Trans-Proteomic Pipeline

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Developer(s) Institute for Systems Biology
Initial release 10 December 2004; 9 years ago (2004-12-10)
Stable release 4.6.2 / 15 February 2013; 16 months ago (2013-02-15)[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

The Trans-Proteomic Pipeline (TPP) is a widely used freely available open-source proteomics data analysis pipeline developed at the Institute for Systems Biology (ISB) by the Ruedi Aebersold group under the Seattle Proteome Center. The TPP includes PeptideProphet, ProteinProphet, ASAPRatio, XPRESS and Libra.

Please see the TPP Wiki for the most up-to-date information, including download information.

The TPP is directly available from the Sashimi project. TPP components are also included in other commercial and non-commercial systems, including:

Software Components[edit]

Probability Assignment and Validation[edit]

PeptideProphet: Allows for statistical validation of peptide-spectra-matches (PSM) using the results of search engines by estimating an False Discovery Rate (FDR) on PSM level.[2] 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.[3] 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.[4]

ProteinProphet: Allows to identify proteins based on the results of PeptideProphet.[5]

Mayu: Allows for statistical validation of protein identification by estimating an False Discovery Rate (FDR) on protein level.[6]

Spectral Library Handling[edit]

The SpectraST tool is able to generate spectral libraries and search datasets using these libraries. For its documentation, see the TPP Wiki page.

See also[edit]


  1. ^ TPP 4.6.2 Release is Available
  2. ^ Keller, A.; Nesvizhskii, AI.; Kolker, E.; Aebersold, R. (Oct 2002). "Empirical statistical model to estimate the accuracy of peptide identifications made by MS/MS and database search.". Anal Chem 74 (20): 5383–92. doi:10.1021/ac025747h. PMID 12403597. 
  3. ^ Choi, H.; Ghosh, D.; Nesvizhskii, AI. (Jan 2008). "Statistical validation of peptide identifications in large-scale proteomics using the target-decoy database search strategy and flexible mixture modeling.". J Proteome Res 7 (1): 286–92. doi:10.1021/pr7006818. PMID 18078310. 
  4. ^ "Re: [spctools-discuss] Re: PeptideProphet algorithm documentation?". Retrieved 1 July 2013. "Hi John, Yes, the semi-supervised and semi-paramteric features from Alexey's papers are implemented. Specifying a decoy tag to PeptideProphet makes it semi-supervised and selecting the non-parametric model makes it semi-parametric; the one remaining parameter in the distributions is the apportionment of non-decoy hits among the positive and negative distributions (which in this mode do not follow the shape of a predetermined parametric distribution e.g. Gamma for negatives and Gaussian for positives, but are learned using Kernel Density Estimates)." 
  5. ^ Nesvizhskii, AI.; Keller, A.; Kolker, E.; Aebersold, R. (Sep 2003). "A statistical model for identifying proteins by tandem mass spectrometry.". Anal Chem 75 (17): 4646–58. doi:10.1021/ac0341261. PMID 14632076. 
  6. ^ 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. PMID 19608599. 


External links[edit]