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Open Mass Spectrometry Search Algorithm
Open accessAbstract
Large numbers of MS/MS peptide spectra generated in proteomics experiments require efficient, sensitive and specific algorithms for peptide identification. In the Open Mass Spectrometry Search Algorithm (OMSSA), specificity is calculated by a classic probability score using an explicit model for matching experimental spectra to sequences. At default thresholds, OMSSA matches more spectra from a standard protein cocktail than a comparable algorithm. OMSSA is designed to be faster than published algorithms in searching large MS/MS datasets. Keywords: protein identification • algorithm • bioinformatics • mass spectrometry • proteomics • significance testing
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Cites
Gapped BLAST and PSI-BLAST: a new generation of protein database search programs
Altschul, S. (1997) · Nucleic Acids Research · doi:10.1093/nar/25.17.3389 · Cited by 73,302
Cited by
The Perseus computational platform for comprehensive analysis of (prote)omics data
Tyanova, S., Temu, T., Sinitcyn, P., Carlson, A., Hein, M. Y., Geiger, T., Mann, M., & Cox, J. (2016) · Nature Methods · doi:10.1038/nmeth.3901 · Cited by 8,803
Comet: An open‐source MS / MS sequence database search tool
Eng, J. K., Jahan, T. A., & Hoopmann, M. R. (2013) · PROTEOMICS · doi:10.1002/pmic.201200439 · Cited by 1,474
Peterson, A. C., Russell, J. D., Bailey, D. J., Westphall, M. S., & Coon, J. J. (2012) · Molecular & Cellular Proteomics · doi:10.1074/mcp.o112.020131 · Cited by 1,288
MS-GF+ makes progress towards a universal database search tool for proteomics
Kim, S., & Pevzner, P. A. (2014) · Nature Communications · doi:10.1038/ncomms6277 · Cited by 1,098
Database resources of the National Center for Biotechnology Information
Wheeler, D. L., Barrett, T., Benson, D. A., Bryant, S. H., Canese, K., Chetvernin, V., Church, D. M., DiCuccio, M., Edgar, R., Federhen, S., et al. (2007) · Nucleic Acids Research · doi:10.1093/nar/gkm1000 · Cited by 1,007
Self-citationInterpretation of Shotgun Proteomic Data
Nesvizhskii, A. I., & Aebersold, R. (2005) · Molecular & Cellular Proteomics · doi:10.1074/mcp.r500012-mcp200 · Cited by 1,025
Database resources of the National Center for Biotechnology Information
Wheeler, D. L., Barrett, T., Benson, D. A., Bryant, S. H., Canese, K., Chetvernin, V., Church, D. M., DiCuccio, M., Edgar, R., Federhen, S., et al. (2007) · Nucleic Acids Research · doi:10.1093/nar/gkl1031 · Cited by 734
Self-citationTarget-Decoy Search Strategy for Mass Spectrometry-Based Proteomics
Elias, J. E., & Gygi, S. P. (2010) · Methods in Molecular Biology · doi:10.1007/978-1-60761-444-9_5 · Cited by 644
Lam, H., Deutsch, E. W., Eddes, J. S., Eng, J. K., King, N., Stein, S. E., & Aebersold, R. (2007) · PROTEOMICS · doi:10.1002/pmic.200600625 · Cited by 517
ProLuCID: An improved SEQUEST-like algorithm with enhanced sensitivity and specificity
Xu, T., Park, S. K., Venable, J. D., Wohlschlegel, J. A., Diedrich, J. K., Cociorva, D., Lu, B., Liao, L., Hewel, J., Han, X., et al. (2015) · Journal of Proteomics · doi:10.1016/j.jprot.2015.07.001 · Cited by 511
A repository of assays to quantify 10,000 human proteins by SWATH-MS
Rosenberger, G., Koh, C. C., Guo, T., Röst, H. L., Kouvonen, P., Collins, B. C., Heusel, M., Liu, Y., Caron, E., Vichalkovski, A., et al. (2014) · Scientific Data · doi:10.1038/sdata.2014.31 · Cited by 413
Guo, T., Kouvonen, P., Koh, C. C., Gillet, L. C., Wolski, W. E., Röst, H. L., Rosenberger, G., Collins, B. C., Blum, L. C., Gillessen, S., et al. (2015) · Nature Medicine · doi:10.1038/nm.3807 · Cited by 376
A complete mass-spectrometric map of the yeast proteome applied to quantitative trait analysis
Picotti, P., Clément-Ziza, M., Lam, H., Campbell, D. S., Schmidt, A., Deutsch, E. W., Röst, H., Sun, Z., Rinner, O., Reiter, L., et al. (2013) · Nature · doi:10.1038/nature11835 · Cited by 318
A dynamic model of proteome changes reveals new roles for transcript alteration in yeast
Lee, M. V., Topper, S. E., Hubler, S. L., Hose, J., Wenger, C. D., Coon, J. J., & Gasch, A. P. (2011) · Molecular Systems Biology · doi:10.1038/msb.2011.48 · Cited by 290
Bioinformatics Methods for Mass Spectrometry-Based Proteomics Data Analysis
Chen, C., Hou, J., Tanner, J. J., & Cheng, J. (2020) · International Journal of Molecular Sciences · doi:10.3390/ijms21082873 · Cited by 280
Human Pumilio Proteins Recruit Multiple Deadenylases to Efficiently Repress Messenger RNAs
Van Etten, J., Schagat, T. L., Hrit, J., Weidmann, C. A., Brumbaugh, J., Coon, J. J., & Goldstrohm, A. C. (2012) · Journal of Biological Chemistry · doi:10.1074/jbc.m112.373522 · Cited by 205
Ting, Y. S., Egertson, J. D., Payne, S. H., Kim, S., MacLean, B., Käll, L., Aebersold, R., Smith, R. D., Noble, W. S., & MacCoss, M. J. (2015) · Molecular & Cellular Proteomics · doi:10.1074/mcp.o114.047035 · Cited by 175
JUMP: a tag-based database search tool for peptide identification with high sensitivity and accuracy
Wang, X., Li, Y., Wu, Z., Wang, H., Tan, H., & Peng, J. (2014) · Molecular & Cellular Proteomics · doi:10.1074/mcp.o114.039586 · Cited by 194
Rudnick, P. A., Clauser, K. R., Kilpatrick, L. E., Tchekhovskoi, D. V., Neta, P., Blonder, N., Billheimer, D. D., Blackman, R. K., Bunk, D. M., Cardasis, H. L., et al. (2010) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m900223-mcp200 · Cited by 183
Menschaert, G., Van Criekinge, W., Notelaers, T., Koch, A., Crappé, J., Gevaert, K., & Van Damme, P. (2013) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m113.027540 · Cited by 173
An automated pipeline for high-throughput label-free quantitative proteomics
Weisser, H., Nahnsen, S., Grossmann, J., Nilse, L., Quandt, A., Brauer, H., Sturm, M., Kenar, E., Kohlbacher, O., Aebersold, R., et al. (2013) · Journal of Proteome Research · doi:10.1021/pr300992u · Cited by 160
Panchaud, A., Affolter, M., & Kussmann, M. (2012) · Journal of Proteomics · doi:10.1016/j.jprot.2011.12.022 · Cited by 159
Protein Identification Using Top-Down Spectra
Liu, X., Sirotkin, Y., Shen, Y., Anderson, G., Tsai, Y. S., Ting, Y. S., Goodlett, D. R., Smith, R. D., Bafna, V., & Pevzner, P. A. (2012) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m111.008524 · Cited by 153
Mapping the Phosphoproteome of Influenza A and B Viruses by Mass Spectrometry
Hutchinson, E. C., Denham, E. M., Thomas, B., Trudgian, D. C., Hester, S. S., Ridlova, G., York, A., Turrell, L., & Fodor, E. (2012) · PLoS Pathogens · doi:10.1371/journal.ppat.1002993 · Cited by 151
Verrastro, I., Pasha, S., Jensen, K., Pitt, A., & Spickett, C. (2015) · Biomolecules · doi:10.3390/biom5020378 · Cited by 114
Analytical tools and current challenges in the modern era of neuroepigenomics
Maze, I., Shen, L., Zhang, B., Garcia, B. A., Shao, N., Mitchell, A., Sun, H., Akbarian, S., Allis, C. D., & Nestler, E. J. (2014) · Nature Neuroscience · doi:10.1038/nn.3816 · Cited by 100
Fucosylation and protein glycosylation create functional receptors for cholera toxin
Wands, A. M., Fujita, A., McCombs, J. E., Cervin, J., Dedic, B., Rodriguez, A. C., Nischan, N., Bond, M. R., Mettlen, M., Trudgian, D. C., et al. (2015) · eLife · doi:10.7554/elife.09545 · Cited by 90
Welte, T., Kudva, R., Kuhn, P., Sturm, L., Braig, D., Müller, M., Warscheid, B., Drepper, F., & Koch, H.-G. (2012) · Molecular Biology of the Cell · doi:10.1091/mbc.e11-07-0590 · Cited by 87
Proteomic Insight into the Molecular Function of the Vitreous
Skeie, J. M., Roybal, C. N., & Mahajan, V. B. (2015) · PLOS ONE · doi:10.1371/journal.pone.0127567 · Cited by 93
Kuhn, P., Weiche, B., Sturm, L., Sommer, E., Drepper, F., Warscheid, B., Sourjik, V., & Koch, H. (2011) · Traffic · doi:10.1111/j.1600-0854.2011.01167.x · Cited by 76
Koch, A., Gawron, D., Steyaert, S., Ndah, E., Crappé, J., De Keulenaer, S., De Meester, E., Ma, M., Shen, B., Gevaert, K., et al. (2014) · PROTEOMICS · doi:10.1002/pmic.201400180 · Cited by 76
Mass Spectrometric Analysis of Histone Proteoforms
Yuan, Z.-F., Arnaudo, A. M., & Garcia, B. A. (2014) · Annual Review of Analytical Chemistry · doi:10.1146/annurev-anchem-071213-015959 · Cited by 64
Aguilar-Cuenca, R., Llorente-González, C., Chapman, J. R., Talayero, V. C., Garrido-Casado, M., Delgado-Arévalo, C., Millán-Salanova, M., Shabanowitz, J., Hunt, D. F., Sellers, J. R., et al. (2020) · Current Biology · doi:10.1016/j.cub.2020.04.057 · Cited by 33
Multienzyme deep learning models improve peptide de novo sequencing by mass spectrometry proteomics
Gueto-Tettay, C., Tang, D., Happonen, L., Heusel, M., Khakzad, H., Malmström, J., & Malmström, L. (2023) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1010457 · Cited by 12
Metadata
- Authors
- Geer, L. Y., Markey, S. P., Kowalak, J. A., Wagner, L., Xu, M., Maynard, D. M., Yang, X., Shi, W., & Bryant, S. H.
- Venue
- Journal of Proteome Research
- Publisher
- American Chemical Society (ACS)
- Published
- 2004-07-02
- Volume
- 3
- Issue
- 5
- Pages
- 958-964
- ISSN
- 1535-3893 (ISSN-L)
- 1535-3907
- OpenAlex
- W2047275456
- Cited by
- 1,431
- References
- 1
- FWCI
- 15.72
- Type
- journal article
- Language
- en
- Open access
- green
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