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Gentrepid V2.0: a web server for candidate disease gene prediction

BACKGROUND: Candidate disease gene prediction is a rapidly developing area of bioinformatics research with the potential to deliver great benefits to human health. As experimental studies detecting associations between genetic intervals and disease proliferate, better bioinformatic techniques that c...

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Detalles Bibliográficos
Autores principales: Ballouz, Sara, Liu, Jason Y, George, Richard A, Bains, Naresh, Liu, Arthur, Oti, Martin, Gaeta, Bruno, Fatkin, Diane, Wouters, Merridee A
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3844418/
https://www.ncbi.nlm.nih.gov/pubmed/23947436
http://dx.doi.org/10.1186/1471-2105-14-249
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author Ballouz, Sara
Liu, Jason Y
George, Richard A
Bains, Naresh
Liu, Arthur
Oti, Martin
Gaeta, Bruno
Fatkin, Diane
Wouters, Merridee A
author_facet Ballouz, Sara
Liu, Jason Y
George, Richard A
Bains, Naresh
Liu, Arthur
Oti, Martin
Gaeta, Bruno
Fatkin, Diane
Wouters, Merridee A
author_sort Ballouz, Sara
collection PubMed
description BACKGROUND: Candidate disease gene prediction is a rapidly developing area of bioinformatics research with the potential to deliver great benefits to human health. As experimental studies detecting associations between genetic intervals and disease proliferate, better bioinformatic techniques that can expand and exploit the data are required. DESCRIPTION: Gentrepid is a web resource which predicts and prioritizes candidate disease genes for both Mendelian and complex diseases. The system can take input from linkage analysis of single genetic intervals or multiple marker loci from genome-wide association studies. The underlying database of the Gentrepid tool sources data from numerous gene and protein resources, taking advantage of the wealth of biological information available. Using known disease gene information from OMIM, the system predicts and prioritizes disease gene candidates that participate in the same protein pathways or share similar protein domains. Alternatively, using an ab initio approach, the system can detect enrichment of these protein annotations without prior knowledge of the phenotype. CONCLUSIONS: The system aims to integrate the wealth of protein information currently available with known and novel phenotype/genotype information to acquire knowledge of biological mechanisms underpinning disease. We have updated the system to facilitate analysis of GWAS data and the study of complex diseases. Application of the system to GWAS data on hypertension using the ICBP data is provided as an example. An interesting prediction is a ZIP transporter additional to the one found by the ICBP analysis. The webserver URL is https://www.gentrepid.org/.
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spelling pubmed-38444182013-12-02 Gentrepid V2.0: a web server for candidate disease gene prediction Ballouz, Sara Liu, Jason Y George, Richard A Bains, Naresh Liu, Arthur Oti, Martin Gaeta, Bruno Fatkin, Diane Wouters, Merridee A BMC Bioinformatics Database BACKGROUND: Candidate disease gene prediction is a rapidly developing area of bioinformatics research with the potential to deliver great benefits to human health. As experimental studies detecting associations between genetic intervals and disease proliferate, better bioinformatic techniques that can expand and exploit the data are required. DESCRIPTION: Gentrepid is a web resource which predicts and prioritizes candidate disease genes for both Mendelian and complex diseases. The system can take input from linkage analysis of single genetic intervals or multiple marker loci from genome-wide association studies. The underlying database of the Gentrepid tool sources data from numerous gene and protein resources, taking advantage of the wealth of biological information available. Using known disease gene information from OMIM, the system predicts and prioritizes disease gene candidates that participate in the same protein pathways or share similar protein domains. Alternatively, using an ab initio approach, the system can detect enrichment of these protein annotations without prior knowledge of the phenotype. CONCLUSIONS: The system aims to integrate the wealth of protein information currently available with known and novel phenotype/genotype information to acquire knowledge of biological mechanisms underpinning disease. We have updated the system to facilitate analysis of GWAS data and the study of complex diseases. Application of the system to GWAS data on hypertension using the ICBP data is provided as an example. An interesting prediction is a ZIP transporter additional to the one found by the ICBP analysis. The webserver URL is https://www.gentrepid.org/. BioMed Central 2013-08-16 /pmc/articles/PMC3844418/ /pubmed/23947436 http://dx.doi.org/10.1186/1471-2105-14-249 Text en Copyright © 2013 Ballouz et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Database
Ballouz, Sara
Liu, Jason Y
George, Richard A
Bains, Naresh
Liu, Arthur
Oti, Martin
Gaeta, Bruno
Fatkin, Diane
Wouters, Merridee A
Gentrepid V2.0: a web server for candidate disease gene prediction
title Gentrepid V2.0: a web server for candidate disease gene prediction
title_full Gentrepid V2.0: a web server for candidate disease gene prediction
title_fullStr Gentrepid V2.0: a web server for candidate disease gene prediction
title_full_unstemmed Gentrepid V2.0: a web server for candidate disease gene prediction
title_short Gentrepid V2.0: a web server for candidate disease gene prediction
title_sort gentrepid v2.0: a web server for candidate disease gene prediction
topic Database
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3844418/
https://www.ncbi.nlm.nih.gov/pubmed/23947436
http://dx.doi.org/10.1186/1471-2105-14-249
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