|SHEN, LISHUANG - IOWA STATE UNIVERSITY
|GONG, JIAN - IOWA STATE UNIVERSITY
|CALDO, RICO - IOWA STATE UNIVERSITY
|NETTLETON, DAN - IOWA STATE UNIVERSITY
|COOK, DIANNE - IOWA STATE UNIVERSITY
|DICKERSON, JULIE - IOWA STATE UNIVERSITY
Submitted to: Nucleic Acids Research
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 10/21/2004
Publication Date: 1/3/2005
Citation: Shen, L., Gong, J., Caldo, R.A., Nettleton, D., Cook, D., Wise, R.P., Dickerson, J.A. 2005. Barleybase - an expression profiling database for plant genomics. Nucleic Acids Research. 33:D614-D618.
Interpretive Summary: BarleyBase is a USDA-NRI-funded public database for cereal microarray data. BarleyBase was first developed to support the new community-designed Affymetrix Barley1 GeneChip, a worldwide uniform platform to investigate the function of 22,000 cereal genes in a single experiment. As the blueprint for new wheat, soybean, grape, rice, and corn GeneChips, Barley1 is a model system that allows researchers to use cutting-edge genomics technology to investigate a range of agricultural problems involving disease defense pathways, responses to abiotic stresses, yield, and biodiversity. The masses of data resulting from use of the publicly available Barley1 GeneChip are hosted in the BarleyBase (http://barleybase.org/) database, facilitating new gene discovery to facilitate new solutions to agricultural problems.
Technical Abstract: BarleyBase (www.BarleyBase.org) is an online database for plant microarrays with integrated tools for data visualization and statistical analysis. BarleyBase houses raw and normalized expression data from the two publicly available Affymetrix genome arrays, Barley1 and Arabidopsis ATH1, with plans to include the new Affymetrix 61K wheat, maize, soybean, and rice arrays as they become available. BarleyBase contains a broad set of query and display options at all data levels, ranging from experiments to individual hybridizations to probe sets down to individual probes. Users can perform cross-experiment queries on probe sets based on observed expression profiles and/or based on known biological information. Probe set queries are integrated with visualization and analysis tools such as the R statistical toolbox, data filters, and a large variety of plot types. Controlled vocabularies for gene and plant ontologies, as well as interconnecting links to physical or genetic map and other genomic data in PlantGDB, Gramene, and GrainGenes, allow users to perform EST alignments and gene function prediction using Barley1 exemplar sequences, thus enhancing cross-species comparison.