Causal Transcription Regulatory Network Inference Using Enhancer Activity as a Causal Anchor.

ABSTRACT: Transcription control plays a crucial role in establishing a unique gene expression signature for each of the hundreds of mammalian cell types. Though gene expression data have been widely used to infer cellular regulatory networks, existing methods mainly infer correlations rather than causality. We developed statistical models and likelihood-ratio tests to infer causal gene regulatory networks using enhancer RNA (eRNA) expression information as a causal anchor and applied the framework to eRNA and transcript expression data from the FANTOM Consortium. Predicted causal targets of transcription factors (TFs) in mouse embryonic stem cells, macrophages and erythroblastic leukaemia overlapped significantly with experimentally-validated targets from ChIP-seq and perturbation data. We further i

SUBMITTER: Vipin D 

PROVIDER: S-EPMC6274755 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature