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Gene Expression Browser: large-scale and cross-experiment microarray data integration, management, search & visualization

Ming Zhang1, Yudong Zhang2, Li Liu2, Lijuan Yu2, Shirley Tsang3, Jing Tan2, Wenhua Yao2, Manjit S Kang4, Yongqiang An5 and Xingming Fan2*

Author Affiliations

1 GeneExp, 310 South Third Street, San Jose, CA 95112, USA

2 Institute of Food Crops, Yunnan Academy of Agricultural Sciences, Kunming 650205, Yunnan Province, China

3 Biomatrix, Rockville, MD 20850, USA

4 Vice Chancellor, Punjab Agricultural Univ., Ludhiana 141 004, India

5 Plant Genetics Research Unit, ARS-USDA, at Donald Danforth Plant Science Center, 975 N. Warson Rd, St. Louis, MO 63132, USA

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BMC Bioinformatics 2010, 11:433  doi:10.1186/1471-2105-11-433

Published: 20 August 2010

Abstract

Background

In the last decade, a large amount of microarray gene expression data has been accumulated in public repositories. Integrating and analyzing high-throughput gene expression data have become key activities for exploring gene functions, gene networks and biological pathways. Effectively utilizing these invaluable microarray data remains challenging due to a lack of powerful tools to integrate large-scale gene-expression information across diverse experiments and to search and visualize a large number of gene-expression data points.

Results

Gene Expression Browser is a microarray data integration, management and processing system with web-based search and visualization functions. An innovative method has been developed to define a treatment over a control for every microarray experiment to standardize and make microarray data from different experiments homogeneous. In the browser, data are pre-processed offline and the resulting data points are visualized online with a 2-layer dynamic web display. Users can view all treatments over control that affect the expression of a selected gene via Gene View, and view all genes that change in a selected treatment over control via treatment over control View. Users can also check the changes of expression profiles of a set of either the treatments over control or genes via Slide View. In addition, the relationships between genes and treatments over control are computed according to gene expression ratio and are shown as co-responsive genes and co-regulation treatments over control.

Conclusion

Gene Expression Browser is composed of a set of software tools, including a data extraction tool, a microarray data-management system, a data-annotation tool, a microarray data-processing pipeline, and a data search & visualization tool. The browser is deployed as a free public web service (http://www.ExpressionBrowser.com webcite) that integrates 301 ATH1 gene microarray experiments from public data repositories (viz. the Gene Expression Omnibus repository at the National Center for Biotechnology Information and Nottingham Arabidopsis Stock Center). The set of Gene Expression Browser software tools can be easily applied to the large-scale expression data generated by other platforms and in other species.