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

Yan Zhang,Ming Hu,Bo Zhang,Feng Yue,Jijun Tang,Jie Xu,W Jim Zheng,Lin An

doi:10.1038/s41467-018-03113-2

Yan Zhang, Ming Hu + Show 6 more

Open Access

https://doi.org/10.1038/s41467-018-03113-2

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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.

Highlights

high-throughput chromosome conformation capture (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
We observe that Hi-C matrices are composed by a series of low-level local patterns, which are shared across all cell types
This work provides a great resource for the study of chromatin interactions, establishes a framework to predict highresolution Hi-C matrix with a fraction of sequencing cost, and identifies potential features underlying the formation of 3D chromatin interactions

Summary

Introduction

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. 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. Owing to high sequencing cost, most available Hi-C datasets have relatively low resolution such as 25 or 40 kb, as the linear increase of resolution requires a quadratic increase in the total number of sequencing reads[6]. These low-resolution Hi-C datasets can be used to define large-scale genomic patterns such as A/B compartment or TADs but cannot be used to identify more refined structures such as sub-domains or enhancer–promoter interactions. This work provides a great resource for the study of chromatin interactions, establishes a framework to predict highresolution Hi-C matrix with a fraction of sequencing cost, and identifies potential features underlying the formation of 3D chromatin interactions

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