The most widespread method of analyzing gene expression data is called hierarchical clustering, which groups together genes with similar levels of expression. This method is powerful because it groups genes together without any prior knowledge of the genes function. GenMAPP takes an opposite, complementary approach. By looking at genes in the context of a known biological process, it is possible to make sense of data that would otherwise be uninterpretable.

Furthermore, small changes in gene expression may be missed in hierarchical clustering, yet have real meaning when displayed on a biological pathway. Hierarchical clustering and the pathway-based GenMAPP work together to help in interpreting biological data.

"This is part of a larger process of putting all the pieces together and has tremendous value for figuring out diseases," Conklin said.

Currently GenMAPP is the only free available program for drawing, viewing, and sharing pathway information in a format that can be used with gene expression data. There are no barriers for any scientist to use the program to modify MAPPs according to his or her hypothesis, to design new pathways and to share the data with others in the community. Conklin said that he hopes that GenMAPP becomes a standard means of viewing microarray data, and pathway information. Already, researchers are using GenMAPP to display results in conferences and published papers. By providing a common format to present and share data, Conklin said that he hopes that GenMAPP will help biologists communicate.

"This puts us one step closer to a common goal of trying to determine how we put all the pieces of the biological puzzle together," he said.

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Contact: Laura Lane
llane@gladstone.ucsf.edu
415-695-3833
University of California - San Francisco
6-May-2002