Bioinformatics Advance Access published online on July 12, 2006
Bioinformatics, doi:10.1093/bioinformatics/btl378
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1 Korea BioInformation Center, Korea Research Institute of Bioscience and Biotechnology, 52 Eoun-dong, Yuseong-gu, Daejeon 305-333, Rep. of Korea
* To whom correspondence should be addressed.
Motivation: Genes are typically expressed in modular manners in biological processes. Recent studies reflect such features in analyzing gene expression patterns by directly scoring gene sets. Gene annotations have been used to define the gene sets, which have served to reveal specific biological themes from expression data. However, current annotations have limited analytical power, because they are classified by single categories providing only unary information for the gene sets. Results: Here we propose a method for discovering composite biological themes from expression data. We intersected two annotated gene sets from different categories of Gene Ontology (GO). We then scored the expression changes of all the single and intersected sets. In this way, we were able to uncover, for example, a gene set with the molecular function F and the cellular component C that showed significant expression change, while the changes in individual gene sets were not significant. We provided an exemplary analysis for HIV-1 immune response. Additionally, we tested the method on 20 public data sets where we found many ‘filtered’ composite terms the number of which reached Availability: We provide a web application (ADGO: http://array.kobic.re.kr/ADGO) for the analysis of differentially expressed gene sets with composite GO annotations. The user can analyze Affymetrix and dual channel array (spotted cDNA and spotted oligo microarray) data for four species: human, mouse, rat, and yeast.
Received April 18, 2006
Revised July 5, 2006
Accepted July 5, 2006
Article
ADGO: analysis of differentially expressed gene sets using composite GO annotation
Dougu Nam 1,
Sang-Bae Kim 1,
Seon-Kyu Kim 1,
Sungjin Yang 1,
Seon-Young Kim 2,
and
In-Sun Chu 1 *
2 Human Genome Laboratory, Genome Research Center, Korea Research Institute of Bioscience and Biotechnology, 52 Eoun-dong, Yuseong-gu, Daejeon 305-333, Rep. of Korea
In-Sun Chu, E-mail: chu{at}kribb.re.kr
Abstract 
34% (a strong criterion, 5% significance) of the number of significant unary terms on average. By using composite annotation, we can derive new and improved information about disease and biological processes from expression data.
Associate Editor: John Quackenbush
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