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DeepHiC: A generative adversarial network for enhancing Hi-C data resolution.

ABSTRACT: 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.

SUBMITTER: Hong H 

PROVIDER: S-EPMC7055922 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature

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DeepHiC: A generative adversarial network for enhancing Hi-C data resolution.

Hong Hao H   Jiang Shuai S   Li Hao H   Du Guifang G   Sun Yu Y   Tao Huan H   Quan Cheng C   Zhao Chenghui C   Li Ruijiang R   Li Wanying W   Yin Xiaoyao X   Huang Yangchen Y   Li Cheng C   Chen Hebing H   Bo Xiaochen X  

PLoS computational biology 20200221 2

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% downsamp  ...[more]

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