Abstract
High-throughput chromosome conformation capture sequencing (Hi-C) is a key technology for studying the three-dimensional (3D) structure of genomes and chromatin folding. Hi-C data reveals important patterns of genome organization such as topologically associating domains (TADs) and chromatin loops with critical roles in transcriptional regulation and disease etiology and progression. However, the relatively low resolution of existing Hi-C data often hinders robust and reliable inference of 3D structures. Hence, we propose TRUHiC, a new computational method that leverages recent state-of-the-art deep generative modeling to augment low-resolution Hi-C data for the characterization of 3D chromatin structures. Applying TRUHiC to publically available Hi-C data for human and mice, we demonstrate that the augmented data significantly improves the characterization of TADs and loops across diverse cell lines and species. We further present a pre-trained TRUHiC on human lymphoblastoid cell lines that can be adaptable and transferable to improve chromatin characterization of various cell lines, tissues, and species.