Utility of RNA-seq and GPMDB protein observation frequency for improving the sensitivity of protein identification by tandem MS

Journal of Proteome Research

Volumn 13, Issue 9, 2014, Pages 4113-4119

  Shanmugam, Avinash K ^a   Yocum, Anastasia K ^a   Nesvizhskii, Alexey I ^a  

^a UNIVERSITY OF MICHIGAN MEDICAL SCHOOL   (United States)

Author keywords

confidence threshold; FDR; GPMDB; integrative analysis; probability adjustment; RNA seq; Tandem mass spectrometry

Indexed keywords

PROTEIN; PROTEOME;

ALGORITHM; ARTICLE; CONTROLLED STUDY; DATA BASE; EMBRYO; GLOBAL PROTEOME MACHINE DATABASE; HUMAN; HUMAN CELL; PROTEIN ANALYSIS; RNA SEQUENCE; TANDEM MASS SPECTROMETRY; CHEMISTRY; CLASSIFICATION; PROCEDURES; PROTEIN DATABASE; PROTEOMICS; REPRODUCIBILITY; SEQUENCE ANALYSIS; STATISTICAL MODEL; TUMOR CELL LINE;

CELL LINE, TUMOR; DATABASES, PROTEIN; HUMANS; MODELS, STATISTICAL; PROTEINS; PROTEOME; PROTEOMICS; REPRODUCIBILITY OF RESULTS; SEQUENCE ANALYSIS, RNA; TANDEM MASS SPECTROMETRY;

EID: 84914126849     PISSN: 15353893     EISSN: 15353907     Source Type: Journal    
DOI: 10.1021/pr500496p     Document Type: Article

References (33)

1. Nesvizhskii, A. I.; Aebersold, R. Interpretation of shotgun proteomic data: the protein inference problem Mol. Cell. Proteomics 2005, 4, 1419-40
2. Choi, H.; Nesvizhskii, A. I. False discovery rates and related statistical concepts in mass spectrometry-based proteomics J. Proteome Res. 2008, 7, 47-50
3. Nesvizhskii, A. I. A survey of computational methods and error rate estimation procedures for peptide and protein identification in shotgun proteomics J. Proteomics. 2010, 73, 2092-123
4. Deutsch, E. W.; Mendoza, L.; Shteynberg, D. A guided tour of the Trans-Proteomic Pipeline Proteomics 2010, 10, 1150-9
5. Malmström, J.; Lee, H.; Nesvizhskii, A. I. Optimized peptide separation and identification for mass spectrometry based proteomics via free-flow electrophoresis J. Proteome Res. 2006, 5, 2241-9
6. Li, Y. F.; Arnold, R. J.; Li, Y.; Radivojac, P.; Sheng, Q.; Tang, H. A bayesian approach to protein inference problem in shotgun proteomics J. Comput. Biol. 2009, 16, 1183-93
7. Ulintz, P. J.; Bodenmiller, B.; Andrews, P. C.; Aebersold, R.; Nesvizhskii, A. I. Investigating MS2/MS3 matching statistics: a model for coupling consecutive stage mass spectrometry data for increased peptide identification confidence Mol. Cell. Proteomics 2008, 7, 71-87
8. Shteynberg, D.; Deutsch, E. W.; Lam, H. iProphet: multi-level integrative analysis of shotgun proteomic data improves peptide and protein identification rates and error estimates Mol. Cell. Proteomics 2011, 10, M111.007690
9. Sheng, Q.; Dai, J.; Wu, Y.; Tang, H.; Zeng, R. BuildSummary: using a group-based approach to improve the sensitivity of peptide/protein identification in shotgun proteomics J. Proteome Res. 2012, 11, 1494-502
10. Ramakrishnan, S. R.; Vogel, C.; Prince, J. T. Integrating shotgun proteomics and mRNA expression data to improve protein identification Bioinformatics. 2009, 25, 1397-1403
11. Li, J.; Zimmerman, L.; Park, B. Network-Assisted protein identification and data interpretation in shotgun proteomics Mol. Syst. Biol. 2009, 5, 303
12. Ramakrishnan, S. R.; Vogel, C.; Kwon, T.; Penalva, L. O.; Marcotte, E. M.; Miranker, D. P. Mining gene functional networks to improve mass-spectrometry-based protein identification Bioinformatics. 2009, 25, 2955-2961
13. Wang, X.; Slebos, R. J. C.; Wang, D. Protein identification using customized protein sequence databases derived from RNA-Seq data J. Proteome Res. 2012, 11, 1009-1017
14. Wang, Z.; Gerstein, M.; Snyder, M. RNA-Seq: a revolutionary tool for transcriptomics Nat. Rev. Genet. 2009, 10, 57-63
15. Craig, R.; Cortens, J. P.; Beavis, R. C. Open source system for analyzing, validating, and storing protein identification data J. Proteome Res. 2004, 3, 1234-1242
16. Korenchuk, S.; Lehr, J. E.; MClean, L. VCaP, a cell-based model system of human prostate cancer In Vivo 2001, 15, 163-168
17. Graham, F. L.; Smiley, J.; Russell, W. C.; Nairn, R. Characteristics of a human cell line transformed by DNA from human adenovirus type 5 J. Gen Virol. 1977, 36, 59-74
18. Sam, L. T.; Lipson, D.; Raz, T. A comparison of single molecule and amplification based sequencing of cancer transcriptomes PLoS One. 2011, 6, e17305
19. Fonslow, B. R.; Stein, B. D.; Webb, K. J. Digestion and depletion of abundant proteins improves proteomic coverage Nat. Methods 2013, 10, 54-56
20. Sultan, M.; Schulz, M. H.; Richard, H. A global view of gene activity and alternative splicing by deep sequencing of the human transcriptome Science 2008, 321, 956-960
21. Yocum, A. K.; Khan, A. P.; Zhao, R.; Chinnaiyan, A. M. Development of selected reaction monitoring-MS methodology to measure peptide biomarkers in prostate cancer Proteomics 2010, 10, 3506-3514
22. Pedrioli, P. G. A. Trans-proteomic pipeline: a pipeline for proteomic analysis Methods Mol. Biol. 2010, 604, 213-238
23. Craig, R.; Beavis, R. C. TANDEM: matching proteins with tandem mass spectra Bioinformatics 2004, 20, 1466-1467
24. Keller, A.; Eng, J.; Zhang, N.; Li, X.; Aebersold, R. A uniform proteomics MS/MS analysis platform utilizing open XML file formats Mol. Syst. Biol. 2005, 1, 2005-0017
25. MacLean, B.; Eng, J. K.; Beavis, R. C.; McIntosh, M. General framework for developing and evaluating database scoring algorithms using the TANDEM search engine Bioinformatics 2006, 22, 2830-2832
26. Choi, H.; Nesvizhskii, A. I. Semisupervised model-based validation of peptide identifications in mass spectrometry-based proteomics J. Proteome Res. 2008, 7, 254-265
27. Fermin, D.; Basrur, V.; Yocum, A. K.; Nesvizhskii, A. I. Abacus: a computational tool for extracting and pre-processing spectral count data for label-free quantitative proteomic analysis Proteomics 2011, 11, 1340-1345
28. Trapnell, C.; Pachter, L.; Salzberg, S. L. TopHat: discovering splice junctions with RNA-Seq Bioinformatics 2009, 25, 1105-1111
29. Mortazavi, A.; Williams, B. A.; McCue, K.; Schaeffer, L.; Wold, B. Mapping and quantifying mammalian transcriptomes by RNA-Seq Nat. Methods 2008, 5, 621-628
30. Gentleman, R. C.; Carey, V. J.; Bates, D. M. Bioconductor: open software development for computational biology and bioinformatics Genome Biol. 2004, 5, R80
31. Morgan, M.; Pagès, H.; Obenchain, V. Rsamtools: binary alignment (BAM), variant call (BCF), or tabix file import; http://bioconductor.org/packages/release/bioc/html/Rsamtools.html.
32. Lawrence, M.; Huber, W.; Pagès, H.; Aboyoun, P.; Carlson, M.; Gentleman, R.; Morgan, M.; Carey, V. Software for computing and annotating genomic ranges PLoS Comput. Biol. 2013, 9, e1003118
33. Leinonen, R.; Sugawara, H.; Shumway, M. The sequence read archive Nucleic Acids Res. 2011, 39, D19-D21