epiregulon

Epiregulon infers transcription factor activity in single cells by constructing gene regulatory networks (regulons) through integration of scATAC-seq, scRNA-seq, and public bulk TF ChIP-seq data.

Key Features:

• Multi-omics integration: Combines single-cell ATAC-seq and RNA-seq data with public bulk TF ChIP-seq data for network construction.
• Regulatory element-gene linking: Establishes links between regulatory elements and target genes by computing correlations between chromatin accessibility and gene expression.
• Motif-agnostic inference: Enables activity inference of transcriptional coregulators and TFs harboring neomorphic mutations through ChIP-seq data integration.
• Single-cell resolution: Predicts TF activity by considering co-occurrence of TF expression and chromatin accessibility at TF binding sites in individual cells.

Scientific Applications:

• Drug response prediction: Predicts effects of androgen receptor inhibition across different drug modalities including AR antagonists and AR degraders.
• Mechanism delineation: Identifies context-dependent interaction partners to delineate mechanisms of degraders such as SMARCA4 degraders.
• Driver prioritization: Prioritizes drivers of lineage reprogramming and tumorigenesis by mapping gene regulation across cellular contexts.
• Therapeutic target discovery: Accelerates discovery of therapeutics targeting transcriptional regulators by evaluating pharmacological agents and identifying disease drivers.

Methodology:

Epiregulon constructs gene regulatory networks by computing correlations between chromatin accessibility and gene expression to link regulatory elements to target genes, and infers TF activity based on co-occurrence of TF expression and chromatin accessibility at TF binding sites in each cell.