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.

Instituto de Salud Carlos III-Fondo Europeo de Desarrollo Regional [CP10/00524 and PI13/00082]; Innovative Medicines Initiative Joint Undertaking [Open PHACTs No. 115191]; resources of which are composed of financial contribution from the European Union's Seventh Framework Programme [FP7/2007-2013]; EFPIA companies’ in kind contribution; European Union Horizon 2020 Programme 2014-2020 [MedBioinformatics No. 634143 and Elixir-Excelerate No. 676559]. The Research Programme on Biomedical Informatics (GRIB) is a member of the Spanish National Bioinformatics Institute (INB), PRB2-ISCIII and is supported by grant PT13/0001/0023, of the PE I+D+i 2013-2016, funded by ISCIII and FEDER. Funding for open access charge: Instituto de Salud Carlos III-Fondo Europeo de Desarrollo Regional [PI13/00082].