Dataset Information

CFM-ID 3.0: Significantly Improved ESI-MS/MS Prediction and Compound Identification.

ABSTRACT: Metabolite identification for untargeted metabolomics is often hampered by the lack of experimentally collected reference spectra from tandem mass spectrometry (MS/MS). To circumvent this problem, Competitive Fragmentation Modeling-ID (CFM-ID) was developed to accurately predict electrospray ionization-MS/MS (ESI-MS/MS) spectra from chemical structures and to aid in compound identification via MS/MS spectral matching. While earlier versions of CFM-ID performed very well, CFM-ID's performance for predicting the MS/MS spectra of certain classes of compounds, including many lipids, was quite poor. Furthermore, CFM-ID's compound identification capabilities were limited because it did not use experimentally available MS/MS spectra nor did it exploit metadata in its spectral matching algorithm. Here, we describe significant improvements to CFM-ID's performance and speed. These include (1) the implementation of a rule-based fragmentation approach for lipid MS/MS spectral prediction, which greatly improves the speed and accuracy of CFM-ID; (2) the inclusion of experimental MS/MS spectra and other metadata to enhance CFM-ID's compound identification abilities; (3) the development of new scoring functions that improves CFM-ID's accuracy by 21.1%; and (4) the implementation of a chemical classification algorithm that correctly classifies unknown chemicals (based on their MS/MS spectra) in >80% of the cases. This improved version called CFM-ID 3.0 is freely available as a web server. Its source code is also accessible online.

SUBMITTER: Djoumbou-Feunang Y

PROVIDER: S-EPMC6523630 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature

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CFM-ID 3.0: Significantly Improved ESI-MS/MS Prediction and Compound Identification.

Djoumbou-Feunang Yannick Y Pon Allison A Karu Naama N Zheng Jiamin J Li Carin C Arndt David D Gautam Maheswor M Allen Felicity F Wishart David S DS

Metabolites 20190413 4

Metabolite identification for untargeted metabolomics is often hampered by the lack of experimentally collected reference spectra from tandem mass spectrometry (MS/MS). To circumvent this problem, Competitive Fragmentation Modeling-ID (CFM-ID) was developed to accurately predict electrospray ionization-MS/MS (ESI-MS/MS) spectra from chemical structures and to aid in compound identification via MS/MS spectral matching. While earlier versions of CFM-ID performed very well, CFM-ID's performance for ...[more]

PMID: 31013937

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Dataset Information

CFM-ID 3.0: Significantly Improved ESI-MS/MS Prediction and Compound Identification.

Publications

CFM-ID 3.0: Significantly Improved ESI-MS/MS Prediction and Compound Identification.

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