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Metabolites. 2019 Apr 13;9(4). pii: E72. doi: 10.3390/metabo9040072.

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

Author information

1
Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada. djoumbou@ualberta.ca.
2
OMx Personal Health Analytics, Edmonton, AB T5J 1B9, Canada. allisonpon@gmail.com.
3
Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada. n.karu@lacdr.leidenuniv.nl.
4
Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada. jiamin3@ualberta.ca.
5
Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada. cbli@ualberta.ca.
6
Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada. darndt@ualberta.ca.
7
Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada. maheswor@ualberta.ca.
8
Wellcome Sanger Institute, Wellcome Trust Genome Campus, Hinxton CB10 1SA, UK. felicity.allen@sanger.ac.uk.
9
Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada. david.wishart@ualberta.ca.
10
Department of Computing Science, University of Alberta, Edmonton, AB T6G 2E8, Canada. david.wishart@ualberta.ca.

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.

KEYWORDS:

MS spectral prediction; combinatorial fragmentation; liquid chromatography; mass spectrometry; metabolite identification; rule-based fragmentation; structure-based chemical classification

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