Charge state determination of peptide tandem mass spectra using support vector machine (SVM)

IEEE Transactions on Information Technology in Biomedicine

Volumn 14, Issue 3, 2010, Pages 552-558

  Zou, An Min ^a   Shi, Jinhong ^b   Ding, Jiarui ^c   Wu, Fang Xiang ^b  

^a RYERSON UNIVERSITY   (Canada)

^b UNIVERSITY OF SASKATCHEWAN   (Canada)

^c UNIVERSITY OF BRITISH COLUMBIA   (Canada)

Author keywords

Charge state determination; Peptide; Support vector machine (SVM); Tandem mass spectra

Indexed keywords

CHARGE STATE; CHARGE STATE DETERMINATION; COMPUTATIONAL EXPERIMENT; DATA SETS; FALSE POSITIVE; HIGH RESOLUTION; ION TRAPS; PEPTIDE IONS; QUADRUPOLES; SEARCH CANDIDATES; SEARCH TIME; SUPPORT VECTOR MACHINE TECHNIQUES; SVM-BASED CLASSIFIERS; TANDEM MASS SPECTRA; TIME-OF-FLIGHT INSTRUMENTS;

CLASSIFIERS; INFORMATION RETRIEVAL; ION ENGINES; IONS; MASS SPECTROMETERS; MASS SPECTROMETRY; SEARCH ENGINES; SOOT BLOWERS; SUPPORT VECTOR MACHINES; VECTORS; WORLD WIDE WEB;

PEPTIDES;

PEPTIDE;

ALGORITHM; ARTICLE; CHEMISTRY; METHODOLOGY; NONLINEAR SYSTEM; PROTEIN DATABASE; PROTEOMICS; TANDEM MASS SPECTROMETRY;

ALGORITHMS; DATABASES, PROTEIN; NONLINEAR DYNAMICS; PEPTIDES; PROTEOMICS; TANDEM MASS SPECTROMETRY;

EID: 77953144851     PISSN: 10897771     EISSN: None     Source Type: Journal    
DOI: 10.1109/TITB.2010.2040287     Document Type: Article

References (21)

1. J.K. Eng, A. L. McCormack, and J. R.Yates, III, "An approach to correlate tandem mass spectral data of peptides with amino acid sequences in a protein database," Amer. Soc. Mass Spectrom., vol.5, pp. 976-989, 1994.
2. D. N. Perkins, D. J. C. Pappin, D. M. Creasy, and J. S. Cottrell, "Probability-based protein identification by searching sequence database using mass spectrometry data," Electrophoresis, vol.20, pp. 3551-3567, 1999.
3. B. Lu and T. Chen, "Algorithms for de novo peptide sequencing using tandemmass spectrometry," Drug Discov. Today, BioSilico, vol.2, pp. 85- 90, 2004.
4. P. James, Ed., Proteome Research: Mass Spectrometry. Berlin, Germany: Springer-Verlag, 2001.
5. H. Steen andM.Mann, "The ABC's (and XYZ's) of peptide sequencing," Nature Rev. Mol. Cell Biol., vol.5, pp. 699-711, 2004.
6. D. L. Tabb, J. K. Eng, and J. R. Yates, III, "Protein identification by SEQUEST," in Proteome Research: Mass Spectrometry, P. James, Ed. Berlin, Germany: Springer-Verlag, 2001.
7. R.G. Sadygov, J. Eng, E. Durr,A. Saraf,H.McDonald,M. J.MacCoss, and J. R.Yates, III, "Code developments to improve the efficiency of automated MS/MS spectra interpretation," J. Proteome Res., vol.1, pp. 211-215, 2002.
8. A. A. Klammer, C. C. Wu, M. J. MacCoss, and W. S. Noble, "Peptide charge state determination for low-resolution tandem mass spectra," in Proc. IEEE Comput. Syst. Bioinformatics Conf. (CSB 2005), pp. 175- 185.
9. S. Na, E. Paek, and C. Lee, "CIFTER: Automated charge-state determination for peptide tandem mass spectra," Anal. Chem., vol.80, pp. 1520- 1528, 2008.
10. M. Bern, D. Goldberg, W. H. McDonald, and J. R. Yates, III, "Automatic quality assessment of peptide tandem mass spectra," Bioinformatics, vol.20, pp. i49-i54, 2004.
11. F. X. Wu, P. Gagne, A. Droit, and G. G. Poirier, "Quality assessment of peptide tandem mass spectra," BMC Bioinformatics, vol.9, pp. S6-S13, 2008.
12. L. Vincent, "Morphological grayscale reconstruction in image analysis: application and efficient algorithm," IEEE Trans. Image Process., vol.2, no.2, pp. 176-201, Apr. 1993.
13. A. B. A. Graf, A. J. Smola, and S. Borer, "Classification in a normalized feature space using support vector machines," IEEE Trans. Neural Netw., vol.14, no.3, pp. 597-605, May 2003.
14. S. Ali and K. A. Smith-Miles, "Improved support vector machine generalization using normalized input space," Lect. Notes Comput. Sci., Adv. Art. Intell., vol.4304, pp. 362-371, 2006.
15. V. Vapnik, Statistical Learning Theory. New York: Willey, 1998.
16. M. Pontil and A. Verri, "Properties of support vector machines," in Artificial Intelligence Laboratory. Cambridge, MA: MIT Press, 1997.
17. A.-M. Zou, F.-X. Wu, J. Ding, and G. G. Poirier, "Quality assessment of tandem mass spectra using support vector machine (SVM)," BMC Bioinformatics, vol.10, no.S-1, p. S49, 2009.
18. J. Platt, "Fast training of support vector machines using sequential minimal optimization," in Advances in Kernel Methods-Support Vector Learning. Cambridge, MA: MIT Press, 1999, pp. 42-65.
19. A. Keller, A. I. Nesvizhskii, E. Kolker, and R. Aebersold, "Empirical statistical model to estimate the accuracy of peptide identifications made by MS/MS and database search," Anal. Chem., vol.74, pp. 5383-5392, 2002.
20. J. P. Gagne, P. Gagne, J.M. Hunter,M. E. Bonicalzi, J. F. Lemay, I. Kelly, C. L. Page, D. Provencher, A. M. Mes, Masson, A. Droit, D. Bourgais, and G. G. Poirier, "Proteome profiling of human epithelial ovarian cancer cell line TOV-112D," Mol. Cell. Biochem., vol.275, pp. 25-55, 2005.
21. R. Craig and R. C. Beavis, "A method for reducing the time required to match protein sequences with tandem mass spectra," Rapid Commun. Mass Spectrom., vol.17, pp. 2310-2316, 2003.