CoRegNet

CoRegNet reconstructs and analyzes co-regulatory networks from large-scale transcriptomic data to identify regulator activity and cooperative transcription factor combinations.

Key Features:

• Implementation: Provided as an R/Bioconductor package for programmatic analysis of transcriptomic datasets.
• Co-regulatory network reconstruction: Reconstructs comprehensive co-regulatory networks from transcriptomic data.
• Integration of regulatory evidence: Integrates external regulatory evidence such as transcription factor binding sites (TFBS) and chromatin immunoprecipitation (ChIP) data to refine inferred networks.
• Sample-specific regulator activity estimation: Estimates regulator activity at the individual sample level.
• Identification of cooperative transcription factors: Detects cooperative transcription factors and specific combinations of active regulators within individual samples.
• Driver regulator detection: Identifies putative driver regulators relevant to specific biological or disease contexts.

Scientific Applications:

• Disease regulatory driver discovery: Identification of key transcriptional drivers in disease contexts, including bladder cancer.
• Transcription factor cooperation analysis: Modeling and characterization of transcription factor cooperation within co-regulatory networks.
• Mechanistic interpretation of transcriptomes: Uncovering regulatory mechanisms underlying complex diseases to inform mechanistic hypotheses and targeted strategies.

Methodology:

Reconstructs co-regulatory networks from transcriptomic data, integrates TFBS and ChIP evidence, estimates sample-specific regulator activity, and models transcription factor cooperation.