This project was prepared as part of a BioQUEST faculty development workshop entitled Bioinformatics in Biology Education: Working with Sequence, Structure and Function at Cornell University in October 2003. The BioQUEST Curriculum Consortium is committed to the reform of undergraduate biology instruction through an emphasis on engaging students in realistic scientific practices. This approach is sometimes characterized as an inquiry driven approach and is captured in BioQUEST's three P's (problem-posing, problem-solving, and peer-persuasion). As part of this workshop groups of faculty were encouraged to initiate innovative curricular projects. We are sharing these works in progress in the hope that they will stimulate further exploration, collaboration and development. Please see the following links for additional information:

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Using GenMAPP and MAPPFinder to analyze publicly available cancer microarray data.
Authors          Audiences          Overview           Materials          Resources           Future Directions



Kam Dahlquist
Loyola Marymount University

Candace Collmer
Cornell University


Possible Audiences:

This exercise can be modified so that it can be presented in introductory biology, genetics, molecular biology, or biochemistry courses.  


Brief Overview:

Students will analyze a publicly available microarray dataset using the GenMAPP and MAPPFinder software. GenMAPP is a program for viewing and analyzing microarray data on biological pathways and any other functional grouping of genes. MAPPFinder matches expression data to the Gene Ontology categories and gives a global overview of the data in terms of all known biology. The students will examine which Gene Ontology processes, functions, and components are up- and down-regulated in the cancer cells. Depending on their level, they can then look at the genes in individual pathways and/or draw additional pathways.  


Project Materials:

GenMAPP, MAPPFinder, and the MAPP Archives can be downloaded free-of-charge from We are evaluating publicly available breast cancer datasets for use in this exercise from: Perou et al., (1999) PNAS 96:9212-9217 and: van t Veer et al. (2002) Nature 415:530-536 Datasets derived from these resources and step-by-step protocols will be forthcoming.