The MassBank contributions of the mFam collaboration

INTRODUCTION: The analysis of metabolic profiles using high resolution mass spectrometry (MS) data provides deep insights into biological processes. In metabolomics, MS analysis generates a large number of features that represent metabolites. However, identifying specific metabolites from these features can be challenging. One of the major bottlenecks in the metabolomics field is the identification of MS features, which is a prerequisite for any biochemical interpretation. By identifying similarities and differences within a metabolite family (mFam), evaluating MS features at the metabolite family level can help assigning functional roles to individual MS features. These data can help interpreting metabolic pathways and processes within a biological system. For the assignment of metabolite families to MS features, it is important to have good quality, reliable, and comprehensive spectral libraries. OBJECTIVE: We initiated a global effort to collect high-resolution MS/MS spectra of metabolites from labs working in different fields, including metabolomics of animals, microorganisms, and plants. The mFam-MS/MS collection delivers valuable training data to assign machine-readable classified information on the unknown metabolites. RESULTS: The mFam collaboration used a standardized metadata template and has developed a globally curated MS/MS spectral library of 7,872 spectra with 2,126 unique metabolites. This library was compiled from 47 datasets contributed by 25 laboratories measured on 12 instrument types, including QTOF, Orbitrap, and Ion Mobility-QTOF systems. It comprises 4,646 spectra in positive mode and 3,226 in negative mode. This standardized resource significantly enhances metabolite identification capabilities, supports the development of machine learning-based annotation tools, and accelerates the discovery of novel metabolites. All spectra are available under the collective contributor label mFam in the MassBank system, including the web interface and the 2025.10 data release available at GitHub and Zenodo.

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Journal
Metabolomics
Published
2026-07-02
DOI
https://doi.org/10.1007/s11306-026-02480-y
Primary Topic
Metabolomics and Mass Spectrometry Studies
Type
article
Field-Weighted Citation Impact
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article

The MassBank contributions of the mFam collaboration

Grégory Mouille, Katyeny Manuela da Silva, Gerd Ulrich Balcke, Otmar Spring et al.
Metabolomics
Metabolomics and Mass Spectrometry Studies
article

The MassBank contributions of the mFam collaboration

Grégory Mouille, Katyeny Manuela da Silva, Gerd Ulrich Balcke, Otmar Spring, David Broadhurst, Ric C. H. de Vos, Jérémy Grosjean, Elias Iturrospe, Asaph Aharoni, Rani Robeyns, Maximilian Frey, Emmanuel Gaquerel, René Meier, Stefanie Döll, Steffen Neumann, Nir Shahaf, Michael Reichelt, Hiroshi Tsugawa, Josep Valls, Ulschan Bathe, Ludger A. Wessjohann, Jean‐Luc Wolfender, Luca Nicolotti, Alena Soboleva, Akshai Parakkal Sreenivasan, Alain Goossens, Romain Larbat, Khabat Vahabi, Kim Kultima, J van Hooft, Stephanie Herman, Micha Gracianna Devi, François Perreau, Vasuk Gautam, Maria Halabalaki, Fredd Vergara, Jean Chrisologue Totozafy, Pierre-Marie Allard, Corey Broeckling, Fischer, Anusha Ahlendorf, David Wishart, Alain Tissier, E V Mikropoulou, Jörg Ziegler, Toni M. Kutchan, Megan Augustin, Adrian Covaci, Esteban Charria-Girón, Stacey N. Reinke, Joerg Buescher, Pierre Pétriacq, Maria van de Lavoir, Andrej Frolov
article en

Abstract

INTRODUCTION: The analysis of metabolic profiles using high resolution mass spectrometry (MS) data provides deep insights into biological processes. In metabolomics, MS analysis generates a large number of features that represent metabolites. However, identifying specific metabolites from these features can be challenging. One of the major bottlenecks in the metabolomics field is the identification of MS features, which is a prerequisite for any biochemical interpretation. By identifying similarities and differences within a metabolite family (mFam), evaluating MS features at the metabolite family level can help assigning functional roles to individual MS features. These data can help interpreting metabolic pathways and processes within a biological system. For the assignment of metabolite families to MS features, it is important to have good quality, reliable, and comprehensive spectral libraries. OBJECTIVE: We initiated a global effort to collect high-resolution MS/MS spectra of metabolites from labs working in different fields, including metabolomics of animals, microorganisms, and plants. The mFam-MS/MS collection delivers valuable training data to assign machine-readable classified information on the unknown metabolites. RESULTS: The mFam collaboration used a standardized metadata template and has developed a globally curated MS/MS spectral library of 7,872 spectra with 2,126 unique metabolites. This library was compiled from 47 datasets contributed by 25 laboratories measured on 12 instrument types, including QTOF, Orbitrap, and Ion Mobility-QTOF systems. It comprises 4,646 spectra in positive mode and 3,226 in negative mode. This standardized resource significantly enhances metabolite identification capabilities, supports the development of machine learning-based annotation tools, and accelerates the discovery of novel metabolites. All spectra are available under the collective contributor label mFam in the MassBank system, including the web interface and the 2025.10 data release available at GitHub and Zenodo.

MetabolomicsVol. 22(4)
University of Hohenheim (DE), University of Geneva (CH), Edith Cowan University (AU), Uppsala University (SE), SIB Swiss Institute of Bioinformatics (CH), Centre National de la Recherche Scientifique (FR), University of Antwerp (BE), Université de Bordeaux (FR), University of Fribourg (CH), University of Alberta (CA), HES-SO University of Applied Sciences and Arts Western Switzerland (CH), National and Kapodistrian University of Athens (GR), Norton Healthcare (US), Heidelberg University (DE), Stellenbosch University (ZA), Université Paris-Saclay (FR), Donald Danforth Plant Science Center (US), German Centre for Integrative Biodiversity Research (DE), Ghent University (BE), Knowledge Integration (United Kingdom) (GB), Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement (FR), Biologie du Fruit et Pathologie (FR), VIB-UGent Center for Plant Systems Biology (BE), Institut Jean-Pierre Bourgin (FR), Leibniz Institute of Vegetable and Ornamental Crops (DE), Institut de Biologie Moléculaire des Plantes (FR), Leibniz Institute of Plant Biochemistry (DE), Max Planck Institute for Chemical Ecology (DE), Unité mixte de recherche Œnologie (FR), Max Planck Institute of Immunobiology and Epigenetics (DE), Université d'Angers (FR), Weizmann Institute of Science (IL), Technical University of Munich (DE), Australian Wine Research Institute (AU), Martin Luther University Halle-Wittenberg (DE), Friedrich Schiller University Jena (DE), Université de Lorraine (FR), Wageningen University & Research (NL), Colorado State University (US), Tokyo University of Agriculture and Technology (JP), Leipzig University (DE)
Leibniz-Institut für Pflanzenbiochemie
Openalex Percentile: Top 12%
Metabolomics and Mass Spectrometry Studies
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