Pathway-oriented visualization of genomic information enables biologists to interpret data in the context of biological processes and systems. We developed GenMAPP (Gene Map Annotator and Pathway Profiler) as a free, open-source, stand-alone computer program for organizing, analyzing, and sharing genome-scale data in the context of biological pathways. This program is widely used for DMA microarray studies (>12,000 unique user registrations, >200 publications). Continuing demands of our users and the ever-increasing size and complexity of datasets now require a major revision of GenMAPP. We have formed key alliances with other open-source bioinformatics pathway projects whose efforts complement GenMAPP. We joined the Cytoscape (www.cytoscape.org) consortium as core developers so that we can build GenMAPP-CS using the advanced layout and visualization tools already available in Cytoscape. To facilitate pathway exchange, we are working closely with community-driven standards, (e.g. BioPAX and SBML) and several major public pathway databases (e.g., Reactome;www.reactome.org) to enhance pathway content and exchange. To implement this plan, we propose three specific aims.
Specific Aim 1 : To build GenMAPP-CS, a client-server version of GenMAPP, to provide a dynamic environment for visualizing and analyzing genomic data on biological pathways. GenMAPP-CS is being developed as an open-source, Java-based program to visualize and analyze datasets that exceed GenMAPP's current capabilities by 10-100-fold, while maintaining user interfaces and specific functions intuitive to biologists.
Specific Aim 2 : To dynamically integrate GenMAPP-CS with major gene and pathway databases for over 20 major model organisms. The new GenMAPP-CS architecture will allow us to integrate gene exon, single nucleotide polymorphism (SNP), and protein domain information with probe information at a scale that is impractical in GenMAPP 2.0.
Specific Aim 3 : To enable GenMAPP-CS to visualize and analyze genome-wide splicing, polymorphism, and interaction datasets. The challenge of analyzing these massive and complex datasets is a major force driving the development of GenMAPP-CS.
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