The MassBank contributions of the mFam collaboration

@article{ahlendorf2026massbank,
  title = {{The MassBank contributions of the mFam collaboration}},
  author = {Anusha Ahlendorf and Asaph Aharoni and Khabat Vahabi and Tillmann Fischer and Pierre-Marie Allard and Megan Augustin and Ulschan Bathe and David Broadhurst and Corey Broeckling and Joerg Buescher and Adrian Covaci and Katyeny Manuela da Silva and Ric de Vos and Micha Gracianna Devi and Stefanie Döll and Maximilian Frey and Andrej Frolov and Emmanuel Gaquerel and Vasuk Gautam and Esteban Charria-Girón and Alain Goossens and Jeremy Grosjean and Maria Halabalaki and Elias Iturrospe and Kim Kultima and Stephanie Herman and Toni Kutchan and Romain Larbat and René Meier and Eleni Mikropoulou and Gregory Mouille and Luca Nicolotti and Nir Shahaf and François Perreau and Pierre Pétriacq and Michael Reichelt and Stacey Reinke and Rani Robeyns and Alena Soboleva and Otmar Spring and Akshai Parakkal Sreenivasan and Alain Tissier and Jean Chrisologue Totozafy and Hiroshi Tsugawa and Josep Valls-Fonayet and Maria van de Lavoir and Justin van der Hooft and Fredd Vergara and David Wishart and Ludger Wessjohann and Jean-Luc Wolfender and Jörg Ziegler and Gerd Ulrich Balcke and Steffen Neumann},
  journal = {Metabolomics},
  year = {2026},
  month = jul,
  doi = {10.1007/s11306-026-02480-y},
  url = {https://hal.inrae.fr/hal-05772013v1},
  note = {CC BY},
}

TY  - JOUR
TI  - The MassBank contributions of the mFam collaboration
AU  - Anusha Ahlendorf
AU  - Asaph Aharoni
AU  - Khabat Vahabi
AU  - Tillmann Fischer
AU  - Pierre-Marie Allard
AU  - Megan Augustin
AU  - Ulschan Bathe
AU  - David Broadhurst
AU  - Corey Broeckling
AU  - Joerg Buescher
AU  - Adrian Covaci
AU  - Katyeny Manuela da Silva
AU  - Ric de Vos
AU  - Micha Gracianna Devi
AU  - Stefanie Döll
AU  - Maximilian Frey
AU  - Andrej Frolov
AU  - Emmanuel Gaquerel
AU  - Vasuk Gautam
AU  - Esteban Charria-Girón
AU  - Alain Goossens
AU  - Jeremy Grosjean
AU  - Maria Halabalaki
AU  - Elias Iturrospe
AU  - Kim Kultima
AU  - Stephanie Herman
AU  - Toni Kutchan
AU  - Romain Larbat
AU  - René Meier
AU  - Eleni Mikropoulou
AU  - Gregory Mouille
AU  - Luca Nicolotti
AU  - Nir Shahaf
AU  - François Perreau
AU  - Pierre Pétriacq
AU  - Michael Reichelt
AU  - Stacey Reinke
AU  - Rani Robeyns
AU  - Alena Soboleva
AU  - Otmar Spring
AU  - Akshai Parakkal Sreenivasan
AU  - Alain Tissier
AU  - Jean Chrisologue Totozafy
AU  - Hiroshi Tsugawa
AU  - Josep Valls-Fonayet
AU  - Maria van de Lavoir
AU  - Justin van der Hooft
AU  - Fredd Vergara
AU  - David Wishart
AU  - Ludger Wessjohann
AU  - Jean-Luc Wolfender
AU  - Jörg Ziegler
AU  - Gerd Ulrich Balcke
AU  - Steffen Neumann
PY  - 2026
DA  - 2026/07/02
JO  - Metabolomics
DO  - 10.1007/s11306-026-02480-y
UR  - https://hal.inrae.fr/hal-05772013v1
AB  - 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.
SN  - 1573-3882
SN  - 1573-3890
ER  -