ArticleBMC bioinformatics2013
Gentrepid V2.0: a web server for candidate disease gene prediction.
Article in BMC bioinformatics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Gene, cell type, and drug prioritization analysis suggest genetic basis for the utility of diuretics in treating Alzheimer disease.HGG advances · 2023Article
- Improved detection of disease-associated variation by sex-specific characterization and prediction of genes required for fertility.Andrology · 2015Article
- Decoding the complex genetic causes of heart diseases using systems biology.Biophysical reviews · 2015Article
- Identification of novel therapeutics for complex diseases from genome-wide association data.BMC medical genomics · 2014Article
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundCandidate 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.
conclusionsThe 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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