Analyzing Shotgun Proteomic Data with PatternLab for Proteomics
互联网
818
- Abstract
- Table of Contents
- Figures
- Literature Cited
Abstract
PatternLab for proteomics is a one?stop shop computational environment for analyzing shotgun proteomic data. Its modules provide means to pinpoint proteins/peptides that are differentially expressed and those that are unique to a state. It can also cluster the ones that share similar expression profiles in time?course experiments, as well as help in interpreting results according to Gene Ontology. PatternLab is user?friendly, simple, and provides a graphical user interface. Curr. Protoc. Bioinform. 30:13.13.1?13.13.15. © 2010 by John Wiley & Sons, Inc.
Keywords: shotgun proteomics; label?free proteomic analysis; label?based proteomic analysis
GO TO THE FULL PROTOCOL:
PDF or HTML at Wiley Online LibraryTable of Contents
- Introduction
- Basic Protocol 1: Parsing Experimental Data into PatternLab's Data Format
- Basic Protocol 2: TFold and ACFold—Pinpointing Differentially Expressed Proteins
- Alternate Protocol 1: Performing a Multi‐Class Comparison (a sparseMatrix.txt File with Three or More Classes)
- Basic Protocol 3: Pinpointing Proteins Unique to a State with the Approximately Area‐Proportional Venn Diagram Module
- Basic Protocol 4: TrendQuest: Clustering Proteins with Similar Expression Profiles
- Basic Protocol 5: Compiling the Latest Gene Ontology and Gene Ontology Annotation (GOA) into GOEx's Precomputed Format
- Guidelines for Understanding Results
- Commentary
- Literature Cited
- Figures
- Tables
GO TO THE FULL PROTOCOL:
PDF or HTML at Wiley Online LibraryMaterials
GO TO THE FULL PROTOCOL:
PDF or HTML at Wiley Online LibraryFigures
-
Figure 13.13.1 The DTASelect parser. This graphical user interface is used to convert spectral counting data obtained from DTASelect into PatternLab's data format (the index and sparse matrix files). View Image -
Figure 13.13.2 A TFold pairwise analysis of two biological states. Each protein (represented as a dot) is mapped according to its log2 (fold change) on the ordinate axis and its −log2 (t‐test p‐value) on the abscissa axis. The latter indicates how likely the observed fold change is a result of chance. Fold changes refer to the ratios of the average relative quantitation values obtained for each state. Accordingly, blue‐dot proteins have p‐values of <0.05 and an absolute fold change >2.5, the established fold‐change cutoff. Orange‐dot proteins did not meet the fold‐change cutoff but were indicated as statistically different. Green‐dot proteins met the fold‐change cutoff but cannot be claimed to be statistically different. Red dots did not satisfy the fold‐change or the statistical cutoffs. The theoretical false‐positive estimator indicates that 125 out of the 148 proteins selected as differentially expressed are likely to be truly differentially expressed. View Image -
Figure 13.13.3 Class selection pop‐up window. When a PatternLab module is set to analyze a sparse matrix containing more than two classes, this pop‐up is displayed so the user can choose which classes to be included in the analysis. The example provided shows a sparse matrix containing seven classes. View Image -
Figure 13.13.4 The XFold graphical user interface. XFold is used when pinpointing differentially expressed proteins in a multi‐class analysis. View Image -
Figure 13.13.5 The AAPVD graphical user interface. This tool's main usage is to pinpoint unique proteins to a state and to obtain a bird's eye view of how different the states are. View Image -
Figure 13.13.6 The TrendQuest graphical user interface. This tool is used to group proteins/peptides that have similar expression profiles. View Image -
Figure 13.13.7 The GOEx graphical user interface. GOEx leverages GO to aid in the interpretation of shotgun proteomic data; it can also consider expression fold changes. View Image
Videos
Literature Cited
| Literature Cited | |
| Ashburner, M., Ball, C.A., Blake, J.A., Botstein, D., Butler, H., Cherry, J.M., Davis, A.P., Dolinski, K., Dwight, S.S., Eppig, J.T., Harris, M.A., Hill, D.P., Issel‐Tarver, L., Kasarskis, A., Lewis, S., Matese, J.C., Richardson, J.E., Ringwald, M., Rubin, G.M., and Sherlock, G. 2000. Gene ontology: Tool for the unification of biology. The Gene Ontology Consortium. Nat. Genet. 25:25‐29. | |
| Benjamini, Y. and Hochberg, Y. 1995. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B 57:289‐300. | |
| Carvalho, P.C., Fischer, J.S., Chen, E.I., Domont, G.B., Carvalho, M.G., Degrave, W.M., Yates, J.R. III, and Barbosa, V.C. 2009. GO Explorer: A gene‐ontology tool to aid in the interpretation of shotgun proteomics data. Proteome. Sci. 7:6. | |
| Carvalho, P.C., Fischer, J.S., Chen, E.I., Yates, J.R. III, and Barbosa, V.C. 2008a. PatternLab for proteomics: A tool for differential shotgun proteomics. BMC. Bioinformatics. 9:316. | |
| Carvalho, P.C., Hewel, J., Barbosa, V.C., and Yates, J.R. III. 2008b. Identifying differences in protein expression levels by spectral counting and feature selection. Genet. Mol. Res. 7:342‐356. | |
| Eng, J.K., McCormack, A., and Yates, J.R. III. 1994. 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. | |
| Liu, H., Sadygov, R.G., and Yates, J.R. III. 2004. A model for random sampling and estimation of relative protein abundance in shotgun proteomics. Anal. Chem. 76:4193‐4201. | |
| Ong, S.E., Blagoev, B., Kratchmarova, I., Kristensen, D.B., Steen, H., Pandey, A., and Mann, M. 2002. Stable isotope labeling by amino acids in cell culture, SILAC, as a simple and accurate approach to expression proteomics. Mol. Cell Proteomics 1:376‐386. | |
| Park, S.K., Venable, J.D., Xu, T., and Yates, J.R. III. 2008. A quantitative analysis software tool for mass spectrometry‐based proteomics. Nat. Methods 5:319‐322. | |
| 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. | |
| Washburn, M.P., Ulaszek, R., Deciu, C., Schieltz, D.M., and Yates, J.R. III. 2002. Analysis of quantitative proteomic data generated via multidimensional protein identification technology. Anal. Chem. 74:1650‐1657. | |
| Xu, T., Venable, J.D., Park, S.K., Cociorva, D., Lu, B., Liao, L., Wohlschlegel, J., Hewel, J., and Yates, J.R. III. 2006. ProLuCID, a fast and sensitive tandem mass spectra‐based protein identification program. Mol. Cell. Proteomics 5:S174. |
GO TO THE FULL PROTOCOL:
PDF or HTML at Wiley Online Library
