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 user-driven 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.
Highlights
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
The CressExpress server will perform a large-scale linear regression experiment, comparing each query to all genes represented on the selected array, using all or just some expression data stored in the CressExpress database, depending on the sample and experiments selected in subsequent steps
CESA4, CESA7, and CESA8 perform a different role from CESA1, CESA3, and CESA6, and we found that the coexpression analysis tends to confirm this view, because the two groups are coexpressed with nonoverlapping groups of genes, as determined by the pathway-level coexpression (PLC) analysis
Summary
Network often perform related functions, demonstrating biological relevance of the approach (for review, see Aoki et al, 2007; Saito et al, 2008). The coexpression networks arising from different inputs may vary greatly, depending on sample or tissue type We address this problem by developing an easy-to-use Web tool (CressExpress) that allows users to select distinct tissue types and experiments to include in an analysis, which executes the calculations offline. Users click the ‘‘Run the Tool’’ link and proceed through a series of screens (Fig. 2) that offer users the opportunity to vary quality control (QC) parameters, specify data release and array platforms, and select subsets of sample types to include in analysis This latter feature can be important for query genes that exert their effects in a tissue- or developmentally restricted fashion. We recommend that when running the tool with a relatively small number of arrays (e.g. ,50), users should utilize the default KS-D value of 0.15, because computing coexpression with a smaller number of arrays makes
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