DeCOOC Deconvoluted Hi-C Map Characterizes the Chromatin Architecture of Cells in Physiologically Distinctive Tissues.

ABSTRACT: Deciphering variations in chromosome conformations based on bulk three-dimensional (3D) genomic data from heterogenous tissues is a key to understanding cell-type specific genome architecture and dynamics. Surprisingly, computational deconvolution methods for high-throughput chromosome conformation capture (Hi-C) data remain very rare in the literature. Here, a deep convolutional neural network (CNN), deconvolve bulk Hi-C data (deCOOC) that remarkably outperformed all the state-of-the-art tools in the deconvolution task is developed. Interestingly, it is noticed that the chromatin accessibility or the Hi-C contact frequency alone is insufficient to explain the power of deCOOC, suggesting the existence of a latent embedded layer of information pertaining to the cell type specific 3D genome

SUBMITTER: Wang J 

PROVIDER: S-EPMC10520690 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature