Enabling pan-repository reanalysis for big data science of public metabolomics data
Yasin El Abiead(University of California San Diego), Michael Strobel(University of California, Riverside), Thomas Payne(European Bioinformatics Institute), Eoin Fahy(San Diego Supercomputer Center), Claire O’Donovan(European Bioinformatics Institute), Shankar Subramamiam(San Diego Supercomputer Center), Juan Antonio Vizcaíno(European Bioinformatics Institute), Simone Zuffa(University of California San Diego), Shipei Xing(University of California San Diego), Helena Mannochio-Russo(University of California San Diego), Ipsita Mohanty(University of California San Diego), Haoqi Nina Zhao(University of California San Diego), Andrés Mauricio Caraballo‐Rodríguez(University of California San Diego), Paulo Wender Portal Gomes(University of California San Diego), Nicole E. Avalon(Scripps Institution of Oceanography), Pieter C. Dorrestein(University of California San Diego), Mingxun Wang(University of California, Riverside)
Cited by 10Open Access
Abstract
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, we've developed pan-repository universal identifiers and harmonized cross-repository metadata. This novel ecosystem facilitates discovery by integrating diverse data sources from public repositories including MetaboLights, Metabolomics Workbench, and GNPS/MassIVE. Our approach simplifies data handling and unlocks previously inaccessible reanalysis workflows, fostering unmatched research opportunities.
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