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.
Highlights
Metabolomics is a field of omics science that characterizes metabolites using high throughput technologies
We have released CFM-ID, a web server that provides three utilities that address important subtasks of the metabolite identification problem: MS/MS spectrum prediction, MS/MS peak annotation and putative metabolite identification [7]. These utilities, which will be further described below, present web-based front-ends to the functionality provided by the Single Energy Competitive Fragmentation Modeling (SE-CFM) technique introduced in [8]
Compound lists and full results for all tests are available in the Data section of the CFM-ID website
Summary
Metabolomics is a field of omics science that characterizes metabolites using high throughput technologies. Tations, and has been trained using spectra for more than 800 metabolites from Metlin, measured on an Agilent 6510 Q-TOF device at the above collision energies, but using negative ionization mode Both models enumerate possible MS/MS fragmentations for a given molecule and assign probabilities to competing fragmentations according to the trained model parameters. Compound identification takes several minutes or more, depending on the number and size of the candidate compounds This task predicts a low (10V), medium (20V) and high (40V) collision energy MS/MS spectrum for a given chemical structure. This list is included in the text file, and printed on the results screen This task allows users to perform putative metabolite identification for one or more input MS/MS spectra (in peak list format). The user can configure the number of top-scoring results that are reported
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