Autonomous METLIN-Guided In-source Fragment Annotation for Untargeted Metabolomics.

ABSTRACT: Computational metabolite annotation in untargeted profiling aims at uncovering neutral molecular masses of underlying metabolites and assign those with putative identities. Existing annotation strategies rely on the observation and annotation of adducts to determine metabolite neutral masses. However, a significant fraction of features usually detected in untargeted experiments remains unannotated, which limits our ability to determine neutral molecular masses. Despite the availability of tools to annotate, relatively few of them benefit from the inherent presence of in-source fragments in liquid chromatography-electrospray ionization-mass spectrometry. In this study, we introduce a strategy to annotate in-source fragments in untargeted data using low-energy tandem mass spectrometry (MS) s

SUBMITTER: Domingo-Almenara X 

PROVIDER: S-EPMC6637741 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature