The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Refinements to Label Free Proteome Quantitation: How to Deal with Peptides Shared by Multiple Proteins
Abstract
Quantitative shotgun proteomics is dependent on the detection, identification, and quantitative analysis of peptides. An issue arises with peptides that are shared between multiple proteins. What protein did they originate from and how should these shared peptides be used in a quantitative proteomics workflow? To systematically evaluate shared peptides in label-free quantitative proteomics, we devised a well-defined protein sample consisting of known concentrations of six albumins from different species, which we added to a highly complex yeast lysate. We used the spectral counts based normalized spectral abundance factor (NSAF) as the starting point for our analysis and compared an exhaustive list of possible combinations of parameters to determine what was the optimal approach for dealing with shared peptides and shared spectral counts. We showed that distributing shared spectral counts based on the number of unique spectral counts led to the most accurate and reproducible results.
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Cites
Interpretation of Shotgun Proteomic Data
Nesvizhskii, A. I., & Aebersold, R. (2005) · Molecular & Cellular Proteomics · doi:10.1074/mcp.r500012-mcp200 · Cited by 1,025
Probabilistic assembly of human protein interaction networks from label-free quantitative proteomics
Sardiu, M. E., Cai, Y., Jin, J., Swanson, S. K., Conaway, R. C., Conaway, J. W., Florens, L., & Washburn, M. P. (2008) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0706983105 · Cited by 226
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Research works printed but not linked: 16
- Zhang B., 2007, J. Proteome Res., 6, 3549, 10.1021/pr070230d · DOI · Paper page
- Usaite R., 2008, J. Proteome Res., 7, 266, 10.1021/pr700580m · DOI
- Jin S., 2008, J. Proteome Res., 7, 164, 10.1021/pr0704175 · DOI
- Liu W. L., 2008, Mol. Cell, 29, 81, 10.1016/j.molcel.2007.11.003 · DOI
- Zybailov B., 2008, PLoS One, 3, e1994, 10.1371/journal.pone.0001994 · DOI · Paper page
- Florens L., 2006, Methods, 40, 303, 10.1016/j.ymeth.2006.07.028 · DOI · Paper page
- Paoletti A. C., 2006, Proc. Natl. Acad. Sci. U.S.A., 103, 18928, 10.1073/pnas.0606379103 · DOI · Paper page
- Zybailov B., 2006, J. Proteome Res., 5, 2339, 10.1021/pr060161n · DOI · Paper page
- Florens L., 2006, Methods Mol. Biol., 328, 159
- Eng J., 1994, J. Am. Soc. Mass Spectrom., 5, 976, 10.1016/1044-0305(94)80016-2 · DOI · Paper page
- Tabb D. L., 2002, J. Proteome Res., 1, 21, 10.1021/pr015504q · DOI · Paper page
- Durbin B. P., 2002, Bioinformatics, 18, S105, 10.1093/bioinformatics/18.suppl_1.S105 · DOI · Paper page
- Lin S. M., 2008, Nucleic Acids Res., 36, e11, 10.1093/nar/gkm1075 · DOI · Paper page
- Pavelka N., 2008, Mol. Cell. Proteomics, 7, 631, 10.1074/mcp.M700240-MCP200 · DOI · Paper page
- Skaar J. R., 2007, Cancer Res., 67, 2006, 10.1158/0008-5472.CAN-06-3241 · DOI
- Sardiu M. E., 2009, PLoS One, 4, e7310, 10.1371/journal.pone.0007310 · DOI
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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
Self-citationVpx relieves inhibition of HIV-1 infection of macrophages mediated by the SAMHD1 protein
Hrecka, K., Hao, C., Gierszewska, M., Swanson, S. K., Kesik-Brodacka, M., Srivastava, S., Florens, L., Washburn, M. P., & Skowronski, J. (2011) · Nature · doi:10.1038/nature10195 · Cited by 1,200
Self-citationQuantitative mass spectrometry in proteomics: critical review update from 2007 to the present
Bantscheff, M., Lemeer, S., Savitski, M. M., & Kuster, B. (2012) · Analytical and Bioanalytical Chemistry · doi:10.1007/s00216-012-6203-4 · Cited by 801
Aneuploidy confers quantitative proteome changes and phenotypic variation in budding yeast
Pavelka, N., Rancati, G., Zhu, J., Bradford, W. D., Saraf, A., Florens, L., Sanderson, B. W., Hattem, G. L., & Li, R. (2010) · Nature · doi:10.1038/nature09529 · Cited by 577
Self-citationCytosolic proteostasis through importing of misfolded proteins into mitochondria
Ruan, L., Zhou, C., Jin, E., Kucharavy, A., Zhang, Y., Wen, Z., Florens, L., & Li, R. (2017) · Nature · doi:10.1038/nature21695 · Cited by 474
Self-citation
Metadata
- Authors
- Zhang, Y., Wen, Z., Washburn, M. P., & Florens, L.
- Venue
- Analytical Chemistry
- Publisher
- American Chemical Society (ACS)
- Published
- 2010-02-18
- Volume
- 82
- Issue
- 6
- Pages
- 2272-2281
- ISSN
- 0003-2700 (ISSN-L)
- 1520-6882
- OpenAlex
- W2078138443
- Cited by
- 385
- References
- 2
- FWCI
- 13.96
- Type
- journal article
- Language
- en
- Open access
- closed
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