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 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 the fragmentation strategy for a particular experiment.

Here is an example showing actual experimental spectra vs our simulated results.

Stable version

ViMMS requires Python 3+. Unfortunately it is not compatible with Python 2. You can install the stable version of ViMMS using pip:through pypi using pip:

pip install vimms

You can download the latest stable release for the paper in review from here:

To use the latest bleeding-edge ViMMS code in this repository:

1. Install Python 3. We recommend Python 3.6 or 3.7.
2. Install pipenv (https://pipenv.readthedocs.io/en/latest/).
3. Clone this repository.
4. In this cloned directory, run $ pipenv install to create a new virtual environment and install all the packages need to run ViMMS.
5. Run jupyter notebook.
6. For IAPI assembly binding to work, copy the file python.exe.config to the same folder as the Python interpreter in the virtual environment.

Notebooks that demonstrate how to use ViMMS are available in the examples folder of this repository.

To reference ViMMS in your work, please cite the following:

• https://www.biorxiv.org/content/10.1101/744227v1?rss=1 (under review).

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