Enabling pan-repository reanalysis for big data science of public metabolomics data

Enabling pan-repository reanalysis for big data science of public metabolomics data

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, here we develop pan-repository universal identifiers and harmonized cross-repository meta...

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Main Authors: Yasin El Abiead, Michael Strobel, Thomas Payne, Eoin Fahy, Claire O’Donovan, Shankar Subramamiam, Juan Antonio Vizcaíno, Ozgur Yurekten, Victoria Deleray, Simone Zuffa, Shipei Xing, Helena Mannochio-Russo, Ipsita Mohanty, Haoqi Nina Zhao, Andres M. Caraballo-Rodriguez, Paulo Wender P. Gomes, Nicole E. Avalon, Trent R. Northen, Benjamin P. Bowen, Katherine B. Louie, Pieter C. Dorrestein, Mingxun Wang
Format: Article
Language:English
Published: Nature Portfolio 2025-05-01
Series:Nature Communications
Online Access:https://doi.org/10.1038/s41467-025-60067-y
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Summary: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, 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.
ISSN:2041-1723

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