POCUS: mining genomic sequence annotation to predict disease genes

Here we present POCUS (prioritization of candidate genes using statistics), a novel computational approach to prioritize candidate disease genes that is based on over-representation of functional annotation between loci for the same disease. We show that POCUS can provide high (up to 81-fold) enrich...

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Detalles Bibliográficos
Autores principales: Turner, Frances S, Clutterbuck, Daniel R, Semple, Colin AM
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2003
Materias:
Method
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC329128/
https://www.ncbi.nlm.nih.gov/pubmed/14611661
Descripción
Sumario:Here we present POCUS (prioritization of candidate genes using statistics), a novel computational approach to prioritize candidate disease genes that is based on over-representation of functional annotation between loci for the same disease. We show that POCUS can provide high (up to 81-fold) enrichment of real disease genes in the candidate-gene shortlists it produces compared with the original large sets of positional candidates. In contrast to existing methods, POCUS can also suggest counterintuitive candidates.

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