Using GenMAPP and MAPPFinder to View Microarray Data on Biological Pathways and Identify Global Trends in the Data

Kam D. Dahlquist1

1 Vassar College, Poughkeepsie, New York
Publication Name:  Current Protocols in Bioinformatics
Unit Number:  Unit 7.5
DOI:  10.1002/0471250953.bi0705s05
Online Posting Date:  May, 2004
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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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Table of Contents

  • 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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Materials

Basic Protocol 1: Importing Gene Expression Data into GenMAPP

  Necessary Resources
  • Hardware
    • PC running Windows 98 or higher, 500 MHz Pentium III class processor or higher, 128 Mb RAM or more, and a minimum of 500 Mb available disk space. Note that large datasets and large genomes may require significantly more RAM and storage for optimum performance.
  • Software
    • GenMAPP 2.0 is available free of charge to any researcher at http://www.GenMAPP.org (see ReadMe file on Web site for installation instructions)
    • Microsoft Internet Explorer 3.0 or higher
    • Spreadsheet or database program, e.g., Microsoft Excel, capable of exporting data to a comma‐separated values file (.csv) or tab‐delimited text file (.txt)
  • Files
    • Raw gene expression data file saved as a comma‐separated values file (.csv) or tab‐delimited text file (.txt), a format which is typically exported by spreadsheet or database programs (e.g., Microsoft Excel). See Figure and step for the correct formatting of this file. When imported, GenMAPP will convert this file to a GenMAPP Expression Dataset file (.gex).
    • MAPP files (.mapp) can be generated using protocol 3 or obtained by downloading the Contributed MAPP Archive from http://www.GenMAPP.org.

Basic Protocol 2: Using MAPPFinder to Identify Global Trends in Gene Expression Data

  Necessary Resources
  • Hardware
    • PC running Windows 98 or higher, 500 MHz Pentium III class processor or higher, 128 Mb RAM or more, and a minimum of 500 Mb available disk space. Note that large datasets and large genomes may require significantly more RAM and storage for optimum performance.
  • Software
    • MAPPFinder 2.0 is available free of charge to all researchers as part of the GenMAPP 2.0 installation package at http://www.GenMAPP.org.
  • Files
    • GenMAPP Expression Dataset file (.gex) generated in protocol 1

Basic Protocol 3: Creating New MAPPs

  Necessary Resources
  • Hardware
    • PC running Windows 98 or higher, 500 MHz Pentium III class processor or higher, 128 Mb RAM or more, and a minimum of 500 Mb available disk space. Note that large datasets and large genomes may require significantly more RAM and storage for optimum performance.
  • Software
    • MAPPBuilder is part of the GenMAPP 2.0 installation package. GenMAPP 2.0 is available free of charge to any researcher at http://www.GenMAPP.org.
    • Microsoft Internet Explorer 3.0 or higher
    • Optional: Spreadsheet or database program, e.g., Microsoft Excel, capable of exporting data to a tab‐delimited text file (.txt)
  • Files
    • MAPPBuilder imports a list of genes in the form of a tab‐delimited text file (.txt) and automatically creates a MAPP file (.mapp) containing those genes, which can be viewed and modified with the GenMAPP Program.

Support Protocol 1: Exporting MAPPs for Publishing in Print or on the Web

  Necessary Resources
  • Hardware
    • PC running Windows 98 or higher, 500 MHz Pentium III class processor or higher, 128 Mb RAM or more, and a minimum of 500 Mb available disk space. Note that large datasets and large genomes may require significantly more RAM and storage for optimum performance.
  • Software
    • GenMAPP 2.0 is available free of charge to any researcher at http://www.GenMAPP.org (see ReadMe file on Web site for installation instructions)
    • Optional: Adobe Acrobat 4.0 or higher
  • Files
    • MAPP files (.mapp) created in protocol 3 or downloaded from http://www.GenMAPP.org and GenMAPP Expression Dataset file (.gex) generated in protocol 1
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Figures

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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