Openly accessible

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 and Wouters, Merridee A, 2013, Gentrepid V2.0: a web server for candidate disease gene prediction, BMC Bioinformatics, vol. 14, no. 1, Article 249, pp. 1-9.

Attached Files
Name Description MIMEType Size Downloads
wouters-gentrepid-2013.pdf Published version application/pdf 1.62MB 10

Title Gentrepid V2.0: a web server for candidate disease gene prediction
Author(s) Ballouz, Sara
Liu, Jason Y.
George, Richard A.
Bains, Naresh
Liu, Arthur
Oti, Martin
Gaeta, Bruno
Fatkin, Diane
Wouters, Merridee A,
Journal name BMC Bioinformatics
Volume number 14
Issue number 1
Season Article 249
Start page 1
End page 9
Total pages 9
Publisher BioMed Central
Place of publication London, England
Publication date 2013-08
ISSN 1471-2105
Keyword(s) candidate disease gene prediction
candidate disease genes
mendelian diseases
complex diseases
genome-wide association studies
genotype
phenotype
candidate gene identification
genetic-association studies
hypertension
Summary 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.
Language eng
Field of Research 060102 Bioinformatics
060412 Quantitative Genetics (incl Disease and Trait Mapping Genetics)
Socio Economic Objective 920110 Inherited Diseases (incl. Gene Therapy)
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2008, BioMed Central
Persistent URL http://hdl.handle.net/10536/DRO/DU:30057302

Document type: Journal Article
Collections: School of Life and Environmental Sciences
Open Access Collection
Connect to link resolver
 
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.

Versions
Version Filter Type
Access Statistics: 19 Abstract Views, 10 File Downloads  -  Detailed Statistics
Created: Sat, 26 Oct 2013, 09:10:35 EST by Merridee Wouters

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.