Target-Decoy Search Strategy for Mass Spectrometry-Based Proteomics
Open accessAbstract
Accurate and precise methods for estimating incorrect peptide and protein identifications are crucial for effective large-scale proteome analyses by tandem mass spectrometry. The target-decoy search strategy has emerged as a simple, effective tool for generating such estimations. This strategy is based on the premise that obvious, necessarily incorrect "decoy" sequences added to the search space will correspond with incorrect search results that might otherwise be deemed to be correct. With this knowledge, it is possible not only to estimate how many incorrect results are in a final data set but also to use decoy hits to guide the design of filtering criteria that sensitively partition a data set into correct and incorrect identifications.
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Peptide-spectrum matches were adjusted to a 1% false discovery rate.
“Peptide-spectrum matches (PSMs) were adjusted to a 1% false discovery rate (FDR)52”
From Human and mouse single-nucleus transcriptomics reveal TREM2-dependent and TREM2-independent cellular responses in Alzheimer’s disease (2020) · cites this paper as [52]
With the assumptions that multiplets are rare and all contributing cell states are present as singlets, simulated doublets can be used to build a target-decoy k-nearest-neighbor classifier that identifies doublets.
“With these assumptions, we show that doublets simulated from empirical datasets can be used to construct a “target-decoy” (Elias and Gygi, 2010) k-nearest neighbor (KNN) classifier capable of identifying doublets, as shown schematically in Figure 1C (Bayesian classifier derived in STAR Methods).”
From Scrublet: computational identification of cell doublets in single-cell transcriptomic data (2019) · cites this paper as Elias and Gygi, 2010
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Cites
Eng, J. K., McCormack, A. L., & Yates, J. R. (1994) · Journal of the American Society for Mass Spectrometry · doi:10.1016/1044-0305(94)80016-2 · Cited by 6,484 · see citing works
Keller, A., Nesvizhskii, A., Kolker, E., & Aebersold, R. (2002) · Analytical Chemistry · doi:10.1021/ac025747h · Cited by 4,970 · see citing works
A statistical model for identifying proteins by tandem mass spectrometry.
Nesvizhskii, A., Keller, A., Kolker, E., & Aebersold, R. (2003) · Analytical Chemistry · doi:10.1021/ac0341261 · Cited by 4,902 · see citing works
Elias, J. E., & Gygi, S. P. (2007) · Nature Methods · doi:10.1038/nmeth1019 · Cited by 3,925 · see citing works
Self-citationThe Universal Protein Resource (UniProt)
Bairoch, A. (2004) · Nucleic Acids Research · doi:10.1093/nar/gki070 · Cited by 3,774 · see citing works
TANDEM: matching proteins with tandem mass spectra
Craig, R., & Beavis, R. C. (2004) · Bioinformatics · doi:10.1093/bioinformatics/bth092 · Cited by 2,553 · see citing works
Semi-supervised learning for peptide identification from shotgun proteomics datasets
Käll, L., Canterbury, J. D., Weston, J., Noble, W. S., & MacCoss, M. J. (2007) · Nature Methods · doi:10.1038/nmeth1113 · Cited by 2,424 · see citing works
Peng, J., Elias, J. E., Thoreen, C. C., Licklider, L., & Gygi, S. P. (2002) · Journal of Proteome Research · doi:10.1021/pr025556v · Cited by 1,652 · see citing works
Self-citationBeausoleil, S. A., Villén, J., Gerber, S. A., Rush, J., & Gygi, S. P. (2006) · Nature Biotechnology · doi:10.1038/nbt1240 · Cited by 1,577 · see citing works
Self-citationOpen Mass Spectrometry Search Algorithm
Geer, L. Y., Markey, S. P., Kowalak, J. A., Wagner, L., Xu, M. Q., Maynard, D., Yang, X., Shi, W., & Bryant, S. H. (2004) · Journal of Proteome Research · doi:10.1021/pr0499491 · Cited by 1,420 · see citing works
The Universal Protein Resource (UniProt)
The UniProt Consortium (2007) · Nucleic Acids Research · doi:10.1093/nar/gkm895 · Cited by 824 · see citing works
Comparative evaluation of mass spectrometry platforms used in large-scale proteomics investigations
Elias, J. E., Haas, W., Faherty, B. K., & Gygi, S. P. (2005) · Nature Methods · doi:10.1038/nmeth785 · Cited by 703 · see citing works
Self-citationThe International Protein Index: An integrated database for proteomics experiments
Kersey, P. J., Duarte, J., Williams, A., Karavidopoulou, Y., Birney, E., & Apweiler, R. (2004) · PROTEOMICS · doi:10.1002/pmic.200300721 · Cited by 693 · see citing works
Assigning Significance to Peptides Identified by Tandem Mass Spectrometry Using Decoy Databases
Käll, L., Storey, J. D., MacCoss, M. J., & Noble, W. S. (2008) · Journal of Proteome Research · doi:10.1021/pr700600n · Cited by 607 · see citing works
PeptideAtlas: a resource for target selection for emerging targeted proteomics workflows
Deutsch, E. W., Lam, H., & Aebersold, R. H. (2008) · EMBO Reports · doi:10.1038/embor.2008.56 · Cited by 541 · see citing works
Sadygov, R. G., Cociorva, D., & Yates, J. R. (2004) · Nature Methods · doi:10.1038/nmeth725 · Cited by 409 · see citing works
Qscore: An algorithm for evaluating SEQUEST database search results
Moore, R. E., Young, M. K., & Lee, T. D. (2002) · Journal of the American Society for Mass Spectrometry · doi:10.1016/s1044-0305(02)00352-5 · Cited by 390 · see citing works
Nonlinear Fitting Method for Determining Local False Discovery Rates from Decoy Database Searches
Tang, W. H., Shilov, I. V., & Seymour, S. L. (2008) · Journal of Proteome Research · doi:10.1021/pr070492f · Cited by 356 · see citing works
Intensity-based protein identification by machine learning from a library of tandem mass spectra
Elias, J. E., Gibbons, F. D., King, O. D., Roth, F. P., & Gygi, S. P. (2004) · Nature Biotechnology · doi:10.1038/nbt930 · Cited by 304 · see citing works
Self-citationPosterior Error Probabilities and False Discovery Rates: Two Sides of the Same Coin
Käll, L., Storey, J. D., MacCoss, M. J., & Noble, W. S. (2007) · Journal of Proteome Research · doi:10.1021/pr700739d · Cited by 293 · see citing works
Optimization and Use of Peptide Mass Measurement Accuracy in Shotgun Proteomics
Haas, W., Faherty, B. K., Gerber, S. A., Elias, J. E., Beausoleil, S. A., Bakalarski, C. E., Li, X., Villén, J., & Gygi, S. P. (2006) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m500339-mcp200 · Cited by 262 · see citing works
Self-citationReporting Protein Identification Data
Bradshaw, R. A., Burlingame, A. L., Carr, S. A., et al. (2006) · Molecular & Cellular Proteomics · doi:10.1074/mcp.e600005-mcp200 · Cited by 222 · see citing works
The need for a public proteomics repository
Prince, J. T., Carlson, M. W., Wang, R., Lü, P., & Marcotte, E. M. (2004) · Nature Biotechnology · doi:10.1038/nbt0404-471 · Cited by 164 · see citing works
Comparative Evaluation of Tandem MS Search Algorithms Using a Target-Decoy Search Strategy
Balgley, B. M., Laudeman, T., Li, Y., Song, T., & Lee, C. S. (2007) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m600469-mcp200 · Cited by 144 · see citing works
Randomized Sequence Databases for Tandem MassSpectrometry Peptide and Protein Identification
Higdon, R., Hogan, J. M., van Belle, G., & Kolker, E. (2005) · OMICS A Journal of Integrative Biology · doi:10.1089/omi.2005.9.364 · Cited by 84 · see citing works
Huttlin, E. L., Hegeman, A. D., Harms, A. C., et al. (2006) · Journal of Proteome Research · doi:10.1021/pr0603194 · Cited by 76 · see citing works
Scheler, C., Li, X., Salnikow, J., Dunn, M. J., & Jungblut, P. R. (1999) · Electrophoresis · doi:10.1002/(sici)1522-2683(19991201)20:18 · Cited by 72 · see citing works
Guidelines for reporting the use of mass spectrometry informatics in proteomics
Binz, P., Barkovich, R. J., Beavis, R. C., Creasy, D. M., Horn, D. M., Julian, R. K., Seymour, S. L., Taylor, C. F., & Vandenbrouck, Y. (2008) · Nature Biotechnology · doi:10.1038/nbt0808-862 · Cited by 71 · see citing works
Bakalarski, C. E., Haas, W., Dephoure, N., & Gygi, S. P. (2007) · Analytical and Bioanalytical Chemistry · doi:10.1007/s00216-007-1563-x · Cited by 64 · see citing works
Self-citationMinimum Reporting Requirements for Proteomics: A MIAPE Primer
Taylor, C. F. (2006) · PROTEOMICS · doi:10.1002/pmic.200600549 · Cited by 56 · see citing works
Jiang, X., Jiang, X., Han, G., et al. (2007) · BMC Bioinformatics · doi:10.1186/1471-2105-8-323 · Cited by 29 · see citing works
Flagged first, then by citations
Cited by
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Wolock, S. L., Lopez, R., & Klein, A. M. (2019) · Cell Systems · doi:10.1016/j.cels.2018.11.005 · Cited by 2,758 · see citing works
Bruderer, R., Bernhardt, O. M., Gandhi, T., Miladinović, S. M., Cheng, L.-Y., Messner, S., Ehrenberger, T., Zanotelli, V., Butscheid, Y., Escher, C., et al. (2015) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m114.044305 · Cited by 1,238 · see citing works
Zhou, Y., Song, W. M., Andhey, P. S., Swain, A., Levy, T., Miller, K. R., Poliani, P. L., Cominelli, M., Grover, S., Gilfillan, S., et al. (2020) · Nature Medicine · doi:10.1038/s41591-019-0695-9 · Cited by 1,150 · see citing works
Comparative analysis of proteome and transcriptome variation in mouse
Ghazalpour, A., Bennett, B., Petyuk, V. A., Orozco, L., Hagopian, R., Mungrue, I. N., Farber, C. R., Sinsheimer, J., Kang, H. M., Furlotte, N., et al. (2011) · PLoS Genetics · doi:10.1371/journal.pgen.1001393 · Cited by 624 · see citing works
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Pozzi, C., Cuomo, A., Spadoni, I., Magni, E., Silvola, A., Conte, A., Sigismund, S., Ravenda, P. S., Bonaldi, T., Zampino, M. G., et al. (2016) · Nature Medicine · doi:10.1038/nm.4078 · Cited by 292 · see citing works
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 · see citing works
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 · see citing works
Protein aggregation mediates stoichiometry of protein complexes in aneuploid cells
Brennan, C. M., Vaites, L. P., Wells, J. N., Santaguida, S., Paulo, J. A., Storchova, Z., Harper, J. W., Marsh, J. A., & Amon, A. (2019) · Genes & Development · doi:10.1101/gad.327494.119 · Cited by 109 · see citing works
Ginno, P. A., Burger, L., Seebacher, J., Iesmantavicius, V., & Schübeler, D. (2018) · Nature Communications · doi:10.1038/s41467-018-06007-5 · Cited by 91 · see citing works
Looking for Missing Proteins in the Proteome of Human Spermatozoa: An Update
Vandenbrouck, Y., Lane, L., Carapito, C., Duek, P., Rondel, K., Bruley, C., Macron, C., Gonzalez de Peredo, A., Couté, Y., Chaoui, K., et al. (2016) · Journal of Proteome Research · doi:10.1021/acs.jproteome.6b00400 · Cited by 77 · see citing works
Brubaker, D. K., Paulo, J. A., Sheth, S., Poulin, E. J., Popow, O., Joughin, B. A., Strasser, S. D., Starchenko, A., Gygi, S. P., Lauffenburger, D. A., et al. (2019) · Cell Systems · doi:10.1016/j.cels.2019.07.006 · Cited by 57 · see citing works
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Shortreed, M. R., Frey, B. L., Scalf, M., Knoener, R. A., Cesnik, A. J., & Smith, L. M. (2016) · Journal of Proteome Research · doi:10.1021/acs.jproteome.5b01090 · Cited by 58 · see citing works
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MethodFert-Bober, J., Murray, C. I., Parker, S. J., & Van Eyk, J. E. (2018) · Circulation Research · doi:10.1161/circresaha.118.310966 · Cited by 45 · see citing works
Zhang, Y., Zhao, Y., Zhang, J., Gao, Y., Li, S., Chang, C., Yu, D., & Yang, G. (2021) · Neuroscience Letters · doi:10.1016/j.neulet.2021.136206 · Cited by 36 · see citing works
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Pham, V., Pitti, R., Tindell, C. A., Cheung, T. K., Masselot, A., Stephan, J.-P., Guler, G. D., Wilson, C., Lill, J., Arnott, D., et al. (2020) · Journal of Proteome Research · doi:10.1021/acs.jproteome.9b00773 · Cited by 26 · see citing works
- Authors
- Elias, J. E., & Gygi, S. P.
- Affiliations
- Harvard University (US)
- Corresponding author
- Gygi, Steven P.
- Venue
- Methods in Molecular Biology
- Publisher
- Humana Press
- Published
- 2009-12-05
- Volume
- 604
- Pages
- 55-71
- ISSN
- 1064-3745 (ISSN-L)
- 1940-6029
- PMC
- PMC2922680
- OpenAlex
- W1418224
- Cited by
- 644
- References
- 31
- Citations by year
- 2026: 23 · 2025: 54 · 2024: 49 · 2023: 62 · 2022: 39 · 2021: 64 · 2020: 54 · 2019: 53 · 2018: 36 · 2017: 43 · 2016: 39 · 2015: 36 · 2014: 30 · 2013: 21 · 2012: 20
- FWCI
- 44.18
- Type
- book chapter
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Methods in molecular biology (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health
- National Human Genome Research Institute (HG000 41, T32HG000041, K22HG000041, R01HG003456)
- National Institute of General Medical Sciences (GM 67945, R01GM067945)
- Topics
- Physical Sciences › Chemistry › Spectroscopy › Advanced Proteomics Techniques and Applications
- Physical Sciences › Chemistry › Spectroscopy › Mass Spectrometry Techniques and Applications
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Keywords
- peptide identification, protein identification, false discovery rate estimation, tandem mass spectrometry, proteomics data analysis
- MeSH terms
- Algorithms, Proteins, Software, Mass Spectrometry, Databases, Protein, Proteomics
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