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

BMC Bioinformatics

Merridee A Wouters¹  Diane Fatkin²  Bruno Gaeta⁶  Martin Oti³  Arthur Liu⁴  Naresh Bains⁴  Richard A George⁴  Jason Y Liu⁴  Sara Ballouz⁵ 

[1]School of Medicine, Deakin University, Geelong, VIC 3217, Australia

[2]Molecular Cardiology and Biophysics Division, Victor Chang Cardiac Research Institute, Darlinghurst, NSW 2010, Australia

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

[4]Structural and Computational Biology Department, Victor Chang Cardiac Research Institute, Darlinghurst, NSW 2010, Australia

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

[6]School of Computer Science and Engineering, University of New South Wales, Kensington, NSW 2052, Australia

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

Others  :  1087787
DOI  :  10.1186/1471-2105-14-249

 received in 2012-11-22, accepted in 2013-08-13,  发布年份 2013

【 摘 要 】

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.

【 授权许可】

   
2013 Ballouz et al.; licensee BioMed Central Ltd.

【 图 表 】

Figure 1.

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