Article | Published:

mProphet: automated data processing and statistical validation for large-scale SRM experiments

Nature Methods volume 8, pages 430435 (2011) | Download Citation

This article has been updated

Abstract

Selected reaction monitoring (SRM) is a targeted mass spectrometric method that is increasingly used in proteomics for the detection and quantification of sets of preselected proteins at high sensitivity, reproducibility and accuracy. Currently, data from SRM measurements are mostly evaluated subjectively by manual inspection on the basis of ad hoc criteria, precluding the consistent analysis of different data sets and an objective assessment of their error rates. Here we present mProphet, a fully automated system that computes accurate error rates for the identification of targeted peptides in SRM data sets and maximizes specificity and sensitivity by combining relevant features in the data into a statistical model.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Change history

  • 06 April 2011

    In the version of this article initially published online, a 'greater than' sign was inadvertently reversed, and an author contribution was incorrectly attributed. The error has been corrected for the print, PDF and HTML versions of this article.

References

  1. 1.

    , , & Selected reaction monitoring for quantitative proteomics: a tutorial. Mol. Syst. Biol. 4, 222 (2008).

  2. 2.

    , , , & Full dynamic range proteome analysis of S. cerevisiae by targeted proteomics. Cell 138, 795–806 (2009).

  3. 3.

    , , & Multiple reaction monitoring for robust quantitative proteomic analysis of cellular signaling networks. Proc. Natl. Acad. Sci. USA 104, 5860–5865 (2007).

  4. 4.

    & Quantitative mass spectrometric multiple reaction monitoring assays for major plasma proteins. Mol. Cell. Proteomics 5, 573–588 (2006).

  5. 5.

    et al. A quantitative targeted proteomics approach to validate predicted microRNA targets in C. elegans. Nat. Methods 7, 837–842 (2010).

  6. 6.

    & Statistical design of quantitative mass spectrometry-based proteomic experiments. J. Proteome Res. 8, 2144–2156 (2009).

  7. 7.

    et al. Multi-site assessment of the precision and reproducibility of multiple reaction monitoring-based measurements of proteins in plasma. Nat. Biotechnol. 27, 633–641 (2009).

  8. 8.

    et al. Integrated pipeline for mass spectrometry-based discovery and confirmation of biomarkers demonstrated in a mouse model of breast cancer. J. Proteome Res. 6, 3962–3975 (2007).

  9. 9.

    , , , & Quantitative, multiplexed assays for low abundance proteins in plasma by targeted mass spectrometry and stable isotope dilution. Mol. Cell. Proteomics 6, 2212–2229 (2007).

  10. 10.

    et al. Quantification of cardiovascular biomarkers in patient plasma by targeted mass spectrometry and stable isotope dilution. Mol. Cell. Proteomics 8, 2339–2349 (2009).

  11. 11.

    et al. Computational prediction of proteotypic peptides for quantitative proteomics. Nat. Biotechnol. 25, 125–131 (2007).

  12. 12.

    , & PeptideAtlas: a resource for target selection for emerging targeted proteomics workflows. EMBO Rep. 9, 429–434 (2008).

  13. 13.

    et al. Targeted quantitative analysis of Streptococcus pyogenes virulence factors by multiple reaction monitoring. Mol. Cell. Proteomics 7, 1489–1500 (2008).

  14. 14.

    et al. A database of mass spectrometric assays for the yeast proteome. Nat. Methods 5, 913–914 (2008).

  15. 15.

    , , & Prediction of high-responding peptides for targeted protein assays by mass spectrometry. Nat. Biotechnol. 27, 190–198 (2009).

  16. 16.

    et al. MaRiMba: a software application for spectral library-based MRM transition list assembly. J. Proteome Res. 8, 4396–4405 (2009).

  17. 17.

    et al. Skyline: an open source document editor for creating and analyzing targeted proteomics experiments. Bioinformatics 26, 966–968 (2010).

  18. 18.

    et al. Expediting the development of targeted SRM assays: using data from shotgun proteomics to automate method development. J. Proteome Res. 8, 2733–2739 (2009).

  19. 19.

    , , & Automated detection of inaccurate and imprecise transitions in peptide quantification by multiple reaction monitoring mass spectrometry. Clin. Chem. 56, 291–305 (2010).

  20. 20.

    et al. High sensitivity detection of plasma proteins by multiple reaction monitoring of N-glycosites. Mol. Cell. Proteomics 6, 1809–1817 (2007).

  21. 21.

    , , & A statistical model for identifying proteins by tandem mass spectrometry. Anal. Chem. 75, 4646–4658 (2003).

  22. 22.

    & Target-decoy search strategy for increased confidence in large-scale protein identifications by mass spectrometry. Nat. Methods 4, 207–214 (2007).

  23. 23.

    , , , & Semi-supervised learning for peptide identification from shotgun proteomics datasets. Nat. Methods 4, 923–925 (2007).

  24. 24.

    et al. Protein identification false discovery rates for very large proteomics data sets generated by tandem mass spectrometry. Mol. Cell. Proteomics 8, 2405–2417 (2009).

  25. 25.

    et al. High-throughput generation of selected reaction-monitoring assays for proteins and proteomes. Nat. Methods 7, 43–46 (2010).

  26. 26.

    , & Qscore: an algorithm for evaluating SEQUEST database search results. J. Am. Soc. Mass Spectrom. 13, 378–386 (2002).

  27. 27.

    , , & How specific is my SRM?: The issue of precursor and product ion redundancy. Proteomics 9, 1120–1123 (2009).

  28. 28.

    & Semisupervised model-based validation of peptide identifications in mass spectrometry-based proteomics. J. Proteome Res. 7, 254–265 (2008).

  29. 29.

    , & Peptide arrays on cellulose support: SPOT synthesis, a time and cost efficient method for synthesis of large numbers of peptides in a parallel and addressable fashion. Nat. Protoc. 2, 1333–1349 (2007).

  30. 30.

    et al. Coherent membrane supports for parallel microsynthesis and screening of bioactive peptides. Biopolymers 55, 188–206 (2000).

  31. 31.

    , , & Empirical statistical model to estimate the accuracy of peptide identifications made by MS/MS and database search. Anal. Chem. 74, 5383–5392 (2002).

  32. 32.

    , & Spectral probabilities and generating functions of tandem mass spectra: a strike against decoy databases. J. Proteome Res. 7, 3354–3363 (2008).

  33. 33.

    et al. Stable isotope labeling by amino acids in cell culture, SILAC, as a simple and accurate approach to expression proteomics. Mol. Cell. Proteomics 1, 376–386 (2002).

  34. 34.

    , , , & Absolute quantification of proteins and phosphoproteins from cell lysates by tandem MS. Proc. Natl. Acad. Sci. USA 100, 6940–6945 (2003).

  35. 35.

    et al. A common open representation of mass spectrometry data and its application to proteomics research. Nat. Biotechnol. 22, 1459–1466 (2004).

  36. 36.

    , , , & A uniform proteomics MS/MS analysis platform utilizing open XML file formats. Mol. Syst. Biol. 1, 2005.0017 (2005).

  37. 37.

    & Statistical significance for genomewide studies. Proc. Natl. Acad. Sci. USA 100, 9440–9445 (2003).

  38. 38.

    R Development Core Team. R: A Language and Environment for Statistical Computing (2008).

Download references

Acknowledgements

We thank J. Malmström and M. Jovanovic for providing the samples that were used as background matrix in the gold-standard data set, M. Jovanovic for careful reading of the manuscript, A. Srebniak for help in generating a software package, and H. Wenschuh. We acknowledge M. Claassen for discussions on machine learning. This work was supported by grants from the Forschungskredit of the University of Zurich, University of Zurich Research Priority Program in Systems Biology and Functional Genomics, GEBERT-RÜF Stiftung and Swiss National Science Foundation (grant 31000-10767), with funds from the US National Heart, Lung, and Blood Institute and the US National Institutes of Health (contract N01-HV-28179), and by SystemsX.ch, the Swiss initiative for systems biology.

Author information

Author notes

    • Paola Picotti
    •  & Martin Beck

    Present addresses: Institute of Biochemistry, Department of Biology, ETH Zurich, Zurich, Switzerland (P.P.) and European Molecular Biology Laboratory, Structural and Computational Biology Unit, Heidelberg, Germany (M.B.).

    • Lukas Reiter
    •  & Oliver Rinner

    These authors contributed equally to this work.

Affiliations

  1. Biognosys AG, Zurich, Switzerland.

    • Lukas Reiter
    •  & Oliver Rinner
  2. Institute of Molecular Systems Biology, Department of Biology, Swiss Federal Institute of Technology (ETH) Zurich, Zurich, Switzerland.

    • Lukas Reiter
    • , Oliver Rinner
    • , Paola Picotti
    • , Ruth Hüttenhain
    • , Martin Beck
    •  & Ruedi Aebersold
  3. Institute of Molecular Life Sciences, University of Zurich, Zurich, Switzerland.

    • Lukas Reiter
    •  & Michael O Hengartner
  4. PhD Program in Molecular Life Sciences Zurich, Zurich, Switzerland.

    • Lukas Reiter
  5. Competence Center for Systems Physiology and Metabolic Diseases, Zurich, Switzerland.

    • Ruth Hüttenhain
    •  & Ruedi Aebersold
  6. Institute for Systems Biology, Seattle, Washington, USA.

    • Mi-Youn Brusniak
  7. Faculty of Science, University of Zurich, Zurich, Switzerland.

    • Ruedi Aebersold

Authors

  1. Search for Lukas Reiter in:

  2. Search for Oliver Rinner in:

  3. Search for Paola Picotti in:

  4. Search for Ruth Hüttenhain in:

  5. Search for Martin Beck in:

  6. Search for Mi-Youn Brusniak in:

  7. Search for Michael O Hengartner in:

  8. Search for Ruedi Aebersold in:

Contributions

L.R., O.R., P.P., M.-Y.B. and R.A. designed the gold-standard data set. P.P. carried out the measurements on the gold-standard data set. L.R., O.R. and R.A. wrote the paper. L.R. and O.R. wrote the software and did the data analysis. L.R. did most of the statistical data analysis. R.H. contributed to the experiment involving the human plasma N-glycopeptide-enriched samples. M.B. contributed to the experiment involving the human u2os cell line. M.O.H. provided critical input on the project. R.A. supervised the project.

Competing interests

O.R. and L.R. are employees of Biognosys AG. This company funded parts of the work.

Corresponding author

Correspondence to Ruedi Aebersold.

Supplementary information

PDF files

  1. 1.

    Supplementary Text and Figures

    Supplementary Figures 1–12, Supplementary Table 1, Supplementary Results and Supplementary Note

Excel files

  1. 1.

    Supplementary Data 1

    Table of transitions, table of peak groups, table with identification statistics and classifier of the gold standard data set analysis. The transitions sheet contains the precursor m/z (Q1), fragment ion m/z (Q3), an id that groups the transitions according to precursor (transition group id), an id for the transition (transition id), a string describing the isotopic labeling of the peptide (isotype), the collision energy used (CE), the expected retention time used for scheduled SRM (tR), the expected relative intensity of the fragment ions (relative intensity %), a string indicating whether the transition is a decoy or target (decoy) and an id to group corresponding target and decoy transition groups (target decoy transition group id). The mProphet peak groups sheet contains a row for each peak group. The most important columns are an id for a transition group measurement (transition_group_record), the features used for scoring (all columns starting with main_var or var_), a column indicating the dilution of the synthetic peptides in the specific matrix (dilution), the species used for the background matrix (background), the class of the peak group in terms of identity as determined by the dilution alignment (real_class), a boolean indicating whether the peak group was derived from decoy or target transitions (real_decoy), a boolean indicating whether treated as decoy or target in the mProphet analysis (decoy) and the mProphet discrimination score (d_score). The mProphet all peak groups sheet contains the all peak groups of the analysis, not only the ones that rank highest in one transition group record (peak_group_rank). The mProphet stat sheet relates the mProphet discrimination score (cutoff) to the false discovery rate (FDR) and the sensitivity (sens). The mProphet classifier weight sheet contains the weights that were determined using the semi-supervised learning approach.

  2. 2.

    Supplementary Data 2

    Table of transitions, table of peak groups, table with identification statistics and classifier of the human u2os cell line analysis. For a detailed description of the sheets see Supplementary Data 1 legend.

  3. 3.

    Supplementary Data 3

    Table of transitions, table of peak groups, table with identification statistics and classifier of the human plasma analysis. For a detailed description of the sheets see Supplementary Data 1 legend.

  4. 4.

    Supplementary Data 4

    Table of transitions and peak groups for the measurement of yeast target and decoy transitions in human plasma. The transitions sheet contains target transitions of yeast peptides and corresponding decoy transitions generated by two different decoy transition generation algorithms (ADD_RANDOM and REVERSE_PEP_AND_INCREASE_Q1). The mQuest peak groups sheet contains the data processed with mQuest. The mProphet analysis does result in meaningful results since the data contains no positive target measurements. For a detailed description of the sheets see Supplementary Data 1 legend.

About this article

Publication history

Received

Accepted

Published

DOI

https://doi.org/10.1038/nmeth.1584

Further reading