Characterizing collaborative transcription regulation with a graph-based deep learning approach.

ABSTRACT: Human epigenome and transcription activities have been characterized by a number of sequence-based deep learning approaches which only utilize the DNA sequences. However, transcription factors interact with each other, and their collaborative regulatory activities go beyond the linear DNA sequence. Therefore leveraging the informative 3D chromatin organization to investigate the collaborations among transcription factors is critical. We developed ECHO, a graph-based neural network, to predict chromatin features and characterize the collaboration among them by incorporating 3D chromatin organization from 200-bp high-resolution Micro-C contact maps. ECHO predicted 2,583 chromatin features with significantly higher average AUROC and AUPR than the best sequence-based model. We observed that ch

SUBMITTER: Zhang Z 

PROVIDER: S-EPMC9203014 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature