The DOI matches its Crossref record.
A Method for Assessing the Statistical Significance of Mass Spectrometry-Based Protein Identifications Using General Scoring Schemes
Abstract
This paper investigates the use of survival functions and expectation values to evaluate the results of protein identification experiments. These functions are standard statistical measures that can be used to reduce various protein identification scoring schemes to a common, easily interpretably representation. The relative merits of scoring systems were explored using this approach, as well as the effects of altering primary identification parameters. We would advocate the widespread use of these simple statistical measures to simplify and standardize the reporting of the confidence of protein identification results, allowing the users of different identification algorithms to compare their results in a straightforward and statistically significant manner. A method is described for measuring these distributions using information that is being discarded by most protein identification search engines, resulting in accurate survival functions that are specific to any combination of scoring algorithms, sequence databases, and mass spectra.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Protein Analysis by Shotgun/Bottom-up Proteomics
Zhang, Y., Fonslow, B. R., Shan, B., Baek, M.-C., & Yates, J. R. (2013) · Chemical Reviews · doi:10.1021/cr3003533 · Cited by 1,491
Interpretation of Shotgun Proteomic Data
Nesvizhskii, A. I., & Aebersold, R. (2005) · Molecular & Cellular Proteomics · doi:10.1074/mcp.r500012-mcp200 · Cited by 1,025
Omenn, G. S., States, D. J., Adamski, M., Blackwell, T. W., Menon, R., Hermjakob, H., Apweiler, R., Haab, B. B., Simpson, R. J., Eddes, J. S., et al. (2005) · PROTEOMICS · doi:10.1002/pmic.200500358 · Cited by 787
Searle, B. C., Pino, L. K., Egertson, J. D., Ting, Y. S., Lawrence, R. T., MacLean, B. X., Villén, J., & MacCoss, M. J. (2018) · Nature Communications · doi:10.1038/s41467-018-07454-w · Cited by 522
A multicenter study benchmarks software tools for label-free proteome quantification
Navarro, P., Kuharev, J., Gillet, L. C., Bernhardt, O. M., MacLean, B., Röst, H. L., Tate, S. A., Tsou, C.-C., Reiter, L., Distler, U., et al. (2016) · Nature Biotechnology · doi:10.1038/nbt.3685 · Cited by 346
An update on LNCipedia: a database for annotated human lncRNA sequences
Volders, P.-J., Verheggen, K., Menschaert, G., Vandepoele, K., Martens, L., Vandesompele, J., & Mestdagh, P. (2015) · Nucleic Acids Research · doi:10.1093/nar/gku1060 · Cited by 355
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
Buschow, S. I., van Balkom, B. W. M., Aalberts, M., Heck, A. J. R., Wauben, M., & Stoorvogel, W. (2010) · Immunology & Cell Biology · doi:10.1038/icb.2010.64 · Cited by 275
Le Bihan, M.-C., Bigot, A., Jensen, S. S., Dennis, J. L., Rogowska-Wrzesinska, A., Lainé, J., Gache, V., Furling, D., Jensen, O. N., Voit, T., et al. (2012) · Journal of Proteomics · doi:10.1016/j.jprot.2012.09.008 · Cited by 153
Fornelli, L., Durbin, K. R., Fellers, R. T., Early, B. P., Greer, J. B., LeDuc, R. D., Compton, P. D., & Kelleher, N. L. (2017) · Journal of Proteome Research · doi:10.1021/acs.jproteome.6b00698 · Cited by 88
Vogels, M. W., van Balkom, B. W. M., Kaloyanova, D. V., Batenburg, J. J., Heck, A. J., Helms, J. B., Rottier, P. J. M., & de Haan, C. A. M. (2011) · PROTEOMICS · doi:10.1002/pmic.201000309 · Cited by 40
Global phosphoproteomic profiling reveals distinct signatures in B-cell non-Hodgkin lymphomas
Rolland, D., Basrur, V., Conlon, K., Wolfe, T., Fermin, D., Nesvizhskii, A. I., Lim, M. S., & Elenitoba-Johnson, K. S. J. (2014) · The American Journal of Pathology · doi:10.1016/j.ajpath.2014.01.036 · Cited by 31
Molecular Characterization of AMPA-Receptor-Containing Vesicles
Peters, J. J., Leitz, J., Oses-Prieto, J. A., Burlingame, A. L., & Brunger, A. T. (2021) · Frontiers in Molecular Neuroscience · doi:10.3389/fnmol.2021.754631 · Cited by 18
The phage protein paratox is a multifunctional metabolic regulator of Streptococcus
Muna, T. H., Rutbeek, N. R., Horne, J., Lao, Y. W., Krokhin, O. V., & Prehna, G. (2025) · Nucleic Acids Research · doi:10.1093/nar/gkae1200
Metadata
- Authors
- Fenyö, D., & Beavis, R. C.
- Venue
- Analytical Chemistry
- Publisher
- American Chemical Society (ACS)
- Published
- 2003-01-10
- Volume
- 75
- Issue
- 4
- Pages
- 768-774
- ISSN
- 0003-2700 (ISSN-L)
- 1520-6882
- OpenAlex
- W2082006641
- Cited by
- 480
- References
- 0
- FWCI
- 14.23
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
