Modeling gene regulation from paired expression and chromatin accessibility data.

Area of Science:

• Genomics
• Epigenomics
• Computational Biology

Background:

• Genome-wide datasets for gene expression, chromatin states, and transcription factor (TF) binding are rapidly expanding.
• Interpreting these datasets requires joint modeling of cis-regulatory element (RE) activation and regulatory factor effects on transcription.

Purpose of the Study:

• To propose a statistical approach, paired expression and chromatin accessibility (PECA), for modeling context-specific gene regulation.
• To infer transcriptional regulatory networks by integrating diverse genomic and epigenomic data.

Main Methods:

• PECA models the localization of chromatin regulators (CRs) to REs via TF interactions.
• It models RE activation by localized CRs.
• It models the effect of TFs on target gene transcription.

Main Results:

• PECA infers context-specific transcriptional regulatory networks.
• The network reveals interactions between trans- and cis-regulatory elements influencing gene expression.
• The approach was validated using mouse ENCODE data.

Conclusions:

• PECA offers a robust statistical framework for analyzing paired expression and chromatin accessibility data.
• It provides a detailed view of gene regulation mechanisms.
• The method has potential for diverse applications in understanding gene expression.