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Anusha Ahlendorf , Khabat Vahabi , Tillmann G. Fischer , Pierre‐Marie Allard , Megan M. Augustin , Ulschan Bathe , David Broadhurst , Corey D. Broeckling , Joerg M. Buescher , Katyeny Manuela da Silva , Ric C. H. de Vos , Minakshi Devi , Stefanie Döll , Maximilian Frey , Tatiana Bilova , Emmanuel Gaquerel , Vasuk Gautam , Alain Goossens , Jérémy Grosjean , Maria Halabalaki , Elias Iturrospe , Kim Kultima , Stephanie Herman , Toni M. Kutchan , Romain Larbat , Tytus D. Mak , René Meier , Eleni V. Mikropoulou , Grégory Mouille , Luca Nicolotti , François Perreau , Pierre Pétriacq , Michael Reichelt , Stacey N. Reinke , Rani Robeyns , Amy M. Sheflin , Alena Soboleva , Otmar Spring , Akshai Parakkal Sreenivasan , Jean Chrisologue Totozafy , Hiroshi Tsugawa , Josep Valls , Maria van de Lavoir , Justin J. J. van der Hooft , Fredd Vergara , David S. Wishart , Ludger A. Wessjohann , Jean‐Luc Wolfender , Jörg Ziegler , Gerd U. Balcke , Steffen Neumann ·

YOU? · · 2025 · Open Access · · DOI: https://doi.org/10.26434/chemrxiv-2025-fz6x8

The analysis of metabolic profiles using high resolution mass spectrometry (MS) data gives deep insights into the biological processes. In metabolomics, MS 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 family, evaluating MS features at the metabolite family level can help assign functional roles to individual MS features. This data can help interpret metabolic pathways and processes within a biological system. For the assignment of metabolite families to MS features, it is very important to have good quality, reliable, and diverse spectral libraries. We initiated a global effort to collect high-resolution MS/MS spectra of metabolites from all biological origins, including mammals, microorganisms, and plants. The mFAM-MS/MS Collection delivers valuable training data to assign machine-readable classified information on the unknown metabolites. The mFam Consortium 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 and leverages 12 instrument types, including QTOF, Orbitrap, and Ion Mobility 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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Landing Page: https://doi.org/10.26434/chemrxiv-2025-fz6x8
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Authors: Anusha Ahlendorf, Khabat Vahabi, Tillmann G. Fischer, Pierre‐Marie Allard, Megan M. Augustin, Ulschan Bathe, David Broadhurst, Corey D. Broeckling, Joerg M. Buescher, Katyeny Manuela da Silva, Ric C. H. de Vos, Minakshi Devi, Stefanie Döll, Maximilian Frey, Tatiana Bilova, Emmanuel Gaquerel, Vasuk Gautam, Alain Goossens, Jérémy Grosjean, Maria Halabalaki, Elias Iturrospe, Kim Kultima, Stephanie Herman, Toni M. Kutchan, Romain Larbat, Tytus D. Mak, René Meier, Eleni V. Mikropoulou, Grégory Mouille, Luca Nicolotti, François Perreau, Pierre Pétriacq, Michael Reichelt, Stacey N. Reinke, Rani Robeyns, Amy M. Sheflin, Alena Soboleva, Otmar Spring, Akshai Parakkal Sreenivasan, Jean Chrisologue Totozafy, Hiroshi Tsugawa, Josep Valls, Maria van de Lavoir, Justin J. J. van der Hooft, Fredd Vergara, David S. Wishart, Ludger A. Wessjohann, Jean‐Luc Wolfender, Jörg Ziegler, Gerd U. Balcke, Steffen Neumann

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