GRaNIE

GRaNIE reconstructs cell-type-specific gene regulatory networks as an R package by integrating enhancer information with chromatin accessibility and RNA-seq covariation to infer transcription factor–enhancer–gene interactions.

Key Features:

• Enhancer Integration: Incorporates enhancer elements into GRN inference to connect noncoding genetic variants to cell-type-specific gene regulation.
• Multi-omic Covariation Analysis: Analyzes covariation between chromatin accessibility and RNA-seq across samples (e.g., individuals or experimental conditions) to infer regulatory interactions.
• Cell-Type Specificity: Infers regulatory networks that reflect context-dependent transcription factor and enhancer activity in specific cell types.
• Joint TF–Enhancer Framework: Jointly models transcription factors and enhancers to produce enhancer-mediated gene regulatory networks.
• Performance Assessment (GRaNPA): Uses the companion GRaNPA (Gene Regulatory Network Performance Analysis) to assess inferred GRN performance in predicting cell-type-specific differential expression.

Scientific Applications:

• Disease Mechanisms: Investigates gene regulatory mechanisms underlying macrophage responses in infections, cancer, and autoimmune diseases.
• Genetic Trait Analysis: Explores how genetic variants associated with common traits influence enhancer activity and gene regulation.
• Regulatory Discovery: Enables identification of candidate regulators, exemplified by the identification of PURA as a potential regulator of pro-inflammatory macrophage polarization.

Methodology:

Constructs enhancer-mediated GRNs by analyzing covariation patterns between chromatin accessibility data and RNA-seq data to identify transcription factor–enhancer–gene regulatory links.