XMSannotator: An R Package for Network-Based Annotation of High-Resolution Metabolomics Data.

ABSTRACT: Improved analytical technologies and data extraction algorithms enable detection of >10 000 reproducible signals by liquid chromatography-high-resolution mass spectrometry, creating a bottleneck in chemical identification. In principle, measurement of more than one million chemicals would be possible if algorithms were available to facilitate utilization of the raw mass spectrometry data, especially low-abundance metabolites. Here we describe an automated computational framework to annotate ions for possible chemical identity using a multistage clustering algorithm in which metabolic pathway associations are used along with intensity profiles, retention time characteristics, mass defect, and isotope/adduct patterns. The algorithm uses high-resolution mass spectrometry data for a series of

SUBMITTER: Uppal K 

PROVIDER: S-EPMC5447360 | biostudies-literature | 2017 Jan

REPOSITORIES: biostudies-literature