Metabolomics workbench: data-driven classification and correlation analysis between metabolite classes, biomarkers, and drugs.
5 affiliations
- 1Department of Pharmaceutical Biosciences; Pharmacology, Uppsala University, Husargatan 3, SE-751 24 Uppsala, Sweden.
- 2Department of Surgical Sciences, Functional Pharmacology and Neuroscience, Uppsala University, Husargatan 3, SE-751 24 Uppsala, Sweden.
- 3Metabolic Research Group, National Institute of Research and Innovation, Aizkraukles iela 21, LV-1006 Riga, Latvia.
- 4Department of Medical Sciences, Molecular Epidemiology, Uppsala University, Dag Hammarskjölds väg 14B, SE-751 85 Uppsala, Sweden.
- 5Department of Clinical Science and Education, Södersjukhuset, Karolinska Institutet, Sjukhusbacken 10, SE-118 83 Stockholm, Sweden.
Abstract
Public repositories now accumulate an unprecedented amount of metabolomics data. Despite the opportunities for large-scale meta-analyses opened by these repositories, the deposited data rarely becomes systematically re-analyzed. We conducted a systematic metadata-based re-analysis of all human blood metabolomics studies in the Metabolomics Workbench performed using Orbitrap or QTOF mass spectrometry, encompassing 44 165 samples. After harmonizing metabolites with PubChem identifiers and embedding them using SMILES representations, we generated a map of 6752 blood metabolites forming 25 structural clusters, and subsequently performed correlation analysis between these clusters, biomarkers, and drugs. Analysis of 337 120 metabolite pairs detected in at least 10 studies showed that structurally related clusters display correlated abundances (ρ up to 0.6). Correlation patterns among established clinical biomarkers revealed candidate metabolic correlates, several with ρ > 0.7. We also developed open-source software enabling similar analyses for any metabolite across the Workbench data explored. Additionally, curating drug-containing datasets enabled meta-analyses for compounds such as acetaminophen, metoprolol, and atenolol, identifying both known and novel metabolic signatures. Besides the biological insights, we have found that Orbitrap and QTOF tend to detect different metabolites, but they give quantitatively coherent results on overlapping compounds. Overall, the present study provides the methodology and software to perform large-scale mining of public metabolomics data enabling its re-analysis to obtain novel biomedical insights.
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BibTeX
@article{khandokhin2026metabolomics,
title = {{Metabolomics workbench: data-driven classification and correlation analysis between metabolite classes, biomarkers, and drugs.}},
author = {Mikhail M Khandokhin and Aleksandr Sokolov and Nikolay Oskolkov and Robert Fredriksson and Olga E Titova and Helgi B Schiöth},
journal = {Briefings in bioinformatics},
year = {2026},
month = sep,
doi = {10.1093/bib/bbag576},
url = {https://europepmc.org/article/MED/42894388},
note = {CC BY},
}
RIS
TY - JOUR
TI - Metabolomics workbench: data-driven classification and correlation analysis between metabolite classes, biomarkers, and drugs.
AU - Mikhail M Khandokhin
AU - Aleksandr Sokolov
AU - Nikolay Oskolkov
AU - Robert Fredriksson
AU - Olga E Titova
AU - Helgi B Schiöth
PY - 2026
DA - 2026/09/01
JO - Briefings in bioinformatics
DO - 10.1093/bib/bbag576
UR - https://europepmc.org/article/MED/42894388
AB - Public repositories now accumulate an unprecedented amount of metabolomics data. Despite the opportunities for large-scale meta-analyses opened by these repositories, the deposited data rarely becomes systematically re-analyzed. We conducted a systematic metadata-based re-analysis of all human blood metabolomics studies in the Metabolomics Workbench performed using Orbitrap or QTOF mass spectrometry, encompassing 44 165 samples. After harmonizing metabolites with PubChem identifiers and embedding them using SMILES representations, we generated a map of 6752 blood metabolites forming 25 structural clusters, and subsequently performed correlation analysis between these clusters, biomarkers, and drugs. Analysis of 337 120 metabolite pairs detected in at least 10 studies showed that structurally related clusters display correlated abundances (ρ up to 0.6). Correlation patterns among established clinical biomarkers revealed candidate metabolic correlates, several with ρ > 0.7. We also developed open-source software enabling similar analyses for any metabolite across the Workbench data explored. Additionally, curating drug-containing datasets enabled meta-analyses for compounds such as acetaminophen, metoprolol, and atenolol, identifying both known and novel metabolic signatures. Besides the biological insights, we have found that Orbitrap and QTOF tend to detect different metabolites, but they give quantitatively coherent results on overlapping compounds. Overall, the present study provides the methodology and software to perform large-scale mining of public metabolomics data enabling its re-analysis to obtain novel biomedical insights.
SN - 1467-5463
SN - 1477-4054
ER -