One of the most challenging problems in biomedical research is to understand the underlying mechanisms of complex diseases. Great effort has been spent on finding the genes associated to diseases (Botstein and Risch, 2003; Kann, 2009). However, more and more evidences indicate that most human diseases cannot be attributed to a single gene but arise due to complex interactions among multiple genetic variants and environmental risk factors (Hirschhorn and Daly, 2005). Several databases have been developed storing associations between genes and diseases such as CTDTM (Davis, et al., 2014), OMIM® (Hamosh et al., 2005) and the NHGRI-EBI GWAS catalog (Welter et al., 2014). Each of these databases focuses on different aspects of the phenotype-genotype relationship, and due to the nature of the database curation process, they are not complete. Hence, integration of different databases with information extracted from the literature is needed to allow a comprehensive view of the state of the art knowledge within this research field. With this need in mind, we have created DisGeNET.
DisGeNET is a discovery platform integrating information on gene-disease associations (GDAs) from several public data sources and the literature (Piñero et al., 2015 ). The current version contains (DisGeNET v4.0) contains 429,036 associations, between 17,381 genes and 15,093 diseases, disorders and clinical or abnormal human phenotypes. Given the large number of GDAs compiled in DisGeNET, we have also developed a score in order to rank the associations based on the supporting evidence. Importantly, useful tools have also been created to explore and analyze the data contained in DisGeNET. DisGeNET can be queried through Search and Browse functionalities available from this web interface, or by a Cytoscape app to query and analyze a network representation of the data. Moreover, DisGeNET data can be queried by downloading the SQLite database to your local repository. Furthermore, an RDF (Resource Description Framework) representation of DisGeNET database is also available. It can be queried using our SPARQL endpoint and a Faceted Browser. Finally, an R package to interrogate DisGeNET is now available (beta version). Follow the link for more information on DisGeNET.
DisGeNET database has been cited by several papers. Some of them can be reviewed here.
DisGeNET is distributed under the GNU GPL 3.0 licence.
Go to DisGeNET web page
N. Queralt-Rosinach, J. Piñero, À. Bravo, F. Sanz, and L. I. Furlong. DisGeNET-RDF: harnessing the innovative power of the Semantic Web to explore the genetic basis of diseases. Bioinformatics. 2016 Jul 15;32(14):2236-8. doi: 10.1093/bioinformatics/btw214
Núria Queralt-Rosinach, Tobias Kuhn, Christine Chichester, Michel Dumontier, Ferran Sanz, Laura I Furlong. Publishing DisGeNET as Nanopublications. Semantic Web, vol. Preprint, no. Preprint, pp. 1-10, 2015. DOI: 10.3233/SW-150189
Pinero Janet, Queralt-Rosinach N, Bravo A, Deu-Pons J, Bauer-Mehren A, Baron M, Sanz F, Furlong LI. DisGeNET: a discovery platform for the dynamical exploration of human diseases and their genes. Database 2015; doi: 10.1093/database/bav028
Anna Bauer-Mehren, Markus Bundschus, Michael Rautschka, Miguel A. Mayer, Ferran Sanz, Laura I. Furlong. Gene-disease network analysis reveals functional modules in mendelian, complex and environmental diseases. PLoS ONE 2011 6(6): e20284. doi:10.1371/journal.pone.0020284. PubMed
Bauer-Mehren A, Rautschka M, Sanz F, Furlong LI. DisGeNET – a Cytoscape plugin to visualize, integrate, search and analyze gene-disease networks. Bioinformatics. 2010 Nov 15;26(22):2924-6. Epub 2010 Sep 21.PubMed