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D NA microarrays are used to measure simultaneously the expression levels of thousands of genes. New tools are needed to relate the large amounts of microarray data generated to known models of cell biology and biochemistry. We have developed a free stand-alone computer program called GenMAPP (Gene Microarray Pathway Profiler), designed for viewing and analyzing gene expression data in the context of biological pathways. GenMAPP displays gene expression data on pathways by color-coding the genes based on data and criteria provided by the investigator. GenMAPP also has graphics tools for constructing and modifying pathways. In addition, it provides access to annotation for genes and a connection with pathway experts. This program thus provides a new format to organize, publish and share DNA microarray data that will increase our understanding of biology in the genomics era. Current tools for analyzing microarray data include statistical filters and pattern-finding algorithms such as hierarchical clustering 1,2. The power of such tools is that they evaluate the gene expression data without the bias of prior knowledge of gene function, providing a valuable first step in analysis by generating lists of interesting genes. The next logical step is to analyze the gene expression changes in the context of known biological pathways 3. Several public and commercial pathway resources currently exist, including the Alliance for Cellular Signaling, BioCarta, EcoCyc 4 and MetaCyc 5 , the Kyoto Encyclopedia of Genes and Genomes 6 (KEGG) and PathDB. These databases contain large amounts of curated information, and some (EcoCyc, MetaCyc and KEGG) can also be used to view simple gene expression data sets in the context of pre-existing pathways 7–9. GenMAPP extends the capabilities of these pathway resources by allowing users to modify pathways for their own use, to design new pathways and to apply complex criteria for viewing gene expression data on those pathways. Gen-MAPP also complements the current pathway databases by providing a means of freely exchanging pathway-related data among investigators. GenMAPP represents biological pathways in a special file format called 'MAPPs'. MAPPs are independent of the gene expression data and can be used to group genes by any organizing principle, for example: metabolic pathways (such as fatty-acid degradation; see Fig. 1 and URLs A–C), signal transduction cascades (see URL D), gene families (see URL E), subcellular components (see Web Fig. F online) or custom MAPPs for hypothesis testing (including the most interesting genes in a microarray experiment). …

GenMAPP, a new tool for viewing and analyzing microarray data on biological pathways