ToxSci Advance Access originally published online on April 6, 2006
Toxicological Sciences 2006 92(2):560-577; doi:10.1093/toxsci/kfj184
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HIGHLIGHTED ARTICLE |
A System-Based Approach to Interpret Dose- and Time-Dependent Microarray Data: Quantitative Integration of Gene Ontology Analysis for Risk Assessment
* Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, Washington 98105; GenMAPP Development Team, Bioinformatics Research Associate/Conklin Lab Gladstone Institute of Cardiovascular Disease/UCSF, San Francisco, California 94158; Cell and Molecular Biology Branch, U.S. Army Medical Research Institute of Chemical Defense, Aberdeen Proving Ground, Maryland 21010; and Institute for Risk Analysis and Risk Communication, Environmental and Occupational Health Sciences, Seattle, Wasington
Received March 30, 2006; accepted April 1, 2006
Although microarray technology has emerged as a powerful tool to explore expression levels of thousands of genes or even complete genomes after exposure to toxicants, the functional interpretation of microarray data sets still represents a time-consuming and challenging task. Gene ontology (GO) and pathway mapping have both been shown to be powerful approaches to generate a global view of biological processes and cellular components impacted by toxicants. However, current methods only allow for comparisons across two experimental settings at one particular time point. In addition, the resulting annotations are presented in extensive gene lists with minimal or limited quantitative information, data that are crucial in the application of toxicogenomic data for risk assessment. To facilitate quantitative interpretation of dose- or time-dependent genomic data, we propose to use combined average raw gene expression values (e.g., intensity or ratio) of genes associated with specific functional categories derived from the GO database. We developed an extended program (GO-Quant) to extract quantitative gene expression values and to calculate the average intensity or ratio for those significantly altered by functional gene category based on MAPPFinder results. To demonstrate its application, we applied this approach to a previously published dose- and time-dependent toxicogenomic data set (J. F. Dillman et al., 2005, Chem. Res. Toxicol. 18, 28–34). Our results indicate that the above systems approach can describe quantitatively the degree to which functional gene systems change across dose or time. Additionally, this approach provides a robust measurement to illustrate results compared to single-gene assessments and enables the user to calculate the corresponding ED50 for each specific functional GO term, important for risk assessment.
Key Words: microarray; GO analysis; dose and time dependent; quantitative.
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