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PeptideClassifier for protein inference and targeted quantitative proteomics

Nature Biotechnology volume 28, pages 647–650 (2010)Cite this article

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To the Editor:

Direct protein profiling offers unique insights beyond those afforded by transcriptomics or genomics technologies. These include information about the abundance level, post-translational modifications and interaction partners of proteins. Shotgun proteomics1 is the method of choice for the comprehensive analysis of complex protein mixtures, and extensive proteome coverage has recently been reported even for eukaryotic model organisms2,3. However, shotgun proteomics also faces significant challenges, such as the protein inference problem4. Protein inference refers to the process of deducing the proteins that were originally present in a sample on the basis of the experimentally identified peptides. Because significant amounts of shared peptides—peptides that could be derived from several proteins—introduce ambiguity, protein inference can be tricky and error prone. Furthermore, the error rate at the protein level is typically substantially higher than that at the peptide level4,5. Informatics solutions that provide accurate and reproducible results are thus needed to minimize the propagation of errors in the literature and in data repositories, and to allow readers to critically evaluate the conclusions of papers6,7.

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Figure 1: Schematic overview of where PeptideClassifier maps onto a shotgun proteomics workflow and selected applications.
Figure 2: Overview of the distinct peptide evidence classes of our classification scheme for eukaryotes and prokaryotes.

References

  1. Washburn, M.P., Wolters, D. & Yates, J.R. III. Nat. Biotechnol. 19, 242–247 (2001).

    Article  CAS  Google Scholar 

  2. Brunner, E. et al. Nat. Biotechnol. 25, 576–583 (2007).

    Article  CAS  Google Scholar 

  3. de Godoy, L.M. et al. Nature 455, 1251–1254 (2008).

    Article  CAS  Google Scholar 

  4. Nesvizhskii, A.I. & Aebersold, R. Mol. Cell. Proteomics 4, 1419–1440 (2005).

    Article  CAS  Google Scholar 

  5. Nesvizhskii, A.I., Keller, A., Kolker, E. & Aebersold, R. Anal. Chem. 75, 4646–4658 (2003).

    Article  CAS  Google Scholar 

  6. Carr, S. et al. Mol. Cell. Proteomics 3, 531–533 (2004).

    Article  CAS  Google Scholar 

  7. Nesvizhskii, A.I., Vitek, O. & Aebersold, R. Nat. Methods 4, 787–797 (2007).

    Article  CAS  Google Scholar 

  8. Grobei, M.A. et al. Genome Res. 19, 1786–1800 (2009).

    Article  CAS  Google Scholar 

  9. Resing, K.A. et al. Anal. Chem. 76, 3556–3568 (2004).

    Article  CAS  Google Scholar 

  10. Searle, B.C., Turner, M. & Nesvizhskii, A.I. J. Proteome Res. 7, 245–253 (2008).

    Article  CAS  Google Scholar 

  11. Ma, Z.Q. et al. J. Proteome Res. 8, 3872–3881 (2009).

    Article  CAS  Google Scholar 

  12. Elias, J.E. & Gygi, S.P. Nat. Methods 4, 207–214 (2007).

    Article  CAS  Google Scholar 

  13. Gupta, N. & Pevzner, P.A. J. Proteome Res. 8, 4173–4181 (2009).

    Article  CAS  Google Scholar 

  14. Ahrens, C.H., Brunner, E., Hafen, E., Aebersold, R. & Basler, K. Fly 1, 182–186 (2007).

    Article  Google Scholar 

  15. Anderson, N.L. et al. Mol. Cell. Proteomics 8, 883–886 (2009).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We thank C. Panse for contributing a first XML parser to extract information from database search engine output files, E. Brunner and G. Hausmann for feedback on the manuscript and K. Basler, U. Grossniklaus, R. Aebersold, M. Hengartner and J. Jiricny for continued support of the Quantitative Model Organism Proteomics bioinformatics core group. E.Q. and C.H.A. are members of the Quantitative Model Organism Proteomics Initiative, which is supported by the University Research Priority Program Systems Biology/Functional Genomics of the University of Zurich.

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Authors and Affiliations

  1. Quantitative Model Organism Proteomics, Institute of Molecular Life Sciences, University of Zürich, Winterthurerstrasse 190, 8057, Zürich, Switzerland

    Ermir Qeli & Christian H Ahrens

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  1. Ermir Qeli
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  2. Christian H Ahrens
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Correspondence to Christian H Ahrens.

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The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Tables 1–3, Supplementary Fig. 1 (PDF 329 kb)

Zipped file 1

Peptide Classifier_Auxiliary Scripts (ZIP 51001 kb)

Zipped file 2

Peptide Classifier_software code (ZIP 8160 kb)

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Qeli, E., Ahrens, C. PeptideClassifier for protein inference and targeted quantitative proteomics. Nat Biotechnol 28, 647–650 (2010). https://doi.org/10.1038/nbt0710-647

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