Proteomics and the Analysis of Proteomic Data: 2013 Overview of Current Protein‐Profiling Technologies

Can Bruce1, Kathryn Stone1, Erol Gulcicek1, Kenneth Williams1

1 W.M. Keck Foundation Biotechnology Resource Laboratory and Molecular Biochemistry and Biophysics Department, Yale University, New Haven, Connecticut
Publication Name:  Current Protocols in Bioinformatics
Unit Number:  Unit 13.21
DOI:  10.1002/0471250953.bi1321s41
Online Posting Date:  March, 2013
GO TO THE FULL TEXT: PDF or HTML at Wiley Online Library

Abstract

Mass spectrometry has become a major tool in the study of proteomes. The analysis of proteolytic peptides and their fragment ions by this technique enables the identification and quantitation of the precursor proteins in a mixture. However, deducing chemical structures and then protein sequences from mass‐to‐charge ratios is a challenging computational task. Software tools incorporating powerful algorithms and statistical methods improved our ability to process the large quantities of proteomics data. Repositories of spectral data make both data analysis and experimental design more efficient. New approaches in quantitative and statistical proteomics make possible a greater coverage of the proteome, the identification of more post‐translational modifications, and a greater sensitivity in the quantitation of targeted proteins. Curr. Protoc. Bioinform. 41:13.21.1‐13.21.17. © 2013 by John Wiley & Sons, Inc.

Keywords: shotgun proteomics; search engines; labeled quantitation; data repository; Selective Reaction Monitoring; data‐independent analysis; data exchange format; quantitative proteomics

     
 
GO TO THE FULL PROTOCOL:
PDF or HTML at Wiley Online Library

Table of Contents

  • Introduction
  • Discovery Proteomics
  • Quantitative Proteomics
  • Conclusion
  • Acknowledgments
  • Literature Cited
     
 
GO TO THE FULL PROTOCOL:
PDF or HTML at Wiley Online Library

Materials

GO TO THE FULL PROTOCOL:
PDF or HTML at Wiley Online Library

Figures

Videos

Literature Cited

Literature Cited
   Alves, G., Ogurtsov, A.Y., Kwok, S., Wu, W.W., Wang, G., Shen, R.F., and Yu, Y.K. 2008. Detection of co‐eluted peptides using database search methods. Biol. Direct 3:27.
   Amanchy, R., Periaswamy, B., Mathivanan, S., Reddy, R., Tattikota, S.G., and Pandey, A. 2007. A curated compendium of phosphorylation motifs. Nat. Biotechnol. 25:285‐286.
   Bantscheff, M., Schirle, M., Sweetman, G., Rick, J., and Kuster, B. 2007. Quantitative mass spectrometry in proteomics: A critical review. Anal. Bioanal. Chem. 389:1017‐1031.
   Beausoleil, S.A., Villen, J., Gerber, S.A., Rush, J., and Gygi, S.P. 2006. A probability‐based approach for high‐throughput protein phosphorylation analysis and site localization. Nat. Biotechnol. 24:1285‐1292.
   Beavis, R.C. 2006. Using the global proteome machine for protein identification. Methods Mol. Biol. 328:217‐228.
   Bern, M. and Goldberg, D. 2006. De novo analysis of peptide tandem mass spectra by spectral graph partitioning. J. Comput. Biol. 13:364‐378.
   Bjornson, R.D., Carriero, N.J., Colangelo, C., Shifman, M., Cheung, K.H., Miller, P.L., and Williams, K. 2008. X!!Tandem, an improved method for running X!tandem in parallel on collections of commodity computers. J. Proteome Res. 7:293‐299.
   Blom, N., Sicheritz‐Ponten, T., Gupta, R., Gammeltoft, S., and Brunak, S. 2004. Prediction of post‐translational glycosylation and phosphorylation of proteins from the amino acid sequence. Proteomics 4:1633‐1649.
   Bodenmiller, B., Campbell, D., Gerrits, B., Lam, H., Jovanovic, M., Picotti, P., Schlapbach, R., and Aebersold, R. 2008. PhosphoPep‐a database of protein phosphorylation sites in model organisms. Nat. Biotechnol. 26:1339‐1340.
   Bruce, C., Shifman, M.A., Miller, P., and Gulcicek, E.E. 2006. Probabilistic enrichment of phosphopeptides by their mass defect. Anal. Chem. 78:4374‐4382.
   Brunner, E., Ahrens, C.H., Mohanty, S., Baetschmann, H., Loevenich, S., Potthast, F., Deutsch, E.W., Panse, C., de Lichtenberg, U., Rinner, O., Lee, H., Pedrioli, P.G., Malmstrom, J., Koehler, K., Schrimpf, S., Krijgsveld, J., Kregenow, F., Heck, A.J., Hafen, E., Schlapbach, R., and Aebersold, R. 2007. A high‐quality catalog of the Drosophila melanogaster proteome. Nat. Biotechnol. 25:576‐583.
   Chen, T., Kao, M.Y., Tepel, M., Rush, J., and Church, G.M. 2001. A dynamic programming approach to de novo peptide sequencing via tandem mass spectrometry. J. Comput. Biol. 8:325‐337.
   Claassen, M. 2012. Inference and validation of protein identifications. Mol. Cell. Proteom. 11:1097‐1104.
   Claassen, M., Aebersold, R., and Buhmann, J.M. 2009. Proteome coverage prediction with infinite Markov models. Bioinformatics 25:I154‐I160.
   Claassen, M., Reiter, L., Hengartner, M.O., Buhmann, J.M., and Aebersold, R. 2012. Generic comparison of protein inference engines. Mol. Cell. Proteom. 11:O110.007088.
   Cox, J. and Mann, M. 2008. MaxQuant enables high peptide identification rates, individualized p.p.b.‐range mass accuracies and proteome‐wide protein quantification. Nat. Biotechnol. 26:1367‐1372.
   Cox, J., Neuhauser, N., Michalski, A., Scheltema, R.A., Olsen, J.V., and Mann, M. 2011. Andromeda: A peptide search engine integrated into the MaxQuant environment. J. Proteome Res. 10:1794‐1805.
   Craig, R. and Beavis, R.C. 2004. TANDEM: Matching proteins with tandem mass spectra. Bioinformatics 20:1466‐1467.
   Dancik, V., Addona, T.A., Clauser, K.R., Vath, J.E., and Pevzner, P.A. 1999. De novo peptide sequencing via tandem mass spectrometry. J. Comput. Biol. 6:327‐342.
   Deutsch, E.W. 2010. The PeptideAtlas project. Methods Mol. Biol. 604:285‐296.
   Deutsch, E.W., Chambers, M., Neumann, S., Levander, F., Binz, P.A., Shofstahl, J., Campbell, D.S., Mendoza, L., Ovelleiro, D., Helsens, K., Martens, L., Aebersold, R., Moritz, R.L., and Brusniak, M.Y. 2012. TraML‐a standard format for exchange of selected reaction monitoring transition lists. Mol. Cell. Proteom. 11:R111.015040.
   Dinkel, H., Chica, C., Via, A., Gould, C.M., Jensen, L.J., Gibson, T.J., and Diella, F. 2011. Phospho.ELM: A database of phosphorylation sites‐update 2011. Nucleic Acids Res. 39:D261‐D267.
   Domanski, D., Percy, A.J., Yang, J., Chambers, A.G., Hill, J.S., Freue, G.V., and Borchers, C.H. 2012. MRM‐based multiplexed quantitation of 67 putative cardiovascular disease biomarkers in human plasma. Proteomics 12:1222‐1243.
   Dong, M.Q., Venable, J.D., Au, N., Xu, T., Park, S.K., Cociorva, D., Johnson, J.R., Dillin, A., and Yates, J.R. 3rd. 2007. Quantitative mass spectrometry identifies insulin signaling targets in C. elegans. Science 317:660‐663.
   Dudoit, S., Shaffer, J.P., and Boldrick, J.C. 2003. Multiple hypothesis testing in microarray experiments. Stat. Sci. 18:71‐103.
   Eisenacher, M. 2011. mzIdentML: An open community‐built standard format for the results of proteomics spectrum identification algorithms. Methods Mol. Biol. 696:161‐177.
   Elias, J.E. and Gygi, S.P. 2010. Target‐decoy search strategy for mass spectrometry‐based proteomics. Methods Mol. Biol. 604:55‐71.
   Ellis, J.J. and Kobe, B. 2011. Predicting protein kinase specificity: Predikin update and performance in the DREAM4 challenge. PloS One 6:e21169.
   Eng, J., McCormack, A.L., and Yates, J.R. 1994a. An approach to correlate tandem mass spectral data of peptides with amino acid sequences in a protein database. J. Am. Soc. Mass Spectrom. 5:976‐989.
   Eng, J.K., McCormack, A.L., and Yates, J.R. 1994b. An approach to correlate tandem mass spectral data of peptides with amino acid sequences in a protein database. J. Am. Soc. Mass Spectrom. 5:976‐989.
   Eriksson, J. and Fenyo, D. 2007. Improving the success rate of proteome analysis by modeling protein‐abundance distributions and experimental designs. Nat. Biotechnol. 25:651‐655.
   Farrah, T., Deutsch, E.W., Omenn, G.S., Campbell, D.S., Sun, Z., Bletz, J.A., Mallick, P., Katz, J.E., Malmstrom, J., Ossola, R., Watts, J.D., Lin, B., Zhang, H., Moritz, R.L., and Aebersold, R. 2011. A high‐confidence human plasma proteome reference set with estimated concentrations in PeptideAtlas. Mol. Cell. Proteom. 10:M110.006353.
   Field, H.I., Fenyo, D., and Beavis, R.C. 2002. RADARS, a bioinformatics solution that automates proteome mass spectral analysis, optimises protein identification, and archives data in a relational database. Proteomics 2:36‐47.
   Fitzgibbon, M., Law, W., May, D., Detter, A., and McIntosh, M. 2008. Open‐source platform for the analysis of liquid chromatography‐mass spectrometry (LC‐MS) data. Methods Mol. Biol. 428:369‐382.
   Frank, A.M. 2009. Predicting intensity ranks of peptide fragment ions. J. Proteome Res. 8:2226‐2240.
   Gattiker, A., Gasteiger, E., and Bairoch, A. 2002. ScanProsite: A reference implementation of a PROSITE scanning tool. Appl. Bioinformatics 1:107‐108.
   Geer, L.Y., Markey, S.P., Kowalak, J.A., Wagner, L., Xu, M., Maynard, D.M., Yang, X., Shi, W., and Bryant, S.H. 2004. Open mass spectrometry search algorithm. J. Proteome Res. 3:958‐964.
   Gupta, N. and Pevzner, P.A. 2009. False discovery rates of protein identifications: A strike against the two‐peptide rule. J. Proteome Res. 8:4173‐4181.
   Hawkridge, A.M. and Muddiman, D.C. 2009. Mass spectrometry‐based biomarker discovery: Toward a global proteome index of individuality. Annu. Rev. Anal. Chem. 2:265‐277.
   Hosack, D.A., Dennis, G., Sherman, B.T., Lane, H.C., and Lempicki, R.A. 2003. Identifying biological themes within lists of genes with EASE. Genome Biol. 4:R70.
   Huang, D.W., Sherman, B.T., Tan, Q., Kir, J., Liu, D., Bryant, D., Guo, Y., Stephens, R., Baseler, M.W., Lane, H.C., and Lempicki, R.A. 2007. DAVID bioinformatics resources: Expanded annotation database and novel algorithms to better extract biology from large gene lists. Nucleic Acids Res. 35:W169‐W175.
   Hughes, C., Ma, B., and Lajoie, G.A. 2010. De novo sequencing methods in proteomics. Methods Mol. Biol. 604:105‐121.
   Jaffe, J.D., Mani, D.R., Leptos, K.C., Church, G.M., Gillette, M.A., and Carr, S.A. 2006. PEPPeR, a platform for experimental proteomic pattern recognition. Mol. Cell. Proteom. 5:1927‐1941.
   Jaffe, J.D., Keshishian, H., Chang, B., Addona, T.A., Gillette, M.A., and Carr, S.A. 2008. Accurate inclusion mass screening: A bridge from unbiased discovery to targeted assay development for biomarker verification. Mol. Cell. Proteom. 7:1952‐1962.
   Jain, N., Thatte, J., Braciale, T., Ley, K., O'Connell, M., and Lee, J.K. 2003. Local pooled error test for identifying differentially expressed genes with a small number of replicated microarrays. Bioinformatics 19:1945‐1951.
   Jain, N., Cho, H., O'Connell, M., and Lee, J.K. 2005. Rank‐invariant resampling based estimation of false discovery rate for analysis of small sample microarray data. BMC Bioinformatics 6:187.
   Jiang, X., Han, G., Feng, S., Ye, M., Yao, X., and Zou, H. 2008. Automatic validation of phosphopeptide identifications by the MS2/MS3 target‐decoy search strategy. J. Proteome Res. 7:1640‐1649.
   Jung, I., Matsuyama, A., Yoshida, M., and Kim, D. 2010. PostMod: Sequence based prediction of kinase‐specific phosphorylation sites with indirect relationship. BMC Bioinformatics 11:S10.
   Kall, L., Storey, J.D., MacCoss, M.J., and Noble, W.S. 2008. Assigning significance to peptides identified by tandem mass spectrometry using decoy databases. J. Proteome Res. 7:29‐34.
   Kapp, E., Schutz, F., and Simpson, R. 2005. An evaluation, comparison, and accurate benchmarking of several publicly available MS/MS search algorithms; Sensitivity and specificity analysis. Mol. Cell. Proteom. 4:S24.
   Keller, A., Eng, J., Zhang, N., Li, X.J., and Aebersold, R. 2005. A uniform proteomics MS/MS analysis platform utilizing open XML file formats. Mol. Syst. Biol. 1:2005.0017.
   Kim, J.H., Lee, J., Oh, B., Kimm, K., and Koh, I. 2004. Prediction of phosphorylation sites using SVMs. Bioinformatics 20:3179‐3184.
   Krokhin, O.V., Ying, S., Cortens, J.P., Ghosh, D., Spicer, V., Ens, W., Standing, K.G., Beavis, R.C., and Wilkins, J.A. 2006. Use of peptide retention time prediction for protein identification by off‐line reversed‐phase HPLC‐MALDI MS/MS. Anal. Chem. 78:6265‐6269.
   Lam, H., Deutsch, E.W., Eddes, J.S., Eng, J.K., King, N., Stein, S.E., and Aebersold, R. 2007. Development and validation of a spectral library searching method for peptide identification from MS/MS. Proteomics 7:655‐667.
   Lange, V., Picotti, P., Domon, B., and Aebersold, R. 2008. Selected reaction monitoring for quantitative proteomics: A tutorial. Mol. Syst. Biol. 4:222.
   Leptos, K.C., Sarracino, D.A., Jaffe, J.D., Krastins, B., and Church, G.M. 2006. MapQuant: Open‐source software for large‐scale protein quantification. Proteomics 6:1770‐1782.
   Link, A.J., Eng, J., Schieltz, D.M., Carmack, E., Mize, G.J., Morris, D.R., Garvik, B.M., and Yates, J.R. 3rd. 1999. Direct analysis of protein complexes using mass spectrometry. Nat. Biotechnol. 17:676‐682.
   Liu, H., Sadygov, R.G., and Yates, J.R. 3rd. 2004. A model for random sampling and estimation of relative protein abundance in shotgun proteomics. Anal. Chem. 76:4193‐4201.
   Lu, B., Ruse, C., Xu, T., Park, S.K., and Yates, J. 3rd. 2007. Automatic validation of phosphopeptide identifications from tandem mass spectra. Anal. Chem. 79:1301‐1310.
   Lu, B., Ruse, C.I., and Yates, J.R. 3rd. 2008. Colander: A probability‐based support vector machine algorithm for automatic screening for CID spectra of phosphopeptides prior to database search. J. Proteome Res. 7:3628‐3634.
   Lundgren, D.H., Hwang, S.I., Wu, L., and Han, D.K. 2010. Role of spectral counting in quantitative proteomics. Exp. Rev. Proteom. 7:39‐53.
   MacLean, B., Tomazela, D.M., Shulman, N., Chambers, M., Finney, G.L., Frewen, B., Kern, R., Tabb, D.L., Liebler, D.C., and MacCoss, M.J. 2010. Skyline: An open source document editor for creating and analyzing targeted proteomics experiments. Bioinformatics 26:966‐968.
   Mallick, P., Schirle, M., Chen, S.S., Flory, M.R., Lee, H., Martin, D., Raught, B., Schmitt, R., Werner, T., Kuster, B., and Aebersold, R. 2007. eComputational prediction of proteotypic peptides for quantitative proteomics. Nat. Biotechnol. 25:125‐131.
   Mann, M. and Kelleher, N.L. 2008. Precision proteomics: The case for high resolution and high mass accuracy. Proc. Natl. Acad. Sci. U.S.A. 105:18132‐18138.
   Mann, M. and Wilm, M. 1994. Error‐tolerant identification of peptides in sequence databases by peptide sequence tags. Anal. Chem. 66:4390‐4399.
   Martin, D.B., Holzman, T., May, D., Peterson, A., Eastham, A., Eng, J., and McIntosh, M. 2008. MRMer, an interactive open source and cross‐platform system for data extraction and visualization of multiple reaction monitoring experiments. Mol. Cell. Proteom. 7:2270‐2278.
   Mead, J.A., Bianco, L., Ottone, V., Barton, C., Kay, R.G., Lilley, K.S., Bond, N.J., and Bessant, C. 2009. MRMaid, the Web‐based tool for designing multiple reaction monitoring (MRM) transitions. Mol. Cell. Proteom. 8:696‐705.
   Miller, M.L., Jensen, L.J., Diella, F., Jorgensen, C., Tinti, M., Li, L., Hsiung, M., Parker, S.A., Bordeaux, J., Sicheritz‐Ponten, T., Olhovsky, M., Pasculescu, A., Alexander, J., Knapp, S., Blom, N., Bork, P., Li, S., Cesareni, G., Pawson, T., Turk, B.E., Yaffe, M.B., Brunak, S., and Linding, R. 2008. Linear motif atlas for phosphorylation‐dependent signaling. Science Signal. 1:ra2.
   Molina, H., Yang, Y., Ruch, T., Kim, J. W., Mortensen, P., Otto, T., Nalli, A., Tang, Q. Q., Lane, M. D., Chaerkady, R., and Pandey, A. 2009. Temporal profiling of the adipocyte proteome during differentiation using a five‐plex SILAC based strategy. J. Proteome Res. 8:48‐58.
   Moore, R.E., Young, M.K., and Lee, T.D. 2002. Qscore: An algorithm for evaluating SEQUEST database search results. J. Am. Soc. Mass Spectrom. 13:378‐386.
   Moruz, L., Staes, A., Foster, J.M., Hatzou, M., Timmerman, E., Martens, L., and Kall, L. 2012. Chromatographic retention time prediction for posttranslationally modified peptides. Proteomics 12:1151‐1159.
   Mueller, L.N., Rinner, O., Schmidt, A., Letarte, S., Bodenmiller, B., Brusniak, M.Y., Vitek, O., Aebersold, R., and Muller, M. 2007. SuperHirn: A novel tool for high resolution LC‐MS‐based peptide/protein profiling. Proteomics 7:3470‐3480.
   Nesvizhskii, A.I. 2010. A survey of computational methods and error rate estimation procedures for peptide and protein identification in shotgun proteomics. J. Proteom. 73:2092‐2123.
   Nesvizhskii, A.I., Keller, A., Kolker, E., and Aebersold, R. 2003. A statistical model for identifying proteins by tandem mass spectrometry. Analytical Chemistry 75:4646‐4658.
   Nesvizhskii, A.I., Vitek, O., and Aebersold, R. 2007. Analysis and validation of proteomic data generated by tandem mass spectrometry. Nat. Methods 4:787‐797.
   Palagi, P.M., Walther, D., Quadroni, M., Catherinet, S., Burgess, J., Zimmermann‐Ivol, C.G., Sanchez, J.C., Binz, P.A., Hochstrasser, D.F., and Appel, R.D. 2005. MSight: An image analysis software for liquid chromatography‐mass spectrometry. Proteomics 5:2381‐2384.
   Panchaud, A., Scherl, A., Shaffer, S.A., von Haller, P.D., Kulasekara, H.D., Miller, S.I., and Goodlett, D.R. 2009. Precursor acquisition independent from ion count: How to dive deeper into the proteomics ocean. Anal. Chem. 81:6481‐6488.
   Pappin, D.J., Hojrup, P., and Bleasby, A.J. 1993. Rapid identification of proteins by peptide‐mass fingerprinting. Curr. Biol. 3:327‐332.
   Pedrioli, P.G. 2010. Trans‐proteomic pipeline: A pipeline for proteomic analysis. Methods Mol. Biol. 604:213‐238.
   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.
   Perkins, D.N., Pappin, D.J., Creasy, D.M., and Cottrell, J.S. 1999. Probability‐based protein identification by searching sequence databases using mass spectrometry data. Electrophoresis 20:3551‐3567.
   Picotti, P., Lam, H., Campbell, D., Deutsch, E.W., Mirzaei, H., Ranish, J., Domon, B., and Aebersold, R. 2008. A database of mass spectrometric assays for the yeast proteome. Nat. Methods 5:913‐914.
   Pluskal, T., Castillo, S., Villar‐Briones, A., and Oresic, M. 2010. MZmine 2: Modular framework for processing, visualizing, and analyzing mass spectrometry‐based molecular profile data. BMC Bioinformatics 11:395.
   Price, T.S., Lucitt, M.B., Wu, W., Austin, D.J., Pizarro, A., Yocum, A.K., Blair, I.A., FitzGerald, G.A., and Grosser, T. 2007. EBP, a program for protein identification using multiple tandem mass spectrometry datasets. Mol. Cell. Proteom. 6:527‐536.
   Reiter, L., Claassen, M., Schrimpf, S.P., Jovanovic, M., Schmidt, A., Buhmann, J.M., Hengartner, M.O., and Aebersold, R. 2009. Protein identification false discovery rates for very large proteomics data sets generated by tandem mass spectrometry. Mol. Cell. Proteom. 8:2405‐2417.
   Reiter, L., Rinner, O., Picotti, P., Huttenhain, R., Beck, M., Brusniak, M.Y., Hengartner, M.O., and Aebersold, R. 2011. mProphet: Automated data processing and statistical validation for large‐scale SRM experiments. Nat. Methods 8:430‐435.
   Rost, H., Malmstrom, L., and Aebersold, R. 2012. A computational tool to detect and avoid redundancy in selected reaction monitoring. Mol. Cell. Proteom. 11:540‐549.
   Ruttenberg, B.E., Pisitkun, T., Knepper, M.A., and Hoffert, J.D. 2008. PhosphoScore: An open‐source phosphorylation site assignment tool for MSn data. J. Proteome Res. 7:3054‐3059.
   Sadygov, R.G., Eng, J., Durr, E., Saraf, A., McDonald, H., MacCoss, M.J., and Yates, J.R. 3rd. 2002. Code developments to improve the efficiency of automated MS/MS spectra interpretation. J. Proteome Res. 1:211‐215.
   Savitski, M.M., Nielsen, M.L., Kjeldsen, F., and Zubarev, R.A. 2005a. Proteomics‐grade de novo sequencing approach. J. Proteome Res. 4:2348‐2354.
   Savitski, M.M., Nielsen, M.L., and Zubarev, R.A. 2005b. New data base‐independent, sequence tag‐based scoring of peptide MS/MS data validates Mowse scores, recovers below threshold data, singles out modified peptides, and assesses the quality of MS/MS techniques. Mol. Cell. Proteom. 4:1180‐1188.
   Savitski, M.M., Lemeer, S., Boesche, M., Lang, M., Mathieson, T., Bantscheff, M., and Kuster, B. 2011. Confident phosphorylation site localization using the Mascot Delta Score. Mol. Cell. Proteom. 10:M110.003830.
   Schmidt, A., Gehlenborg, N., Bodenmiller, B., Mueller, L.N., Campbell, D., Mueller, M., Aebersold, R., and Domon, B. 2008. An integrated, directed mass spectrometric approach for in‐depth characterization of complex peptide mixtures. Mol. Cell. Proteom. 7:2138‐2150.
   Searle, B.C. 2010. Scaffold: A bioinformatic tool for validating MS/MS‐based proteomic studies. Proteomics 10:1265‐1269.
   Sherwood, C.A., Eastham, A., Lee, L.W., Peterson, A., Eng, J.K., Shteynberg, D., Mendoza, L., Deutsch, E.W., Risler, J., Tasman, N., Aebersold, R., Lam, H., and Martin, D.B. 2009. MaRiMba: A software application for spectral library‐based MRM transition list assembly. J. Proteome Res. 8:4396‐4405.
   Shifman, M.A., Li, Y., Colangelo, C.M., Stone, K.L., Wu, T.L., Cheung, K.H., Miller, P.L., and Williams, K.R. 2007. YPED: A Web‐accessible database system for protein expression analysis. J. Proteome Res. 6:4019‐4024.
   Stergachis, A.B., MacLean, B., Lee, K., Stamatoyannopoulos, J.A., and MacCoss, M.J. 2011. Rapid empirical discovery of optimal peptides for targeted proteomics. Nat. Methods 8:1041‐1043.
   Sturm, M., Bertsch, A., Gropl, C., Hildebrandt, A., Hussong, R., Lange, E., Pfeifer, N., Schulz‐Trieglaff, O., Zerck, A., Reinert, K., and Kohlbacher, O. 2008. OpenMS‐an open‐source software framework for mass spectrometry. BMC Bioinformatics 9:163.
   Tabb, D.L., Saraf, A., and Yates, J.R. 3rd. 2003. GutenTag: High‐throughput sequence tagging via an empirically derived fragmentation model. Anal. Chem. 75:6415‐6421.
   Tabb, D.L., Fernando, C.G., and Chambers, M.C. 2007. MyriMatch: Highly accurate tandem mass spectral peptide identification by multivariate hypergeometric analysis. J. Proteome Res. 6:654‐661.
   Tanner, S., Shu, H., Frank, A., Wang, L.C., Zandi, E., Mumby, M., Pevzner, P.A., and Bafna, V. 2005. InsPecT: Identification of posttranslationally modified peptides from tandem mass spectra. Anal. Chem. 77:4626‐4639.
   Taus, T., Kocher, T., Pichler, P., Paschke, C., Schmidt, A., Henrich, C., and Mechtler, K. 2011. Universal and confident phosphorylation site localization using phosphoRS. J. Proteome Res. 10:5354‐5362.
   Taylor, C.F., Paton, N.W., Lilley, K.S., Binz, P.A., Julian, R.K. Jr., Jones, A.R., Zhu, W., Apweiler, R., Aebersold, R., Deutsch, E.W., Dunn, M.J., Heck, A.J., Leitner, A., Macht, M., Mann, M., Martens, L., Neubert, T.A., Patterson, S.D., Ping, P., Seymour, S.L., Souda, P., Tsugita, A., Vandekerckhove, J., Vondriska, T.M., Whitelegge, J.P., Wilkins, M.R., Xenarios, I., Yates, J.R. 3rd, and Hermjakob, H. 2007. The minimum information about a proteomics experiment (MIAPE). Nat. Biotechnol. 25:887‐893.
   Turewicz, M. and Deutsch, E.W. 2011. Spectra, chromatograms, Metadata: mzML‐the standard data format for mass spectrometer output. Methods Mol. Biol. 696:179‐203.
   Vahamaa, H., Koskinen, V.R., Hosia, W., Moulder, R., Nevalainen, O.S., Lahesmaa, R., Aittokallio, T., and Salmi, J. 2011. PolyAlign: A versatile LC‐MS data alignment tool for landmark‐selected and ‐automated use. Int. J. Proteomics 2011:450290.
   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.
   Vizcaino, J.A., Cote, R., Reisinger, F., Barsnes, H., Foster, J.M., Rameseder, J., Hermjakob, H., and Martens, L. 2010. The proteomics identifications database: 2010 update. Nucleic Acids Res. 38:D736‐D742.
   Wong, Y.H., Lee, T.Y., Liang, H.K., Huang, C.M., Wang, T.Y., Yang, Y.H., Chu, C.H., Huang, H.D., Ko, M.T., and Hwang, J.K. 2007. KinasePhos 2.0: A web server for identifying protein kinase‐specific phosphorylation sites based on sequences and coupling patterns. Nucleic Acids Res. 35:W588‐W594.
   Xu, C.X. and Ma, B. 2006. Software for computational peptide identification from MS‐MS data. Drug Discov. Today 11:595‐600.
   Yates, J.R., Ruse, C.I., and Nakorchevsky, A. 2009. Proteomics by mass spectrometry: Approaches, advances, and applications. Ann. Rev. Biomed. Eng. 11:49‐79.
   Zerck, A., Nordhoff, E., Resemann, A., Mirgorodskaya, E., Suckau, D., Reinert, K., Lehrach, H., and Gobom, J. 2009. An iterative strategy for precursor ion selection for LC‐MS/MS based shotgun proteomics. J. Proteome Res. 8:3239‐3251.
   Zhang, B., VerBerkmoes, N.C., Langston, M.A., Uberbacher, E., Hettich, R.L., and Samatova, N.F. 2006. Detecting differential and correlated protein expression in label‐free shotgun proteomics. J. Proteome Res. 5:2909‐2918.
   Zhang, J., Xin, L., Shan, B., Chen, W., Xie, M., Yuen, D., Zhang, W., Zhang, Z., Lajoie, G.A., and Ma, B. 2012. PEAKS DB: De novo sequencing assisted database search for sensitive and accurate peptide identification. Mol. Cell. Proteom. 11:M111.010587.
   Zhang, N., Aebersold, R., and Schwikowski, B. 2002. ProbID: A probabilistic algorithm to identify peptides through sequence database searching using tandem mass spectral data. Proteomics 2:1406‐1412.
   Zhang, Y., Wen, Z., Washburn, M.P., and Florens, L. 2010. Refinements to label free proteome quantitation: How to deal with peptides shared by multiple proteins. Anal. Chem. 82:2272‐2281.
   Zhou, F.F., Xue, Y., Chen, G.L., and Yao, X. 2004. GPS: A novel group‐based phosphorylation predicting and scoring method. Biochem. Biophys. Res. Commun. 325:1443‐1448.
   Zhu, W.H., Smith, J.W., and Huang, C.M. 2010. Mass spectrometry‐based label‐free quantitative proteomics. J. Biomed. Biotechnology. 2010:840518.
   Zubarev, R.A., Zubarev, A.R., and Savitski, M.M. 2008. Electron capture/transfer versus collisionally activated/induced dissociations: Solo or duet? J. Am. Soc. Mass Spectrom. 19:753‐761.
   Zybailov, B., Mosley, A.L., Sardiu, M.E., Coleman, M.K., Florens, L., and Washburn, M.P. 2006. Statistical analysis of membrane proteome expression changes in Saccharomyces cerevisiae. J. Proteome Res. 5:2339‐2347.
GO TO THE FULL PROTOCOL:
PDF or HTML at Wiley Online Library