Computational metabolomics at scale: From open data to insight
Author Identifier (ORCID)
Abstract
Metabolomics data are currently generated at scale thanks to the evolution of technologies that have led to marked improvements in the number of metabolites detected, spanning all chemical classes. These data are increasingly submitted to public repositories for data reuse, integration, and interpretation. Despite the availability of public resources and associated computational tools, the field still lacks a widely adopted, consistent data and analytics infrastructure capable of transforming this wealth of information into scientific insight. Indeed, the metabolomics field is just now scratching the surface of being able to harness the power of new computational technologies. In this review, we summarize discussions from the “Dagstuhl-Seminar 24181 Computational Metabolomics: Towards Molecules, Models, and their Meaning” with a focus on public data availability, open data standards, data and knowledge integration, and education. Our goal is to raise awareness and adoption of the latest open science resources while highlighting key areas needing further development.
Keywords
metabolomics, computational metabolomics, data integration, open science, public repositories, data standards
Document Type
Journal Article
Date of Publication
10-1-2026
Article Number
103557
E-ISSN
18790429
ISSN
09581669
Volume
101
Publication Title
Current Opinion in Biotechnology
Publisher
Elsevier
School
School of Science
Funding Information
This research was supported in part by the Intramural Research Program of the National Institutes of Health (NIH). This work was supported in part by the intramural program at the National Center for Advancing Translational Sciences (ZIA TR000547). SH was supported by the National Institute of General Medical Sciences of the National Institutes of Health under award number R35GM148219.
Copyright
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Recommended Citation
Mathé, E. A., Van Der Hooft, J. J., Chatelaine, H., Nothias, L., Reinke, S. N., Vizcaíno, J. A., Willighagen, E. L., Ebbels, T. M., & Hassoun, S. (2026). Computational metabolomics at scale: From open data to insight. Current Opinion in Biotechnology, 101, Article 103557. https://doi.org/10.1016/j.copbio.2026.103557