resolution
%A Cheng Li
%A Cheng Quan
%A Chenghui Zhao
%A Guifang Du
%A Hao Hong
%A Hao Li
%A Hebing Chen
%A Huan Tao
%A Ruijiang Li
%A Shuai Jiang
%A Wanying Li
%A Xiaochen Bo
%A Xiaoyao Yin
%A Yangchen Huang
%A Yu Sun
%J -
%D 2020
%R 10.1371/journal.pcbi.1007287
%X Hi-C is commonly used to study three-dimensional genome organization. However, due to the high sequencing cost and technical constraints, the resolution of most Hi-C datasets is coarse, resulting in a loss of information and biological interpretability. Here we develop DeepHiC, a generative adversarial network, to predict high-resolution Hi-C contact maps from low-coverage sequencing data. We demonstrated that DeepHiC is capable of reproducing high-resolution Hi-C data from as few as 1% downsampled reads. Empowered by adversarial training, our method can restore fine-grained details similar to those in high-resolution Hi-C matrices, boosting accuracy in chromatin loops identification and TADs detection, and outperforms the state-of-the-art methods in accuracy of prediction. Finally, application of DeepHiC to Hi-C data on mouse embryonic development can facilitate chromatin loop detection. We develop a web-based tool (DeepHiC, http://sysomics.com/deephic) that allows researchers to enhance their own Hi-C data with just a few clicks
%K Chromatin
%K Comparative genomics
%K Mammalian genomics
%K Deep learning
%K Genetic loci
%K Genome analysis
%K Structural genomics
%K Chromosome mapping
%U https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1007287