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MetaboAnalystR 2.0: From Raw Spectra to Biological Insights
Article

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

1
Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
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OMx Personal Health Analytics, Edmonton, AB T5J 1B9, Canada
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Wellcome Sanger Institute, Wellcome Trust Genome Campus, Hinxton CB10 1SA, UK
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Department of Computing Science, University of Alberta, Edmonton, AB T6G 2E8, Canada
*
Author to whom correspondence should be addressed.
Current address: Corteva Agriscience, 9300 Zionsville Road, Indianapolis, IN 46268, USA.
Current address: Leiden Academic Centre for Drug Research, Leiden University, 2300RA Leiden, The Netherlands.
Metabolites 2019, 9(4), 72; https://doi.org/10.3390/metabo9040072
Received: 4 March 2019 / Revised: 31 March 2019 / Accepted: 8 April 2019 / Published: 13 April 2019
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. View Full-Text
Keywords: mass spectrometry; liquid chromatography; MS spectral prediction; metabolite identification; structure-based chemical classification; rule-based fragmentation; combinatorial fragmentation mass spectrometry; liquid chromatography; MS spectral prediction; metabolite identification; structure-based chemical classification; rule-based fragmentation; combinatorial fragmentation
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MDPI and ACS Style

Djoumbou-Feunang, Y.; Pon, A.; Karu, N.; Zheng, J.; Li, C.; Arndt, D.; Gautam, M.; Allen, F.; Wishart, D.S. CFM-ID 3.0: Significantly Improved ESI-MS/MS Prediction and Compound Identification. Metabolites 2019, 9, 72. https://doi.org/10.3390/metabo9040072

AMA Style

Djoumbou-Feunang Y, Pon A, Karu N, Zheng J, Li C, Arndt D, Gautam M, Allen F, Wishart DS. CFM-ID 3.0: Significantly Improved ESI-MS/MS Prediction and Compound Identification. Metabolites. 2019; 9(4):72. https://doi.org/10.3390/metabo9040072

Chicago/Turabian Style

Djoumbou-Feunang, Yannick, Allison Pon, Naama Karu, Jiamin Zheng, Carin Li, David Arndt, Maheswor Gautam, Felicity Allen, and David S. Wishart 2019. "CFM-ID 3.0: Significantly Improved ESI-MS/MS Prediction and Compound Identification" Metabolites 9, no. 4: 72. https://doi.org/10.3390/metabo9040072

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