GENEVESTIGATOR. Arabidopsis Microarray Database and Analysis Toolbox^{<a href="#fn1" rid="fn1" class=" fn">1</a>,}^{<a href="#fn3" rid="fn3" class=" fn">[w]</a>}

Abstract

A major challenge in biology today is the large-scale determination of gene function (Boyes et al., 2001). First, the establishment of standards and controlled vocabularies facilitates the integration of experimental data into a computational framework, thereby allowing structured and systematic processing of information (Ashburner et al., 2000; Brazma et al., 2001). Second, structured databases and data querying tools provide the means to assign putative functional information to genes.

The complete sequencing of the Arabidopsis genome achieved in the year 2000 (The Arabidopsis Genome Initiative, 2000) enables us to monitor gene expression of this flowering plant on a genome-scale using microarrays. In situ synthesis of high-density oligonucleotides on glass slides (Lockhart et al., 1996) has become a powerful tool to rapidly integrate the sequence knowledge into expression profiling platforms, such as the ATH1 full genome array developed by Affymetrix and The Institute for Genomic Research (TIGR), which represents approximately 23,750 genes from Arabidopsis (Redman et al., 2004). The availability of a full-genome array and the complete technical environment provided by the Affymetrix system led to a wide use of the GeneChip technology in the plant community. Thousands of arrays have since been processed, of which a significant number are publicly available through services and repositories such as Nottingham Arabidopsis Stock Centre Transcriptomics Service (NASCArrays; Craigon et al., 2004), ArrayExpress at the European Bioinformatics Institute (EBI; Brazma et al., 2003), or Gene Expression Omnibus (GEO) at the National Center for Biotechnology Information (NCBI; Edgar et al., 2002).

The exploitation of large-scale gene expression datasets, mainly from Saccharomyces cerevisiae and Escherichia coli, has already led to the discovery of global structures governing metabolic and regulatory networks (Lee et al., 2002; Ravasz et al., 2002; Stelling et al., 2002; Ihmels et al., 2004). Multiple-genome comparisons have also yielded interesting observations on the modularity and connectivity distributions of gene expression data (Bergmann et al., 2004). Nevertheless, the combination of multiple datasets still raises a number of questions concerning their compatibility, in particular when comparing data from different platforms and organisms. While analyses revealing global properties of networks or modules may not necessarily require full compatibility of expression datasets, the details are often noisy (Friedman, 2004) and the comparative search for the function of individual genes requires a more stringent selection.

The Affymetrix platform provides a standardized system with a high degree of reproducibility (Hennig et al., 2003; Redman et al., 2004). Although data from different experiments may not be pooled for a rigorous expression profiling analysis, one can assume that the large-scale combination and analysis of expression data from a single organism using a single platform like the Affymetrix system allows the identification of biologically meaningful expression patterns of individual genes. To date, few tools have been developed for biologists to query large gene expression databases. The Yeast Microarray Global Viewer (yMGV) is a database providing online tools for the analysis of transcriptional expression profiles of yeast genes among 82 different datasets (Lelandais et al., 2004). In the plant community, NASCArrays (Craigon et al., 2004) provides a repository for Arabidopsis microarray data and some simple “gene-centric” data mining tools.

Here, we describe a novel online tool called GENEVESTIGATOR comprising a gene expression database and a number of querying and analysis functionalities developed to facilitate gene functional discovery. GENEVESTIGATOR allows the data to be presented in the context of plant development, plant organ, and environmental conditions, both for individual genes or for families of genes, thereby answering questions such as “in which growth stage is my gene of interest expressed?” or “which genes are specifically expressed in roots?” The main objective of the software is to assign contextual information to gene expression data, directing the design of new experiments and gene functional discovery.

Genes expressed preferentially (A) in stamina and pollen, (B) in seeds and siliques, (C) during seedling stage, and (D) during early flowering stage. For the description of growth stage groups (labeled A–J), see Table I. See also Supplemental Table II, which provides lists of genes expressed preferentially in roots, green tissues, photosynthetic active leaves, senescent leaves, stem and node, carpel, petal, sepal, and shoot apex.

Acknowledgments

Notes

^{This work was supported by ETH, Strategic Excellence Project 2–74213–02/TH–8/02–2, and by the Functional Genomics Center Zurich.}

^{The online version of this article contains Web-only data.}

www.plantphysiol.org/cgi/doi/10.1104/pp.104.046367.