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
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author Turner, Frances S
Clutterbuck, Daniel R
Semple, Colin AM
author_facet Turner, Frances S
Clutterbuck, Daniel R
Semple, Colin AM
author_sort Turner, Frances S
collection PubMed
description 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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spelling pubmed-3291282004-02-05 POCUS: mining genomic sequence annotation to predict disease genes Turner, Frances S Clutterbuck, Daniel R Semple, Colin AM Genome Biol Method 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. BioMed Central 2003 2003-10-10 /pmc/articles/PMC329128/ /pubmed/14611661 Text en Copyright © 2003 Turner et al.; licensee BioMed Central Ltd. This is an Open Access article: verbatim copying and redistribution of this article are permitted in all media for any purpose, provided this notice is preserved along with the article's original URL.
spellingShingle Method
Turner, Frances S
Clutterbuck, Daniel R
Semple, Colin AM
POCUS: mining genomic sequence annotation to predict disease genes
title POCUS: mining genomic sequence annotation to predict disease genes
title_full POCUS: mining genomic sequence annotation to predict disease genes
title_fullStr POCUS: mining genomic sequence annotation to predict disease genes
title_full_unstemmed POCUS: mining genomic sequence annotation to predict disease genes
title_short POCUS: mining genomic sequence annotation to predict disease genes
title_sort pocus: mining genomic sequence annotation to predict disease genes
topic Method
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC329128/
https://www.ncbi.nlm.nih.gov/pubmed/14611661
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AT clutterbuckdanielr pocusmininggenomicsequenceannotationtopredictdiseasegenes
AT semplecolinam pocusmininggenomicsequenceannotationtopredictdiseasegenes

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