Abstract
The complexity of proteomes makes good experimental design essential for their successful investigation. Here, we describe how proteomics experiments can be modeled and how computer simulations of these models can be used to improve experimental designs.
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References
S. Ghaemmaghami, W.K. Huh, K. Bower, R.W. Howson, A. Belle, N. Dephoure, E.K. O’Shea, and J.S. Weissman (2003) Global analysis of protein expression in yeast, Nature, 425, 737–41.
N.L. Anderson and N.G. Anderson (2002) The human plasma proteome: history, character, and diagnostic prospects, Mol Cell Proteomics, 1, 845–67.
R. Aebersold and M. Mann (2003) Mass spectrometry-based proteomics, Nature, 422, 198–207.
H. Wang, S.G. Clouthier, V. Galchev, D.E. Misek, U. Duffner, C.K. Min, R. Zhao, J. Tra, G.S. Omenn, J.L. Ferrara, and S.M. Hanash (2005) Intact-protein-based high-resolution three-dimensional quantitative analysis system for proteome profiling of biological fluids, Mol Cell Proteomics, 4, 618–25.
Y. Ishihama (2005) Proteomic LC-MS systems using nanoscale liquid chromatography with tandem mass spectrometry, J Chromatogr A, 1067, 73–83.
B.J. Cargile, J.L. Bundy, T.W. Freeman, and J.L. Stephenson, Jr. (2004) Gel based isoelectric focusing of peptides and the utility of isoelectric point in protein identification, J Proteome Res, 3, 112–9.
J.J. Coon, J.E. Syka, J. Shabanowitz, and D.F. Hunt (2005) Tandem mass spectrometry for peptide and protein sequence analysis, Biotechniques, 38, 519, 521, 523.
R.S. Johnson, M.T. Davis, J.A. Taylor, and S.D. Patterson (2005) Informatics for protein identification by mass spectrometry, Methods, 35, 223–36.
L. McHugh and J.W. Arthur (2008) Computational methods for protein identification from mass spectrometry data, PLoS Comput Biol, 4, e12.
D. Fenyo (2000) Identifying the proteome: software tools, Curr Opin Biotechnol, 11, 391–5.
J. Eriksson and D. Fenyo (2007) Improving the success rate of proteome analysis by modeling protein-abundance distributions and experimental designs, Nat Biotechnol, 25, 651–5.
O.V. Krokhin, R. Craig, V. Spicer, W. Ens, K.G. Standing, R.C. Beavis, and J.A. Wilkins (2004) An improved model for prediction of retention times of tryptic peptides in ion pair reversed-phase HPLC: its application to protein peptide mapping by off-line HPLC-MALDI MS, Mol Cell Proteomics, 3, 908–19.
Acknowledgments
This work was supported by funding provided by the National Institutes of Health Grants RR00862 and RR022220, the Carl Trygger foundation, and the Swedish research council.
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Eriksson, J., Fenyö, D. (2010). Modeling Experimental Design for Proteomics. In: Fenyö, D. (eds) Computational Biology. Methods in Molecular Biology, vol 673. Humana Press, Totowa, NJ. https://doi.org/10.1007/978-1-60761-842-3_14
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DOI: https://doi.org/10.1007/978-1-60761-842-3_14
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