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

Shilu Zhang, Deborah Chasman, Sara Knaack and Sushmita Roy ()
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Shilu Zhang: Wisconsin Institute for Discovery
Deborah Chasman: Wisconsin Institute for Discovery
Sara Knaack: Wisconsin Institute for Discovery
Sushmita Roy: Wisconsin Institute for Discovery

Nature Communications, 2019, vol. 10, issue 1, 1-18

Abstract: 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.

Date: 2019
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DOI: 10.1038/s41467-019-13423-8

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