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United States Department of Agriculture

Agricultural Research Service


item Wise, Roger
item Caldo, Rico
item Turner, Stacy
item Ashlock, Dan
item Dickerson, Julie

Submitted to: Plant and Animal Genome Conference Proceedings
Publication Type: Abstract Only
Publication Acceptance Date: 1/15/2003
Publication Date: 1/15/2003
Citation: Wise, R.P., Caldo, R.A., Turner, S., Ashlock, D., Dickerson, J. 2003. Parallel expression analysis using barley microarrays. Plant and Animal Genome Conference Proceedings. XI Conference. p. W50.

Interpretive Summary:

Technical Abstract: In small grain Triticeae crops, the molecular characterization of genes coincident with disease, response to biotic or abiotic stresses, or cellular development has traditionally followed a "one-gene-at-a-time" approach. However, recent advances in microarray technology now allow the parallel investigation of up to 22,500 genes in a single experiment. In 2003, a USDA-IFAFS funded barley GeneChip will be available from Affymetrix. We are using these arrays to monitor gene-for-gene interactions on a global scale. Pairwise expression profiling is being performed with plants containing the Mla6 and Mla13 resistance specificities in response to the powdery mildew isolates 5874 (AvrMla6, virMla13) and K1 (AvrMla13, virMla6). Our objective is to identify genes induced/repressed during early stages of pathogen infection to fully elucidate the molecular mechanisms orchestrating incompatible and compatible barley-powdery mildew interactions. In order to effectively utilize up to 500,000 data points per experiment, researchers must be able to easily access, compare, and manipulate the generated data. This project will create an on-line interactive database component, BarleyBase, and develop a set of web-accessible tools for the analysis of Affymetrix GeneChip data. BarleyBase will feature "click through" integration of the data on the web and it will be interoperable with the Gramene comparative mapping resource for grains ( The web-based analysis tools will enable database users to identify subsets of genes that change expression in response to drought, cold stress, disease, or other treatments.

Last Modified: 08/22/2017
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