Modeling gene regulation from matched expression and chromatin accessibility data
Ontology highlight
ABSTRACT: We propose a statistical method to model gene regulation by paired expression and chromatin accessibility (PECA) data, which dissects the regulatory elements (RE) of the genome by modeling their interaction with general chromatin regulators (CR) and sequence-specific transcriptional regulators (TF) and consequent effects on transcriptional changes.
ORGANISM(S): Mus musculus
PROVIDER: GSE98479 | GEO | 2017/05/03
SECONDARY ACCESSION(S): PRJNA385189
REPOSITORIES: GEO
ACCESS DATA