The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.
Nonlinear Fitting Method for Determining Local False Discovery Rates from Decoy Database Searches
Abstract
False discovery rate (FDR) analyses of protein and peptide identification results using decoy database searching conventionally report aggregate or global FDRs for a whole set of identifications, which are often not very informative about the error rates of individual members in the set. We describe a nonlinear curve fitting method for calculating the local FDR, which estimates the chance that an individual protein (or peptide) is incorrect, and present a simple tool that implements this analysis. The goal of this method is to offer a simple extension to the now commonplace decoy database searching, providing additional valuable information.
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Cites
Numerical recipes in C: the art of scientific computing
(1993) · Choice Reviews Online · doi:10.5860/choice.30-5638 · Cited by 17,933
Numerical recipes in Pascal: the art of scientific computing
(1990) · Choice Reviews Online · doi:10.5860/choice.27-5158 · Cited by 11,811
Statistical significance for genomewide studies
Storey, J. D., & Tibshirani, R. (2003) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1530509100 · Cited by 9,692
Elias, J. E., & Gygi, S. P. (2007) · Nature Methods · doi:10.1038/nmeth1019 · Cited by 3,925
Peng, J., Elias, J. E., Thoreen, C. C., Licklider, L. J., & Gygi, S. P. (2003) · Journal of Proteome Research · doi:10.1021/pr025556v · Cited by 1,647
Beausoleil, S. A., Villén, J., Gerber, S. A., Rush, J., & Gygi, S. P. (2006) · Nature Biotechnology · doi:10.1038/nbt1240 · Cited by 1,577
Shilov, I. V., Seymour, S. L., Patel, A. A., Loboda, A., Tang, W. H., Keating, S. P., Hunter, C. L., Nuwaysir, L. M., & Schaeffer, D. A. (2007) · Molecular & Cellular Proteomics · doi:10.1074/mcp.t600050-mcp200 · Cited by 1,298
Self-citationThe Need for Guidelines in Publication of Peptide and Protein Identification Data
Carr, S., Aebersold, R., Baldwin, M., Burlingame, A., Clauser, K., & Nesvizhskii, A. (2004) · Molecular & Cellular Proteomics · doi:10.1074/mcp.t400006-mcp200 · Cited by 499
Size, power and false discovery rates
Efron, B. (2007) · The Annals of Statistics · doi:10.1214/009053606000001460 · Cited by 411
Posterior Error Probabilities and False Discovery Rates: Two Sides of the Same Coin
Käll, L., Storey, J. D., MacCoss, M. J., & Noble, W. S. (2008) · Journal of Proteome Research · doi:10.1021/pr700739d · Cited by 293
Cited by
Target-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
Horowitz, A. M., Fan, X., Bieri, G., Smith, L. K., Sanchez-Diaz, C. I., Schroer, A. B., Gontier, G., Casaletto, K. B., Kramer, J. H., Williams, K. E., et al. (2020) · Science · doi:10.1126/science.aaw2622 · Cited by 496
Proteomic analysis of human follicular fluid from fertile women
Zamah, A. M., Hassis, M. E., Albertolle, M. E., & Williams, K. E. (2015) · Clinical Proteomics · doi:10.1186/s12014-015-9077-6 · Cited by 132
Velimezi, G., Liontos, M., Vougas, K., Roumeliotis, T., Bartkova, J., Sideridou, M., Dereli-Oz, A., Kocylowski, M., Pateras, I. S., Evangelou, K., et al. (2013) · Nature Cell Biology · doi:10.1038/ncb2795 · Cited by 131
Li, Y., Champion, M. M., Sun, L., Champion, P. A. D., Wojcik, R., & Dovichi, N. J. (2012) · Analytical Chemistry · doi:10.1021/ac202899p · Cited by 128
Metadata
- Authors
- Tang, W. H., Shilov, I. V., & Seymour, S. L.
- Venue
- Journal of Proteome Research
- Publisher
- American Chemical Society (ACS)
- Published
- 2008-08-14
- Volume
- 7
- Issue
- 9
- Pages
- 3661-3667
- ISSN
- 1535-3893 (ISSN-L)
- 1535-3907
- OpenAlex
- W1993457485
- Cited by
- 356
- References
- 13
- FWCI
- 4.35
- Type
- journal article
- Language
- en
- Open access
- closed
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