Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Perspective
  • Published:

OpenMS: a flexible open-source software platform for mass spectrometry data analysis

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.

This is a preview of subscription content, access via your institution

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Prices may be subject to local taxes which are calculated during checkout

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.

Similar content being viewed by others

References

  1. Weisser, H. et al. An automated pipeline for high-throughput label-free quantitative proteomics. J. Proteome Res. 12, 1628–1644 (2013).Extended description of the OpenMS label-free workflow; compares the results to those obtained with other software.

    Article  CAS  Google Scholar 

  2. Martens, L. et al. mzML—a community standard for mass spectrometry data. Mol. Cell. Proteomics 10, R110.000133 (2011).

    Article  Google Scholar 

  3. Walzer, M. et al. The mzQuantML data standard for mass spectrometry-based quantitative studies in proteomics. Mol. Cell. Proteomics 12, 2332–2340 (2013).

    Article  CAS  Google Scholar 

  4. Griss, J. et al. The mzTab data exchange format: communicating mass-spectrometry-based proteomics and metabolomics experimental results to a wider audience. Mol. Cell. Proteomics 13, 2765–2775 (2014).

    Article  CAS  Google Scholar 

  5. Jones, A.R. et al. The mzIdentML data standard for mass spectrometry-based proteomics results. Mol. Cell. Proteomics 11, M111.014381 (2012).

    Article  Google Scholar 

  6. Deutsch, E.W. et al. A guided tour of the trans-proteomic pipeline. Proteomics 10, 1150–1159 (2010).

    Article  CAS  Google Scholar 

  7. Chambers, M.C. et al. A cross-platform toolkit for mass spectrometry and proteomics. Nat. Biotechnol. 30, 918–920 (2012).

    Article  CAS  Google Scholar 

  8. Sturm, M. et al. OpenMS—an open-source software framework for mass spectrometry. BMC Bioinformatics 9, 163 (2008).Contains the first description of OpenMS as a C++ software library.

    Article  Google Scholar 

  9. Vaudel, M. et al. PeptideShaker enables reanalysis of MS-derived proteomics data sets. Nat. Biotechnol. 33, 22–24 (2015).

    Article  CAS  Google Scholar 

  10. Wang, R. et al. PRIDE Inspector: a tool to visualize and validate MS proteomics data. Nat. Biotechnol. 30, 135–137 (2012).

    Article  Google Scholar 

  11. Devil in the details. Nature 470, 305–306 (2011).

  12. Code share. Nature 514, 536 (2014).

  13. Berthold, M.R. et al. KNIME: The Konstanz Information Miner (Springer, 2008).

  14. Goecks, J., Nekrutenko, A. & Taylor, J. Galaxy: a comprehensive approach for supporting accessible, reproducible, and transparent computational research in the life sciences. Genome Biol. 11, R86 (2010).

    Article  Google Scholar 

  15. Döring, A., Weese, D., Rausch, T. & Reinert, K. SeqAn an efficient, generic C. library for sequence analysis. BMC Bioinformatics 9, 11 (2008).

    Article  Google Scholar 

  16. Walzer, M. et al. qcML: an exchange format for quality control metrics from mass spectrometry experiments. Mol. Cell. Proteomics 13, 1905–1913 (2014).

    Article  CAS  Google Scholar 

  17. Deutsch, E.W. et al. TraML—a standard format for exchange of selected reaction monitoring transition lists. Mol. Cell. Proteomics 11, R11.015040 (2012).

    Article  Google Scholar 

  18. Röst, H.L., Schmitt, U., Aebersold, R. & Malmström, L. pyOpenMS: a Python-based interface to the OpenMS mass-spectrometry algorithm library. Proteomics 14, 74–77 (2014).

    Article  Google Scholar 

  19. Kiefer, P., Schmitt, U. & Vorholt, J.A. eMZed: an open source framework in Python for rapid and interactive development of LC/MS data analysis workflows. Bioinformatics 29, 963–964 (2013).

    Article  CAS  Google Scholar 

  20. Röst, H.L., Rosenberger, G., Aebersold, R. & Malmström, L. Efficient visualization of high-throughput targeted proteomics experiments: TAPIR. Bioinformatics 31, 2415–2417 (2015).

    Article  Google Scholar 

  21. DeLano, W.L. The PyMOL Molecular Graphics System (DeLano Scientific, 2002).

  22. Craig, R. & Beavis, R.C. TANDEM: matching proteins with tandem mass spectra. Bioinformatics 20, 1466–1467 (2004).

    Article  CAS  Google Scholar 

  23. Tyanova, S. et al. The Perseus computational platform for comprehensive analysis of (prote)omics data. Nat. Methods http://dx.doi.org/10.1038/nmeth.3901 (2016).

  24. Junker, J. et al. TOPPAS: a graphical workflow editor for the analysis of high-throughput proteomics data. J. Proteome Res. 11, 3914–3920 (2012).

    Article  CAS  Google Scholar 

  25. Aiche, S. et al. Workflows for automated downstream data analysis and visualization in large-scale computational mass spectrometry. Proteomics 15, 1443–1447 (2015).Highlights the importance of workflows in the world of MS and discusses open-source software solutions for workflow management.

    Article  CAS  Google Scholar 

  26. Kunszt, P. et al. iPortal: the swiss grid proteomics portal: requirements and new features based on experience and usability considerations. Concurr. Comput. 27, 433–445 (2015).

    Article  Google Scholar 

  27. Kessner, D., Chambers, M., Burke, R., Agus, D. & Mallick, P. ProteoWizard: open source software for rapid proteomics tools development. Bioinformatics 24, 2534–2536 (2008).

    Article  CAS  Google Scholar 

  28. Geer, L.Y. et al. Open mass spectrometry search algorithm. J. Proteome Res. 3, 958–964 (2004).

    Article  CAS  Google Scholar 

  29. Kim, S. et al. The generating function of CID, ETD, and CID/ETD pairs of tandem mass spectra: applications to database search. Mol. Cell. Proteomics 9, 2840–2852 (2010).

    Article  CAS  Google Scholar 

  30. Käll, L., Canterbury, J.D., Weston, J., Noble, W.S. & MacCoss, M.J. Semi-supervised learning for peptide identification from shotgun proteomics datasets. Nat. Methods 4, 923–925 (2007).

    Article  Google Scholar 

  31. Serang, O., MacCoss, M.J. & Noble, W.S. Efficient marginalization to compute protein posterior probabilities from shotgun mass spectrometry data. J. Proteome Res. 9, 5346–5357 (2010).

    Article  CAS  Google Scholar 

  32. Kenar, E. et al. Automated label-free quantification of metabolites from liquid chromatography-mass spectrometry data. Mol. Cell. Proteomics 13, 348–359 (2014).First application of OpenMS to metabolomics.

    Article  CAS  Google Scholar 

  33. Kramer, K. et al. Photo-cross-linking and high-resolution mass spectrometry for assignment of RNA-binding sites in RNA-binding proteins. Nat. Methods 11, 1064–1070 (2014).Describes the use of OpenMS to investigate RNA–protein cross-linking.

    Article  CAS  Google Scholar 

  34. Röst, H.L. et al. OpenSWATH enables automated, targeted analysis of data-independent acquisition MS data. Nat. Biotechnol. 32, 219–223 (2014).First publication of an automated workflow for targeted analysis of SWATH-MS data, implemented in OpenMS.

    Article  Google Scholar 

  35. Nahnsen, S., Bertsch, A., Rahnenführer, J., Nordheim, A. & Kohlbacher, O. Probabilistic consensus scoring improves tandem mass spectrometry peptide identification. J. Proteome Res. 10, 3332–3343 (2011).

    Article  CAS  Google Scholar 

  36. Nilse, L., Sigloch, F.C., Biniossek, M.L. & Schilling, O. Toward improved peptide feature detection in quantitative proteomics using stable isotope labeling. Proteomics Clin. Appl. 9, 706–714 (2015).

    Article  CAS  Google Scholar 

  37. Röst, H.L., Schmitt, U., Aebersold, R. & Malmström, L. Fast and efficient XML data access for next-generation mass spectrometry. PLoS One 10, e0125108 (2015).

    Article  Google Scholar 

  38. Bielow, C., Aiche, S., Andreotti, S. & Reinert, K. MSSimulator: simulation of mass spectrometry data. J. Proteome Res. 10, 2922–2929 (2011).

    Article  CAS  Google Scholar 

  39. Liu, Y. et al. Quantitative variability of 342 plasma proteins in a human twin population. Mol. Syst. Biol. 11, 786 (2015).

    Article  Google Scholar 

  40. Gillet, L.C. et al. Targeted data extraction of the MS/MS spectra generated by data-independent acquisition: a new concept for consistent and accurate proteome analysis. Mol. Cell. Proteomics 11, O111.016717 (2012).

    Article  Google Scholar 

  41. Lai, Z.W. et al. Formalin-fixed, paraffin-embedded tissues (FFPE) as a robust source for the profiling of native and protease-generated protein amino termini. Mol. Cell. Proteomics 15, 2203–2213 (2016).

    Article  CAS  Google Scholar 

  42. Tholen, S. et al. Contribution of cathepsin L to secretome composition and cleavage pattern of mouse embryonic fibroblasts. Biol. Chem. 392, 961–971 (2011).

    Article  CAS  Google Scholar 

  43. Wright, J.C. et al. Improving GENCODE reference gene annotation using a high-stringency proteogenomics workflow. Nat. Commun. 7, 11778 (2016).

    Article  CAS  Google Scholar 

  44. Harrow, J. et al. GENCODE: producing a reference annotation for ENCODE. Genome Biol. 7, S4.1–S4.9 (2006).

    Article  Google Scholar 

  45. Petryszak, R. et al. Expression Atlas update—an integrated database of gene and protein expression in humans, animals and plants. Nucleic Acids Res. 44, D746–D752 (2016).

    Article  CAS  Google Scholar 

  46. Choi, M. et al. MSstats: an R package for statistical analysis of quantitative mass spectrometry-based proteomic experiments. Bioinformatics 30, 2524–2526 (2014).

    Article  CAS  Google Scholar 

  47. Rosenberger, G., Ludwig, C., Röst, H.L., Aebersold, R. & Malmström, L. aLFQ: an R-package for estimating absolute protein quantities from label-free LC-MS/MS proteomics data. Bioinformatics 30, 2511–2513 (2014).

    Article  CAS  Google Scholar 

  48. MacLean, B. et al. Skyline: an open source document editor for creating and analyzing targeted proteomics experiments. Bioinformatics 26, 966–968 (2010).

    Article  CAS  Google Scholar 

Download references

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

Authors and Affiliations

Authors

Corresponding author

Correspondence to Oliver Kohlbacher.

Ethics declarations

Competing interests

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

Supplementary information

Supplementary Text and Figures

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nmeth.3959

This article is cited by

Search

Quick links

Nature Briefing: Translational Research

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Get what matters in translational research, free to your inbox weekly. Sign up for Nature Briefing: Translational Research