In silico prediction of high-resolution Hi-C interaction matrices

Shilu Zhang,Sara Knaack,Deborah Chasman,Sushmita Roy

doi:10.1038/s41467-019-13423-8

Shilu Zhang, Sara Knaack + Show 2 more

Open Access

https://doi.org/10.1038/s41467-019-13423-8

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Abstract

The three-dimensional (3D) organization of the genome plays an important role in gene regulation bringing distal sequence elements in 3D proximity to genes hundreds of kilobases away. Hi-C is a powerful genome-wide technique to study 3D genome organization. Owing to experimental costs, high resolution Hi-C datasets are limited to a few cell lines. Computational prediction of Hi-C counts can offer a scalable and inexpensive approach to examine 3D genome organization across multiple cellular contexts. Here we present HiC-Reg, an approach to predict contact counts from one-dimensional regulatory signals. HiC-Reg predictions identify topologically associating domains and significant interactions that are enriched for CCCTC-binding factor (CTCF) bidirectional motifs and interactions identified from complementary sources. CTCF and chromatin marks, especially repressive and elongation marks, are most important for HiC-Reg’s predictive performance. Taken together, HiC-Reg provides a powerful framework to generate high-resolution profiles of contact counts that can be used to study individual locus level interactions and higher-order organizational units of the genome.

Highlights

The three-dimensional (3D) organization of the genome plays an important role in gene regulation bringing distal sequence elements in 3D proximity to genes hundreds of kilobases away
We described a novel computational approach, HiC-Reg that can predict the contact count of two genomic regions from their one-dimensional regulatory signals, which are available for a large number of cell lines and experimentally more tractable to generate than Hi-C datasets
The former is relevant to predict interactions among regions that might not have been experimentally assayed. The latter is useful for cell types and developmental stages that may not be amenable to large-scale high-throughput 3C experiments and computational predictions could prioritize regions for targeted experimental studies

Summary

Introduction

The three-dimensional (3D) organization of the genome plays an important role in gene regulation bringing distal sequence elements in 3D proximity to genes hundreds of kilobases away. To overcome the limitations of a classification approach and to maximally exploit the information in high-throughput assays such Hi-C, we develop a Random Forests regression-based approach, HiC-Reg. HiC-Reg integrates published Hi-C datasets with one-dimensional regulatory genomic datasets such as chromatin marks, architectural and transcription factor proteins, and chromatin accessibility, to predict interaction counts between two genomic loci in a cell line-specific manner. HiC-Reg provides a computational approach for predicting the interaction counts discovered by Hi-C technology, which can be used for examining long-range interactions for individual loci as well as for global studies of chromosome conformation organization

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