The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Proteomics
Abstract
▪ Abstract Fueled by ever-growing DNA sequence information, proteomics-the large scale analysis of proteins-has become one of the most important disciplines for characterizing gene function, for building functional linkages between protein molecules, and for providing insight into the mechanisms of biological processes in a high-throughput mode. It is now possible to examine the expression of more than 1000 proteins using mass spectrometry technology coupled with various separation methods. High-throughput yeast two-hybrid approaches and analysis of protein complexes using affinity tag purification have yielded valuable protein-protein interaction maps. Large-scale protein tagging and subcellular localization projects have provided considerable information about protein function. Finally, recent developments in protein microarray technology provide a versatile tool to study protein-protein, protein-nucleic acid, protein-lipid, enzyme-substrate, and protein-drug interactions. Other types of microarrays, though not fully developed, also show great potential in diagnostics, protein profiling, and drug identification and validation. This review discusses high-throughput technologies for proteome analysis and their applications. Also discussed are the approaches used for the integrated analysis of the voluminous sets of data generated by proteome analysis conducted on a global scale.
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Cites
A comprehensive analysis of protein–protein interactions in Saccharomyces cerevisiae
Uetz, P., Giot, L., Cagney, G., Mansfield, T. A., Judson, R. S., Knight, J. R., Lockshon, D., Narayan, V., Srinivasan, M., Pochart, P., et al. (2000) · Nature · doi:10.1038/35001009 · Cited by 4,706
Quantitative analysis of complex protein mixtures using isotope-coded affinity tags
Gygi, S. P., Rist, B., Gerber, S. A., Turecek, F., Gelb, M. H., & Aebersold, R. (1999) · Nature Biotechnology · doi:10.1038/13690 · Cited by 4,683
The complete genome sequence of the gastric pathogen Helicobacter pylori
Tomb, J.-F., White, O., Kerlavage, A. R., Clayton, R. A., Sutton, G. G., Fleischmann, R. D., Ketchum, K. A., Klenk, H. P., Gill, S., Dougherty, B. A., et al. (1997) · Nature · doi:10.1038/41483 · Cited by 3,529
Global Analysis of Protein Activities Using Proteome Chips
Zhu, H., Bilgin, M., Bangham, R., Hall, D., Casamayor, A., Bertone, P., Lan, N., Jansen, R., Bidlingmaier, S., Houfek, T., et al. (2001) · Science · doi:10.1126/science.1062191 · Cited by 1,884
Self-citationA genomic regulatory network for development
Davidson, E. H., Rast, J. P., Oliveri, P., Ransick, A., Calestani, C., Yuh, C.-H., Minokawa, T., Amore, G., Hinman, V., Arenas-Mena, C., et al. (2002) · Science · doi:10.1126/science.1069883 · Cited by 1,567
Modeling the Heart--from Genes to Cells to the Whole Organ
Noble, D. (2002) · Science · doi:10.1126/science.1069881 · Cited by 622
Covey, T. R., Huang, E. C., & Henion, J. D. (1991) · Analytical Chemistry · doi:10.1021/ac00013a003 · Cited by 446
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The challenge of emerging and re-emerging infectious diseases
Morens, D. M., Folkers, G. K., & Fauci, A. S. (2004) · Nature · doi:10.1038/nature02759 · Cited by 2,103
Mass spectrometry–based proteomics turns quantitative
Ong, S.-E., & Mann, M. (2005) · Nature Chemical Biology · doi:10.1038/nchembio736 · Cited by 1,572
Zhao, R., Davey, M., Hsu, Y.-C., Kaplanek, P., Tong, A., Parsons, A. B., Krogan, N., Cagney, G., Mai, D., Greenblatt, J., et al. (2005) · Cell · doi:10.1016/j.cell.2004.12.024 · Cited by 819
Magliery, T. J., Wilson, C. G. M., Pan, W., Mishler, D., Ghosh, I., Hamilton, A. D., & Regan, L. (2005) · Journal of the American Chemical Society · doi:10.1021/ja046699g · Cited by 441
Hall, D. A., Ptacek, J., & Snyder, M. (2007) · Mechanisms of Ageing and Development · doi:10.1016/j.mad.2006.11.021 · Cited by 286
Self-citation
Metadata
- Authors
- Zhu, H., Bilgin, M., & Snyder, M.
- Venue
- Annual Review of Biochemistry
- Publisher
- Annual Reviews
- Published
- 2003-06
- Volume
- 72
- Issue
- 1
- Pages
- 783-812
- ISSN
- 0066-4154 (ISSN-L)
- 1545-4509
- OpenAlex
- W4247666721
- Cited by
- 332
- References
- 7
- FWCI
- 2.26
- Type
- journal article
- Language
- en
- Open access
- closed
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