OpenMS has tools for analysis of proteomics data, 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). OpenMS also supports metabolomics workflows and targeted analysis of DIA/SWATH data.[2] Furthermore, OpenMS provides tools for the analysis of cross linking data, including protein-protein, protein-RNA and protein-DNA cross linking. Lastly, OpenMS provides tools for analysis of RNA mass spectrometry data.
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.[4][5]
In 2009, the visualization tool TOPPView was published[6] and in 2012, the workflow manager and editor TOPPAS was described.[7] In 2013, a complete high-throughput label-free analysis pipeline using OpenMS 1.8 was described and compared with similar, proprietary software (such as MaxQuant and Progenesis QI). 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 [...]".[8]
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.[9] The OpenMS 1.11 release was the first release to contain fully integrated bindings to the Python programming language (termed pyOpenMS).[10] In addition, new tools were added to support QcML (for quality control) and for metabolomics accurate mass analysis. Multiple tools were significantly improved with regard to memory and CPU performance.[11]
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 improvements in the kernel allow for faster access to data stored in mzML and provide a novel API for accessing mass spectrometric data.[12] In 2016, the new features of OpenMS 2.0 were described in an article in Nature Methods.[2]
In 2024, OpenMS 3.0[3] was released, providing support for a wide array of data analysis task in proteomics, metabolomics and MS-based transcriptomics.
OpenMS provides a set of over 100 different executable tools than can be chained together into pipelines for mass spectrometry 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).
The following graphical applications are part an OpenMS release:
TOPPView is a viewer 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). OpenMS is compatible with current and upcoming Proteomics Standard Initiative (PSI) formats for mass spectrometric data.
TOPPAS is a graphical application to build and execute data processing pipelines which consist of TOPP tools.
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
^ abPfeuffer J, Bielow C, Wein S, Jeong K, Netz E, Walter A, Alka O, Nilse L, Colaianni PD, McCloskey D, Kim J, Rosenberger G, Bichmann L, Walzer M, Veit J, Boudaud B, Bernt M, Patikas N, Pilz M, Startek MP, Kutuzova S, Heumos L, Charkow J, Sing JC, Feroz A, Siraj A, Weisser H, Dijkstra TM, Perez-Riverol Y, Röst H, Kohlbacher O, Sachsenberg T (2024). "OpenMS 3 enables reproducible analysis of large-scale mass spectrometry data". Nat. Methods. 21 (3): 365–67. doi:10.1038/s41592-024-02197-7. PMID38366242.
^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. PMID19425593.
^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. PMID22583024.
^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. PMID23391308.
