Diseases 2.0: a weekly updated database of disease–gene associations from text mining and data integration

Abstract

The scientific knowledge about which genes are involved in which diseases grows rapidly, which makes it difficult to keep up with new publications and genetics datasets. The DISEASES database aims to provide a comprehensive overview by systematically integrating and assigning confidence scores to evidence for disease–gene associations from curated databases, genome-wide association studies (GWAS) and automatic text mining of the biomedical literature. Here, we present a major update to this resource, which greatly increases the number of associations from all these sources. This is especially true for the text-mined associations, which have increased by at least 9-fold at all confidence cutoffs. We show that this dramatic increase is primarily due to adding full-text articles to the text corpus, secondarily due to improvements to both the disease and gene dictionaries used for named entity recognition, and only to a very small extent due to the growth in number of PubMed abstracts. DISEASES now also makes use of a new GWAS database, Target Illumination by GWAS Analytics, which considerably increased the number of GWAS-derived disease–gene associations. DISEASES itself is also integrated into several other databases and resources, including GeneCards/MalaCards, Pharos/Target Central Resource Database and the Cytoscape stringApp. All data in DISEASES are updated on a weekly basis and is available via a web interface at https://diseases.jensenlab.org, from where it can also be downloaded under open licenses. Database URL: https://diseases.jensenlab.org

Glioblastoma is associated with extensive accelerated brain ageing

“…g. PGSEA analysis for unaffected control (n = 5) and NA-GBM (n = 5) samples against the Jensen disease database 41 , scale bar indicates the Z-score for each sample. Left plot shows the top 5 hits (p < 0.02), and the right plot shows the results for selected neurodegenerative diseases (p > 0.02).…”

Potential of ATP5MG to Treat Metabolic Syndrome-Associated Cardiovascular Diseases

“…The online tool DISEASES (18) was used to analyze the associations of ATP5MG with diseases. Only the human gene ATP5MG was selected, and the Z-score data were downloaded by selecting "text mining."…”

Pharos 2023: an integrated resource for the understudied human proteome

“…Very briefly, the TDL is one of four potential values: Tclin, Tchem, Tbio or Tdark. Tclin are targets for which an approved drug exists ( 5 , 6 ), which currently includes 704 human proteins; Tchem are proteins that are not Tclin, but are known to bind small molecules with high potency (currently N = 1971); Tbio includes proteins that have Gene Ontology ( 7 ) leaf term annotations based on experimental evidence; or meet two of the following three conditions: A fractional publication count ( 8 ) >5, three or more Gene RIF, ‘Reference Into Function’ annotations ( https://www.ncbi.nlm.nih.gov/gene/about-generif ), or 50 or more commercial antibodies, as counted in the Antibodypedia portal ( 9 ). The fourth category, Tdark, currently includes ∼31% of the human proteins that were manually curated at the primary sequence level in UniProt, but do not meet any of the Tclin, Tchem or Tbio criteria.…”

“…Pharos typically links to external sites for more information, and to TCRD’s primary data sources, when a website is available. Many other sites link to Pharos as well, including: PDB ( 12 ), ChEMBL ( 13 ), DISEASES ( 8 ), DrugCentral ( 14 ), MARRVEL ( 15 ), Reactome ( 16 ), KEGG ( 17 ), Guide to Pharmacology ( 18 ), GlyGen ( 19 ), UniProt ( 20 ) and more. Data is accessible by full TCRD download ( http://juniper.health.unm.edu/tcrd/ ), or via the GraphQL API ( https://pharos-api.ncats.io/graphql ).…”