Enhancement of Hi-C experimental data using deep convolutional neural network

Friday, October 6, 2017 - 2:20pm to 3:10pm
Swearingen room 2A14

I would like to invite you to attend this week's CSCE 791 seminar. These seminars highlight research being performed in our department and across the world. All CSCE 791 seminars are open to anybody who wishes to attend - not just students registered for the course.

Speaker: Dr. Jijun Tang, University of South Carolina

Abstract: Hi-C technology is one of the most popular tools for measuring the spatial organization of mammalian genomes. Although an increasing number of Hi-C datasets have been generated in a variety of tissue/cell types, due to high sequencing cost, the resolution of most Hi-C datasets are coarse and cannot be used to infer enhancer-promoter interactions or link disease-related non-coding variants to their target genes. To address this challenge, 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. Through extensive testing, we demonstrate that HiCPlus can impute interaction matrices highly similar to the original ones, while using only as few as 1/16 of the total sequencing reads. We observe that Hi-C interaction matrix contains unique local features that are consistent across different cell types, and such features can be effectively captured by the deep learning framework. We further apply HiCPlus to enhance and expand the usability of Hi-C data sets in a variety of tissue and cell types. In summary, our work not only provides a framework to generate high-resolution Hi-C matrix with a fraction of the sequencing cost, but also reveals features underlying the formation of 3D chromatin interactions.