Linking Mass Spectrometry Computational Ecosystems to Enhance Biological Insights of Publicly-Available Data

University Of California-San Diego, La Jolla CA

Investigators

Abstract

Metabolite annotation is a key process to derive biological knowledge from metabolic/metabolomic datasets. The number of metabolomics research projects being published in peer reviewed scientific journals and whose data are being deposited in metabolomics data repositories, including MetaboLights (https://www.ebi.ac.uk/metabolights/ ) in the UK and Global Natural Products Social Molecular Networking (GNPS: https://gnps.ucsd.edu/) in the USA, is exponentially increasing (the volume of data increases by 300% every two years). The volume of data now present in metabolomics data repositories can be described as 'big data' and provides significant opportunities for reuse, re-analysis and for meta-analysis studies to investigate the roles of metabolites in biological systems including microbes, plants, mammals and other invertebrates. The majority of data deposited into metabolomics data repositories do not have a chemical structure assigned to it and no biological data integration or interpretation is achievable without this information, defined as the metabolite annotation. There can be hundreds or low thousands of metabolites which do not have a chemical structure assigned in each study. The majority of data present is derived from liquid chromatography-mass spectrometry chemical assays where the chromatographic retention time, mass of the intact metabolite and mass of gas phase collision induced (MS/MS) fragment ions are applied to derive a chemical structure. The data and tools available through GNPS and Metabolights have significant differences and this limits their full benefit to all users. In this project the University of California San Diego, the University of Binghamton, and EMBL-European Bioinformatics Institute will collaborate to expand and strengthen the computational workflow to further increase the number of metabolites annotated and to increase the confidence of these annotations and will then subsequently apply the additional tools to all datasets available in MetaboLights as well as for all future submitted datasets. This international effort to develop a workflow to apply to all deposited data in a metabolomics data repository and combining data and expertise from two resources will greatly benefit the global 'omics community. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.