DeepCAGE: Incorporating Transcription Factors in Genome-wide Prediction of Chromatin Accessibility.

ABSTRACT: Although computational approaches have been complementing high-throughput biological experiments for the identification of functional regions in the human genome, it remains a great challenge to systematically decipher interactions between transcription factors (TFs) and regulatory elements to achieve interpretable annotations of chromatin accessibility across diverse cellular contexts. To solve this problem, we propose DeepCAGE, a deep learning framework that integrates sequence information and binding statuses of TFs, for the accurate prediction of chromatin accessible regions at a genome-wide scale in a variety of cell types. DeepCAGE takes advantage of a densely connected deep convolutional neural network architecture to automatically learn sequence signatures of known chromatin access

SUBMITTER: Liu Q 

PROVIDER: S-EPMC9801045 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature