ms-mint-app 0.1.7.3

Metabolomics Integrator (Mint)

MINT (Metabolomics Integrator)

The Metabolomics Integrator (MINT) is a post-processing tool for liquid chromatography-mass spectrometry (LCMS) based metabolomics. Metabolomics is the study of all metabolites (small chemical compounds) in a biological sample e.g. from bacteria or a human blood sample. The metabolites can be used to define biomarkers used in medicine to find treatments for diseases or for the development of diagnostic tests or for the identification of pathogens such as methicillin resistant Staphylococcus aureus (MRSA). More information on how to install and run the program can be found in the Documentation or check out the Quickstart to jump right into it.

Browser based as standalone application or server based

A demo server is available here. Be mindful, you share the server with others.

Metabolomics with Python

MINT originally started as Python project. The core of MINT is a Python class for targeted metabolomics that can be used independent of the graphical application.

from ms_mint.notebook import Mint
mint.ms_files = glob('/path/to/files/*mzML')
mint.peaklist_files = '/path/to/peaklist/file/peaklist.csv'
mint.run()
mint.results

Contributions are welcome

MINT integrates open-source software and packages into a Python library for metabolomics with a browser based GUI. It is programmed by scientists for scientists to contribute to better and faster science integrating best practices of data management and computer science. Contributions are welcome that improve the efficiency of the code, bug fixes, feature implementations, security enhancements among others. If you want to contribute to MINT please send me a notification.

How to contribute

Errors, Feedback, Feature Requests

If you encounter an error, if you have a request for a new feature, or for general feedback, please open a new ticket at the issue tracker.

Code contributions

If your are up to enhance the codebase yourself, we ask you to followowing steps:

1. fork the repository
2. implement the new feature or bug-fix
3. add corresponding tests
4. run flake8
5. submit a pull request

Code standards

Before submitting a pull request please run flake8.

Get in touch

Open an issue or join the slack channel.

Acknowledgements

This project would not be possible without the help of the open-source community. The tools and resources provided by GitHub, Docker-Hub, the Python Package Index, as well the answers from dedicated users on Stackoverflow and the Plotly community, as well as the free open-source packages used are the foundation of this project. Several people have made direct contributions to the codebase and we are extremely grateful for that.

• @rokm refactored the specfile for Pyinstaller to create a windows package.
• @bucknerns helped with the configuration of the versioneer file.

Last but not least, we want to thank all the users and early adopters that drive the development with feature requests and bug reports.