Protein and Gene Model Inference Based on Statistical Modeling in K-partite Graphs

Overview

Journal Proc Natl Acad Sci U S A

Specialty Science

Date 2010 Jun 22

PMID 20562346

Citations 17

Authors

Sarah Gerster

Ermir Qeli

Christian H Ahrens

Peter Buhlmann

Affiliations

Soon will be listed here.

Abstract

One of the major goals of proteomics is the comprehensive and accurate description of a proteome. Shotgun proteomics, the method of choice for the analysis of complex protein mixtures, requires that experimentally observed peptides are mapped back to the proteins they were derived from. This process is also known as protein inference. We present Markovian Inference of Proteins and Gene Models (MIPGEM), a statistical model based on clearly stated assumptions to address the problem of protein and gene model inference for shotgun proteomics data. In particular, we are dealing with dependencies among peptides and proteins using a Markovian assumption on k-partite graphs. We are also addressing the problems of shared peptides and ambiguous proteins by scoring the encoding gene models. Empirical results on two control datasets with synthetic mixtures of proteins and on complex protein samples of Saccharomyces cerevisiae, Drosophila melanogaster, and Arabidopsis thaliana suggest that the results with MIPGEM are competitive with existing tools for protein inference.

Citing Articles

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Characterization of peptide-protein relationships in protein ambiguity groups via bipartite graphs.

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Detecting and Testing Altered Brain Connectivity Networks with K-partite Network Topology.

Chen S, Bowman F, Xing Y Comput Stat Data Anal. 2020; 141:109-122.

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References

Brunner E, Ahrens C, Mohanty S, Baetschmann H, Loevenich S, Potthast F . A high-quality catalog of the Drosophila melanogaster proteome. Nat Biotechnol. 2007; 25(5):576-83. DOI: 10.1038/nbt1300. View

Feng J, Naiman D, Cooper B . Probability model for assessing proteins assembled from peptide sequences inferred from tandem mass spectrometry data. Anal Chem. 2007; 79(10):3901-11. DOI: 10.1021/ac070202e. View

Keller A, Nesvizhskii A, 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(20):5383-92. DOI: 10.1021/ac025747h. View

Eng J, McCormack A, Yates J . An approach to correlate tandem mass spectral data of peptides with amino acid sequences in a protein database. J Am Soc Mass Spectrom. 2013; 5(11):976-89. DOI: 10.1016/1044-0305(94)80016-2. View

Moore R, Young M, Lee T . Qscore: an algorithm for evaluating SEQUEST database search results. J Am Soc Mass Spectrom. 2002; 13(4):378-86. DOI: 10.1016/S1044-0305(02)00352-5. View

Bern M, Goldberg D . Improved ranking functions for protein and modification-site identifications. J Comput Biol. 2008; 15(7):705-19. DOI: 10.1089/cmb.2007.0119. View

Washburn M, Wolters D, Yates 3rd J . Large-scale analysis of the yeast proteome by multidimensional protein identification technology. Nat Biotechnol. 2001; 19(3):242-7. DOI: 10.1038/85686. View

Grobei M, Qeli E, Brunner E, Rehrauer H, Zhang R, Roschitzki B . Deterministic protein inference for shotgun proteomics data provides new insights into Arabidopsis pollen development and function. Genome Res. 2009; 19(10):1786-800. PMC: 2765272. DOI: 10.1101/gr.089060.108. View

Nesvizhskii A, Keller A, Kolker E, Aebersold R . A statistical model for identifying proteins by tandem mass spectrometry. Anal Chem. 2003; 75(17):4646-58. DOI: 10.1021/ac0341261. View

10.

Price T, Lucitt M, Wu W, Austin D, Pizarro A, Yocum A . EBP, a program for protein identification using multiple tandem mass spectrometry datasets. Mol Cell Proteomics. 2006; 6(3):527-36. DOI: 10.1074/mcp.T600049-MCP200. View

11.

Shen C, Wang Z, Shankar G, Zhang X, Li L . A hierarchical statistical model to assess the confidence of peptides and proteins inferred from tandem mass spectrometry. Bioinformatics. 2007; 24(2):202-8. DOI: 10.1093/bioinformatics/btm555. View

12.

Aebersold R, Mann M . Mass spectrometry-based proteomics. Nature. 2003; 422(6928):198-207. DOI: 10.1038/nature01511. View

13.

Ramakrishnan S, Vogel C, Prince J, Li Z, Penalva L, Myers M . Integrating shotgun proteomics and mRNA expression data to improve protein identification. Bioinformatics. 2009; 25(11):1397-403. PMC: 2682515. DOI: 10.1093/bioinformatics/btp168. View

14.

Weatherly D, Atwood 3rd J, Minning T, Cavola C, Tarleton R, Orlando R . A Heuristic method for assigning a false-discovery rate for protein identifications from Mascot database search results. Mol Cell Proteomics. 2005; 4(6):762-72. DOI: 10.1074/mcp.M400215-MCP200. View

15.

Nesvizhskii A, Vitek O, Aebersold R . Analysis and validation of proteomic data generated by tandem mass spectrometry. Nat Methods. 2007; 4(10):787-97. DOI: 10.1038/nmeth1088. View

16.

Tabb D, Fernando C, Chambers M . MyriMatch: highly accurate tandem mass spectral peptide identification by multivariate hypergeometric analysis. J Proteome Res. 2007; 6(2):654-61. PMC: 2525619. DOI: 10.1021/pr0604054. View

17.

Tabb D, McDonald W, Yates 3rd J . DTASelect and Contrast: tools for assembling and comparing protein identifications from shotgun proteomics. J Proteome Res. 2003; 1(1):21-6. PMC: 2811961. DOI: 10.1021/pr015504q. View

18.

Keller A, Purvine S, Nesvizhskii A, Stolyar S, Goodlett D, Kolker E . Experimental protein mixture for validating tandem mass spectral analysis. OMICS. 2002; 6(2):207-12. DOI: 10.1089/153623102760092805. View

19.

Li Y, Arnold R, Li Y, Radivojac P, Sheng Q, Tang H . A bayesian approach to protein inference problem in shotgun proteomics. J Comput Biol. 2009; 16(8):1183-93. PMC: 2799497. DOI: 10.1089/cmb.2009.0018. View

20.

Nesvizhskii A, Aebersold R . Analysis, statistical validation and dissemination of large-scale proteomics datasets generated by tandem MS. Drug Discov Today. 2004; 9(4):173-81. DOI: 10.1016/S1359-6446(03)02978-7. View