Fiche publication
Date publication
juillet 2026
Journal
Metabolomics : Official journal of the Metabolomic Society
Auteurs
Membres identifiés du Cancéropôle Est :
Pr GAQUEREL Emmanuel
Tous les auteurs :
Ahlendorf A, Aharoni A, Vahabi K, Fischer TG, Allard PM, Augustin M, Bathe U, Broadhurst DI, Broeckling C, Buescher J, Covaci A, da Silva KM, de Vos RCH, Devi MG, Döll S, Frey M, Frolov A, Gaquerel E, Gautam V, Charria-Girón E, Goossens A, Grosjean J, Halabalaki M, Iturrospe E, Kultima K, Herman S, Kutchan TM, Larbat R, Meier R, Mikropoulou EV, Mouille G, Nicolotti L, Shahaf N, Perreau F, Pétriacq P, Reichelt M, Reinke SN, Robeyns R, Soboleva A, Spring O, Sreenivasan AP, Tissier A, Totozafy JC, Tsugawa H, Valls-Fonayet J, van de Lavoir M, van der Hooft JJJ, Vergara F, Wishart D, Wessjohann LA, Wolfender JL, Ziegler J, Balcke GU, Neumann S
Lien Pubmed
Résumé
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.
Mots clés
Data processing, FAIR data, Metabolomics, Open science, Reference spectra, Spectral libraries, Tandem mass spectrometry
Référence
Metabolomics. 2026 07 2;22(4):
