Abstract
Quantitative proteomics is a growing field where several experimental techniques such as those based around stable isotope labelling are reaching maturity. These advances require the parallel development of informatics tools to process and analyse the data, especially for high-throughput experiments seeking to quantify large numbers of proteins. We have developed a novel algorithm for the quantitative analysis of stable isotope-based proteomics data at the peptide level. Without prior formal identification of the peptides by MS/MS, the algorithm determines the mass charge ratio m/z and retention time t of stable isotope-labelled peptide pairs and calculates their relative ratios. It supports several non-proprietary XML input formats and requires only minimal parameter tuning and runs fully automated. We have tested its performance on a low complexity peptide sample in an initial study. In comparison to a manual analysis and an automated approach using MSQuant, it performs as well or better and therefore we believe it has utility for groups wishing to perform high-throughput experiments.
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References
Cox, J., Mann, M.: Is proteomics the new genomics? Cell 130(3), 395–398 (2007)
Alterovitz, G., Liu, J., Chow, J., Ramoni, M.F.: Automation, parallelism, and robotics for proteomics. Proteomics 6(14), 4016–4022 (2006)
de Godoy, L.M.F., Olsen, J.V., Cox, J., Nielsen, M.L., Hubner, N.C., Frohlich, F., Walther, T.C., Mann, M.: Comprehensive mass-spectrometry-based proteome quantification of haploid versus diploid yeast. Nature 455(7217), 1251–1254 (2008)
Ong, S.-E., Blagoev, B., Kratchmarova, I., Kristensen, D.B., Steen, H., Pandey, A., Mann, M.: Stable isotope labeling by amino acids in cell culture, SILAC, as a simple and accurate approach to expression proteomics. Mol. Cell. Proteomics 1(5), 376–386 (2002)
Beynon, R.J., Doherty, M.K., Pratt, J.M., Gaskell, S.J.: Multiplexed absolute quantification in proteomics using artificial QCAT proteins of concatenated signature peptides. Nat. Methods 2(8), 587–589 (2005)
Schulze, W.X., Mann, M.: A novel proteomic screen for peptide-protein interactions. J. Biol. Chem. 279(11), 10756–10764 (2004)
Cox, J., Mann, M.: MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat. Biotechnol. 26(12), 1367–1372 (2008)
Kohlbacher, O., Reinert, K., Gröpl, C., Lange, E., Pfeifer, N., Schulz-Trieglaff, O., Sturm, M.: TOPP – the OpenMS proteomics pipeline. Bioinformatics 23(2), e191–e197 (2007)
Martens, L., Orchard, S., Apweiler, R., Hermjakob, H.: Human Proteome Organization Proteomics Standards Initiative: data standardization, a view on developments and policy. Mol. Cell. Proteomics 6(9), 1666–1667 (2007)
Sturm, M., Bertsch, A., Gröpl, C., Hildebrandt, A., Hussong, R., Lange, E., Pfeifer, N., Schulz-Trieglaff, O., Zerck, A., Reinert, K., Kohlbacher, O.: OpenMS - an open-source software framework for mass spectrometry. BMC Bioinformatics 9, 163 (2008)
Rousseeuw, P.J.: Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics 20, 53–65 (1987)
Orchard, S., Hermjakob, H., Taylor, C., Binz, P.-A., Hoogland, C., Julian, R., Garavelli, J.S., Aebersold, R., Apweiler, R.: Autumn 2005 Workshop of the Human Proteome Organisation Proteomics Standards Initiative (HUPO-PSI) Geneva, September 4-6 (2005); Proteomics 6 (3), 738–741 (2006)
Pedrioli, P., et al.: A common open representation of mass spectrometry data and its application to proteomics research. Nat. Biotechnol. 22(11), 1459–1466 (2004)
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Nilse, L. et al. (2010). SILACAnalyzer - A Tool for Differential Quantitation of Stable Isotope Derived Data. In: Masulli, F., Peterson, L.E., Tagliaferri, R. (eds) Computational Intelligence Methods for Bioinformatics and Biostatistics. CIBB 2009. Lecture Notes in Computer Science(), vol 6160. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14571-1_4
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DOI: https://doi.org/10.1007/978-3-642-14571-1_4
Publisher Name: Springer, Berlin, Heidelberg
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