An Automated Identification Tool for LC-MS Based Metabolomics Studies

Abstract

Liquid chromatography/mass spectrometer (LC/MS) has become one of the most popular analytical platform for metabolomics studies owing to its wide range of detectable polarity and molecular mass. However, metabolite identification remains quite costly and time-consuming in LC/MS-based metabolomics, mostly due to lower database integrity and a separated MS/MS spectra generation process. In this work, we constructed an automated, user-friendly, and freely available tool. From a peak list, the tool first groups peaks, which are usually associated with a metabolite, based on their retention time and abundance correlation across samples. In each group, different ions are annotated and the mass of the underlying metabolite is derived. Finally, the fragments are used to match with low-energy MS/MS spectra in public databases for metabolite identification. To identify metabolites without accessible MS/MS spectra, we have developed characteristic fragment and common substructure matches. Through the above approach, we anticipate facilitating the metabolite identification in LC-MS-based metabolomics studies.