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This release is a pre-release and may not be stable for production use.

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MINT (Metabolomics Integrator)

A powerful post-processing tool for LC-MS based metabolomics that simplifies peak integration, quality control, and data analysis.

Key Features

  • Targeted Peak Integration - Extract chromatograms and quantify peaks from mzML/mzXML files
  • Interactive Visualization - Explore chromatograms, heatmaps, and clustering results
  • ROI Optimization - Refine Regions of Interest (ROIs) with interactive feedback
  • Optional Quantification (SCALiR) - Available in the Processing tab for absolute quantification when needed
  • DuckDB Backend - Fast, efficient storage for large datasets
  • Desktop App - Available as standalone Windows and Linux executable

Hierarchical Clustering

Quick Start

# Create conda environment. Requires Python 3.12+
conda create -n ms-mint-app2 python==3.12
conda activate ms-mint-app2

# Install the package from PyPI
pip install ms-mint-app2

# Run MINT
Mint

Builds are provided with all dependencies integrated for Windows and Linux.

For detailed installation instructions, see the Installation Guide.

Documentation

Publications Using MINT

  1. Brown K, et al. Microbiota alters the metabolome in an age- and sex-dependent manner in mice. Nat Commun. 2023;14: 1348.

  2. Ponce LF, et al. SCALiR: A Web Application for Automating Absolute Quantification of Mass Spectrometry-Based Metabolomics Data. Anal Chem. 2024;96: 6566–6574.

Contributing

All contributions are welcome! This includes:

  • Bug reports and fixes
  • Documentation improvements
  • Feature requests and enhancements
  • Code reviews

Please open a GitHub issue to get started.

Acknowledgements

This project builds on the amazing open-source community:

Special thanks to GitHub,PyPI, and the Plotly Community for their invaluable resources.

License

This project is licensed under the Apache License 2.0.


Metadata

Release files for ms-mint-app2 2.0.0rc6

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