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 benchmarki

SUBMITTER: Venafra V 

PROVIDER: S-EPMC11343843 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature