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
• RT Optimization - Fine-tune retention time windows with visual 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

Quick Start

Installation with pip (Recommended)

# 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

• Full Documentation - Complete user guide
• Quick Start Tutorial - Get up and running in 5 minutes

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.

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