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

Nature Methods

Volumn 13, Issue 9, 2016, Pages 741-748

  Röst, Hannes L ^a,b   Sachsenberg, Timo ^c   Aiche, Stephan ^d,m   Bielow, Chris ^e,f   Weisser, Hendrik ^g   Aicheler, Fabian ^c   Andreotti, Sandro ^d   Ehrlich, Hans Christian ^d,m   Gutenbrunner, Petra ^d   Kenar, Erhan ^c   Liang, Xiao ^h   Nahnsen, Sven ^c   Nilse, Lars ⁱ   Pfeuffer, Julianus ^c   Rosenberger, George ^a   Rurik, Marc ^c   Schmitt, Uwe ^a   Veit, Johannes ^c   Walzer, Mathias ^c   Wojnar, David ^c   Wolski, Witold E ^a   Schilling, Oliver ⁱ   Choudhary, Jyoti S ^g   Malmström, Lars ^a,j   Aebersold, Ruedi ^a,j   Reinert, Knut ^d,k   Kohlbacher, Oliver ^c,l   more..

^a ETH ZURICH   (Switzerland)

^b STANFORD UNIVERSITY   (United States)

^c UNIVERSITY OF TÜBINGEN   (Germany)

^d FREIE UNIVERSITÄT BERLIN   (Germany)

^e MAX DELBRÜCK CENTER FOR MOLECULAR MEDICINE   (Germany)

^f BERLIN INSTITUTE OF HEALTH   (Germany)

^g WELLCOME TRUST SANGER INSTITUTE   (United Kingdom)

^h INTERNATIONAL MAX PLANCK RESEARCH SCHOOL FOR COMPUTATIONAL BIOLOGY AND SCIENTIFIC COMPUTING   (Germany)

ⁱ UNIVERSITY OF FREIBURG   (Germany)

^j UNIVERSITY OF ZURICH   (Switzerland)

^k MAX PLANCK INSTITUTE FOR MOLECULAR GENETICS   (Germany)

^l MAX PLANCK INSTITUTE FOR DEVELOPMENTAL BIOLOGY   (Germany)

^m SAP RESEARCH   (Germany)

more..

Author keywords

[No Author keywords available]

Indexed keywords

BIOMICS; DATA ANALYSIS; DATA PROCESSING; DEGRADOMICS; MASS SPECTROMETRY; OPENMS; PRIORITY JOURNAL; PROTEOGENOMICS; REVIEW; SOFTWARE; WORKFLOW; AGING; BIOLOGY; BLOOD; CHEMISTRY; HUMAN; INFORMATION PROCESSING; MOLECULAR GENETICS; PROCEDURES; PROTEOMICS;

PLASMA PROTEIN;

AGING; AUTOMATIC DATA PROCESSING; BLOOD PROTEINS; COMPUTATIONAL BIOLOGY; HUMANS; MASS SPECTROMETRY; MOLECULAR SEQUENCE ANNOTATION; PROTEOGENOMICS; PROTEOMICS; SOFTWARE; WORKFLOW;

EID: 84984878984     PISSN: 15487091     EISSN: 15487105     Source Type: Journal    
DOI: 10.1038/nmeth.3959     Document Type: Review

References (48)

1. Weisser, H. et al. An automated pipeline for high-throughput label-free quantitative proteomics. J. Proteome Res. 12, 1628-1644 (2013).
2. Martens, L. et al. mzML-a community standard for mass spectrometry data. Mol. Cell. Proteomics 10, R110.000133 (2011).
3. Walzer, M. et al. The mzQuantML data standard for mass spectrometry-based quantitative studies in proteomics. Mol. Cell. Proteomics 12, 2332-2340 (2013).
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).
5. Jones, A.R. et al. The mzIdentML data standard for mass spectrometry-based proteomics results. Mol. Cell. Proteomics 11, M111.014381 (2012).
6. Deutsch, E.W. et al. A guided tour of the trans-proteomic pipeline. Proteomics 10, 1150-1159 (2010).
7. Chambers, M.C. et al. A cross-platform toolkit for mass spectrometry and proteomics. Nat. Biotechnol. 30, 918-920 (2012).
8. Sturm, M. et al. OpenMS-an open-source software framework for mass spectrometry. BMC Bioinformatics 9, 163 (2008).
9. Vaudel, M. et al. PeptideShaker enables reanalysis of MS-derived proteomics data sets. Nat. Biotechnol. 33, 22-24 (2015).
10. Wang, R. et al. PRIDE Inspector: a tool to visualize and validate MS proteomics data. Nat. Biotechnol. 30, 135-137 (2012).
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).
15. Döring, A., Weese, D., Rausch, T. & Reinert, K. SeqAn an efficient, generic C. library for sequence analysis. BMC Bioinformatics 9, 11 (2008).
16. Walzer, M. et al. qcML: an exchange format for quality control metrics from mass spectrometry experiments. Mol. Cell. Proteomics 13, 1905-1913 (2014).
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).
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).
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).
20. Röst, H.L., Rosenberger, G., Aebersold, R. & Malmström, L. Efficient visualization of high-throughput targeted proteomics experiments: TAPIR. Bioinformatics 31, 2415-2417 (2015).
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).
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).
25. Aiche, S. et al. Workflows for automated downstream data analysis and visualization in large-scale computational mass spectrometry. Proteomics 15, 1443-1447 (2015).
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).
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).
28. Geer, L.Y. et al. Open mass spectrometry search algorithm. J. Proteome Res. 3, 958-964 (2004).
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).
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).
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).
32. Kenar, E. et al. Automated label-free quantification of metabolites from liquid chromatography-mass spectrometry data. Mol. Cell. Proteomics 13, 348-359 (2014).
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).
34. Röst, H.L. et al. OpenSWATH enables automated, targeted analysis of data-independent acquisition MS data. Nat. Biotechnol. 32, 219-223 (2014).
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).
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).
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).
38. Bielow, C., Aiche, S., Andreotti, S. & Reinert, K. MSSimulator: simulation of mass spectrometry data. J. Proteome Res. 10, 2922-2929 (2011).
39. Liu, Y. et al. Quantitative variability of 342 plasma proteins in a human twin population. Mol. Syst. Biol. 11, 786 (2015).
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).
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).
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).
43. Wright, J.C. et al. Improving GENCODE reference gene annotation using a high-stringency proteogenomics workflow. Nat. Commun. 7, 11778 (2016).
44. Harrow, J. et al. GENCODE: producing a reference annotation for ENCODE. Genome Biol. 7, S4.1-S4.9 (2006).
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).
46. Choi, M. et al. MSstats: an R package for statistical analysis of quantitative mass spectrometry-based proteomic experiments. Bioinformatics 30, 2524-2526 (2014).
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).
48. MacLean, B. et al. Skyline: an open source document editor for creating and analyzing targeted proteomics experiments. Bioinformatics 26, 966-968 (2010).