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Updated: Apr 28, 2026

Large Scale Non-targeted Metabolomic Profiling of Serum by Ultra Performance Liquid Chromatography-Mass Spectrometry UPLC-MS
Published on: March 14, 2013
CFM-ID: a web server for annotation, spectrum prediction and metabolite identification from tandem mass spectra
Felicity Allen1, Allison Pon2, Michael Wilson2
1Department of Computing Science, Athabasca Hall, University of Alberta, Edmonton T6G 2E8, Canada. felicity.allen@ualberta.ca.
Abstract:
CFM-ID is a web server supporting three tasks associated with the interpretation of tandem mass spectra (MS/MS) for the purpose of automated metabolite identification: annotation of the peaks in a spectrum for a known chemical structure; prediction of spectra for a given chemical structure and putative metabolite identification--a predicted ranking of possible candidate structures for a target spectrum. The algorithms used for these tasks are based on Competitive Fragmentation Modeling (CFM), a recently introduced probabilistic generative model for the MS/MS fragmentation process that uses machine learning techniques to learn its parameters from data. These algorithms have been extensively tested on multiple datasets and have been shown to out-perform existing methods such as MetFrag and FingerId. This web server provides a simple interface for using these algorithms and a graphical display of the resulting annotations, spectra and structures. CFM-ID is made freely available at http://cfmid.wishartlab.com.
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