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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Artículo científico |
| Language: | en |
| Published: |
Nature communications
2025
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| Subjects: | |
| Online Access: | https://pubmed.ncbi.nlm.nih.gov/40413169/ |
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Table of Contents:
- Enabling pan-repository reanalysis for big data science of public metabolomics data. El Abiead, Yasin Strobel, Michael Payne, Thomas Fahy, Eoin O'Donovan, Claire Subramamiam, Shankar Vizcaíno, Juan Antonio Yurekten, Ozgur Deleray, Victoria Zuffa, Simone Xing, Shipei Mannochio-Russo, Helena Mohanty, Ipsita Zhao, Haoqi Nina Caraballo-Rodriguez, Andres M P Gomes, Paulo Wender Avalon, Nicole E Northen, Trent R Bowen, Benjamin P Louie, Katherine B Dorrestein, Pieter C Wang, Mingxun Metabolomics Big Data Humans Data Science Databases, Factual Metadata 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.