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

Sara Ballouz127, Jason Y Liu1, Richard A George1, Naresh Bains1, Arthur Liu1, Martin Oti3, Bruno Gaeta2, Diane Fatkin45 and Merridee A Wouters6*

Author Affiliations

1 Structural and Computational Biology Department, Victor Chang Cardiac Research Institute, Darlinghurst, NSW 2010, Australia

2 School of Computer Science and Engineering, University of New South Wales, Kensington, NSW 2052, Australia

3 Centre for Molecular and Biomolecular Informatics, Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands

4 School of Medical Sciences, University of New South Wales, Kensington, NSW 2052, Australia

5 Molecular Cardiology and Biophysics Division, Victor Chang Cardiac Research Institute, Darlinghurst, NSW 2010, Australia

6 School of Medicine, Deakin University, Geelong, VIC 3217, Australia

7 Stanley Institute for Cognitive Genomics, Cold Spring Harbor Laboratory, 500 Sunnyside Boulevard, 11797, Woodbury, NY, USA

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BMC Bioinformatics 2013, 14:249  doi:10.1186/1471-2105-14-249

Published: 16 August 2013

Abstract

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/ webcite.

Keywords:
Candidate disease gene prediction; Candidate disease genes; Mendelian diseases; Complex diseases; Genome-wide association studies; Genotype; Phenotype; Candidate gene identification; Genetic-association studies; Hypertension