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OpenMS 3 enables reproducible analysis of large-scale mass spectrometry data

Nature Methods volume 21, pages 365–367 (2024)Cite this article

Subjects

The OpenMS framework provides developers and users with a comprehensive set of MS-related algorithms and modular tools that employ open-standard file formats for interoperability. Ready-to-go workflows allow users to analyze their data while developers can create custom workflows and write new tools and algorithms for all major platforms (Windows, Linux and macOS) in C++ (OpenMS C++ library) or Python (pyOpenMS2). The permissive BSD license encourages academic and commercial use, as well as integration into other projects. The source code of OpenMS and pyOpenMS is publicly hosted on GitHub, where continuous integration, testing and code reviews ensure that contributions meet high quality standards. Communication between developers and users is facilitated by various online channels and an annual meeting. The OpenMS library offers native and modern C++ compiler support on all major platforms. Part of the library is an extensible graphical user interface module, which forms the basis for a powerful data viewer3.

Built on top of the C++ library, pyOpenMS gives access to algorithms and data structures using the Python scripting language, enabling seamless interaction with Python libraries for data science, machine learning and visualization. Non-experts can easily install pyOpenMS using pip or Conda and begin building scripts with our revised documentation and examples. Extended access to significant parts of OpenMS algorithms and data structures through Python enables developers to quickly prototype algorithms and serves teaching purposes. Furthermore, a recently developed template for web applications written in Python eases the deployment of graphical user interface applications. We now present a brief overview of major developments and user-facing changes.

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Fig. 1: OpenMS.

Code availability

All OpenMS source code is available on GitHub (https://github.com/OpenMS/OpenMS) under a permissive three-clause BSD license. Documentation for users (https://openms.readthedocs.io/), Python programmers (https://pyopenms.readthedocs.io/en/latest/index.html), and C++ developers (https://www.openms.org/documentation) can be accessed online.

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Acknowledgements

C.B. was in part supported by the Chan Zuckerberg EOSS program (179). J.P. was funded by Forschungscampus MODAL (project grant 3FO18501). K.J., E.N. and T.S. were supported by the Ministry of Science, Research and Arts Baden-Württemberg. A.S. and A.F. are part of the MSCA-ITN-2020 PROTrEIN project, which received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No: 956148. J.C. was funded by the Government of Canada through Genome Canada and the Ontario Genomics Institute (OGI-164) and supported by the Ontario Graduate Scholarship. J.C.S. was funded by the European Research Area Network Personalized Medicine Cofund (PerProGlio). A.W. was supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation TRR 261/1,Z03). S.W. was supported by de.NBI (031A535A), Analytics for Biologics (H2020-MSCA-ITN-2017), the EU H2020 project EPIC-XS (H2020-INFRAIA), TRR (TP Z03), and TUE.AI. S.K. was funded by Horizon 2020 under grant agreement No 686070. M.P.S. was funded by Polish National Science Centre grant no. 2017/26/D/ST6/00304. Y.P.-R. acknowledges funding from EMBL core funding, Wellcome grants (208391/Z/17/Z, 223745/Z/21/Z), and the EU H2020 project EPIC-XS (823839).

Author information

Author notes

  1. These authors contributed equally: Julianus Pfeuffer, Chris Bielow, Samuel Wein, Kyowon Jeong, Eugen Netz, Axel Walter.

Authors and Affiliations

  1. Algorithmic Bioinformatics, Freie Universität Berlin, Berlin, Germany

    Julianus Pfeuffer

  2. Visual and Data-Centric Computing, Zuse Institute Berlin, Berlin, Germany

    Julianus Pfeuffer

  3. Bioinformatics Solution Center, Institut für Mathematik und Informatik, Freie Universität Berlin, Berlin, Germany

    Chris Bielow

  4. Applied Bioinformatics, Department of Computer Science, University of Tuebingen, Tübingen, Germany

    Samuel Wein, Kyowon Jeong, Eugen Netz, Axel Walter, Oliver Alka, Jihyung Kim, Johannes Veit, Matteo Pilz, Ayesha Feroz, Arslan Siraj, Oliver Kohlbacher & Timo Sachsenberg

  5. Institute for Bioinformatics and Medical Informatics, University of Tuebingen, Tübingen, Germany

    Samuel Wein, Kyowon Jeong, Eugen Netz, Axel Walter, Oliver Alka, Jihyung Kim, Johannes Veit, Matteo Pilz, Ayesha Feroz, Arslan Siraj, Oliver Kohlbacher & Timo Sachsenberg

  6. Institute of Molecular Medicine and Cell Research, University of Freiburg, Freiburg, Germany

    Lars Nilse

  7. Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark

    Pasquale Domenico Colaianni, Douglas McCloskey & Bertrand Boudaud

  8. BioMed X Institute, Heidelberg, Germany

    Douglas McCloskey

  9. Department of Systems Biology, Columbia University, New York, NY, USA

    George Rosenberger

  10. Yale Center for Systems and Engineering Immunology and Department of Immunobiology, Yale University School of Medicine, New Haven, CT, USA

    Leon Bichmann

  11. European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), , Wellcome Trust Genome Campus, Hinxton, UK

    Mathias Walzer & Yasset Perez-Riverol

  12. Department of Computational Biology, Helmholtz Centre for Environmental Research GmbH-UFZ, Leipzig, Germany

    Matthias Bernt

  13. Evergrande Center for Immunologic Diseases Harvard Medical School and Brigham and Women’s Hospital, Boston, MA, USA

    Nikolaos Patikas

  14. Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Warsaw, Poland

    Michał Piotr Startek

  15. Institute for Immunology, University Medical Center of the Johannes-Gutenberg University, Mainz, Germany

    Michał Piotr Startek

  16. Department of Computer Science/Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark

    Svetlana Kutuzova

  17. Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Oberschleißheim, Germany

    Lukas Heumos

  18. Institute of Lung Health and Immunity and Comprehensive Pneumology Center with the CPC-M bioArchive, Helmholtz Zentrum Munich, Member of the German Center for Lung Research (DZL), Munich, Germany

    Lukas Heumos

  19. TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany

    Lukas Heumos

  20. Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada

    Joshua Charkow, Justin Cyril Sing & Hannes Röst

  21. The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada

    Joshua Charkow, Justin Cyril Sing & Hannes Röst

  22. STORM Therapeutics Limited, Cambridge, UK

    Hendrik Weisser

  23. Department for Women’s Health, University Clinic Tübingen, Tübingen, Germany

    Tjeerd M. H. Dijkstra

  24. Institute for Translational Bioinformatics, University Hospital Tübingen, Tübingen, Germany

    Tjeerd M. H. Dijkstra & Oliver Kohlbacher

Authors
  1. Julianus Pfeuffer
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  30. Hannes Röst
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  31. Oliver Kohlbacher
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  32. Timo Sachsenberg
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Contributions

J.P., C.B., S.W., K.J., E.N., A.W., T.D., T.S. wrote the manuscript. All authors contributed to the development of the software.

Corresponding author

Correspondence to Timo Sachsenberg.

Ethics declarations

Competing interests

L.H. is an employee of Laminlabs. H.W. is an employee of STORM Therapeutics. L.B. is an employee of BioNTech SE. L.N. is an employee of OpenMS Consulting. O.K., T.S. and S.W. are officers in OpenMS Inc., a non-profit foundation that manages the international coordination of OpenMS development. All remaining authors declare no competing interests.

Peer review

Peer review information

Nature Methods thanks Brian Searle and Wen-Feng Zeng for their contribution to the peer review of this work.

Supplementary information

Supplementary Information

Supplementary Note and Figs. 1–7

Supplementary Table

List of all OpenMS tools.

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Cite this article

Pfeuffer, J., Bielow, C., Wein, S. et al. OpenMS 3 enables reproducible analysis of large-scale mass spectrometry data. Nat Methods 21, 365–367 (2024). https://doi.org/10.1038/s41592-024-02197-7

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