Taxonomically Informed Scoring Enhances Confidence in Natural Products Annotation.

ABSTRACT: Mass spectrometry (MS) offers unrivalled sensitivity for the metabolite profiling of complex biological matrices encountered in natural products (NP) research. The massive and complex sets of spectral data generated by such platforms require computational approaches for their interpretation. Within such approaches, computational metabolite annotation automatically links spectral data to candidate structures via a score, which is usually established between the acquired data and experimental or theoretical spectral databases (DB). This process leads to various candidate structures for each MS features. However, at this stage, obtaining high annotation confidence level remains a challenge notably due to the extensive chemodiversity of specialized metabolomes. The design of a metascore is a w

SUBMITTER: Rutz A 

PROVIDER: S-EPMC6824209 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature