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

By Janet Piñero González, À. Bravo, Núria Queralt-Rosinach, Alba Gutiérrez-Sacristán, Jordi Deu-Pons, Emilio Centeno, Javier García-García, Ferran Sanz, Laura Inés Furlong. 2016

2,488 citations recorded by the source corpus.

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.

Subfields: Genetics, Molecular Biology

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Citations in the corpus

WebGestalt 2017: a more comprehensive, powerful, flexible and interactive gene set enrichment analysis toolkit (2017, background): “Under the Disease class, DisGeNET (12), a comprehensive disease–gene association database that covers the whole landscape of human diseases and the phenotype–genotype relationships, has been added.”
TransmiR v2.0: an updated transcription factor-microRNA regulation database (2018, methodology): “The TFs were annotated with the following four items: Entrez gene ID, Ensembl gene ID, gene associated diseases (from DisGeNET database (22)), and cancer prognostic association data (from Human Protein Atlas database (23)).”
Artificial intelligence to deep learning: machine intelligence approach for drug discovery (2021, background): “Likewise, DisGeNET (https:// www. disge net. org/) is a text mining-driven database that contains a plethora of information on gene-disease and variants-disease relationships [90].”
Open Targets: a platform for therapeutic target identification and validation (2016, background): “DisGeNET (41) is the closest resource to the Open Targets Target Validation Platform in terms of information on Mendelian and complex diseases to help prioritization of disease genes as targets.”
Dual Proteome-scale Networks Reveal Cell-specific Remodeling of the Human Interactome (2020, methodology): “, 2000) and DisGeNET disease associations (Piñero et al., 2017).”
Mutation severity spectrum of rare alleles in the human genome is predictive of disease type (2019, methodology): “However, the gene does not belong to our disease-causing set and has no disease associations in DisGeNET.”
HMDD v3.0: a database for experimentally supported human microRNA–disease associations (2018, background): “Users can click one disease to view the interaction network between the miRNAs and genes associated with this disease (the disease genes are from DisGeNET (23)).”
Integrating biomedical research and electronic health records to create knowledge-based biologically meaningful machine-readable embeddings (2019, methodology): “In SPOKE, the ASSOCIATES_- DaG edges represent known associations between Diseases and Genes and were obtained from the GWAS Catalog 15 , DISEASES 16 , DisGeNET 17,18 , and DOAF 19 .”
The DisGeNET knowledge platform for disease genomics: 2019 update (2019, background): “Originally implemented in 2010 as a Cytoscape plugin (6), during the last years DisGeNET has evolved into different formats and tools (7–10), and it now undergoes its sixth release as a knowledge management platform aimed at supporting different application scenarios and users.”
Using clusterProfiler to characterize multiomics data (2024, neutral): “This makes it ideal for analyzing gene functions and localizations within biological processes, particularly for initial comprehensive studies on gene functions • KEGG offers detailed annotations on biochemical pathways and functions, including metabolic and signaling pathways, making it suitable fo”