Using GenMAPP and MAPPFinder to View Microarray Data on Biological Pathways and Identify Global Trends in the Data
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Abstract
GenMAPP (Gene MicroArray Pathway Profiler) is a free, stand?alone computer program designed for viewing and analyzing gene expression data on MAPPs representing biological pathways or any other functional grouping of genes. A MAPP is a special file format produced with the graphics tools in GenMAPP that depicts the biological relationship between genes or gene products. When a MAPP is linked to an expression dataset, GenMAPP automatically and dynamically color?codes the genes on the MAPP according to criteria supplied by the user. MAPPFinder is an accessory program that works with GenMAPP and the annotations from the Gene Ontology (GO) Consortium to identify global biological trends in gene expression data. MAPPFinder relates the microarray dataset to the GO hierarchy and calculates a percentage and statistical score for genes meeting the user's criterion for a meaningful gene expression change for each GO biological process, cellular component, and molecular function term.
Keywords: GenMAPP; MAPPFinder; microarray; pathways; Gene Ontology; gene expression profile
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- Basic Protocol 1: Importing Gene Expression Data into GenMAPP
- Basic Protocol 2: Using MAPPFinder to Identify Global Trends in Gene Expression Data
- Basic Protocol 3: Creating New MAPPs
- Support Protocol 1: Exporting MAPPs for Publishing in Print or on the Web
- Guidelines for Understanding Results
- Commentary
- Literature Cited
- Figures
- Tables
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Basic Protocol 1: Importing Gene Expression Data into GenMAPP
Necessary Resources
Basic Protocol 2: Using MAPPFinder to Identify Global Trends in Gene Expression Data
Necessary Resources
Basic Protocol 3: Creating New MAPPs
Necessary Resources
Support Protocol 1: Exporting MAPPs for Publishing in Print or on the Web
Necessary Resources
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Figure 7.5.1 Screen shot of the main Drafting Board window in GenMAPP 2.0. The top row below the title bar is the Drafting Board menu through which all of the functions of the program can be accessed. Below this is the Drafting Toolbar, containing the tools used for placing the graphical elements on a MAPP. The Color Set currently in use to color‐code gene objects with expression data is displayed to the right of the Drafting Toolbar. To the right of the Color Set is the zoom factor. The Drafting Board is displaying the Fatty Acid Degradation pathway for mouse and is color‐coded with expression data from a mouse model of cardiomyopathy (Redfern et al., ). View Image -
Figure 7.5.2 Flow chart of how a typical user would follow the protocols in this chapter. View Image -
Figure 7.5.3 The raw data format of expression data to be imported into GenMAPP as it would appear in a spreadsheet program. View Image -
Figure 7.5.4 The GenMAPP Expression Dataset Manager through which expression data are imported and the instructions for color‐coding gene objects on a MAPP defined. View Image -
Figure 7.5.5 The MAPPFinder Calculate New Results window. View Image -
Figure 7.5.6 The MAPPFinder Browser for viewing results. The Browser is highlighting Gene Ontology terms that have an overrepresentation of genes with decreased gene expression in a 12.5‐day mouse embyro heart, as compared to an adult mouse heart (Doniger et al., ). View Image -
Figure 7.5.7 Example of MAPPFinder results for (A ) GO terms and (B ) Local MAPPS, as they would appear in the MAPPFinder Browser window. View Image -
Figure 7.5.8 The Gene Finder, through which a gene object is given a gene identifier for connecting the graphical object with gene expression data and annotations stored in the underlying Gene Database. View Image -
Figure 7.5.9 The format of the .txt file for importing a list of genes into GenMAPP using MAPPBuilder, as it would appear in a spreadsheet program. View Image -
Figure 7.5.10 The MAPP Sets window for exporting an entire folder of MAPPs to HTML. View Image
Videos
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. Nat. Genet. 25:25‐29. | |
| Dahlquist, K.D., Salomonis, N., Vranizan, K., Lawlor, S.C., and Conklin, B.R. 2002. GenMAPP, a new tool for viewing and analyzing microarray data on biological pathways. Nat. Genet. 31:19‐20. | |
| Doniger, S.W., Salomonis, N., Dahlquist, K.D., Vranizan, K., Lawlor, S.C., and Conklin, B.R. 2003. MAPPFinder: Using Gene Ontology and GenMAPP to create a global gene‐expression profile from microarray data. Genome Biol. 4:R7. | |
| Eisen, M.B., Spellman, P.T., Brown, P.O., and Botstein, D. 1998. Cluster analysis and display of genome‐wide expression patterns. Proc. Natl. Acad. Sci. U.S.A. 95:14863‐14868. | |
| Grosu, P., Townsend, J.P., Hartl, D.L., and Cavalieri, D. 2002. Pathway processor: A tool for integrating whole‐genome expression results into metabolic networks. Genome Res. 12:1121‐1126. | |
| Hosack, D.A., Dennis, G., Jr., Sherman, B.T., Lane, H.C., and Lempicki, R.A. 2003. Identifying biological themes within lists of genes with EASE. Genome Biol. 4:R70. | |
| Karp, P.D., Riley, M., Paley, S.M., and Pellegrini‐Toole, A. 2002. The MetaCyc database. Nucleic Acids Res. 30:59‐61. | |
| Nakao, M., Bono, H., Kawashima, S., Kamiya, T., Sato, K., Goto, S., and Kanehisa, M. 1999. Genome‐scale gene expression analysis and pathway reconstruction in KEGG. Genome Inform. Ser. Workshop Genome Inform. 10:94‐103. | |
| Redfern, C.H., Degtyarev, M.Y., Kwa, A.T., Salomonis, N., Cotte, N., Nanevicz, T., Fidelman, N., Desai, K., Vranizan, K., Lee, E.K., Coward, P., Shah, N., Warrington, J.A., Fishman, G.I., Bernstein, D., Baker, A.J., and Conklin, B.R. 2000. Conditional expression of a Gi‐coupled receptor causes ventricular conduction delay and a lethal cardiomyopathy. Proc. Natl. Acad. Sci. U.S.A. 97:4826‐4831. | |
| Segal, M.R., Dahlquist, K.D., and Conklin, B.R. 2003. Regression approaches for microarray data analysis. J. Comput. Biol. 10:961‐980. | |
| Tamayo, P., Slonim, D., Mesirov, J., Zhu, Q., Kitareewan, S., Dmitrovsky, E., Lander, E.S., and Golub, T.R. 1999. Interpreting patterns of gene expression with self‐organizing maps: Methods and application to hematopoeitic differentiation. Proc. Natl. Acad. Sci. U.S.A. 96:2907‐2912. | |
| Zeeberg, B.R., Feng, W., Wang, G., Wang, M.D, Fojo, A.T., Sunshine, M., Narasimhan, S., Kane, D.W., Reinhold, W.C., Lababidi, S., Bussey, K.J., Riss, J., Barrett, J.C., and Weinstein, J.N. 2003. GoMiner: A resource for biological interpretation of genomic and proteomic data. Genome Biol. 4:R28. | |
| Key References | |
| Dahlquist et al., 2002. See above. | |
| A description of the GenMAPP software. | |
| Doniger et al., 2002. See above. | |
| A description of the MAPPFinder software: how it works and how it calculates the z score. | |
| Internet Resources | |
| http://www.GenMAPP.org | |
| GenMAPP Web site, download software, gene databases, MAPP archives, and documentation. | |
| http://www.geneontology.org | |
| Home for the Gene Ontology Project used for MAPPFinder. | |
| http://www.stat.berkeley.edu/users/terry/zarray/TechReport/578.pdf | |
| Dudoit, S., Yang, Y.H., Callow, M.J., and Speed, T.P. 2000. Statistical methods for identifying differentially expressed genes in replicated cDNA microarray experiments. | |
| http://stat‐www.berkeley.edu/users/laan/Research/Research_subpages/Papers/hopach.pdf | |
| van der Laan, M.J. and Pollard, K.S. 2001. Hybrid clustering of gene expression data with visualization and the bootstrap. |
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