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ToxSci Advance Access originally published online on January 10, 2006
Toxicological Sciences 2006 90(2):558-568; doi:10.1093/toxsci/kfj097
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© The Author 2006. Published by Oxford University Press on behalf of the Society of Toxicology. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

dbZach: A MIAME-Compliant Toxicogenomic Supportive Relational Database

Lyle D. Burgoon*,{dagger},{ddagger}, Paul C. Boutros{dagger},§, Edward Dere{dagger},§ and Timothy R. Zacharewski{dagger},{ddagger},§,1

* Department of Pharmacology & Toxicology, {dagger} National Food Safety & Toxicology Center, {ddagger} Center for Integrative Toxicology, and § Department of Biochemistry & Molecular Biology, Michigan State University, East Lansing, Michigan 48824

Received October 3, 2005; accepted January 3, 2006

Quantitative risk assessment and the elucidation of mechanisms of toxicity requires computational infrastructure and innovative analysis approaches that systematically consider available data at all levels of biological organization. dbZach (http://dbzach.fst.msu.edu) is a modular relational database with associated data insertion, retrieval, and mining tools that manages traditional toxicology and complementary toxicogenomic data to facilitate comprehensive data integration, analysis, and sharing. It consists of four Core Subsystems (i.e., Clones, Genes, Sample Annotation, and Protocols), four Experimental Subsystems (i.e., Microarray, Affymetrix, Real-Time PCR, and Toxicology), and three Computational Subsystems (i.e., Gene Regulation, Pathways, Orthology) that comply with the Minimum Information About a Microarray Experiment (MIAME) standard. It is capable of including emerging technologies and other model systems, including ecologically relevant species. dbZach represents an enterprise toxicogenomic data management system which facilitates data integration and analysis, and reduces uncertainties in the continuum from initial exposure to toxicity while facilitating more comprehensive elucidations of mechanisms of toxicity and supporting mechanistically-based quantitative risk assessment.

Key Words: dbZach; database; MIAME compliant; toxicogenomic data management system.


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