SignalingProfiler 2.0

SignalingProfiler 2.0 integrates proteogenomic data with prior knowledge causal networks to draw mechanistic hypotheses on signaling events impacting cellular phenotypes.

Key Features:

• Multi-omics integration: Incorporates statistical, footprint-based, and graph algorithms to accelerate the integration and interpretation of multi-omics data.
• Context-specific network derivation: Derives context-specific signaling networks by integrating proteogenomic data with prior knowledge-causal networks.
• Hierarchical network generation: Generates hierarchical mechanistic networks recapitulating novel and known perturbed signaling and phenotypic outcomes.
• Cross-species applicability: Functions in both human and mice contexts.

Scientific Applications:

• Signaling remodeling analysis: Unravels how cellular signaling is remodeled upon perturbation to understand disease mechanisms.
• Drug target identification: Supports the identification of potential drug targets through mechanistic hypothesis generation.
• Proof-of-concept benchmarking: Demonstrated through benchmarking on three proof-of-concept studies showing network recapitulation of perturbed signaling and phenotypic outcomes.

Methodology:

SignalingProfiler 2.0 employs a multi-step pipeline incorporating statistical, footprint-based, and graph algorithms to integrate proteogenomic data with prior knowledge-causal networks.