Protein inference: A review

Briefings in Bioinformatics

Volumn 13, Issue 5, 2012, Pages 586-614

  Huang, Ting ^a   Wang, Jingjing ^b   Yu, Weichuan ^c   He, Zengyou ^a  

^a DALIAN UNIVERSITY OF TECHNOLOGY   (China)

^b UNIVERSITY OF TOKYO   (Japan)

^c HONG KONG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Hong Kong)

Author keywords

False discovery rate; Peptide identification; Protein inference; Shotgun proteomics; Supplementary information model

Indexed keywords

EID: 84865783204     PISSN: 14675463     EISSN: 14774054     Source Type: Journal    
DOI: 10.1093/bib/bbs004     Document Type: Review

References (91)

1. Bern M, Goldberg D. Improved ranking functions for protein and modification-site identifications. J Comput Biol 2008;15:705-19.
2. Fenyo D. Identifying the proteome: software tools. Curr Opin Biotechnol 2000;11:391-5.
3. Chakravarti DN, Chakravarti B, Moutsatsos I. Informatic tools for proteome profiling. Biotechniques 2002;32(Suppl): 4-15.
4. Brunner E, Ahrens CH. A high-quality catalog of the Drosophila melanogaster proteome. Nat Biotechnol 2007; 25(5):576-83.
5. Eriksson J, Fenyo D. Improving the success rate of proteome analysis by modeling protein-abundance distributions and experimental designs. Nat Biotechnol 2007;25(6): 651-55.
6. Mallick P, Schirle M, Chen SS, et al. Computational prediction of proteotypic peptides for quantitative proteomics. Nat Biotechnol 2007;25(1):125-31.
7. Eng JK, McCormack AL, Yates JR. 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(11):976-89.
8. Perkins DN, Pappin DJ, Creasy DM, etal. Probability-based protein identification by searching sequence databases using mass spectrometry data. Electrophoresis 1999;20(18): 3551-67.
9. David CM, Cottrell JS. Unimod: Protein modifications for mass spectrometry. Proteomics 2004;4(6):1534-6.
10. Rappsilber J, Mann M. What does it mean to identify a protein in proteomics? Trends Biochem Sci 2002;27(2):74-8.
11. Nesvizhskii AI, Vitek O, Aebersold R. Interpretation of shotgun proteomic data: the protein inference problem. Mole Cell Proteomics 2005;4(10):1419-40.
12. Nesvizhskii AI, Keller A, Kolker E, et al. A statistical model for identifying proteins by tandem mass spectrometry. Anal Chem 2003;75(17):4646-58.
13. Shen C, Wang Z, Shankar G, et al. A hierarchical statistical model to assess the confidence of peptides and proteins inferred from tandem mass spectrometry. Bioinformatics 2008;24(2):202-8.
14. Gerster S, Qelib E, Ahrensb CH, et al. Protein and gene model inference based on statistical modeling in k-partite graphs. Proc Nat Acad Sci USA 2010;107(27):12101-6.
15. Tabb DL, McDonald H, Yates JR. DTASelect and Contrast: tools for assembling and comparing protein identifications from shotgun proteomics. J Proteome Res 2002; 1(1):21-6.
16. Cormen TH, Leiserson CE, Rivest RL, et al. Introduction to Algorithms. 3rd edn. Cambridge, MA, USA: MIT Press, 2009.
17. Hochbaum DS. Approximating covering and packing problems: set cover, vertex cover, independent set, and related problems. Approximation Algorithms for NP-Hard Problems 1997;94-143.
18. Yang X, Dondeti V, Dezube R, et al. DBParser: Web-based software for shotgun proteomic data analyses. J ProteomeRes 2004;3(5):1002-8.
19. Zhang B, Chambers MC, Tabb DL. Proteomic parsimony through bipartite graph analysis improves accuracy and transparency. J Proteome Res 2007;6(9):3549-57.
20. He Z, Yang C, Yu W. A partial set covering model for protein mixture identification using mass spectrometry data. IEEE/ACM Trans Comput Biol Bioinformatics 2011;8(2): 368-80.
21. Bergeron JJ, Hallett M. Peptides you can count on. Nat Biotechnol 2007;25(1):61-2.
22. Fusaro VA, Mani DR, Mesirov JP, et al. Prediction of high-responding peptides for targeted protein assays by mass spectrometry. Nat Biotechnol 2009;27(2):190-8.
23. Tang H, Arnold RJ, Alves P, et al. A computational approach toward label-free protein quantification using predicted peptide detectability. Bioinformatics 2006;22(14): 481-8.
24. Alves P, Arnold RJ, Novotny MV, et al. Advancement in protein inference from shotgun proteomics using peptide detectability. Pac Symp Biocomput 2007;409-20.
25. Feng J, Naiman DQ, Cooper B. Probability model for assessing proteins assembled from peptides sequences inferred from tandem mass spectrometry data. Anal Chem 2007; 79(10):3901-11.
26. Elias JE, Gygi SP. Target-decoy search strategy for increased confidence in large-scale protein identifications by mass spectrometry. NatMethods 2007;4(3):207-14.
27. Keller A, Nesvizhskii AI, Kolker E, et al. Empirical statistical model to estimate the accuracy of peptide identifications made by MS/MS and database search. Anal Chem 2002; 74(20):5383-92.
28. Zhang J, Li J, Liu X, et al. A nonparametric model for quality control of database search results in shotgun proteomics. BMC Bioinformatics 2008;9:29.
29. Zhang J, Ma J, Dou L, et al. Bayesian nonparametric model for the validation of peptide identification in shotgun proteomics. Mol Cell Proteomics 2009;8(3):547-57.
30. Martnez-Bartolom S, Navarro P, Martn-Maroto F, et al. Properties of average score distributions of SEQUEST: the probability ratio method. Mol Cell Proteomics 2008;7(6): 1135-45.
31. Choi H, Ghosh D, Nesvizhskii AI. Statistical validation of peptide identifications in large-scale proteomics using the target-decoy database search strategy and flexible mixture modeling. J ProteomeRes 2008;7(1):286-92.
32. Choi H, Nesvizhskii AI. Semisupervised model-based validation of peptide identifications in mass spectrometry-based proteomics. J ProteomeRes 2008;7(1):254-65.
33. Helsens K, Colaert N, Barsnes H, et al. ms_lims, a simple yet powerful open source laboratory information management system for ms-driven proteomics. Proteomics 2010;10(6): 1261-64.
34. Mueller M, Martens L, Reidegeld KA, et al. Functional annotation of proteins identified in human brain during the HUPO brain proteome project pilot study. Proteomics 2006;6(18):5059-75.
35. Geer LY, Markey SP, Kowalak JA, et al. Open mass spectrometry search algorithm. J Proteome Res 2004;3(5): 958-64.
36. Ma B, Zhang K, Hendrie C, et al. Peaks: powerful software for peptide de novo sequencing by tandem mass spectrometry. Rapid CommunMass Spectrometry 2003;17(20):2337-42.
37. Frank A, Pevzner P. PepNovo: de novo peptide sequencing via probabilistic network modeling. Anal Chem 2005;77(4): 964-73.
38. Tabb DL, Saraf A, Yates JR. GutenTag: High-throughput sequence tagging via an empirically derived fragmentation model. Anal Chem 2003;75(23):6415-21.
39. Tanner S, Shu H, Frank A, et al. InsPect: Fast and accurate identification of post-translationally modified peptides from tandem mass spectra. Anal Chem 2005;77(14): 4626-4639.
40. Pappin DJ, Hojrup P, Bleasby AJ. Rapid identification of proteins by peptide-mass fingerprinting. CurrBiol 1993;3(6): 327-32.
41. McHugh L, Arthur JW. Computational methods for protein identification from mass spectrometry data. PLoS Comput Biol 2008;4(2):e12.
42. Moore RE, Young MK, Lee TD. Qscore: an algorithm for evaluating sequest database search results. J Am Soc Mass Spectrometry 2002;13(4):378-86.
43. Weatherly DB, Atwood JA, Minning TA, et al. 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.
44. Peng J, Elias JE, Thoreen CC. Evaluation of multidimensional chromatography coupled with tandem mass spectrometry (LC/LC-MS/MS) for large-scale protein analysis: the yeast proteome. J ProteomeRes 2003;2(1):43-50.
45. Ma Z-Q, Dasari S, Chambers MC, et al. IDPicker 2.0: Improved protein assembly with high discrimination peptide identification filtering. J Proteome Res 2009;8(8): 3872-81.
46. Price TS, Lucitt MB, Wu W, et al. EBP: Protein identification using multiple tandem mass spectrometry datasets. Mol Cell Proteomics 2007;6(3):527-36.
47. Dowe DL, Gardner S, Oppy G. Bayes not bust! Why simplicity is no problem for Bayesians. BritJ Phil Sci 2007;58(4): 709-54.
48. Slotta DJ, McFarland MA, Markey SP. MassSieve: Panning MS/MS peptide data for proteins. Proteomics 2010;10(16): 3035-9.
49. Li YF, Arnold RJ, Li Y, et al. A Bayesian approach to protein inference problem in shotgun proteomics. JComputBiol 2009;16(8):1-11.
50. Serang O, MacCoss MJ, Noble WS. Efficient marginalization to compute protein posterior probabilities from shotgun mass spectrometry data. J Proteome Res 2010;9(10): 5346-57.
51. Sadygov RG, Liu H, Yates JR. Statistical models for protein validation using tandem mass spectral data and protein amino acid sequence databases. Anal Chem 2004;76(6): 1664-71.
52. Kearney P, Butler H, Eng K, et al. Protein identification and peptide expression resolver: Harmonizing protein identification with protein expression data. J Proteome Res 2008; 7(1):234-44.
53. Li J, Zimmerman LJ, Park B-H, et al. Network-assisted protein identification and data interpretation in shotgun proteomics. Mol Syst Biol 2009;5:303.
54. Ramakrishnan SR, Vogel C, Kwon T, et al. Mining gene functional networks to improve mass-spectrometry based protein identification. Bioinformatics 2009;25(22):2955-61.
55. Ramakrishnan SR, Vogel C, Prince JT, et al. Integrating shotgun proteomics and mRNA expression data to improve protein identification. Bioinformatics 2009;25(11):1397-403.
56. Li Q, MacCoss M, Stephens M. A nested mixture model for protein identification using mass spectrometry. Ann Appl Stat 2010;4(2):962-87.
57. Spivak M, Weston J, MacCoss MJ, et al. Direct maximization of protein identifications from tandem mass spectra. Mol Cell Proteomics 2012;11(2):M111.012161.
58. Grobei MA, Qeli E, Brunner E, et al. Deterministic protein inference for shotgun proteomics data provides new insights into Arabidopsis pollen development and function. Genome Res 2009;19(10):1786-800.
59. Qeli E, Ahrens CH. PeptideClassifier for protein inference and targeted quantitative proteomics. Nat Biotechnol 2010; 28(7):647-50.
60. Lu B, Motoyama A, Ruse C, et al. Improving protein identification sensitivity by combining MS and MS/MS information for shotgun proteomics using LTQ-Orbitrap high mass accuracy data. Anal Chem 2008;80(6):2018-25.
61. Searle BC. Scaffold: a bioinformatic tool for validating MS/MS-based proteomic studies. Proteomics 2010;10(6):1265-69.
62. Magnin J, Masselot A, Menzel C, et al. OLAV-PMF: a novel scoring scheme for high-throughput peptide mass fingerprinting. J Proteome Res 2004;3(1):55-60.
63. Zhang W, Chait BT. ProFound: an expert system for protein identification using mass spectrometric peptide mapping information. Anal Chem 2000;72(11):2482-9.
64. Colinge J, Bennett KL. Introduction to computational proteomics. PLoSComput Biol 2007;3(7):e114.
65. Gygi SP, Corthals GL, Zhang Y, et al. Evaluation of two-dimensional gel electrophoresis-based proteome analysis technology. Proc Natl Acad Sci USA 2000;97(17): 9390-5.
66. He Z, Yang C, Yang C, et al. Optimization-based peptide mass fingerprinting for protein mixture identification. JComputBiol 2010;17(3):221-35.
67. Neilson KA, Ali NA, Muralidharan S, et al. Less label, more free: approaches in label-free quantitative mass spectrometry. Proteomics 2011;11(4):535-53.
68. Lundgren DH, Hwang S-I, Wu L, et al. Role of spectral counting in quantitative proteomics. Exp Rev Proteomics 2010;7(1):39-53.
69. Choi H, Fermin D, Nesvizhskii AI. Significance analysis of spectral count data in label-free shotgun proteomics. Mol Cell Proteomics 2008;7(12):2373-85.
70. Booth JG, Eilertson KE, Olinares PDB, et al. A Bayesian mixture model for comparative spectral count data in shotgun proteomics. Mol Cell Proteomics 2011;10(8): M110.007203.
71. Sharan R, Ulitsky I, Shamir R. Network-based prediction of protein function. Mol Syst Biol 2007;3:88.
72. Klimek J, Eddes JS, Hohmann L, et al. The standard protein mix database: a diverse dataset to assist in the production of improved peptide and protein identification software tools. J ProteomeRes 2008;7(1):96-103.
73. Schulz-Trieglaff O, Pfeifer N, Gropl C, et al. LC-MSsim- a simulation software for liquid chromatography mass spectrometry data. BMC Bioinformatics 2008;9:423.
74. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J Roy Stat Soc Ser B (Stat Methodol) 1993;57(1): 289-300.
75. Reiter L, Claassen M, Schrimpf SP, et al. Protein identification false discovery rates for very large proteomics datasets generated by tandem mass spectrometry. Mol Cell Proteomics 2009;8(11):2405-17.
76. Elias JE, Gygi SP. Target-decoy search strategy for increased confidence in large-scale protein identifications by mass spectrometry. NatMethods 2007;4(3):207-214.
77. Adamski M, Blackwell T, Menon R, et al. Data management and preliminary data analysis in the pilot phase of the HUPO Plasma Proteome Project. Proteomics 2005;5(13): 3246-61.
78. Gupta N, Pevzner PA. False discovery rates of protein identifications: a strike against the two-peptide rule. J Proteome Res 2008;8(9):4173-81.
79. Hather G, Higdon R, Bauman A, et al. Estimating false discovery rates for peptide and protein identification using randomized databases. Proteomics 2010;10(12):2369-76.
80. Klammer AA, Park CY, Noble WS. Statistical calibration of the SEQUEST Xcorr function. J Proteome Res 2009;8(4): 2106-13.
81. Spirin V, Shpunt A, Seebacher J, et al. Assigning spectrum-specific p-values to protein identifications by mass spectrometry. Bioinformatics 2011;27(8):1128-34.
82. Mosteller F, Fisher RA. Questions and answers. Am Stat 2008;2(5):30-1.
83. Keller A, Eng J, Zhang N, et al. A uniform proteomics MS/MS analysis platform utilizing open XML file formats. Mol Syst Biol 2005;1:2005.0017.
84. Xue X, Wu S, Wang Z, et al. Protein probabilities in shotgun proteomics: evaluating different estimation methods using a semi-random sampling model. Proteomics 2006; 6(23):6134-45.
85. Li X, Gong Y, Wang Y, et al. Comparison of alternative analytical techniques for the characterisation of the human serum proteome in HUPO Plasma Proteome Project. Proteomics 2005;5(13):3423-41.
86. Alves G, Wu WW, Wang G, et al. Enhancing peptide identification confidence by combining search methods. J ProteomeRes 2008;7(8):3102-13.
87. Claassen M, Aebersold R, Buhmann JM. Proteome coverage prediction with infinite Markov models. Bioinformatics 2009;25(12):i154-60.
88. Ma B. Challenges in computational analysis of mass spectrometry data for proteomics. JCompSciTechnol 2010;25(1): 107-23.
89. Shi J, Wu F-X. Protein inference by assembling peptides identified from tandem mass spectra. Curr Bioinform 2009; 4(3):226-33.
90. Nesvizhskii AI. A survey of computational methods and error rate estimation procedures for peptide and protein identification in shotgun proteomics. J Proteomics 2010; 73(11):2092-123.
91. Claassen M. Design and Validation of Proteome Measurements. PhD Thesis, ETH Zurich, 2010.