BICORN

BICORN infers context-specific cis-regulatory modules (CRMs) by integrating transcription factor (TF)–gene binding events and gene expression profiles to characterize cooperative TF regulation in particular cell types.

Key Features:

• Hierarchical Bayesian Modeling: Employs a hierarchical Bayesian framework to model regulatory dependencies among TFs and genes.
• Integration of TF binding and gene expression: Integrates TF–gene binding events with gene expression data to link binding to regulatory effects.
• Context-Specific CRM Inference: Infers CRMs in specific cellular contexts to capture cell-type–specific regulatory modules.
• Automated CRM Identification: Identifies candidate CRMs by analyzing TF binding at regulatory regions associated with target genes.
• Iterative Parameter Estimation (Gibbs sampling): Uses Gibbs sampling to iteratively estimate parameters for CRMs, TF activities, and regulatory impacts.
• Sparse Network Modeling: Models regulation of target genes as a sparse network of functional CRMs.

Scientific Applications:

• Genome-wide transcriptional regulation analysis: Enables genome-wide analysis of transcriptional regulation and TF cooperation.
• Context-specific gene expression studies: Supports study of gene expression dynamics across different cell types or conditions by inferring context-specific CRMs.
• Domain-specific research: Applicable to investigations in developmental biology, cancer research, and systems biology that require inference of cooperative TF regulation.

Methodology:

Hierarchical Bayesian modeling of target gene regulation as a sparse network of CRMs, integrating TF binding data and gene expression profiles, with iterative parameter estimation via Gibbs sampling to infer CRMs, TF activities, and regulatory impacts.