Comet: An open-source MS/MS sequence database search tool

Proteomics

Volumn 13, Issue 1, 2013, Pages 22-24

  Eng, Jimmy K ^a   Jahan, Tahmina A ^a   Hoopmann, Michael R ^b  

^a UNIVERSITY OF WASHINGTON   (United States)

^b INSTITUTE FOR SYSTEMS BIOLOGY   (United States)

Author keywords

Bioinformatics; Identification; MS; Peptide; Protein

Indexed keywords

ALGORITHM; ARTICLE; PERFORMANCE; PRIORITY JOURNAL; SEARCH ENGINE; SEQUENCE DATABASE; TANDEM MASS SPECTROMETRY;

ALGORITHMS; AMINO ACID SEQUENCE; DATABASES, PROTEIN; HUMANS; PEPTIDES; PROTEINS; PROTEOMICS; SEARCH ENGINE; SOFTWARE; TANDEM MASS SPECTROMETRY;

EID: 84872371907     PISSN: 16159853     EISSN: 16159861     Source Type: Journal    
DOI: 10.1002/pmic.201200439     Document Type: Article

References (26)

1. Eng, J. K., McCormack, A. L., Yates, J. R., 3rd, 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, 976-989.
2. Picotti, P., Aebersold, R., Selected reaction monitoring-based proteomics: workflows, potential, pitfalls and future directions. Nat. Meth. 2012, 9, 555-566.
3. Geiger, T., Cox, J., Mann, M., Proteomics on an Orbitrap Benchtop mass spectrometer using all-ion fragmentation. Mol. Cell. Proteome. 2010, 9, 2252-2261.
4. Panchaud, A., Scherl, A., Shaffer, S. A., von Haller, P. D. et al., Precursor acquisition independent from ion count: how to dive deeper into the proteomics ocean. Anal. Chem. 2009, 81, 6481-6488.
5. Weisbrod, C. R., Eng, J. K., Hoopmann, M. R., Baker, T., Bruce, J. E., Accurate peptide fragment mass analysis: multiplexed peptide identification and quantification. J. Proteome Res. 2012, 11, 1621-1632.
6. Gillet, L. C., Navarro, P., Tate, S., Röst, H. et al., Targeted data extraction of the MS/MS spectra generated by data-independent acquisition: a new concept for consistent and accurate proteome analysis. Mol. Cell. Proteome. 2012, 11, O111.16717.
7. Nesvizhskii, A. I., A survey of computational methods and error rate estimation procedures for peptide and protein identification in shotgun proteomics. J. Proteome. 2010, 73, 2092-2123.
8. Pedrioli, P. G., Eng, J. K., Hubley, R., Vogelzang, M. et al., A common open representation of mass spectrometry data and its application to proteomics research. Nat. Biotechnol. 2004, 22, 1459-1466.
9. Martens, L., Chambers, M., Sturm, M., Kessner, D. et al., mzML-a community standard for mass spectrometry data. Mol. Cell. Proteome. 2011, 10.
10. McDonald, W. H., Tabb, D. L., Sadygov, R. G., MacCoss, M. J. et al., MS1, MS2, and SQT-three unified, compact, and easily parsed file formats for the storage of shotgun proteomic spectra and identifications. Rapid Commun. Mass Spectrom. 2004, 18, 2162-2168.
11. Keller, A., Eng, J., Zhang, N., Li, X. J., Aebersold, R., A uniform proteomics MS/MS analysis platform utilizing open XML file formats. Mol. Syst. Biol. 2005, 1, 2005.0017.
12. Eng, J. K., Fischer, B., Grossmann, J., MacCoss, M. J., A fast SEQUEST cross correlation algorithm. J. Proteome Res. 2008, 7, 4598-4602.
13. Craig, R., Beavis, R. C., TANDEM: matching proteins with tandem mass spectra. Bioinformatics 2004, 20, 1466-1467.
14. Geer, L. Y., Markey, S. P., Kowalak, J. A., Wagner, L. et al., Open mass spectrometry search algorithm. J. Proteome. Res. 2004, 3, 958-964.
15. Phanstiel, D. H., Brumbaugh, J., Wenger, C. D., Tian, S. et al., Proteomic and phosphoproteomic comparison of human ES and iPS cells. Nat. Meth. 2011, 8, 821-827.
16. Kersey, P. J., Duarte, J., Williams, A., Karavidopoulou, Y. et al., The International Protein Index: An integrated database for proteomics experiments. Proteomics 2004, 4, 1985-1988.
17. Storey, J. D., Tibshirani, R., Statistical significance for genomewide studies. Proc. Natl. Acad. Sci. 2003, 100, 9440-9445.
18. Elias, J. E., Gygi, S. P., Target-decoy search strategy for increased confidence in large-scale protein identifications by mass spectrometry. Nat. Meth. 2007, 4, 207-214.
19. Keller, A., Nesvizhskii, A. I., 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, 5383-5392.
20. Nesvizhskii, A., Keller, A., Kolker, E., Aebersold, R., A statistical model for identifying proteins by tandem mass spectrometry. Anal. Chem. 2003, 75, 4646-4658.
21. Searle, B. C., Scaffold: a bioinformatic tool for validating MS/MS-based proteomic studies. Proteomics 2010, 10, 1265-1269.
22. Kall, L., Canterbury, J. D., Weston, J., Noble, W. S., MacCoss, M. J., Semi-supervised learning for peptide identification from shotgun proteomics datasets. Nat. Meth. 2007, 4, 923-925.
23. Park, C. Y., Klammer, A. A., Kall, L., Maccoss, M. J., Noble, W. S., Rapid and accurate peptide identification from tandem mass spectra. J. Proteome Res. 2008, 3022-3027.
24. Searle, B. C., Turner, M., Nesvizhskii, A. I., Improving sensitivity by probabilistically combining results from multiple MS/MS search methodologies. J. Proteome Res. 2008, 7, 245-253.
25. Shteynberg, D., Deutsch, E. W., Lam, H., Eng, J. K. et al., iProphet: Multi-level integrative analysis of shotgun proteomic data improves peptide and protein identification rates and error estimates. Mol. Cell. Proteome. 2011, 10, M111.007690.
26. Wedge, D. C., Krishna, R., Blackhurst, P., Siepen, J. A. et al., FDRAnalysis: a tool for the integrated analysis of tandem mass spectrometry identification results from multiple search engines. J. Proteome Res. 2011, 10, 2088-2094.