When using MZmine, please cite the corresponding paper: Robin Schmid, Steffen Heuckeroth, Ansgar Korf et al. Integrative analysis of multimodal mass spectrometry data in MZmine 3. Nature Biotechnology (2023), doi:10.1038/s41587-023-01690-2.
Welcome to the MZmine 3 wiki!¶
MZmine 3 is an open-source and platform-independent software for mass spectrometry (MS) data processing and visualization. It enables large-scale metabolomics and lipidomics research by spectral preprocessing, feature detection, and various options for compound identification, including spectral library querying and creation.
Since the introduction of MZmine 2 in 2010, the project has matured into a community-driven, highly collaborative platform and its functions continue to expand based on the users' needs and feedbacks. This has also enabled the tight integration of the MZmine ecosystem with popular third-party software for MS data analysis, such as the SIRIUS suite for in silico metabolite annotation, the GNPS platform with Ion Identity Molecular Networking, the MetaboAnalyst web app for univariate and multivariate statistical anlysis, etc.
Such a great progress was made possible by the invaluable contribution of many developers from research labs distributed all over the world!
Want to get started with MZmine 3?¶
What's new compared to MZmine 2?¶
MZmine 3 comes with a redesigned and fully customizable GUI based on the JavaFX technology that allow an interactive visualization and validation of results from every processing step.
A completely new data structure provides the flexibility to process any type of mass spectrometry, including LC-MS, GC-MS and MS-imaging. Moreover, MZmine 3 now supports ion mobility, with a dedicated LC-IM-MS data visualization module and feature detection algorithms.
Finally, significant effort was devoted to trace memory issues and bottlenecks, resulting in an unprecendent processing performance and scalability.
The latest changelog can be found here!
About this documentation¶
Here you can find documentation for both processing and visualization modules in MZmine 3. Moreover, data processing pipelines for untargeted LC-MS and LC-IMS-MS feature detection are described and general recommendations are given.
COMING SOON! We are currently working on a series of short videotutorials to help get you started with the main features of MZmine 3!
How to contribute¶
The MZmine community is always welcoming new developers and contributions! You can contribute by improving existing modules or even adding new featurs in MZmine 3! Please, check out our brief tutorial.
You can also contribute to this wiki and help new users to get started with MZmine 3! See here how to contribute to the documentation.