DeepTACT: predicting high-resolution chromatin contacts via bootstrapping deep learning

Abstract

High-resolution interactions among regulatory elements are of crucial importance for the understanding of transcriptional regulation and the interpretation of disease mechanism. Hi-C technique allows the genome-wide detection of chromatin contacts. However, unless extremely deep sequencing is performed on a very large number of input cells, current Hi-C experiments do not have high enough resolution to resolve contacts among regulatory elements. Here, we develop DeepTACT, a bootstrapping deep learning model, to integrate genome sequences and chromatin accessibility data for the prediction of chromatin contacts among regulatory elements. In tests based on promoter capture Hi-C data, DeepTACT is seen to offer improved resolution over existing methods. DeepTACT analysis also identifies a class of hub promoters, which are active across cell lines, enriched in housekeeping genes, functionally related to fundamental biological processes, and capable of reflecting cell similarity. Finally, the utility of high-resolution chromatin contact information in the study of human diseases is illustrated by the association of IFNA2 and IFNA1 to coronary artery disease via an integrative analysis of GWAS data and high-resolution contacts inferred by DeepTACT.