Revealing disease-associated pathways by network integration of untargeted metabolomics.

ABSTRACT: Uncovering the molecular context of dysregulated metabolites is crucial to understand pathogenic pathways. However, their system-level analysis has been limited owing to challenges in global metabolite identification. Most metabolite features detected by untargeted metabolomics carried out by liquid-chromatography-mass spectrometry cannot be uniquely identified without additional, time-consuming experiments. We report a network-based approach, prize-collecting Steiner forest algorithm for integrative analysis of untargeted metabolomics (PIUMet), that infers molecular pathways and components via integrative analysis of metabolite features, without requiring their identification. We demonstrated PIUMet by analyzing changes in metabolism of sphingolipids, fatty acids and steroids in a Hunting

SUBMITTER: Pirhaji L 

PROVIDER: S-EPMC5209295 | biostudies-literature | 2016 Sep

REPOSITORIES: biostudies-literature