NetBID2

NetBID2 infers hidden driver genes and their network activities from large-scale multi-omics data to identify regulators of phenotypes such as tumorigenesis that act via post-translational modifications or other non-genetic mechanisms.

Key Features:

• Reverse-engineered context-specific interactomes: Employs algorithms to reconstruct gene regulatory and signaling networks specific to biological contexts.
• Multi-omics data integration: Integrates large-scale multi-omics datasets to enable network-based analysis across omic layers.
• Network activity inference and Bayesian driver inference: Infers network activity and applies Bayesian inference to detect hidden drivers not apparent from genomic or differential expression analyses.
• Statistical analysis and visualization: Provides statistical analyses and data visualization for interpreting inferred network activities and driver candidates.
• Extensive network coverage: Includes 145 context-specific gene regulatory and signaling networks across normal tissues and pediatric and adult cancers.

Scientific Applications:

• Cancer driver discovery: Identifies non-genetic and post-translational drivers implicated in tumorigenesis.
• Therapeutic target prioritization: Prioritizes candidate regulators and pathways as potential therapeutic targets.
• Regulatory mechanism exploration: Enables investigation of complex regulatory interactions across multiple omic layers to elucidate disease mechanisms.

Methodology:

Reverse-engineering of context-specific gene regulatory and signaling interactomes, integration of large-scale multi-omics data, inference of network activity, Bayesian inference to identify drivers, and subsequent statistical analyses and visualization.