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

Hao Hong,Yu Sun,Hebing Chen,Chenghui Zhao,Wanying Li,Yangchen Huang,Hao Li,Xiaochen Bo,Xiaoyao Yin,Ruijiang Li,Shuai Jiang,Cheng Li,Huan Tao,Cheng Quan,Guifang Du,Ferhat Ay

doi:10.1371/journal.pcbi.1007287

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.

Highlights

The high-throughput chromosome conformation capture (Hi-C) technique [1] is a genomewide technique used to investigate three-dimensional (3D) chromatin conformation inside the nucleus
We demonstrated that DeepHiC is capable of reproducing high-resolution Hi-C data from as few as 1% downsampled reads
We developed a novel method, DeepHiC, for enhancing Hi-C data resolution from lowcoverage sequencing data using generative adversarial network

Summary

Introduction

The high-throughput chromosome conformation capture (Hi-C) technique [1] is a genomewide technique used to investigate three-dimensional (3D) chromatin conformation inside the nucleus. It has facilitated the identification and characterization of multiple structural elements, such as the A/B compartments [1], topological associating domains (TADs) [2, 3], enhancer-promoter interactions [4] and stripes [5] over recent decades. Low-resolution data may be sufficient for detecting large-scale genomic patterns such as A/B compartments, but the decrease in resolution when analyzing Hi-C data may prevent identification of fine-scale genomic elements such as sub-TADs [20, 21] and enhancer-promoter interactions, and even lead to inconsistent results when detecting interactions and TADs in replicated samples [22]. Developing a computational model to impute a higher-resolution Hi-C contact matrix from currently available Hi-C datasets show its potency and usefulness

Methods

Results

Discussion

Conclusion