实现公共代谢组学数据大数据科学的全库重新分析。

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

作者信息

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

机构信息

Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA.

Department of Computer Science and Engineering, University of California Riverside, Riverside, CA, USA.

出版信息

Nat Commun. 2025 May 24;16(1):4838. doi: 10.1038/s41467-025-60067-y.

DOI:10.1038/s41467-025-60067-y

PMID:40413169

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.

摘要

公共非靶向代谢组学数据是用于发现代谢物和表型的一种不断增长的资源；然而，跨存储库访问和利用这些数据面临重大挑战。因此，我们在此开发了泛存储库通用标识符和统一的跨存储库元数据。这个生态系统通过整合来自公共存储库（包括MetaboLights、代谢组学工作台和GNPS/MassIVE）的各种数据源来促进发现。我们的方法简化了数据处理，并开启了以前无法进行的重新分析工作流程，带来了无与伦比的研究机会。