In-silico Optimisation of Mass Spectrometry Fragmentation Strategies in Metabolomics

Wandy, J., Davies, V. , van der Hooft, J., Weidt, S., Daly, R. and Rogers, S. (2019) In-silico Optimisation of Mass Spectrometry Fragmentation Strategies in Metabolomics. [Data Collection]

Original publication URL: https://doi.org/10.1101/744227

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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 optimise strategies before validations on real machines. We introduce Virtual Metabolomics Mass Spectrometer (ViMMS), a modular 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 in-silico to allow the testing of different fragmentation strategies. It allows the comparison of different fragmentation strategies on real data, with the resulting scan results extractable as mzML files. To demonstrate its utility, we show how our proposed framework can be used to take the output of a real tandem mass spectrometry analysis and examine the effect of varying parameters in Top-N Data Dependent Acquisition protocol. We also demonstrate how ViMMS can be used to compare a recently published Data-set-Dependent Acquisition strategy with a standard Top-N strategy. We expect that ViMMS will save method development time by allowing for offline evaluation of novel fragmentation strategies and optimisation of fragmentation strategy for a particular experiment.

College / School: College of Medical Veterinary and Life Sciences
Date Deposited: 28 Aug 2019 08:28
URI: http://researchdata.gla.ac.uk/id/eprint/870

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Wandy, J., Davies, V. , van der Hooft, J., Weidt, S., Daly, R. and Rogers, S. (2019); In-silico Optimisation of Mass Spectrometry Fragmentation Strategies in Metabolomics

University of Glasgow

DOI: 10.5525/gla.researchdata.870

Retrieved: 2020-04-07

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