WHAT IS OPENMS?

As part of the deNBI Center for integrative Bioinformatics, OpenMS offers an open-source software C++ library (+ python bindings) for LC/MS data management and analyses. It provides an infrastructure for the rapid development of mass spectrometry related software as well as a rich toolset built on top of it. OpenMS is free software available under the three clause BSD license and runs under Windows, macOS and Linux.

Download OpenMS

PROTEOMICS

Almost 200 ready-made and customisable tools for analysing your proteomics data. Combined in flexible workflows they provide solutions to a broad range of problems arising in state-of-the art proteomics labs.

METABOLOMICS

OpenMS provides a wide range of customisable tools, specifically designed for all steps in the analysis of your targeted and untargeted metabolomics data.

WORKFLOWS

Every tool that is developed within OpenMS is available on flexible, scalable and easy-to-use workflow engines (such as KNIME, Galaxy, and nextflow) facilitating reproducible science

QUICK FACTS

Programming Language:


C++11

License:


BSD-clause-3

Supported Platforms:


Windows


Linux


macOS

Recent News

OpenMS 2.6.0 released

30.09.2020

We are proud to announce release 2.6.0 of OpenMS. Get it now!

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Testimonials

Recent Tweets

OpenMS Team
4 Oct
OpenMS Team
@OpenMSTeam

We are proud to announce OpenMS 2.6.0 github.com/OpenMS/OpenMS/…. Bioconda packages: anaconda.org/search?q=OpenMS. KNIME plugins are available via the KNIME update mechanism and our pyOpenMS 2.6.0 packages will be available here (soon): pypi.org/project/pyopen…

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How to cite OpenMS

Röst, H.L., Sachsenberg, T., Aiche, S., Bielow, C., Weisser, H., Aicheler, F., Andreotti, S., Ehrlich, H.-C., Gutenbrunner, P., Kenar, E., Liang, X., Nahnsen, S., Nilse, L., Pfeuffer, J., Rosenberger, G., Rurik, M., Schmitt, U., Veit, J., Walzer, M., Wojnar, D., Wolski, W.E., Schilling, O., Choudhary, J.S., Malmström, L., Aebersold, R., Reinert, K., Kohlbacher, O.

OpenMS: A flexible open-source software platform for mass spectrometry data analysis. Nature Methods, vol. 13, 2016. doi:10.1038/nmeth.3959