Trans-Proteomic Pipeline, a standardized data processing pipeline for large-scale reproducible proteomics informatics

Proteomics - Clinical Applications

Volumn 9, Issue 7-8, 2015, Pages 745-754

  Deutsch, Eric W ^a   Mendoza, Luis ^a   Shteynberg, David ^a   Slagel, Joseph ^a   Sun, Zhi ^a   Moritz, Robert L ^a  

^a INSTITUTE FOR SYSTEMS BIOLOGY   (United States)

Author keywords

Bioinformatics; Mass spectrometry

Indexed keywords

PROTEOME;

AMINO ACID SEQUENCE; AUTOMATION; BIOINFORMATICS; COMPUTER INTERFACE; DATA ANALYSIS; GENE ONTOLOGY; GENOMICS; GENOTYPE; INFORMATION SYSTEM; LABORATORY INFORMATION MANAGEMENT SYSTEM; MASS SPECTROMETRY; PRIORITY JOURNAL; PROTEIN ANALYSIS; PROTEIN DATABASE; PROTEIN EXPRESSION; PROTEIN PROTEIN INTERACTION; PROTEOMICS; REVIEW; RNA SEQUENCE; BIOLOGY; HUMAN; METABOLISM; PROCEDURES; REPRODUCIBILITY; SOFTWARE; STATISTICS;

COMPUTATIONAL BIOLOGY; HUMANS; PROTEOME; PROTEOMICS; REPRODUCIBILITY OF RESULTS; SOFTWARE; STATISTICS AS TOPIC;

EID: 84948987628     PISSN: 18628346     EISSN: 18628354     Source Type: Journal    
DOI: 10.1002/prca.201400164     Document Type: Review

References (54)

1. Reiter, L., Rinner, O., Picotti, P., Huttenhain, R. et al., mProphet: automated data processing and statistical validation for large-scale SRM experiments. Nat. Methods 2011, 8, 430-435.
2. Li, X. J., Hayward, C., Fong, P. Y., Dominguez, M. et al., A blood-based proteomic classifier for the molecular characterization of pulmonary nodules. Sci. Transl. Med. 2013, 5, 207ra142.
3. Boja, E. S., Rodriguez, H., Regulatory considerations for clinical mass spectrometry: multiple reaction monitoring. Clin. Lab. Med. 2011, 31, 443-453.
4. Boja, E. S., Fehniger, T. E., Baker, M. S., Marko-Varga, G., Rodriguez, H., Analytical validation considerations of multiplex mass spectrometry-based proteomic platforms for measuring protein biomarkers. J. Proteome Res. 2014, 13, 5325-5332.
5. Theis, J. D., Dasari, S., Vrana, J. A., Kurtin, P. J., Dogan, A., Shotgun-proteomics-based clinical testing for diagnosis and classification of amyloidosis. J. Mass Spectrom. 2013, 48, 1067-1077.
6. Nesvizhskii, A. I., A survey of computational methods and error rate estimation procedures for peptide and protein identification in shotgun proteomics. J. Proteomics 2010, 73, 2092-2123.
7. Cox, J., Mann, M., MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat. Biotechnol. 2008, 26, 1367-1372.
8. Kohlbacher, O., Reinert, K., Gropl, C., Lange, E. et al., TOPP-the OpenMS proteomics pipeline. Bioinformatics 2007, 23, e191-197.
9. Keller, A., Eng, J., Zhang, N., Li, X. J., Aebersold, R., A uniform proteomics MS/MS analysis platform utilizing open XML file formats. Mol. Syst. Biol. 2005, 1, 2005.0017.
10. Deutsch, E. W., Mendoza, L., Shteynberg, D., Farrah, T. et al., A guided tour of the Trans-Proteomic Pipeline. Proteomics 2010, 10, 1150-1159.
11. Deutsch, E. W., Lam, H., Aebersold, R., Data analysis and bioinformatics tools for tandem mass spectrometry in proteomics. Physiol. Genomics 2008, 33, 18-25.
12. MacLean, B., Tomazela, D. M., Shulman, N., Chambers, M. et al., Skyline: an open source document editor for creating and analyzing targeted proteomics experiments. Bioinformatics 2010, 26, 966-968.
13. Kessner, D., Chambers, M., Burke, R., Agus, D., Mallick, P., ProteoWizard: open source software for rapid proteomics tools development. Bioinformatics 2008, 24, 2534-2536.
14. Chambers, M. C., Maclean, B., Burke, R., Amodei, D. et al., A cross-platform toolkit for mass spectrometry and proteomics. Nat. Biotechnol. 2012, 30, 918-920.
15. Deutsch, E. W., Albar, J. P., Binz, P.-A., Eisenacher, M. et al., Development of Data Representation Standards by the Human Proteome Organization Proteomics Standards Initiative. JAMIA 2015, accepted.
16. Martens, L., Chambers, M., Sturm, M., Kessner, D. et al., mzML-a community standard for mass spectrometry data. Mol. Cell. Proteomics 2011, 10, R110 000133.
17. Deutsch, E. W., File formats commonly used in mass spectrometry proteomics. Mol. Cell. Proteomics 2012, 11, 1612-1621.
18. Craig, R., Beavis, R. C., TANDEM: matching proteins with tandem mass spectra. Bioinformatics 2004, 20, 1466-1467.
19. MacLean, B., Eng, J. K., Beavis, R. C., McIntosh, M., General framework for developing and evaluating database scoring algorithms using the TANDEM search engine. Bioinformatics 2006, 22, 2830-2832.
20. Eng, J. K., Jahan, T. A., Hoopmann, M. R., Comet: an open source tandem mass spectrometry sequence database search tool. Proteomics 2013, 13, 22-24.
21. Eng, J. K., Searle, B. C., Clauser, K. R., Tabb, D. L., A face in the crowd: recognizing peptides through database search. Mol. Cell. Proteomics 2011, 10, R111 009522.
22. Lam, H., Deutsch, E. W., Eddes, J. S., Eng, J. K. et al., Development and validation of a spectral library searching method for peptide identification from MS/MS. Proteomics 2007, 7, 655-667.
23. Lam, H., Deutsch, E. W., Eddes, J. S., Eng, J. K. et al., Building consensus spectral libraries for peptide identification in proteomics. Nat. Methods 2008, 5, 873-875.
24. Keller, A., Nesvizhskii, A. I., Kolker, E., Aebersold, R., Empirical statistical model to estimate the accuracy of peptide identifications made by MS/MS and database search. Anal. Chem. 2002, 74, 5383-5392.
25. Shteynberg, D., Deutsch, E. W., Lam, H., Eng, J. K. et al., iProphet: multi-level integrative analysis of shotgun proteomic data improves peptide and protein identification rates and error estimates. Mol. Cell. Proteomics 2011, 10, M111 007690.
26. Shteynberg, D., Mendoza, L., Sun, Z., Moritz, R. L., Deutsch, E. W., PTMProphet: statistical analysis of post-translational modification localization for shotgun proteomics datasets. 2015, in preparation.
27. Pedrioli, P. G., Raught, B., Zhang, X. D., Rogers, R. et al., Automated identification of SUMOylation sites using mass spectrometry and SUMmOn pattern recognition software. Nat. Methods 2006, 3, 533-539.
28. Zieske, L. R., A perspective on the use of iTRAQ reagent technology for protein complex and profiling studies. J. Exp. Bot. 2006, 57, 1501-1508.
29. Gygi, S. P., Rist, B., Gerber, S. A., Turecek, F. et al., Quantitative analysis of complex protein mixtures using isotope-coded affinity tags. Nat. Biotechnol. 1999, 17, 994-999.
30. Ong, S. E., Blagoev, B., Kratchmarova, I., Kristensen, D. B. et al., Stable isotope labeling by amino acids in cell culture, SILAC, as a simple and accurate approach to expression proteomics. Mol. Cell. Proteomics 2002, 1, 376-386.
31. Li, X. J., Zhang, H., Ranish, J. A., Aebersold, R., Automated statistical analysis of protein abundance ratios from data generated by stable-isotope dilution and tandem mass spectrometry. Anal. Chem. 2003, 75, 6648-6657.
32. Nesvizhskii, A. I., Keller, A., Kolker, E., Aebersold, R., A statistical model for identifying proteins by tandem mass spectrometry. Anal. Chem. 2003, 75, 4646-4658.
33. Marzolf, B., Deutsch, E. W., Moss, P., Campbell, D. et al., SBEAMS-Microarray: database software supporting genomic expression analyses for systems biology. BMC Bioinformatics 2006, 7, 286.
34. Trudgian, D. C., Mirzaei, H., Cloud CPFP: a shotgun proteomics data analysis pipeline using cloud and high performance computing. J. Proteome Res. 2012, 11, 6282-6290.
35. Rauch, A., Bellew, M., Eng, J., Fitzgibbon, M. et al., Computational Proteomics Analysis System (CPAS): an extensible, open-source analytic system for evaluating and publishing proteomic data and high throughput biological experiments. J. Proteome Res. 2006, 5, 112-121.
36. Shifman, M. A., Li, Y., Colangelo, C. M., Stone, K. L. et al., YPED: a web-accessible database system for protein expression analysis. J. Proteome Res. 2007, 6, 4019-4024.
37. Deutsch, E. W., Lam, H., Aebersold, R., PeptideAtlas: a resource for target selection for emerging targeted proteomics workflows. EMBO Rep. 2008, 9, 429-434.
38. Farrah, T., Deutsch, E. W., Hoopmann, M. R., Hallows, J. L. et al., The state of the human proteome in 2012 as viewed through PeptideAtlas. J. Proteome Res. 2013, 12, 162-171.
39. Griss, J., Foster, J. M., Hermjakob, H., Vizcaino, J. A., PRIDE Cluster: building a consensus of proteomics data. Nat. Methods 2013, 10, 95-96.
40. Craig, R., Cortens, J. P., Beavis, R. C., Open source system for analyzing, validating, and storing protein identification data. J. Proteome Res. 2004, 3, 1234-1242.
41. Vizcaino, J. A., Deutsch, E. W., Wang, R., Csordas, A. et al., ProteomeXchange provides globally coordinated proteomics data submission and dissemination. Nat. Biotechnol. 2014, 32, 223-226.
42. Wolstencroft, K., Haines, R., Fellows, D., Williams, A. et al., The Taverna workflow suite: designing and executing workflows of Web Services on the desktop, web or in the cloud. Nucleic Acids Res. 2013, 41, W557-W561.
43. Jones, A. R., Eisenacher, M., Mayer, G., Kohlbacher, O. et al., The mzIdentML data standard for mass spectrometry-based proteomics results. Mol. Cell. Proteomics 2012, 11, M111 014381.
44. Walzer, M., Qi, D., Mayer, G., Uszkoreit, J. et al., The mzQuantML data standard for mass spectrometry-based quantitative studies in proteomics. Mol. Cell. Proteomics 2013, 12, 2332-2340.
45. Griss, J., Jones, A. R., Sachsenberg, T., Walzer, M. et al., The mzTab data exchange format: communicating MS-based proteomics and metabolomics experimental results to a wider audience. Mol. Cell. Proteomics 2014, 13, 2765-2775.
46. Slagel, J., Mendoza, L., Shteynberg, D., Deutsch, E. W., Moritz, R. L., Processing shotgun proteomics data on the Amazon Cloud with the Trans-Proteomic Pipeline. Mol. Cell. Proteomics 2015, 14, 399-404.
47. Apweiler, R., Bairoch, A., Wu, C. H., Barker, W. C. et al., UniProt: the Universal Protein knowledgebase. Nucleic Acids Res. 2004, 32, D115-D119.
48. Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D. et al., Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat. Genet. 2000, 25, 25-29.
49. Ellis, M. J., Gillette, M., Carr, S. A., Paulovich, A. G. et al., Connecting genomic alterations to cancer biology with proteomics: the NCI Clinical Proteomic Tumor Analysis Consortium. Cancer Discov. 2013, 3, 1108-1112.
50. Zhang, B., Wang, J., Wang, X., Zhu, J. et al., Proteogenomic characterization of human colon and rectal cancer. Nature 2014, 513, 382-387.
51. Knudsen, G. M., Chalkley, R. J., The effect of using an inappropriate protein database for proteomic data analysis. PloS One 2011, 6, e20873.
52. Reiter, L., Claassen, M., Schrimpf, S. P., Jovanovic, M. et al., Protein identification false discovery rates for very large proteomics data sets generated by tandem mass spectrometry. Mol. Cell. Proteomics 2009, 8, 2405-2417.
53. Farrah, T., Deutsch, E. W., Omenn, G. S., Campbell, D. S. et al., A high-confidence human plasma proteome reference set with estimated concentrations in PeptideAtlas. Mol. Cell. Proteomics 2011, 10, M110 006353.
54. Gillet, L. C., Navarro, P., Tate, S., Rost, H. 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 2012, 11, O111 016717.