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Updated: May 2, 2026

A Strategy for Sensitive, Large Scale Quantitative Metabolomics
Published on: May 27, 2014
Enabling pan-repository reanalysis for big data science of public metabolomics data
Yasin El Abiead1, Michael Strobel2, Thomas Payne3
1Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA.
None:
Public untargeted metabolomics data is a growing resource for metabolite and phenotype discovery; however, accessing and utilizing these data across repositories pose significant challenges. Therefore, here we develop pan-repository universal identifiers and harmonized cross-repository metadata. This ecosystem facilitates discovery by integrating diverse data sources from public repositories including MetaboLights, Metabolomics Workbench, and GNPS/MassIVE. Our approach simplified data handling and unlocks previously inaccessible reanalysis workflows, fostering unmatched research opportunities.

