Trans-Proteomic Pipeline, a standardized data processing pipeline for large-scale reproducible proteomics informatics
Democratization of genomics technologies has enabled the rapid determination of genotypes. More recently the democratization of comprehensive proteomics technologies is enabling the determination of the cellular phenotype and the molecular events that define its dynamic state. Core proteomic technol...
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| Published in | Proteomics. Clinical applications Vol. 9; no. 7-8; pp. 745 - 754 |
|---|---|
| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Germany
Blackwell Publishing Ltd
01.08.2015
Wiley Subscription Services, Inc |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1862-8346 1862-8354 1862-8354 |
| DOI | 10.1002/prca.201400164 |
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| Abstract | Democratization of genomics technologies has enabled the rapid determination of genotypes. More recently the democratization of comprehensive proteomics technologies is enabling the determination of the cellular phenotype and the molecular events that define its dynamic state. Core proteomic technologies include MS to define protein sequence, protein:protein interactions, and protein PTMs. Key enabling technologies for proteomics are bioinformatic pipelines to identify, quantitate, and summarize these events. The Trans‐Proteomics Pipeline (TPP) is a robust open‐source standardized data processing pipeline for large‐scale reproducible quantitative MS proteomics. It supports all major operating systems and instrument vendors via open data formats. Here, we provide a review of the overall proteomics workflow supported by the TPP, its major tools, and how it can be used in its various modes from desktop to cloud computing. We describe new features for the TPP, including data visualization functionality. We conclude by describing some common perils that affect the analysis of MS/MS datasets, as well as some major upcoming features. |
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| AbstractList | Democratization of genomics technologies has enabled the rapid determination of genotypes. More recently the democratization of comprehensive proteomics technologies is enabling the determination of the cellular phenotype and the molecular events that define its dynamic state. Core proteomic technologies include mass spectrometry to define protein sequence, protein:protein interactions, and protein post-translational modifications. Key enabling technologies for proteomics are bioinformatic pipelines to identify, quantitate, and summarize these events. The Trans-Proteomics Pipeline (TPP) is a robust open-source standardized data processing pipeline for large-scale reproducible quantitative mass spectrometry proteomics. It supports all major operating systems and instrument vendors via open data formats. Here we provide a review of the overall proteomics workflow supported by the TPP, its major tools, and how it can be used in its various modes from desktop to cloud computing. We describe new features for the TPP, including data visualization functionality. We conclude by describing some common perils that affect the analysis of tandem mass spectrometry datasets, as well as some major upcoming features. Democratization of genomics technologies has enabled the rapid determination of genotypes. More recently the democratization of comprehensive proteomics technologies is enabling the determination of the cellular phenotype and the molecular events that define its dynamic state. Core proteomic technologies include MS to define protein sequence, protein:protein interactions, and protein PTMs. Key enabling technologies for proteomics are bioinformatic pipelines to identify, quantitate, and summarize these events. The Trans-Proteomics Pipeline (TPP) is a robust open-source standardized data processing pipeline for large-scale reproducible quantitative MS proteomics. It supports all major operating systems and instrument vendors via open data formats. Here, we provide a review of the overall proteomics workflow supported by the TPP, its major tools, and how it can be used in its various modes from desktop to cloud computing. We describe new features for the TPP, including data visualization functionality. We conclude by describing some common perils that affect the analysis of MS/MS datasets, as well as some major upcoming features. Democratization of genomics technologies has enabled the rapid determination of genotypes. More recently the democratization of comprehensive proteomics technologies is enabling the determination of the cellular phenotype and the molecular events that define its dynamic state. Core proteomic technologies include MS to define protein sequence, protein:protein interactions, and protein PTMs. Key enabling technologies for proteomics are bioinformatic pipelines to identify, quantitate, and summarize these events. The Trans-Proteomics Pipeline (TPP) is a robust open-source standardized data processing pipeline for large-scale reproducible quantitative MS proteomics. It supports all major operating systems and instrument vendors via open data formats. Here, we provide a review of the overall proteomics workflow supported by the TPP, its major tools, and how it can be used in its various modes from desktop to cloud computing. We describe new features for the TPP, including data visualization functionality. We conclude by describing some common perils that affect the analysis of MS/MS datasets, as well as some major upcoming features.Democratization of genomics technologies has enabled the rapid determination of genotypes. More recently the democratization of comprehensive proteomics technologies is enabling the determination of the cellular phenotype and the molecular events that define its dynamic state. Core proteomic technologies include MS to define protein sequence, protein:protein interactions, and protein PTMs. Key enabling technologies for proteomics are bioinformatic pipelines to identify, quantitate, and summarize these events. The Trans-Proteomics Pipeline (TPP) is a robust open-source standardized data processing pipeline for large-scale reproducible quantitative MS proteomics. It supports all major operating systems and instrument vendors via open data formats. Here, we provide a review of the overall proteomics workflow supported by the TPP, its major tools, and how it can be used in its various modes from desktop to cloud computing. We describe new features for the TPP, including data visualization functionality. We conclude by describing some common perils that affect the analysis of MS/MS datasets, as well as some major upcoming features. |
| Author | Deutsch, Eric W. Sun, Zhi Moritz, Robert L. Shteynberg, David Mendoza, Luis Slagel, Joseph |
| AuthorAffiliation | 1 Institute for Systems Biology, Seattle, WA, USA |
| AuthorAffiliation_xml | – name: 1 Institute for Systems Biology, Seattle, WA, USA |
| Author_xml | – sequence: 1 givenname: Eric W. surname: Deutsch fullname: Deutsch, Eric W. email: eric.deutsch@systemsbiology.org organization: Institute for Systems Biology, WA, Seattle, USA – sequence: 2 givenname: Luis surname: Mendoza fullname: Mendoza, Luis organization: Institute for Systems Biology, WA, Seattle, USA – sequence: 3 givenname: David surname: Shteynberg fullname: Shteynberg, David organization: Institute for Systems Biology, WA, Seattle, USA – sequence: 4 givenname: Joseph surname: Slagel fullname: Slagel, Joseph organization: Institute for Systems Biology, WA, Seattle, USA – sequence: 5 givenname: Zhi surname: Sun fullname: Sun, Zhi organization: Institute for Systems Biology, WA, Seattle, USA – sequence: 6 givenname: Robert L. surname: Moritz fullname: Moritz, Robert L. organization: Institute for Systems Biology, WA, Seattle, USA |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/25631240$$D View this record in MEDLINE/PubMed |
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| Keywords | Bioinformatics Mass spectrometry |
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| Notes | National Institute of Biomedical Imaging and Bioengineering - No. U54EB020406 istex:EB20B983CD858F97707C606A79F23C206E63C082 ark:/67375/WNG-WD0JRLJ3-0 National Institutes of Health from the NHGRI - No. RC2 HG005805 National Science Foundation MRI - No. 0923536 NIGMS - No. R01 GM087221 and 2P50 GM076547 ProteomeXchange" - No. 260558 ArticleID:PRCA1636 See the article online to view Figs. 1–3 in colour. Colour Online ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 ObjectType-Review-4 content type line 23 ObjectType-Undefined-3 |
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| PublicationTitle | Proteomics. Clinical applications |
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| Publisher | Blackwell Publishing Ltd Wiley Subscription Services, Inc |
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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. 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. Kessner, D., Chambers, M., Burke, R., Agus, D., Mallick, P., ProteoWizard: open source software for rapid proteomics tools development. Bioinformatics 2008, 24, 2534-2536. 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. 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. 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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. 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. 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. 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. 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. 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. 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. Craig, R., Beavis, R. C., TANDEM: matching proteins with tandem mass spectra. Bioinformatics 2004, 20, 1466-1467. Boja, E. S., Rodriguez, H., Regulatory considerations for clinical mass spectrometry: multiple reaction monitoring. Clin. Lab. Med. 2011, 31, 443-453. Knudsen, G. M., Chalkley, R. J., The effect of using an inappropriate protein database for proteomic data analysis. PloS One 2011, 6, e20873. 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. 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. 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. 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. Deutsch, E. W., Mendoza, L., Shteynberg, D., Farrah, T. et al., A guided tour of the Trans-Proteomic Pipeline. Proteomics 2010, 10, 1150-1159. 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. 2010; 10 2004; 20 2015; 14 2014; 513 2013; 48 2013; 3 2002; 74 2000; 25 2006; 57 2013; 41 2011; 31 2002; 1 2006; 7 2008; 9 2004; 3 2006; 5 2011; 10 2008; 5 2006; 3 2008; 33 2013; 5 2011; 6 2012; 11 2011; 8 2003; 75 2012; 30 2004; 32 2010; 26 2013; 10 2013; 13 2006; 22 2013; 12 1999; 17 2008; 26 2007; 6 2014; 13 2008; 24 2007; 7 2009; 8 2005; 1 2015 2007; 23 2010; 73 2014; 32 e_1_2_12_4_1 e_1_2_12_6_1 e_1_2_12_19_1 e_1_2_12_2_1 e_1_2_12_17_1 e_1_2_12_38_1 e_1_2_12_20_1 e_1_2_12_41_1 e_1_2_12_22_1 e_1_2_12_43_1 e_1_2_12_24_1 e_1_2_12_45_1 e_1_2_12_26_1 e_1_2_12_47_1 Deutsch E. W. (e_1_2_12_16_1) 2015 e_1_2_12_28_1 e_1_2_12_49_1 e_1_2_12_31_1 e_1_2_12_52_1 e_1_2_12_33_1 e_1_2_12_54_1 e_1_2_12_35_1 e_1_2_12_37_1 e_1_2_12_14_1 e_1_2_12_12_1 e_1_2_12_8_1 e_1_2_12_10_1 e_1_2_12_50_1 e_1_2_12_3_1 e_1_2_12_5_1 e_1_2_12_18_1 e_1_2_12_39_1 e_1_2_12_42_1 e_1_2_12_21_1 e_1_2_12_44_1 e_1_2_12_23_1 e_1_2_12_46_1 e_1_2_12_25_1 e_1_2_12_48_1 e_1_2_12_40_1 e_1_2_12_27_1 e_1_2_12_29_1 e_1_2_12_30_1 e_1_2_12_53_1 e_1_2_12_32_1 e_1_2_12_55_1 e_1_2_12_34_1 e_1_2_12_36_1 e_1_2_12_15_1 e_1_2_12_13_1 e_1_2_12_11_1 e_1_2_12_7_1 e_1_2_12_51_1 e_1_2_12_9_1 |
| References_xml | – reference: 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. – reference: 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. – reference: 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. – reference: 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. – reference: 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. – reference: 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. – reference: 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. – reference: 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. – reference: 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. – reference: Boja, E. S., Rodriguez, H., Regulatory considerations for clinical mass spectrometry: multiple reaction monitoring. Clin. Lab. Med. 2011, 31, 443-453. – reference: 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. – reference: 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. – reference: 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. – reference: 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. – reference: 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. – reference: 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. – reference: 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. – reference: 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. – reference: Craig, R., Beavis, R. C., TANDEM: matching proteins with tandem mass spectra. Bioinformatics 2004, 20, 1466-1467. – reference: 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. – reference: 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. – reference: Deutsch, E. W., Lam, H., Aebersold, R., PeptideAtlas: a resource for target selection for emerging targeted proteomics workflows. EMBO Rep. 2008, 9, 429-434. – reference: 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. – reference: 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. – reference: 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. – reference: Knudsen, G. M., Chalkley, R. J., The effect of using an inappropriate protein database for proteomic data analysis. PloS One 2011, 6, e20873. – reference: Deutsch, E. W., File formats commonly used in mass spectrometry proteomics. Mol. Cell. Proteomics 2012, 11, 1612-1621. – reference: Deutsch, E. W., Mendoza, L., Shteynberg, D., Farrah, T. et al., A guided tour of the Trans-Proteomic Pipeline. Proteomics 2010, 10, 1150-1159. – reference: 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. – reference: Kessner, D., Chambers, M., Burke, R., Agus, D., Mallick, P., ProteoWizard: open source software for rapid proteomics tools development. Bioinformatics 2008, 24, 2534-2536. – reference: 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. – reference: 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. – reference: Deutsch, E. W., Lam, H., Aebersold, R., Data analysis and bioinformatics tools for tandem mass spectrometry in proteomics. Physiol. Genomics 2008, 33, 18-25. – reference: 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. – reference: 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. – reference: 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. – reference: 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. – reference: Griss, J., Foster, J. M., Hermjakob, H., Vizcaino, J. A., PRIDE Cluster: building a consensus of proteomics data. Nat. Methods 2013, 10, 95-96. – reference: Ellis, M. J., Gillette, M., Carr, S. A., Paulovich, A. 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| Title | Trans-Proteomic Pipeline, a standardized data processing pipeline for large-scale reproducible proteomics informatics |
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