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Nives Škunca
Researcher at Swiss Institute of Bioinformatics
Publications - 30
Citations - 8236
Nives Škunca is an academic researcher from Swiss Institute of Bioinformatics. The author has contributed to research in topics: Ontology (information science) & Protein function prediction. The author has an hindex of 22, co-authored 30 publications receiving 6730 citations. Previous affiliations of Nives Škunca include University College London & Baylor College of Medicine.
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Book ChapterDOI
Primer on the Gene Ontology
TL;DR: The Gene Ontology (GO) project is the largest resource for cataloguing gene function as discussed by the authors, and the combination of solid conceptual underpinnings and a practical set of features have made the GO a widely adopted resource in the research community and an essential resource for data analysis.
Journal ArticleDOI
CAFA and the Open World of protein function predictions
Christophe Dessimoz,Christophe Dessimoz,Christophe Dessimoz,Nives Škunca,Nives Škunca,Paul Thomas +5 more
TL;DR: The plummeting cost of DNA sequencing means that vast amounts of gene sequences are becoming available across all domains of life, but discovering the function(s) of a gene remains painstaking work that is largely restricted to a handful of model species.
Book ChapterDOI
Primer on the Gene Ontology
TL;DR: This chapter provides a concise primer for all users of the Gene Ontology, briefly introducing the structure of the ontology and explaining how to interpret annotations associated with the GO.
Journal ArticleDOI
Polyketide synthase genes and the natural products potential of Dictyostelium discoideum
Jurica Zucko,Nives Škunca,Tomaz Curk,Blaz Zupan,Paul F. Long,J. Cullum,R.H. Kessin,D. Hranueli +7 more
TL;DR: The promoters of PKS genes were much more divergent than the structural genes, although the authors have identified motifs that are unique to some PKS gene promoters.
Journal ArticleDOI
Phyletic Profiling with Cliques of Orthologs Is Enhanced by Signatures of Paralogy Relationships
TL;DR: A novel model for computational annotation that refines two established concepts: annotation based on homology and annotations based on phyletic profiling is introduced, which will contribute to making experimental validation of computational predictions more approachable, both in cost and time.