eGOn – a new tool for mapping microarray data onto the Gene
Ontology structure
Vidar Beisvag1, Lars Jolsum1, Wacek Kusnierczyk2, Mette Langaas2, Bjørn Alsberg3,
Hallgeir Bergum1, Jan Komorowski4, Arne K. Sandvik1 and Astrid Lægreid1
Norwegian University of Science and Technology, 1Department of Clinical and
Molecular Medicine, 2Department of Mathematical Sciences, 3Department of
Chemistry, 4Department of Computer and Information Science and Uppsala
University, The Linnaeus Centre for Bioinformatics.
Gene expression profiles obtained through microarray experiments represent massive
amounts of numerical data. In order to facilitate biological interpretation of these
profiles, a new web-based tool has been developed as a support for the Gene
Ontology (GO). eGOn (explore Gene Ontology (http://nova.idi.ntnu.no/)) is a
bioinformatics software that correlates the investigated genes with their biological
characteristics. A set of selected gene identifiers is submitted to the server, and eGOn
performs a search over the publicly available gene databases (e.g. Proteome available
through LocusLink) to retrieve GO terms that have been annotated to these genes. The
information comes from all the three top-level branches in the Ontology: molecular
function, biological process, and cellular component. The results are visualized in a
hierarchical structure corresponding to the most recent GO database version. The
view of the structure may be customized and stored as a template to be used with
other gene lists. An essential feature of eGOn is that several input files may be
analyzed simultaneously to compare the distribution of the annotated genes for two or
more experiments over the whole GO hierarchy. The GO annotations for all gene lists
are visualized in the GO hierarchical structure and can be exported in various text
formats. Similarity/distance measures are introduced to enable identification of
significant differences between the 'biological’ profiles of the different gene lists
submitted to eGOn. The information gained assists researchers in discovering if
differentially regulated genes create 'biological’ profiles in states such as normality,
disease, disease subtypes or treatment responses.