Pattern recognition of topologically associating domains using deep learning.

ABSTRACT:

Background

Recent increasing evidence indicates that three-dimensional chromosome structure plays an important role in genomic function. Topologically associating domains (TADs) are self-interacting regions that have been shown to be a chromosomal structural unit. During evolution, these are conserved based on checking synteny block cross species. Are there common TAD patterns across species or cell lines?

Results

To address the above question, we propose a novel task-TAD recognition-as opposed to traditional TAD identification. Specifically, we treat Hi-C maps as images, thus re-casting TAD recognition as image pattern recognition, for which we use a convolutional neural network and a residual neural network. In addition, we propose an elegant way to generate non-TAD data f

SUBMITTER: Yang JY 

PROVIDER: S-EPMC9732975 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature