Dynamic regulatory module networks for inference of cell type-specific transcriptional networks.

ABSTRACT: Changes in transcriptional regulatory networks can significantly alter cell fate. To gain insight into transcriptional dynamics, several studies have profiled bulk multi-omic data sets with parallel transcriptomic and epigenomic measurements at different stages of a developmental process. However, integrating these data to infer cell type-specific regulatory networks is a major challenge. We present dynamic regulatory module networks (DRMNs), a novel approach to infer cell type-specific cis-regulatory networks and their dynamics. DRMN integrates expression, chromatin state, and accessibility to predict cis-regulators of context-specific expression, where context can be cell type, developmental stage, or time point, and uses multitask learning to capture network dynamics acros

SUBMITTER: Siahpirani AF 

PROVIDER: S-EPMC9341506 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature