OpenMS

OpenMS is an open-source project for data analysis and processing in protein mass spectrometry and is released under the 3-clause BSD licence. It supports most common operating systems including Microsoft Windows, OS X and Linux.[2]

OpenMS
Developer(s)Over 65 individuals
Initial release1 July 2007 (2007-07-01)
Stable release
2.6.0 / 30 September 2020 (2020-09-30)
Repository
Written inC++ (with bindings to Python)
Operating systemLinux, Windows, OS X
Size215 MB [1]
Available inEnglish
TypeBioinformatics / Mass spectrometry software
LicenseBSD licenses 3-clause
Websiteopenms.de

OpenMS has tools for many common data analysis pipelines used in proteomics, providing algorithms for signal processing, feature finding (including de-isotoping), visualization in 1D (spectra or chromatogram level), 2D and 3D, map mapping and peptide identification. It supports label-free and isotopic-label based quantification (such as iTRAQ and TMT and SILAC). Furthermore, it also supports metabolomics workflows and targeted analysis of DIA/SWATH data.[2]

To achieve a wide variety of tasks in proteomics, OpenMS provides The OpenMS Proteomics Pipeline (TOPP) which is a set of computational tools that can be chained together to tailor problem-specific analysis pipelines for HPLC-MS data. It transforms most of the OpenMS functionality into small command line tools that are the building blocks for more complex analysis pipelines.[2]

History

OpenMS was originally released in 2007 in version 1.0 and was described in two articles published in Bioinformatics in 2007 and 2008 and has since seen continuous releases.[3][4] In 2009, the visualization tool TOPPView was published[5] and in 2012, the workflow manager and editor TOPPAS was described in a scientific article.[6] In 2013, a complete high-throughput label-free analysis pipeline using OpenMS 1.8 was described in the literature and compared with similar, proprietary software (such as MaxQuant and Progenesis). The authors conclude that "[...] all three software solutions produce adequate and largely comparable quantification results; all have some weaknesses, and none can outperform the other two in every aspect that we examined. However, the performance of OpenMS is on par with that of its two tested competitors [...]".[7]

The OpenMS 1.10 release contained several new analysis tools, including OpenSWATH (a tool for targeted DIA data analysis), a metabolomics feature finder and a TMT analysis tool. Furthermore, full support for TraML 1.0.0 and the search engine MyriMatch were added.[8] The OpenMS 1.11 release was the first release to contain fully integrated bindings to the Python programming language (termed pyOpenMS).[9] In addition, several new tools were added such as tools relating to QcML (for quality control) and for metabolomics accurate mass analysis. Multiple tools were significantly improved with regard to memory and CPU performance.[10]

With OpenMS 2.0, released in April 2015, the project provides a new version that has been completely cleared of GPL code and uses git (in combination with GitHub) for its version control and ticketing system. Other changes include support for mzIdentML, mzQuantML and mzTab while multiple improvements in the kernel allowed for faster access to data stored in mzML and provided a novel API for accessing mass spectrometric data.[11] In 2016, the new features of OpenMS 2.0 were described in an article in Nature Methods.[12]

Since the inception of the project, a yearly OpenMS user meeting has been held at several universities where developers and users of the framework had the chance to present new features of OpenMS and direct, biological applications of OpenMS. The 3rd OpenMS user meeting took place in March 2010 in Dortmund,[13] with the next meetings taking place in Berlin (4th meeting in September 2011),[14] Salzburg (5th meeting in October 2012),[15] Zurich (6th meeting in September 2013),[16] Berlin (7th meeting in September 2014),[17] Bochum (8th user meeting in September 2015)[18] and Tübingen (9th meeting in September 2016).[19]

OpenMS is currently developed in the groups of Knut Reinert[20] at the Free University of Berlin, in the group of Oliver Kohlbacher[21] at the University of Tübingen and in the group of Ruedi Aebersold[22] at ETH Zurich.

Features

OpenMS provides several features to users and developers, foremost providing a set of over 100 different executable tools than can be chained together into pipelines for proteomics data analysis (the TOPP Tools). It also provides visualization tools for spectra and chromatograms (1D), mass spectrometric heat maps (2D m/z vs RT) as well as a three-dimensional visualization of a mass spectrometry experiment. Finally, OpenMS also provides a C++ library (with bindings to Python available since 1.11) for LC/MS data management and analyses accessible to developers to create new tools and implement their own algorithms using the OpenMS library. OpenMS is free software available under the 3-clause BSD licence (previously under the LGPL).

Among others, it provides algorithms for signal processing, feature finding (including de-isotoping), visualization, map mapping and peptide identification. It supports label-free and isotopic-label based quantification (such as iTRAQ and TMT and SILAC).

TOPPView is a viewer software that allows visualization of mass spectrometric data on MS1 and MS2 level as well as in 3D; additionally it also displays chromatographic data from SRM experiments (in version 1.10). TOPPAS is a graphic integrated workflow manager that allows chaining the TOPP tools into a reusable and reproducible workflow.[6]

OpenMS is compatible with the current and the upcoming Proteomics Standard Initiative (PSI) formats for mass spectrometric data.

Releases
Version Date Features
Old version, no longer maintained: 1.6.0 November 2009 New version of TOPPAS, reading of compressed XML files, identification-based alignment
Old version, no longer maintained: 1.7.0 September 2010 Protein quantification, protXML support, create Inclusion/Exclusion lists
Old version, no longer maintained: 1.8.0 March 2011 Display identification results, QT Clustering-based feature linking
Old version, no longer maintained: 1.9.0 February 2012 metabolomics support, feature detection in raw (profile) data
Old version, no longer maintained: 1.10.0 March 2013 KNIME integration, support for targeted SWATH-MS analysis, TraML support, SuperHirn integration, MyriMatch support
Old version, no longer maintained: 1.11.0 August 2013 Support for Python bindings, performance improvements, Mascot 2.4 support
Old version, no longer maintained: 2.0 April 2015 mzQuantL, mzIdentML, mzTab, indexed mzML, Removal of GPL code, Switch to git, Support for Fido, MSGF+, Percolator
Old version, no longer maintained: 2.0.1 April 2016 faster file reading, improved support for mzIdentML and mzTab, elemental flux analysis, targeted assay generation, Support for Comet and Luciphor
Old version, no longer maintained: 2.1.0 November 2016 Metabolite SWATH-MS support, lowess-transformations for RT alignment, improved metabolic feature finding
Old version, no longer maintained: 2.2.0 July 2017 Fast feature linking using a KD tree, RNA cross-linking support, SpectraST support, scanning SWATH support, SQLite file formats
Old version, no longer maintained: 2.3.0 January 2018 Protein-Protein Crosslinking, support for Comet, support for fractions, TMT 11plex, improved build for Python bindings
Old version, no longer maintained: 2.4.0 October 2018 Support MaraCluster, Crux, MSFragger, MSstats, SIRIUS, visualization of ion mobility and DIA, library improvements
Old version, no longer maintained: 2.5.0 February 2020 Support RNA mass spectrometery, QualityControl workflow, extended OpenSWATH support, ProteomicsLFQ
Current stable version: 2.6.0 September 2020 PyOpenMS wheel builds, Database suitability tool, SLIM labelling support
Legend:
Old version
Older version, still maintained
Latest version
Latest preview version
Future release

See also

References
  1. OpenMS releases
  2. Röst HL, Sachsenberg T, Aiche S, Bielow C, Weisser H, Aicheler F, Andreotti S, Ehrlich HC, 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 WE, Schilling O, Choudhary JS, Malmström L, Aebersold R, Reinert K, Kohlbacher O (2016). "OpenMS: a flexible open-source software platform for mass spectrometry data analysis" (PDF). Nat. Methods. 13 (9): 741–8. doi:10.1038/nmeth.3959. PMID 27575624. S2CID 873670.
  3. Sturm, M.; Bertsch, A.; Gröpl, C.; Hildebrandt, A.; Hussong, R.; Lange, E.; Pfeifer, N.; Schulz-Trieglaff, O.; Zerck, A.; Reinert, K.; Kohlbacher, O. (2008). "OpenMS – an open-source software framework for mass spectrometry". BMC Bioinformatics. 9: 163. doi:10.1186/1471-2105-9-163. PMC 2311306. PMID 18366760.
  4. Kohlbacher, O.; Reinert, K.; Gropl, C.; Lange, E.; Pfeifer, N.; Schulz-Trieglaff, O.; Sturm, M. (2007). "TOPP--the OpenMS proteomics pipeline". Bioinformatics. 23 (2): e191–e197. doi:10.1093/bioinformatics/btl299. PMID 17237091.
  5. Sturm, M.; Kohlbacher, O. (2009). "TOPPView: An Open-Source Viewer for Mass Spectrometry Data". Journal of Proteome Research. 8 (7): 3760–3763. doi:10.1021/pr900171m. PMID 19425593.
  6. Junker, J.; Bielow, C.; Bertsch, A.; Sturm, M.; Reinert, K.; Kohlbacher, O. (2012). "TOPPAS: A Graphical Workflow Editor for the Analysis of High-Throughput Proteomics Data". Journal of Proteome Research. 11 (7): 3914–3920. doi:10.1021/pr300187f. PMID 22583024.
  7. Weisser, H.; Nahnsen, S.; Grossmann, J.; Nilse, L.; Quandt, A.; Brauer, H.; Sturm, M.; Kenar, E.; Kohlbacher, O.; Aebersold, R.; Malmström, L. (2013). "An Automated Pipeline for High-Throughput Label-Free Quantitative Proteomics". Journal of Proteome Research. 12 (4): 1628–44. doi:10.1021/pr300992u. PMID 23391308.
  8. "OpenMS 1.10 released". Retrieved 4 July 2013.
  9. "pyopenms 1.11 : Python Package Index". Retrieved 27 October 2013.
  10. "OpenMS 1.11 released". Retrieved 27 October 2013.
  11. Röst HL, Schmitt U, Aebersold R, Malmström L (2015). "Fast and Efficient XML Data Access for Next-Generation Mass Spectrometry". PLOS ONE. 10 (4): e0125108. Bibcode:2015PLoSO..1025108R. doi:10.1371/journal.pone.0125108. PMC 4416046. PMID 25927999.
  12. Röst HL, Sachsenberg T, Aiche S, et al. (2016). "OpenMS: a flexible open-source software platform for mass spectrometry data analysis" (PDF). Nature Methods. 13 (9): 741–8. doi:10.1038/nmeth.3959. PMID 27575624. S2CID 873670.
  13. "OpenMS user meeting on the 1-2nd of March 2010". Retrieved 27 October 2013.
  14. "Fall User Meeting 2011". Retrieved 27 October 2013.
  15. "5th OpenMS User Meeting – High-performance software for high-throughput proteomics and metabolomics". Retrieved 4 July 2013.
  16. "6th OpenMS User Meeting – High-performance software for high-throughput proteomics and metabolomics". Retrieved 27 October 2013.
  17. "7th OpenMS User Meeting – High-performance software for high-throughput proteomics and metabolomics | OpenMS".
  18. "8th OpenMS User Meeting – High-performance software for high-throughput proteomics and metabolomics | OpenMS". Retrieved 2016-03-30.
  19. http://open-ms.sourceforge.net/um2016/
  20. Reinert group
  21. Kohlbacher group
  22. "Aebersold group". Archived from the original on 2011-07-20. Retrieved 2013-07-01.
  • Sturm M, Bertsch A, Groepl C, Hildebrandt A, Hussong R, Lange E, Pfeifer N, Schulz-Trieglaff O, Zerck A, Reinert K, Kohlbacher O: OpenMS – An open-source software framework for mass spectrometry. BMC Bioinformatics 2008, 9:163.(fulltext)
  • Kohlbacher O, Reinert K, Gröpl C, Lange E, Pfeifer N, Schulz-Trieglaff O, Sturm M: TOPP - the OpenMS proteomics pipeline. Bioinformatics 2007, 23(2):e191-7. (fulltext)
  • Röst HL, Sachsenberg T, Aiche S, Bielow C, Weisser H, Aicheler F, Andreotti S, Ehrlich HC, 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 WE, Schilling O, Choudhary JS, Malmström L, Aebersold R, Reinert K, Kohlbacher O (2016). "OpenMS: a flexible open-source software platform for mass spectrometry data analysis" (PDF). Nat. Methods. 13 (9): 741–8. doi:10.1038/nmeth.3959. PMID 27575624. S2CID 873670.
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