Abstract
Metabolite identification is the major bottle-neck in LC-MS based metabolomic investigations. The mass-based search approach often leaves a large fraction of metabolites with either no identification or multiple putative identifications. As manual verification of metabolites is laborious, computational approaches are needed to obtain more reliable putative identifications and prioritize them. In this paper, a computational pipeline is proposed to assist metabolite identification with improved coverage and prioritization capability. The pipeline is based on multiple pieces of publicly-available software and databases. The proposed pipeline is successfully applied in an LC-MS/MS-based metabolomic study, where mass, retention time, and MS/MS spectrum were used to improve the accuracy of metabolite identification and to prioritize putative identifications for subsequent metabolite verification.
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