A System for Visualizing Gene Expressions Using Metabolic Networks

Abstract

It has become possible to monitor gene expressions comprehensively with the rapid progress of the DNA microarray technique. Microarray data is useful for estimating the functions of a gene because a set of genes is considered to have similar functions if the genes have similar gene expression profiles. However, it is difficult to extract biologically meaningful data from such data. One promising approach for browsing gene expression data is to map them onto genetic networks. It is a well-known fact that genes interact with each other and form complex networks. Genetic networks are modeled as graphs where a node represents a gene and an edge represents an interaction of the genes at the ends. Estimating genetic networks using gene expression data is one of the most important tasks in the field of bioinformatics. There have been several related studies to visualize gene expression data onto metabolic networks, such as KEGG (Kyoto Encyclopedia of Genes and Genomes) [2], ExPASy [1], and EcoCyc[3]. However, most studies only provide static images. Obviously, the cost of maintaining massive images of metabolic networks is expensive, and it is also difficult to update the images based on the latest information. Although some studies support automatic graph drawing, their functionalities are limited; that is, they only support one particular species or they cannot handle data based on multiple pathways. To cope with this problem, we have implemented a system that supports automatic drawing of metabolic pathways. It allows us to visualize microarray data by overlaying gene expression statuses without complex operations. In this paper, we propose a system for visualizing DNA microarray data by mapping them onto metabolic pathways. In our implementation, we employ SVG for drawing graphs to support dynamic redrawing of metabolic networks. As a result, when we have a set of gene expression data, we can quickly spot the distinctions among those data by browsing them contrastively.