CFM-ID: A web server for annotation, spectrum prediction and metabolite identification from tandem mass spectra. Allen, F., Wilson, M., Pon, A., Greiner, R., & Wishart, D. Nucleic Acids Res, 42(W1):W94-W99, 2014.
doi  abstract   bibtex   
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.
@Article{allen14cfm-id,
  author    = {Felicity Allen and Michael Wilson and Allison Pon and Russ Greiner and David Wishart},
  journal   = {Nucleic Acids Res},
  title     = {{CFM-ID:} A web server for annotation, spectrum prediction and metabolite identification from tandem mass spectra},
  year      = {2014},
  number    = {W1},
  pages     = {W94-W99},
  volume    = {42},
  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.},
  doi       = {10.1093/nar/gku436},
  file      = {AllenEtAl_CFMIDWebServer_NuclAcidsRes_2014.pdf:2014/AllenEtAl_CFMIDWebServer_NuclAcidsRes_2014.pdf:PDF},
  keywords  = {MS; metabolite identification; CFM; CFM-ID;},
  owner     = {kaidu},
  timestamp = {2014.05.06},
}

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