SignalingProfiler 2.0 a network-based approach to bridge multi-omics data to phenotypic hallmarks

Abstract

Unraveling how cellular signaling is remodeled upon perturbation is crucial for understanding disease mechanisms and identifying potential drug targets. In this pursuit, computational tools generating mechanistic hypotheses from multi-omics data have invaluable potential. Here, we present a newly implemented version (2.0) of SignalingProfiler, a multi-step pipeline to draw mechanistic hypotheses on the signaling events impacting cellular phenotypes. SignalingProfiler 2.0 derives context-specific signaling networks by integrating proteogenomic data with the prior knowledge-causal network. This is a freely accessible and flexible tool that incorporates statistical, footprint-based, and graph algorithms to accelerate the integration and interpretation of multi-omics data. Through a benchmarking process on three proof-of-concept studies, we demonstrate the tool’s ability to generate hierarchical mechanistic networks recapitulating novel and known perturbed signaling and phenotypic outcomes, in both human and mice contexts. In summary, SignalingProfiler 2.0 addresses the emergent need to derive biologically relevant information from complex multi-omics data by extracting interpretable networks.