Naslov
REViGO: Redundancy Elimination and Visualization of Gene Ontology Term Lists

Autori
Škunca, Nives ; Šmuc, Tomislav ; Supek, Fran

Vrsta, podvrsta i kategorija rada
Sažeci sa skupova, sažetak, znanstveni

Izvornik
The 3rd Adriatic Meeting on Computational Solutions in the Life Sciences - Book of Abstracts / Babić, Darko ; Došlić, Nađa ; Smith, David ; Tomić, Sanja ; Vlahoviček, Kristian - Zagreb : Institute of Computational Life Sciences (ICLS), 2009, 75-75

ISBN
978-953-6690-80-0

Skup
The 3rd Adriatic Meeting on Computational Solutions in the Life Sciences

Mjesto i datum
Primošten, Hrvatska, 01.09.2009. - 05.09.2009

Vrsta sudjelovanja
Poster

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
Gene Ontology; visualization; semantic similarity

Sažetak
The Gene Ontology (GO) is a controlled, hierarchically organized vocabulary for describing function of gene products taking part in biological systems. Today's high-throughput experiments measure expression of thousands of genes simultaneously using microarrays or various proteomics approaches. Afterwards, researchers typically focus on genes whose expression differs between e.g. healthy and diseased tissue, with the resulting gene lists being interpreted by statistical testing for over– and under-representation within GO categories [1]. Such a way of summarizing experimental results may prove inadequate in the future. As the high-throughput techniques become cheaper and more accurate, they will dependably detect even slight changes in gene expression. Consequently, the lists of relevant genes will grow in size, and so will the resulting lists of GO categories. Additionally, the interpretation of results is made difficult by high redundancy between individual GO categories. We propose a computational approach that would (a) enable flexible reduction in size for large user-supplied lists of overlapping GO categories, and (b) visualize the remaining GO terms in a two-dimensional space which reflects the terms' semantic interrelations. Our simple clustering-like algorithm relies on previously defined measures of semantic similarity in the GO space [2]. Dimensionality reduction techniques and graph—based visualization approaches will be used to derive informative visualizations. For more information refer to http://revigo.irb.hr. [1] I. Rivals, L.Personnaz, L. Taing, M-C. Potier. Bioinformatics, 23(4) (2007) 401 [2] A. Schlicker, M. Albrecht. Nucl. Acids Res., 36(Database issue) (2007) D434

Izvorni jezik
Engleski

Znanstvena područja
Biologija, Računarstvo

POVEZANOST RADA