Quality assessments of peptide-spectrum matches in shotgun proteomics

Proteomics

Volumn 11, Issue 6, 2011, Pages 1086-1093

  Granholm, Viktor ^a   Käll, Lukas ^a  

^a STOCKHOLM UNIVERSITY   (Sweden)

Author keywords

Bioinformatics; High throughput proteomics; Statistical models; Tandem mass spectra

Indexed keywords

ARTICLE; PRIORITY JOURNAL; PROTEIN ANALYSIS; PROTEOMICS; QUALITY CONTROL; SCORING SYSTEM; SEARCH ENGINE; SPECTROSCOPY; STATISTICAL ANALYSIS; VARIANCE;

ANALYSIS OF VARIANCE; COMPUTATIONAL BIOLOGY; DATABASES, PROTEIN; MODELS, STATISTICAL; PEPTIDES; PROTEOMICS; QUALITY CONTROL; SEARCH ENGINE; TANDEM MASS SPECTROMETRY;

EID: 79952309062     PISSN: 16159853     EISSN: 16159861     Source Type: Journal    
DOI: 10.1002/pmic.201000432     Document Type: Article

References (35)

1. Eng, J. K., McCormack, A. L., Yates, J. R. An approach to correlate tandem mass spectral data of peptides with amino acid sequences in a protein database. J. Am. Soc. Mass Spectrom. 1994, 5, 976-989.
2. Perkins, D. N., Pappin, D. J. C., Creasy, D. M., Cottrell, J. S., Probability-based protein identification by searching sequence databases using mass spectrometry data. Electrophoresis 1999, 20, 3551-3567.
3. Craig, R., Beavis, R. C., TANDEM: matching proteins with tandem mass spectra. Bioinformatics 2004, 20, 1466-1467.
4. Geer, L. Y., Markey, S. P., Kowalak, J. A., Wagner, L. et al., Open mass spectrometry search algorithm. J. Proteome Res. 2004, 3, 958-964.
5. Park, C. Y., Käll, L., Klammer, A. A., MacCoss, M. J., Noble, W. S., Rapid and accurate peptide identification from tandem mass spectra. J. Proteome Res. 2008, 7, 3022.
6. Karlin, S., Altschul, S. F., Methods for assessing the statistical significance of molecular sequence features by using general scoring schemes. Proc. Natl. Acad. Sci. USA 1990, 87, 2264.
7. Sorić, B., Statistical "discoveries" and effect-size estimation. J. Am. Stat. Assoc. 1989, 84, 608-610.
8. Benjamini, Y., Hochberg, Y., Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R Stat. Soc. B (Methodol.) 1995, 57, 289-300.
9. Storey, J. D., Tibshirani, R., Statistical significance for genomewide studies. Proc. Natl. Acad. Sci. USA 2003, 100, 9440.
10. Käll, L., Storey, J. D., MacCoss, M. J., Noble, W. S., Posterior error probabilities and false discovery rates: two sides of the same coin. J. Proteome Res. 2008, 7, 40-44.
11. Efron, B., Tibshirani, R., Storey, J. D., Tusher, V., Empirical Bayes analysis of a microarray experiment. J. Am. Stat. Assoc. 2001, 96, 1151-1160.
12. Käll, L., Storey, J. D., Noble, W. S., Non-parametric estimation of posterior error probabilities associated with peptides identified by tandem mass spectrometry. Bioinformatics 2008, 24, i42.
13. Käll, L., Canterbury, J. D., Weston, J., Noble, W. S., MacCoss, M. J., Semi-supervised learning for peptide identification from shotgun proteomics datasets. Nat. Methods 2007, 4, 923-925.
14. Kall, L., Storey, J. D., Noble, W. S., QVALITY: non-parametric estimation of q-values and posterior error probabilities. Bioinformatics 2009, 25, 964.
15. Davison, A. C., Hinkley, D. V., Bootstrap methods and their application. Cambridge University Press, Cambridge 1997.
16. Fenyö, D., Beavis, R. C., A method for assessing the statistical significance of mass spectrometry-based protein identifications using general scoring schemes. Anal. Chem. 2003, 75, 768-774.
17. Klammer, A. A., Park, C. Y., Noble, W. S., Statistical calibration of the SEQUEST XCorr function. J. Proteome Res. 2009, 8, 2106-2113.
18. Kim, S., Gupta, N., Pevzner, P. A., Spectral probabilities and generating functions of tandem mass spectra: a strike against decoy databases. J. Proteome Res. 2008, 7, 3354-3363.
19. Kim, S., Mischerikow, N., Bandeira, N., Navarro, J. D. et al., The generating function of CID, ETD and CID/ETD pairs of tandem mass spectra: applications to database search. Mol. Cell. Proteomics 2010.
20. Dempster, A. P., Laird, N. M., Rubin, D. B., Maximum likelihood from incomplete data via the EM algorithm. J. R Stat. Soc. B (Methodol.) 1977, 39, 1-38.
21. 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.
22. Choi, H., Ghosh, D., Nesvizhskii, A. I., Statistical validation of peptide identifications in large-scale proteomics using the target-decoy database search strategy and flexible mixture modeling. J. Proteome Res. 2008, 7, 286.
23. Choi, H., Nesvizhskii, A. I., Semisupervised model-based validation of peptide identifications in mass spectrometry-based proteomics. J. Proteome Res. 2008, 7, 254.
24. Moore, R. E., Young, M. K., Lee, T. D., Qscore: an algorithm for evaluating SEQUEST database search results. J. Am. Soc. Mass Spectrom. 2002, 13, 378-386.
25. Storey, J. D., The positive false discovery rate: a Bayesian interpretation and the q-value. Ann. Stat. 2003, 31, 2013-2035.
26. Peng, J., Elias, J. E., Thoreen, C. C., Licklider, L. J., Gygi, S. P., Evaluation of multidimensional chromatography coupled with tandem mass spectrometry (LC/LC-MS/MS) for large-scale protein analysis: the yeast proteome. J. Proteome Res. 2003, 2, 43-50.
27. Elias, J. E., Gygi, S. P., Target-decoy search strategy for increased confidence in large-scale protein identifications by mass spectrometry. Nat. Methods 2007, 4, 207-214.
28. Elias, J. E., Gygi, S. P., Target-decoy search strategy for mass spectrometry-based proteomics. Methods Mol. Biol. (Clifton, NJ) 2010, 604, 55.
29. Fitzgibbon, M., Li, Q., McIntosh, M., Modes of inference for evaluating the confidence of peptide identifications. J. Proteome Res. 2008, 7, 35-39.
30. Klammer, A. A., MacCoss, M. J., Effects of modified digestion schemes on the identification of proteins from complex mixtures. J. Proteome Res. 2006, 5, 695-700.
31. Colinge, J., Masselot, A., Giron, M., Dessingy, T., Magnin, J., OLAV: towards high-throughput tandem mass spectrometry data identification. Proteomics 2003, 3, 1454-1463.
32. Feng, J., Naiman, D. Q., Cooper, B., Probability-based pattern recognition and statistical framework for randomization: modeling tandem mass spectrum/peptide sequence false match frequencies. Bioinformatics 2007, 23, 2210.
33. Huttlin, E. L., Hegeman, A. D., Harms, A. C., Sussman, M. R., Prediction of error associated with false-positive rate determination for peptide identification in large-scale proteomics experiments using a combined reverse and forward peptide sequence database strategy. J. Proteome Res. 2007, 6, 392-398.
34. Venable, J. D., Yates, J. R., Impact of ion trap tandem mass spectra variability on the identification of peptides. Anal. Chem. 2004, 76, 2928-2937.
35. Spivak, M., Weston, J., Bottou, L., Käll, L., Noble, W. S., Improvements to the percolator algorithm for peptide identification from shotgun proteomics data sets. J. Proteome Res. 2009, 8, 3737-3745.