Pregled bibliografske jedinice broj: 843386
Visualizing GO Annotations
Visualizing GO Annotations // The Gene Ontology Handbook / Dessimoz, Christophe ; Škunca, Nives (ur.).
New York (NY): Humana Press, 2016. str. 207-220
CROSBI ID: 843386 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
Visualizing GO Annotations
Autori
Supek, Fran ; Škunca, Nives
Vrsta, podvrsta i kategorija rada
Poglavlja u knjigama, pregledni
Knjiga
The Gene Ontology Handbook
Urednik/ci
Dessimoz, Christophe ; Škunca, Nives
Izdavač
Humana Press
Grad
New York (NY)
Godina
2016
Raspon stranica
207-220
ISBN
978-1-4939-3741-7
Ključne riječi
Gene Ontology, Visualization, Interpretation, Redundancy, Enrichment, Tools
Sažetak
Contemporary techniques in biology produce readouts for large numbers of genes simultaneously, the typical example being differential gene expression measurements. Moreover, those genes are often richly annotated using GO terms that describe gene function and that can be used to summarize the results of the genome-scale experiments. However, making sense of such GO enrichment analyses may be challenging. For instance, overrepresented GO functions in a set of differentially expressed genes are typically output as a flat list, a format not adequate to capture the complexities of the hierarchical structure of the GO annotation labels. In this chapter, we survey various methods to visualize large, difficult-to- interpret lists of GO terms. We catalog their availability—Web-based or standalone, the main principles they employ in summarizing large lists of GO terms, and the visualization styles they support. These brief commentaries on each software are intended as a helpful inventory, rather than comprehensive descriptions of the underlying algorithms. Instead, we show examples of their use and suggest that the choice of an appropriate visualization tool may be crucial to the utility of GO in biological discovery.
Izvorni jezik
Engleski
Znanstvena područja
Biologija
POVEZANOST RADA
Projekti:
098-0000000-3168 - Strojno učenje prediktivnih modela u računalnoj biologiji (Šmuc, Tomislav, MZOS ) ( CroRIS)
ICT-2013-612944
HRZZ-IP-2013-11-9623 - Postupci strojnog učenja za dubinsku analizu složenih struktura podataka (DescriptiveInduction) (Gamberger, Dragan, HRZZ - 2013-11) ( CroRIS)
Ustanove:
Institut "Ruđer Bošković", Zagreb