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Link to original content: http://dx.doi.org/10.1038/nmeth.3959
OpenMS: a flexible open-source software platform for mass spectrometry data analysis | Nature Methods
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OpenMS: a flexible open-source software platform for mass spectrometry data analysis

Nature Methods volume 13, pages 741–748 (2016)Cite this article

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Abstract

High-resolution mass spectrometry (MS) has become an important tool in the life sciences, contributing to the diagnosis and understanding of human diseases, elucidating biomolecular structural information and characterizing cellular signaling networks. However, the rapid growth in the volume and complexity of MS data makes transparent, accurate and reproducible analysis difficult. We present OpenMS 2.0 (http://www.openms.de), a robust, open-source, cross-platform software specifically designed for the flexible and reproducible analysis of high-throughput MS data. The extensible OpenMS software implements common mass spectrometric data processing tasks through a well-defined application programming interface in C++ and Python and through standardized open data formats. OpenMS additionally provides a set of 185 tools and ready-made workflows for common mass spectrometric data processing tasks, which enable users to perform complex quantitative mass spectrometric analyses with ease.

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Figure 1: The structure of the OpenMS framework.
Figure 2: Automated and interactive data analysis using OpenMS.
Figure 3: The data-analysis workflow for a SWATH-MS analysis of human blood plasma.

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Acknowledgements

We gratefully acknowledge the contributions of all OpenMS developers as well as of our users who helped to improve the software. This work was funded by ETH (ETH-30 11-2 to H.L.R.), SNSF (P2EZP3_162268 to H.L.R.), the Swiss Federal Commission for Technology and Innovation CTI (13539.1 PFFLI-LS to G.R.), ERC Proteomics v3.0 (233226 to R.A.), the PhosphonetX project of SystemsX.ch (R.A.), the Swiss National Science Foundation (R.A.), the Wellcome Trust (grant WT098051 to the Sanger Institute/H.W., P.G. and J.S.C.), the German Academic Exchange Service (DAAD, grant 57076385 to X.L.), the European Union (FP7, Predict-IV, GA 202222 to K.R. and C.B.), the Deutsche Forschungsgemeinschaft (SCHI 871/5, SCHI 871/6, SCHI 871/8, SCHI 871/9, GR 1748/6, INST 39/900-1, and SFB850-Project B8 to O.S. and L.N.), the European Research Council (ERC-2011-StG 282111-ProteaSys to O.S. and L.N.), DFG (QBiC to S.N., D.W. and O.K.; SFB685 to M.W. and O.K.), the European Union's Seventh Framework Programme (FP7/2007-2013 under EC-GA No. 263215 “MARINA” to M.R., F.A., S.A., K.R. and O.K.), the European Union (PRIME-XS to M.W., S.N. and O.K.), and BMBF (grant 01GI1104A to E.K. and O.K.; grant 01ZX1301F to E.K., J.P., T.S. and O.K.; grant 031A430C to F.A. and O.K.; grant 0315395B to J.V., L.N. and O.K.; grants 031A367 and 031A535A to O.K., K.R., S.A., J.P. and T.S.). We are deeply grateful for the support of the KNIME team, the Proteome Discoverer team and the Compound Discoverer team.

Author information

Author notes

  1. Stephan Aiche & Hans-Christian Ehrlich

    Present address: Present address: SAP SE, Potsdam, Germany.,

  2. Hannes L Röst, Timo Sachsenberg, Stephan Aiche, Chris Bielow and Hendrik Weisser: These authors contributed equally to this work.

Authors and Affiliations

  1. Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland

    Hannes L Röst, George Rosenberger, Witold E Wolski, Lars Malmström & Ruedi Aebersold

  2. Department of Genetics, Stanford University, Stanford, California, USA

    Hannes L Röst

  3. Department of Computer Science, University of Tübingen, Tübingen, Germany

    Timo Sachsenberg, Fabian Aicheler, Erhan Kenar, Julianus Pfeuffer, Marc Rurik, Johannes Veit, Mathias Walzer & Oliver Kohlbacher

  4. Center for Bioinformatics, University of Tübingen, Tübingen, Germany

    Timo Sachsenberg, Fabian Aicheler, Erhan Kenar, Julianus Pfeuffer, Marc Rurik, Johannes Veit, Mathias Walzer & Oliver Kohlbacher

  5. Department of Mathematics and Computer Science, Freie Universität Berlin, Berlin, Germany

    Stephan Aiche, Sandro Andreotti, Hans-Christian Ehrlich & Knut Reinert

  6. Berlin Institute for Medical Systems Biology, Max-Delbrück-Center for Molecular Medicine, Berlin, Germany

    Chris Bielow

  7. Metabolomics Core Facility, Berlin Institute of Health, Berlin, Germany

    Chris Bielow

  8. Proteomic Mass Spectrometry, Wellcome Trust Sanger Institute, Hinxton, UK

    Hendrik Weisser, Petra Gutenbrunner & Jyoti S Choudhary

  9. Quantitative Biology Center, University of Tübingen, Tübingen, Germany

    Erhan Kenar, Sven Nahnsen, David Wojnar & Oliver Kohlbacher

  10. International Max Planck Research School for Computational Biology and Scientific Computing (IMPRS-CBSC), Berlin, Germany

    Xiao Liang

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

    Lars Nilse & Oliver Schilling

  12. ID Scientific IT Services, ETH Zurich, Zurich, Switzerland

    Uwe Schmitt

  13. Functional Genomics Center Zurich, ETH Zurich, Zurich, Switzerland

    Witold E Wolski

  14. BIOSS Centre for Biological Signalling Studies, University of Freiburg, Freiburg, Germany

    Oliver Schilling

  15. S3IT, University of Zurich, Zurich, Switzerland

    Lars Malmström

  16. Faculty of Science, University of Zurich, Zurich, Switzerland

    Ruedi Aebersold

  17. Max Planck Institute for Molecular Genetics, Berlin, Germany

    Knut Reinert

  18. Faculty of Medicine, University of Tübingen, Tübingen, Germany

    Oliver Kohlbacher

  19. Biomolecular Interactions, Max Planck Institute for Developmental Biology, Tübingen, Germany

    Oliver Kohlbacher

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Corresponding author

Correspondence to Oliver Kohlbacher.

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Competing interests

C.B. is a part-time employee of CodeMS, which operates in the field covered in the Perspective.

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Supplementary Notes 1–3, and Supplementary Protocols 1 and 2 (PDF 5944 kb)

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Röst, H., Sachsenberg, T., Aiche, S. et al. OpenMS: a flexible open-source software platform for mass spectrometry data analysis. Nat Methods 13, 741–748 (2016). https://doi.org/10.1038/nmeth.3959

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