The Trans-Proteomic Pipeline (TPP) is an open-source data analysis software for proteomics developed at the Institute for Systems Biology (ISB) by the Ruedi Aebersold group under the Seattle Proteome Center. The TPP includes PeptideProphet,[2] ProteinProphet,[3] ASAPRatio, XPRESS and Libra.
| Developer(s) | Institute for Systems Biology |
|---|---|
| Initial release | 10 December 2004 |
| Stable release | 5.0.0
/ 11 October 2016[1] |
| Written in | C , Perl, Java |
| Operating system | Linux, Windows, OS X |
| Type | Bioinformatics / Mass spectrometry software |
| License | GPL v. 2.0 and LGPL |
| Website | TPP Wiki |
Software Components
editProbability Assignment and Validation
editPeptideProphet performs statistical validation of peptide-spectra-matches (PSM) using the results of search engines by estimating a false discovery rate (FDR) on PSM level.[4] The initial PeptideProphet used a fit of a Gaussian distribution for the correct identifications and a fit of a gamma distribution for the incorrect identification. A later modification of the program allowed the usage of a target-decoy approach, using either a variable component mixture model or a semi-parametric mixture model.[5] In the PeptideProphet, specifying a decoy tag will use the variable component mixture model while selecting a non-parametric model will use the semi-parametric mixture model.
ProteinProphet identifies proteins based on the results of PeptideProphet.[6]
Mayu performs statistical validation of protein identification by estimating a false discovery rate (FDR) on protein level.[7]
Spectral library handling
editThe SpectraST tool is able to generate spectral libraries and search datasets using these libraries.[8]
See also
editReferences
edit- ^ TPP 5.0.0 Release is Available
- ^ Software:PeptideProphet - SPCTools
- ^ Software:ProteinProphet - SPCTools
- ^ Keller, A; Nesvizhskii, A; Kolker, E; Aebersold, R. (2002). "Empirical statistical model to estimate the accuracy of peptide identifications made by MS/MS and database search". Anal Chem. 74 (20): 5383–5392. doi:10.1021/ac025747h. PMID 12403597.5383-5392&rft.date=2002&rft_id=info:doi/10.1021/ac025747h&rft_id=info:pmid/12403597&rft.aulast=Keller&rft.aufirst=A&rft.au=Nesvizhskii, A&rft.au=Kolker, E&rft.au=Aebersold, R.&rfr_id=info:sid/en.wikipedia.org:Trans-Proteomic Pipeline" class="Z3988">
- ^ Choi, Hyungwon; Ghosh, Debashis; Nesvizhskii, Alexey I. (2008). "Statistical Validation of Peptide Identifications in Large-Scale Proteomics Using the Target-Decoy Database Search Strategy and Flexible Mixture Modeling" (PDF). Journal of Proteome Research. 7 (1): 286–292. doi:10.1021/pr7006818. ISSN 1535-3893. PMID 18078310.286-292&rft.date=2008&rft.issn=1535-3893&rft_id=info:pmid/18078310&rft_id=info:doi/10.1021/pr7006818&rft.aulast=Choi&rft.aufirst=Hyungwon&rft.au=Ghosh, Debashis&rft.au=Nesvizhskii, Alexey I.&rft_id=http://www.stat.osu.edu/~statgen/joul_spr2011/Choi.pdf&rfr_id=info:sid/en.wikipedia.org:Trans-Proteomic Pipeline" class="Z3988">
- ^ Nesvizhskii AI, Keller A, Kolker E, Aebersold R. (2003) "A statistical model for identifying proteins by tandem mass spectrometry." Anal Chem 75:4646-58
- ^ Reiter, L.; Claassen, M.; Schrimpf, SP.; Jovanovic, M.; Schmidt, A.; Buhmann, JM.; Hengartner, MO.; Aebersold, R. (Nov 2009). "Protein identification false discovery rates for very large proteomics data sets generated by tandem mass spectrometry". Mol Cell Proteomics. 8 (11): 2405–17. doi:10.1074/mcp.M900317-MCP200. PMC 2773710. PMID 19608599.2405-17&rft.date=2009-11&rft_id=https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2773710#id-name=PMC&rft_id=info:pmid/19608599&rft_id=info:doi/10.1074/mcp.M900317-MCP200&rft.aulast=Reiter&rft.aufirst=L.&rft.au=Claassen, M.&rft.au=Schrimpf, SP.&rft.au=Jovanovic, M.&rft.au=Schmidt, A.&rft.au=Buhmann, JM.&rft.au=Hengartner, MO.&rft.au=Aebersold, R.&rft_id=https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2773710&rfr_id=info:sid/en.wikipedia.org:Trans-Proteomic Pipeline" class="Z3988">
- ^ Software:SpectraST - SPCTools