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Transformation, Normalization, and Batch Effect in the Analysis of Mass Spectrometry Data for Omics Studies Read chapter Read first chapter

2017 | OriginalPaper | Chapter

Probabilistic and Likelihood-Based Methods for Protein Identification from MS/MS Data

Authors : Ryan Gill, Susmita Datta

Published in: Statistical Analysis of Proteomics, Metabolomics, and Lipidomics Data Using Mass Spectrometry

Publisher: Springer International Publishing

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Abstract

The process of identification of peptides from the mass spectra and the constituent proteins in a sample is called protein identification. In the current literature, there exist many proposed approaches for the protein identification problem based on tandem mass spectrometry (MS/MS) data. While there are many two-step protein identification procedures that first identify peptides in a separate process and then use the results in protein identification, in recent years there have been attempts to develop a one-step solution to the problem through simultaneous identification of proteins and peptides in a sample. We briefly introduce the probabilistic and likelihood-based two-step and one-step procedures and report some comparative performances of these procedures for different MS/MS data.

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previous chapter The Analysis of Peptide-Centric Mass-Spectrometry Data Utilizing Information About the Expected Isotope Distribution
next chapter An MCMC-MRF Algorithm for Incorporating Spatial Information in IMS Proteomic Data Processing
Literature
1.
Yates, J. R., Ruse, C. I., & Nakorchevsky, M. (2009). Proteomics by mass spectrometry: Approaches, advances, and applications. Annual Review of Biomedical Engineering, 11(1), 49–79.CrossRef
2.
Eng, J. K., McCormack, A. L., & Yates, J. R., III. (1994). An approach to correlate tandem mass spectral data of peptides with amino acid sequences in a protein database. Journal of the American Society for Mass Spectrometry, 5(11), 976–989.CrossRef
3.
Eng, J. K., Fischer, B., Grossmann, J., & Maccoss, M. J. (2008). A fast SEQUEST cross correlation algorithm. Journal of Proteome Research, 7(10), 4598–4602.CrossRef
4.
Diament, B. J., & Noble, W. S. (2011). Faster SEQUEST searching for peptide identification from tandem mass spectra. Journal of Proteome Research, 10(9), 3871–3879.CrossRef
5.
Craig, R., & Beavis, R. C. (2004). TANDEM: Matching proteins with tandem mass spectra. Bioinformatics, 20(9), 1466–1467.CrossRef
6.
Perkins, D. N., Pappin, D. J., Creasy, D. M., & Cottrell, J. S. (1999). Probability-based protein identification by searching sequence databases using mass spectrometry. Electrophoresis, 20(18), 3551–3567.CrossRef
7.
Clauser, K. R., Baker, P., & Burlingame, A. L. (1999). Role of accurate mass measurement (+/− 10 ppm) in protein identification strategies employing MS or MS/MS and database searching. Analytical Chemistry, 71(14), 2871–2882.CrossRef
8.
Kim, S., Gupta, N., & Pevzner, P. A. (2008). Spectral probabilities and generating functions of tandem mass spectra: A strike against decoy databases. Journal of Proteome Research, 7(8), 3354–3363.CrossRef
9.
Swaney, D. L., Wenger, C. D., & Coon, J. J. (2010). Value of using multiple proteases for large-scale mass spectrometry-based proteomics. Journal of Proteome Research, 9(3), 1323–1329.CrossRef
10.
Granholm, V., Kim, S., Navarro, J. C. F., Sjolund, E., Smith, R. D., & Kall, L. (2014). Fast and accurate database searches with MSGF+ Percolator. Journal of Proteome Research, 13(2), 890–897.CrossRef
11.
Keller, A., Purvine, S., Nesvizhskii, A. I., Stolyar, S., Goodlett, D. R., & Kolker, E. (2002). Experimental protein mixture for validating tandem mass spectral analysis. Omics, 6(2), 207–212.CrossRef
12.
Nesvizhskii, A. I., & Aebersold, R. (2004). Analysis, statistical validation and dissemination of large-scale proteomics data sets generated by tandem MS. Drug Discovery Today, 9(4), 173–181.CrossRef
13.
Nesvizhskii, A. I., Keller, A., Kolker, E., & Aebersold, R. (2003). A statistical model for identifying proteins by tandem mass spectrometry. Analytical Chemistry, 75(17), 4646–4658.CrossRef
14.
Shen, C., Wang, Z., Shankar, G., Zhang, X., & Li, L. (2008). A hierarchical statistical model to assess the confidence of peptides and proteins inferred from tandem mass spectrometry. Bioinformatics, 24(2), 202–208.CrossRef
15.
Sikdar, S., Gill, R., & Datta, S. (2015). Improving protein identification from tandem mass spectrometry data by one-step methods and integrating data from other platforms. Briefings in Bioinformatics, 17(2), 262–269.
16.
Keller, A., Nesvizhskii, A. I., Kolker, E., & Aebersold, R. (2002). Empirical statistical model to estimate the accuracy of peptide identifications made by MS/MS and database search. Analytical Chemistry, 74(20), 5383–5592.CrossRef
17.
Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning: Data mining, inference, and prediction. New York: Springer.CrossRefMATH
18.
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, Series B, 39(1), 1–38.MathSciNetMATH
19.
Shteynberg, D., Deutsch, E. W., Lam, H., Eng, J. K., Sun, Z., Tasman, N., et al. (2011). iProphet: Multi-level integrative analysis of shotgun proteomic data improves peptide and protein identification rates and error estimates. Molecular & Cellular Proteomics, 10(12), 1–15.CrossRef
20.
Mitra, R., Gill, R., Sikdar, S., & Datta, S. (2015). Bayesian hierarchical model for protein identifications. Under review.
21.
Li, Q., MacCoss, M., & Stephens, M. (2010). A nested mixture model for protein identification using mass spectrometry. The Annals of Applied Statistics, 4(2), 962–987.MathSciNetCrossRefMATH
22.
Huang, T., Wang, J., Yu, W., & He, Z. (2012). Protein inference: A review. Briefings in Bioinformatics, 13(5), 586–614.CrossRef
23.
Nesvizhskii, A. I., Vitek, O., & Aebersold, R. (2007). Analysis and validation of proteomic data generated by tandem mass spectrometry. Nature Methods, 4(10), 787–797.CrossRef
24.
Serang, O., & Noble, W. (2012). A review of statistical methods for protein identification using tandem mass spectrometry. Stat Interface, 5(1), 3–20.MathSciNetCrossRefMATH
25.
Bern, M. W., & Kil, Y. J. (2011). Two-dimensional target decoy strategy for shotgun proteomics. Journal of Proteome Research, 10(12), 5296–5301.CrossRef
26.
Shi, J., & Wu, F.-X. (2012). A feedback framework for protein inference with peptides identified from tandem mass spectra. Proteome Science, 10, 68.CrossRef
27.
Shi, J., Chen, B., & Wu, F.-X. (2013). Unifying protein inference and peptide identification with feedback to update consistency between peptides. Proteomics, 13(2), 239–247.CrossRef
28.
Spivak, M., Weston, J., Tomazela, D., Maccoss, M. J., & Noble, W. S. (2012). Direct maximization of protein identifications from tandem mass spectra. Molecular & Cellular Proteomics, 11(2), M111.012161.
29.
Purvine, S., Picone, A. F., & Kolker, E. (2004). Standard mixtures for proteome studies. OMICS, 8(1), 79–92.CrossRef
30.
Elias, J. E., Haas, W., Faherty, B. K., & Gygi, S. P. (2005). Comparative evaluation of mass spectrometry platforms used in large-scale proteomics investigations. Nature Methods, 2(9), 667–675.CrossRef
31.
Kall, L., Canterbury, J., Weston, J., Noble, M. J., & MacCoss, W. S. (2007). A semi-supervised machine learning technique for peptide identification from shotgun proteomics datasets. Nature Methods, 4, 923–925.CrossRef
Metadata
Title
Probabilistic and Likelihood-Based Methods for Protein Identification from MS/MS Data
Authors
Ryan Gill
Susmita Datta
Copyright Year
2017
Book
Statistical Analysis of Proteomics, Metabolomics, and Lipidomics Data Using Mass Spectrometry

Print ISBN: 978-3-319-45807-6

Electronic ISBN: 978-3-319-45809-0

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-45809-0_4

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