Introduction

MS-MINT (Mass Spectrometry-Metabolomics Integration Toolkit) is a Python library for large-scale targeted metabolomics. The library includes a range of functions for processing LCMS data from targeted metabolomics experiments, and it is particularly well-suited for handling large amounts of data (10,000+ files). To usems-mint, you provide it with a target list of the specific metabolites you want to analyze, as well as the names of the mass spectrometry files containing the data. ms-mint then extracts peak intensities and other relevant information from the data, allowing you to gain insights into the concentrations and profiles of the metabolites in your samples. This information can be used to identify biomarkers, which are indicators of disease or other physiological changes that can be used in the development of diagnostic tests or other medical applications. ms-mint can be used in Python scripts, interactively in Jupyter Notebooks, or with the web-app ms-mint-app.

Citation

When using MS-MINT in your research, please cite the library in your publications DOI: 10.5281/zenodo.12733875

Support

• GitHub Repository: https://github.com/LewisResearchGroup/ms-mint
• Issue Tracker: https://github.com/LewisResearchGroup/ms-mint/issues

Contributing

MS-MINT is an open-source project. Contributions are welcome!

• Report issues on GitHub
• Submit pull requests
• Share improvements and extensions

Disclaimer

MS-MINT is provided as-is. Always validate results and consult domain experts.