CFM-ID 3.0: Significantly Improved ESI-MS/MS Prediction and Compound Identification
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 ionizat...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-04-01
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| Series: | Metabolites |
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| Online Access: | https://www.mdpi.com/2218-1989/9/4/72 |
| _version_ | 1818425723394719744 |
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| author | Yannick Djoumbou-Feunang Allison Pon Naama Karu Jiamin Zheng Carin Li David Arndt Maheswor Gautam Felicity Allen David S. Wishart |
| author_facet | Yannick Djoumbou-Feunang Allison Pon Naama Karu Jiamin Zheng Carin Li David Arndt Maheswor Gautam Felicity Allen David S. Wishart |
| author_sort | Yannick Djoumbou-Feunang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-14T14:18:28Z |
| format | Article |
| id | doaj.art-615de7509d534f90ba45aa205d3a2b56 |
| institution | Directory Open Access Journal |
| issn | 2218-1989 |
| language | English |
| last_indexed | 2024-12-14T14:18:28Z |
| publishDate | 2019-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Metabolites |
| spelling | doaj.art-615de7509d534f90ba45aa205d3a2b562022-12-21T22:58:09ZengMDPI AGMetabolites2218-19892019-04-01947210.3390/metabo9040072metabo9040072CFM-ID 3.0: Significantly Improved ESI-MS/MS Prediction and Compound IdentificationYannick Djoumbou-Feunang0Allison Pon1Naama Karu2Jiamin Zheng3Carin Li4David Arndt5Maheswor Gautam6Felicity Allen7David S. Wishart8Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, CanadaOMx Personal Health Analytics, Edmonton, AB T5J 1B9, CanadaDepartment of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, CanadaDepartment of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, CanadaDepartment of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, CanadaDepartment of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, CanadaDepartment of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, CanadaWellcome Sanger Institute, Wellcome Trust Genome Campus, Hinxton CB10 1SA, UKDepartment of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, CanadaMetabolite 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.https://www.mdpi.com/2218-1989/9/4/72mass spectrometryliquid chromatographyMS spectral predictionmetabolite identificationstructure-based chemical classificationrule-based fragmentationcombinatorial fragmentation |
| spellingShingle | Yannick Djoumbou-Feunang Allison Pon Naama Karu Jiamin Zheng Carin Li David Arndt Maheswor Gautam Felicity Allen David S. Wishart CFM-ID 3.0: Significantly Improved ESI-MS/MS Prediction and Compound Identification Metabolites mass spectrometry liquid chromatography MS spectral prediction metabolite identification structure-based chemical classification rule-based fragmentation combinatorial fragmentation |
| title | CFM-ID 3.0: Significantly Improved ESI-MS/MS Prediction and Compound Identification |
| title_full | CFM-ID 3.0: Significantly Improved ESI-MS/MS Prediction and Compound Identification |
| title_fullStr | CFM-ID 3.0: Significantly Improved ESI-MS/MS Prediction and Compound Identification |
| title_full_unstemmed | CFM-ID 3.0: Significantly Improved ESI-MS/MS Prediction and Compound Identification |
| title_short | CFM-ID 3.0: Significantly Improved ESI-MS/MS Prediction and Compound Identification |
| title_sort | cfm id 3 0 significantly improved esi ms ms prediction and compound identification |
| topic | mass spectrometry liquid chromatography MS spectral prediction metabolite identification structure-based chemical classification rule-based fragmentation combinatorial fragmentation |
| url | https://www.mdpi.com/2218-1989/9/4/72 |
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