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Open AccessArticle

In Silico Optimization of Mass Spectrometry Fragmentation Strategies in Metabolomics

1
Glasgow Polyomics, University of Glasgow, Glasgow G61 1BD, UK
2
School of Computing Science, University of Glasgow, Glasgow G12 8RZ, UK
3
Bioinformatics Group, Department of Plant Sciences, Wageningen University, 6780 PB Wageningen, The Netherlands
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Metabolites 2019, 9(10), 219; https://doi.org/10.3390/metabo9100219
Received: 28 August 2019 / Revised: 27 September 2019 / Accepted: 2 October 2019 / Published: 9 October 2019
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.
Keywords: liquid chromatography–mass spectrometry (LC/MS); fragmentation (MS/MS); data-dependent acquisition (DDA); simulator; in silico liquid chromatography–mass spectrometry (LC/MS); fragmentation (MS/MS); data-dependent acquisition (DDA); simulator; in silico
MDPI and ACS Style

Wandy, J.; Davies, V.; J. J. van der Hooft, J.; Weidt, S.; Daly, R.; Rogers, S. In Silico Optimization of Mass Spectrometry Fragmentation Strategies in Metabolomics. Metabolites 2019, 9, 219.

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