Linked Life Data

16601082

Source:http://linkedlifedata.com/resource/pubmed/id/16601082

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
2
pubmed:dateCreated
2006-7-7
pubmed:abstractText
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 ED(50) for each specific functional GO term, important for risk assessment.
pubmed:grant
pubmed:commentsCorrections
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Aug
pubmed:issn
1096-6080
pubmed:author
pubmed:issnType
Print
pubmed:volume
92
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
560-77
pubmed:dateRevised
2010-9-17
pubmed:meshHeading
pubmed:year
2006
pubmed:articleTitle
A system-based approach to interpret dose- and time-dependent microarray data: quantitative integration of gene ontology analysis for risk assessment.
pubmed:affiliation
Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, 98105, USA.
pubmed:publicationType
Journal Article, Research Support, U.S. Gov't, Non-P.H.S., Research Support, N.I.H., Extramural