CressExpress: A Tool For Large-Scale Mining of Expression Data from Arabidopsis    

Abstract

CressExpress is a user-friendly, online, coexpression analysis tool for Arabidopsis (Arabidopsis thaliana) microarray expression data that computes patterns of correlated expression between user-entered query genes and the rest of the genes in the genome. Unlike other coexpression tools, CressExpress allows characterization of tissue-specific coexpression networks through userdriven filtering of input data based on sample tissue type. CressExpress also performs pathway-level coexpression analysis on each set of query genes, identifying and ranking genes based on their common connections with two or more query genes. This allows identification of novel candidates for involvement in common processes and functions represented by the query group. Users launch experiments using an easy-to-use Web-based interface and then receive the full complement of results, along with a record of tool settings and parameters, via an e-mail link to the CressExpress Web site. Data sets featured in CressExpress are strictly versioned and include expression data from MAS5, GCRMA, and RMA array processing algorithms. To demonstrate applications for CressExpress, we present coexpression analyses of cellulose synthase genes, indolic glucosinolate biosynthesis, and flowering. We show that subselecting sample types produces a richer network for genes involved in flowering in Arabidopsis. CressExpress provides direct access to expression values via an easy-to-use URL-based Web service, allowing users to determine quickly if their query genes are coexpressed with each other and likely to yield informative pathway-level coexpression results. The tool is available at http://www.cressexpress.org.Availability of abundant, high-quality data sets from microarray expression experiments has stimulated rapid progress in gene networks analysis for a variety of plant and animal species (Stuart et al., 2003;Craigon et al., 2004;Wille et al., 2004;Wei et al., 2006;Zhong and Sternberg, 2006). These data are making it possible to explore correlated expression patterns for the entire genome, as well as answer focused questions regarding specific pathways and processes. By examining correlated expression patterns between genes, investigators can infer new functions for previously uncharacterized genes or identify potential causal relationships between regulators and their targets. Although the details of individual analyses and applications vary, most are based on the idea that correlated expression, or coexpression, implies biologically relevant relationships between gene products. Many applications of this idea utilize variations ofPearson's correlation coefficient and linear regression to quantify coexpression relationships. Figure 1 presents an example scatter plot that illustrates the idea. Each point on the plot represents data from one array; x and y coordinates represent expression values for genes indicated on the horizontal and vertical axes, respectively. In this case, there is a strong positive relationship between the two genes' expression value...

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