ADGO: analysis of differentially expressed gene sets using composite GO annotation.
BACKGROUND
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. In addition, we tested the method on 20 public datasets where we found many 'filtered' composite terms the number of which reached approximately 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.


BACKGROUND
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.


BACKGROUND
chu@kribb.re.kr


BACKGROUND
http://array.kobic.re.kr/ADGO.
Nam
Dougu
D
Korean BioInformation Center, Korea Research Institute of Bioscience and Biotechnology, 52 Eoun-dong Yuseong-gu, Daejeon 305-333, Korea.
Kim
Sang-Bae
SB
Kim
Seon-Kyu
SK
Yang
Sungjin
S
Kim
Seon-Young
SY
Chu
In-Sun
IS
D000465:Algorithms; D003198:Computer Simulation; D030562:Databases, Protein; D004282:Documentation / Q000379:methods*; D020869:Gene Expression Profiling / Q000379:methods*; D008954:Models, Biological; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D011506:Proteins / Q000737:chemistry* / Q000145:classification / Q000378:metabolism*; D012984:Software
D011506:Proteins