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GenMAPP Microarray Visualization Software Proves There s Life After Clustering


While microarray analysis software offerings continue to proliferate, most are variations on the same idea: they identify up- and down-regulated genes and cluster those with similar expression patterns. While clustering is an effective way to glean biologically relevant knowledge from large amounts of data, Bruce Conklin at the Gladstone Institute felt that it was only half of the equation.

“Clustering tools take an unbiased view and assume you know nothing about biology, and for many genes that’s exactly what we know — nothing,” Conklin said. “But if you know a lot about the genes, you want to take a totally biased view of your genomic experiment in the context of known biochemical pathways.”

With this in mind, Conklin and his colleagues developed GenMAPP (Gene MicroArray Pathway Profiler), a freely available Windows-based tool that graphically represents gene expression values on pathway diagrams.

Starting with a MAPP, a drawing program that represents pathways with graphical objects such as genes, receptors, subcellular components, lines, and arrows that can be placed and manipulated on a “drafting board,” users can then upload any set of gene expression data as a comma-separated file in order to color-code the gene objects to represent their degree of up- and down-regulation.

Conklin said his group was spending quite a bit of time with colored pencils and paper manually adding expression data to known biological pathways, and devised GenMAPP as a way to automate the process. The group has created a “starter set” of pathways based on textbooks, review articles, and public pathway databases. There are currently 1,009 MAPPs for mouse, 1,905 for human, 345 for rat, and 633 for yeast at

Users can modify the existing MAPPs or create their own, and can share MAPPs with colleagues. This capability gives Conklin hope that the system will eventually become the Adobe Acrobat of gene expression data — the standard format for sharing information in the field. He envisions researchers creating and sharing MAPPs and eventually including them in research papers so that readers can download the file and add their own gene expression data to the pathway.

Since GenMAPP was launched a year ago, more than 1,300 researchers have registered to use the software, Conklin said. Around half of these are commercial users.

A paper on GenMAPP appears in this month’s Nature Genetics.

— BT

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