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A framework to develop, test and optimise fragmentation strategies in LC-MS metabolomics.

Project description

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About

Liquid-Chromatography (LC) coupled with tandem mass spectrometry (MS/MS) is a prevalent technique for identifying small molecules in untargeted metabolomics. There are various strategies for acquiring MS/MS fragmentation spectra, but devising new methods is often challenging due to the absence of a structured environment where researchers can prototype, compare, and optimize strategies before testing on real equipment.

To solve this, we introduce the Virtual Metabolomics Mass Spectrometer (VIMMS), a flexible and modular framework designed to simulate fragmentation strategies in tandem mass spectrometry-based metabolomics.

Quick Start & Documentation

Eager to start using ViMMS? Take advantage of these resources:

Key Features

ViMMS provides scan-level control simulation of the MS2 acquisition process in a virtual environment. You can generate new LC-MS/MS data based on empirical data or virtually replay a previous LC-MS/MS analysis using existing data, which allows for testing different fragmentation strategies. With ViMMS, you can evaluate diverse fragmentation strategies using real data, and extract the scan results as mzML files.

Moreover, ViMMS serves as a platform for the development, optimization, and testing of new fragmentation strategies. These strategies can be implemented by extending a Controller class in ViMMS, and can be tested on both the simulator and actual mass spectrometry instruments that support compatible APIs.

To see a more thorough explanation of the use cases of ViMMS, please refer to the Use Cases section.

Contributions

As an open-source project licensed under MIT, we welcomes all forms of contributions, including bug fixes, new features, and more. You can find our community contribution guidelines here.

Citing ViMMS

To cite ViMMS or read about the list of publications that are built on top of ViMMS, please refer to the Publications page. ViMMS is also actively presented in various computational biology venues.

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