DisGeNET user guides

The DisGeNET Web Interface

The DisGeNET web interface has been created using django framework.

In the DisGeNET Web Interface User Guide, you will find a tutorial addressing the following type of questions:

  • What genes are associated to a specific Disease?
  • How to retrieve the genes associated to several diseases at once?
  • What diseases are associated to a specific protein class ?
  • What are the variants associated to a specific disease?
  • How can I retrieve GDAs annotated with a specific evidence level?
  • How can I retrieve diseases associated to a particular variant consequence type?

The DisGeNET Cytoscape App

In the DisGeNET Cytoscape App User Guide, you will find a tutorial addressing the following type of questions:

  • How to create gene and variant-disease networks?
  • What diseases are associated to a specific gene, or variant, or lists of genes or variants ?
  • How to create networks by disease class?
  • How can I filter the GDAs with a specific evidence level?
  • How can I color the networks by disease class?
  • How can I annotate my protein-protein network with DisGeNET data?
  • How can I use the app in an automatic fashion from R or Python?

FAQs

These are the FAQs. If you have questions or comments about DisGeNET data, the website, the Cytoscape App, the web interface, DisGeNET RDF, or the downloads, please email us at: support(at)disgenet(dot)org

What kind of information can I find in DisGeNET?

DisGeNET contains a compilation of genes associated to diseases, that comes from different publicly available databases: the Comparative Toxicogenomics DatabaseTM (CTDTM), UniProt/SwissProt, the Cancer Genome Interpreter, Orphanet, the Mouse Genome Database (MGD), PsyGeNET, Genomics England, ClinGen, and the Rat Genome Database (RGD). The variant-disease associations in DisGeNET are annotated from ClinVar, the NHGRI-EBI GWAS Catalog, and the GWAS db. It also contains gene-disease, and variant-disease associations retrieved using text mining approaches (LHGDN and BeFree data).

How is the score for a gene-disease pair, or variant-disease pair computed?

For a seamless integration and ranking of gene-disease associations, we developed a gene-disease association score. The DisGeNET score for GDAs takes into account the number and type of sources (level of curation, organisms), and the number of publications supporting the association, while the score for the VDAs takes into account sources, and number of papers. The scores range from 0 to 1. For further details, click here .

What kind of questions can be answered with DisGeNET?

You can answer these type of questions:

  • what are the genes, and variants involved in a particular disease
  • what are the diseases associated to a particular gene
  • what are the diseases associated to a list of variants
  • what is the type of association between a particular gene and a disease
  • what are the more recent genes/variants reported as associated to a particular disease

What is meant by "association type"?

The "association type" describes the different flavors of the gene-disease relationship, which are described in the GeneDiseaseAssociation.owl. For instance, the Genetic Variation association type is used when a sequence variation is associated to the disease phenotype. This ontology was developed to achieve a seamless integration of gene-disease association data coming from different databases.

What is the disease vocabulary used for diseases?

All entries in DisGeNET are mapped to the UMLS® CUIs. The source databases use MeSH, or MIM identifiers, or disease names for disease terms.

What species are covered in DisGeNET?

DisGeNET is focused on human genes and their association to diseases. We also include gene-disease associations described in animal models (mouse, rat) and map the genes to the human orthologs.

How can I download the information in DisGeNET?

DisGeNET data can be obtained in different ways: by downloading the SQLite database, by downloading the RDF data dump, through the Cytoscape app, or by downloading the tab separated files at the Downloads page. Alternatively, you can download the results of your analysis by using the downloads button on the top right in the web interface.

8. How is computed the disease similarity in the search box?

Disease searches can now be expanded using semantic similarities between disease concepts. Once you search for a disease, you will see the button "Similar diseases" the disease card description (in the image below, inside the red box).

By clicking in "Similar diseases", a pop-up window will appear showing the top-10 most similar diseases according to the Sokal-Sneath semantic similarity distance (Sánchez et al., 2011). See an example in the figure below.

It is possible to explore the next 10 most similar diseases using the pagination buttons. You can then select one or more concepts and include them in the search by clicking on the “Expand your search using the selected diseases” button. In this way, the results of your search will include those of your original concept as well as the added concepts. This expanded search applies to all the results (GDAs, VDAs and DDAs). The disease similarity between concepts is computed using the Sokal-Sneath semantic similarity distance (Sánchez et al., 2011) on the taxonomic relations provided by the Unified Medical Language System Metathesaurus (Bodenreider, 2004). Only the relationships of type is-a (which describe the taxonomy in any ontology) are taken into account.

DisGeNET presentations

  • Slides of the presentation "DisGeNET platform of disease genomics to support variant interpretation", Seminar CBIO-MINES ParisTech, Tuesday, February 4th 2020
  • Slides of the presentation of DisGeNET in the Lecture series Translational Bioinformatics and Systems Biomedicine in Luxembourg (February, 2019).
  • Slides of the presentation of DisGeNET in the Translational Bioinformatics Conference in Cambridge, United Kingdom (June, 2017).
  • Slides of the presentation of DisGeNET in the PRBB computational seminars (June, 2017).
  • Slides of the DisGeNET tutorial at the ECCB 2016 in The Hague, Netherlands (September, 2016).
  • Poster of disgenet2r, an R package to explore the molecular underpinnings of human diseases at JBI in Valencia, Spain (May, 2016).
  • Poster of DisGeNET at the conference Linking Life Science Data: Design to Implementation, and Beyond, Vienna, Austria (February, 2016).
  • Slides of the DisGeNET presentation at the BioHackathon 2015 in Nagasaki, Japan (September, 2015).
  • Slides of the DisGeNET tutorial at the SWAT4LS 2015 in Cambridge, UK (December, 2015).
  • Slides of the DisGeNET presentation at the Big Data in Biomedicine debate. Barcelona, Spain (November, 2014).