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        Census for Proteome Quantification

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        • Abstract
        • Table of Contents
        • Figures
        • Literature Cited

        Abstract

         

        Quantitative analysis has become increasingly important in the proteomics field; however, the large amount of mass spectrometric data and the different types of quantitative strategies make data analysis ever challenging. Here we describe a quantitative software tool called Census to analyze high?throughput mass spectrometry data from shotgun proteomics experiments in an efficient way. Census is capable of analyzing various stable isotope labeling experiments (using, e.g., 15 N, 18 O, SILAC, iTRAQ, TMT) in addition to labeling?free experiments. With high?resolution data, Census increases the quantitative accuracy by minimizing the contributions of interfering peaks and chemical noise with a small accuracy tolerance for each isotope peak. Census provides various scoring algorithms including least?squares correlation, weight average, singleton peptide detection with discriminant analysis, and probability score for each peptide. Furthermore, Census has built?in multiple statistical filters to maintain robust quality control on quantitative results. Curr. Protoc. Bioinform. 29:13.12.1?13.12.11. © 2010 by John Wiley & Sons, Inc.

        Keywords: Census; mass spectrometry data; proteomics data; stable isotope label; label?free analysis

             
         
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        Table of Contents

        • Introduction
        • Basic Protocol 1: Using Census to Quantitatively Analyze Proteomic Data
        • Support Protocol 1: Downloading and Installing Census
        • Guidelines for Understanding Results
        • Commentary
        • Literature Cited
        • Figures
        • Tables
             
         
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        Materials

         
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        Figures

        •   Figure 13.12.1 Label‐free configuration file. The sample tag defines a group of experiments, and the each_sample tag inside it defines an individual experiment. The path in the file tag can have specific spectral file name or wildcard (asterisk) for all spectral files in the same directory.
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        •   Figure 13.12.2 Quantified protein list after loading the census_chro.xml file. To display quantified peptide information, double‐click on one of the proteins.
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        •   Figure 13.12.3 Quantified peptide list with chromatogram graph and quantitative scores.
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        •   Figure 13.12.4 A panel to generate Census report file. Various filtering options can be defined to control the quality of the output.
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        •   Figure 13.12.5 An example of a Census report file named census‐out.txt. The upper part shows the filtering options applied, and the lower part shows a protein/peptide list with scores. The report file is a tab‐delimited text file, so it is recommended to align columns properly by loading it into Microsoft Excel or OpenOffice Calc.
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        Literature Cited

        Literature Cited
           Denison, C., Rudner, A.D., Gerber, S.A., Bakalarski, C.E., Moazed, D., and Gygi, S.P. 2005. A proteomic strategy for gaining insights into protein sumoylation in yeast. Mol. Cell Proteomics 4:246‐254.
           Dieguez‐Acuna, F.J., Gerber, S.A., Kodama, S., Elias, J.E., Beausoleil, S.A., Faustman, D., and Gygi, S.P. 2005. Characterization of mouse spleen cells by subtractive proteomics. Mol. Cell Proteomics 4:1459‐1470.
           Julka, S. and Regnier, F. 2004. Quantification in proteomics through stable isotope coding: A review. J. Proteome Res. 3:350‐363.
           MacCoss, M.J., Wu, C.C., Liu, H., Sadygov, R., and Yates, J.R. 3rd. 2003. A correlation algorithm for the automated analysis of quantitative ‘shotgun’ proteomics data. Anal. Chem. 75:6912‐6921.
           Makarov, A., Denisov, E., Kholomeev, A., Balschun, W., Lange, O., Strupat, K., and Horning, S. 2006. Performance evaluation of a hybrid linear ion trap/orbitrap mass spectrometer. Anal. Chem. 78:2113‐2120.
           McDonald, W.H., Tabb, D.L., Sadygov, R.G., MacCoss, M.J., Venable, J., Graumann, J., Johnson, J.R., Cociorva, D., and Yates, J.R. 3rd. 2004. MS1, MS2, and SQT‐three unified, compact, and easily parsed file formats for the storage of shotgun proteomic spectra and identifications. Rapid Commun. Mass Spectrom. 18:2162‐2168.
           Olsen, J.V., de Godoy, L.M., Li, G., Macek, B., Mortensen, P., Pesch, R., Makarov, A., Lange, O., Horning, S., and Mann, M. 2005. Parts per million mass accuracy on an Orbitrap mass spectrometer via lock mass injection into a C‐trap. Mol. Cell Proteomics 4:2010‐2021.
           Park, S.K., Venable, J.D., Xu, T., and Yates, J.R. 3rd. 2008. A quantitative analysis software tool for mass spectrometry‐based proteomics. Nat. Methods 5:319‐322.
           Park, S.K., Liao, L., Kim, J.Y., and Yates, J.R. 3rd. 2009. A computational approach to correct arginine‐to‐proline conversion in quantitative proteomics. Nat. Methods 6:184‐185.
           Pedrioli, P.G., Eng, J.K., Hubley, R., Vogelzang, M., Deutsch, E.W., Raught, B., Pratt, B., Nilsson, E., Angeletti, R.H., Apweiler, R., Cheung, K., Costello, C.E., Hermjakob, H., Huang, S., Julian, R.K., Kapp, E., McComb, M.E., Oliver, S.G., Omenn, G., Paton, N.W., Simpson, R., Smith, R., Taylor, C.F., Zhu, W., and Aebersold, R. 2004. A common open representation of mass spectrometry data and its application to proteomics research. Nat. Biotechnol. 22:1459‐1466.
           Tabb, D.L., McDonald, W.H., and Yates, J.R. 3rd. 2002. DTASelect and Contrast: Tools for assembling and comparing protein identifications from shotgun proteomics. J. Proteome Res. 1:21‐26.
           Venable, J.D., Dong, M.Q., Wohlschlegel, J., Dillin, A., and Yates, J.R. 2004. Automated approach for quantitative analysis of complex peptide mixtures from tandem mass spectra. Nat. Methods 1:39‐45.
           Yates, J.R., Cociorva, D., Liao, L., and Zabrouskov, V. 2006. Performance of a linear ion trap‐Orbitrap hybrid for peptide analysis. Anal. Chem. 78:493‐500.
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