Bioinformatics Laboratory, Academic Medical Center, Amsterdam
To enhance the exploration of gene expression data in metabolic context, one requires an application that allows the integration of this data and which presents this data in a (genome-wide) metabolic map. The layout of this metabolic map must be highly flexible to enable discoveries of biological phenomena. Moreover, it must allow the simultaneous presentation of additional information about genes and enzymes. Since the layout and properties of existing maps didn't fulfill our requirements, we developed a new way of presenting gene expression data in metabolic charts. ViMAc generates user-specified (genome-wide) metabolic maps to explore gene expression data. To enhance the interpretation of these maps information such as sub-cellular localization is included. ViMAc can be used to analyze human or yeast expression data obtained with DNA microarrays or SAGE. We demonstrate how it can be applied to explore DNA microarray data for yeast.