dbZach: A MIAME-Compliant Toxicogenomic Supportive Relational Database

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 databas...

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Bibliographic Details
Published in:Toxicological sciences 2006-04, Vol.90 (2), p.558-568
Main Authors: Burgoon, Lyle D., Boutros, Paul C., Dere, Edward, Zacharewski, Timothy R.
Format: Article
Language:English
Subjects:
Animals
Database Management Systems
Databases, Factual
dbZach
Humans
MIAME compliant
Oligonucleotide Array Sequence Analysis
Polymerase Chain Reaction
Risk Assessment
Toxicogenetics
toxicogenomic data management system
Toxicology
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Summary: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.
ISSN:1096-6080
1096-0929
DOI:10.1093/toxsci/kfj097