Welcome to CFM-ID!
We have recently updated to CFM-ID 2.0! You may notice some small changes to the user interface, as CFM-ID now supports EI spectra and the .msp file format. Please contact us if you notice any irregularities or errors. Thank you!
CFM-ID provides a method for accurately and efficiently identifying metabolites in spectra generated by electrospray tandem mass spectrometry (ESI-MS/MS). The program uses Competitive Fragmentation Modeling to produce a probabilistic generative model for the MS/MS fragmentation process and machine learning techniques to adapt the model parameters from data. This generated model can be used for:
|Spectra Prediction:||Predicting the spectra for a given chemical structure. This task predicts low/10V, medium/20V, and high/40V energy MS/MS spectra for an input structure provided in SMILES or InChI format.|
|Peak Assignment:||Annotating the peaks in set of spectra given a known chemical structure. This task takes a set of three input spectra (for ESI spectra, low/10V, medium/20V, and high/40V energy levels) or a single input spectra (for EI spectra, 70eV energy level) in peak list format and a chemical structure in SMILES or InChI format, then assigns a putative fragment annotation to the peaks in each spectrum.|
|Compound Identification:||Predicted ranking of possible candidate structures for a target spectrum. This task takes a set of three input spectra (for ESI spectra, low/10V, medium/20V, and high/40V energy levels) or a single input spectra (for EI spectra, 70eV energy level) in peak list format, and ranks a list of candidate structures according to how well they match the input spectra. This list may be provided by the user, or can be generated from HMDB or KEGG. The match is determined by predicting the spectra for each candidate compound and computing a score (Jaccard or DotProduct) based on the match between the predicted spectra and the input spectra.|
The models used here was trained using Single Energy Competitive Fragmentation Modeling on ESI-MS/MS spectra measured at three different collision energies (low/10V, medium/20V, and high/40V) and EI-MS spectra measured at 70eV that were obtained from the METLIN Metabolite Database.
More information on the Competitive Fragmentation Modeling method and this web server can be found in the following publications:
Allen F, Greiner R, and Wishart D. Computational prediction of electron ionization mass spectra to assist in GC-MS compound identification. Submitted, 2016.
Supporting Data: https://sourceforge.net/p/cfm-id/code/HEAD/tree/supplementary_material/2016_ei_ms_paper/
Allen F, Greiner R, and Wishart D. Competitive fragmentation modeling of ESI-MS/MS spectra for putative metabolite identification. Metabolomics. June 2014.
Supporting Data: https://sourceforge.net/p/cfm-id/code/HEAD/tree/supplementary_material/2015_esi_msms_paper/
Allen F, Pon A, Wilson M, Greiner R, and Wishart D. CFM-ID: a web server for annotation, spectrum prediction and metabolite identification from tandem mass spectra. Nucleic Acids Res. June 2014.Windows executables and cross-platform source code are freely available at http://sourceforge.net/projects/cfm-id. Supplementary files containing test molecule lists and trained models are also available on that site.