Scholarlink

Introduction <p>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.</p> Objective <p>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.</p> Results <p>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.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

The MassBank contributions of the mFam collaboration

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月2日

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

摘要

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.