In silico optimization of mass spectrometry fragmentation strategies in metabolomics

Wandy, J. , Davies, V. , van der Hooft, J. J.J. , Weidt, S., Daly, R. and Rogers, S. (2019) In silico optimization of mass spectrometry fragmentation strategies in metabolomics. Metabolites, 9(10), 219. (doi: 10.3390/metabo9100219) (PMID:31600991)

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Liquid chromatography (LC) coupled to tandem mass spectrometry (MS/MS) is widely used in identifying small molecules in untargeted metabolomics. Various strategies exist to acquire MS/MS fragmentation spectra; however, the development of new acquisition strategies is hampered by the lack of simulators that let researchers prototype, compare, and optimize strategies before validations on real machines. We introduce Virtual Metabolomics Mass Spectrometer (ViMMS), a metabolomics LC-MS/MS simulator framework that allows for scan-level control of the MS2 acquisition process in silico. ViMMS can generate new LC-MS/MS data based on empirical data or virtually re-run a previous LC-MS/MS analysis using pre-existing data to allow the testing of different fragmentation strategies. To demonstrate its utility, we show how ViMMS can be used to optimize N for Top-N data-dependent acquisition (DDA) acquisition, giving results comparable to modifying N on the mass spectrometer. We expect that ViMMS will save method development time by allowing for offline evaluation of novel fragmentation strategies and optimization of the fragmentation strategy for a particular experiment.

Item Type:Articles
Glasgow Author(s) Enlighten ID:Weidt, Dr Stefan and Davies, Dr Vinny and Daly, Dr Ronan and Wandy, Dr Joe and Van Der Hooft, Mr Justin and Rogers, Dr Simon
Authors: Wandy, J., Davies, V., van der Hooft, J. J.J., Weidt, S., Daly, R., and Rogers, S.
College/School:College of Medical Veterinary and Life Sciences
College of Medical Veterinary and Life Sciences > School of Infection & Immunity
College of Science and Engineering > School of Computing Science
Journal Name:Metabolites
ISSN (Online):2218-1989
Copyright Holders:Copyright © 2019 The Authors
First Published:First published in Metabolites 9(10):219
Publisher Policy:Reproduced under a Creative Commons License

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
3009820Exploiting Closed-Loop Aspects in Computationally and Data Intensive AnalyticsRoderick Murray-SmithEngineering and Physical Sciences Research Council (EPSRC)EP/R018634/1Computing Science