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

Author

Listed:
  • Yasin El Abiead

    (University of California San Diego)

  • Michael Strobel

    (University of California Riverside)

  • Thomas Payne

    (Wellcome Genome Campus, Hinxton)

  • Eoin Fahy

    (University of California, San Diego)

  • Claire O’Donovan

    (Wellcome Genome Campus, Hinxton)

  • Shankar Subramamiam

    (University of California, San Diego)

  • Juan Antonio Vizcaíno

    (Wellcome Genome Campus, Hinxton)

  • Ozgur Yurekten

    (Wellcome Genome Campus, Hinxton)

  • Victoria Deleray

    (University of California San Diego)

  • 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)

  • Andres M. Caraballo-Rodriguez

    (University of California San Diego)

  • Paulo Wender P. Gomes

    (University of California San Diego
    Federal University of Pará)

  • Nicole E. Avalon

    (University of California San Diego
    University of California, Irvine)

  • Trent R. Northen

    (Lawrence Berkeley National Lab
    Lawrence Berkeley National Laboratory)

  • Benjamin P. Bowen

    (Lawrence Berkeley National Lab
    Lawrence Berkeley National Laboratory)

  • Katherine B. Louie

    (Lawrence Berkeley National Laboratory)

  • Pieter C. Dorrestein

    (University of California San Diego
    University of California San Diego
    University of California San Diego
    University of California San Diego)

  • Mingxun Wang

    (University of California Riverside)

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.

Suggested Citation

  • 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 , 2025. "Enabling pan-repository reanalysis for big data science of public metabolomics data," Nature Communications, Nature, vol. 16(1), pages 1-7, December.
    • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-60067-y
    DOI: 10.1038/s41467-025-60067-y
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    References listed on IDEAS

    as
    1. Robert A. Quinn & Alexey V. Melnik & Alison Vrbanac & Ting Fu & Kathryn A. Patras & Mitchell P. Christy & Zsolt Bodai & Pedro Belda-Ferre & Anupriya Tripathi & Lawton K. Chung & Michael Downes & Ryan , 2020. "Global chemical effects of the microbiome include new bile-acid conjugations," Nature, Nature, vol. 579(7797), pages 123-129, March.
    2. Chengxin Dai & Anja Füllgrabe & Julianus Pfeuffer & Elizaveta M. Solovyeva & Jingwen Deng & Pablo Moreno & Selvakumar Kamatchinathan & Deepti Jaiswal Kundu & Nancy George & Silvie Fexova & Björn Grüni, 2021. "A proteomics sample metadata representation for multiomics integration and big data analysis," Nature Communications, Nature, vol. 12(1), pages 1-8, December.
    3. Emily C. Gentry & Stephanie L. Collins & Morgan Panitchpakdi & Pedro Belda-Ferre & Allison K. Stewart & Marvic Carrillo Terrazas & Hsueh-han Lu & Simone Zuffa & Tingting Yan & Julian Avila-Pacheco & D, 2024. "Reverse metabolomics for the discovery of chemical structures from humans," Nature, Nature, vol. 626(7998), pages 419-426, February.
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