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Nucleic Acids Res. 2017 Jan 4;45(D1):D833-D839. doi: 10.1093/nar/gkw943. Epub 2016 Oct 19.

DisGeNET: a comprehensive platform integrating information on human disease-associated genes and variants.

Author information

  • 1Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Medical Research Institute (IMIM), Department of Experimental and Health Sciences (DCEXS), Universitat Pompeu Fabra (UPF), C/Dr Aiguader 88, E-08003 Barcelona, Spain.
  • 2Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Medical Research Institute (IMIM), Department of Experimental and Health Sciences (DCEXS), Universitat Pompeu Fabra (UPF), C/Dr Aiguader 88, E-08003 Barcelona, Spain laura.furlong@upf.edu.

Abstract

The information about the genetic basis of human diseases lies at the heart of precision medicine and drug discovery. However, to realize its full potential to support these goals, several problems, such as fragmentation, heterogeneity, availability and different conceptualization of the data must be overcome. To provide the community with a resource free of these hurdles, we have developed DisGeNET (http://www.disgenet.org), one of the largest available collections of genes and variants involved in human diseases. DisGeNET integrates data from expert curated repositories, GWAS catalogues, animal models and the scientific literature. DisGeNET data are homogeneously annotated with controlled vocabularies and community-driven ontologies. Additionally, several original metrics are provided to assist the prioritization of genotype-phenotype relationships. The information is accessible through a web interface, a Cytoscape App, an RDF SPARQL endpoint, scripts in several programming languages and an R package. DisGeNET is a versatile platform that can be used for different research purposes including the investigation of the molecular underpinnings of specific human diseases and their comorbidities, the analysis of the properties of disease genes, the generation of hypothesis on drug therapeutic action and drug adverse effects, the validation of computationally predicted disease genes and the evaluation of text-mining methods performance.

PMID:
27924018
PMCID:
PMC5210640
DOI:
10.1093/nar/gkw943
[PubMed - in process]
Free PMC Article
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