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, TIGA, 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/TCRD, and the Cytoscape stringApp. All data in DISEASES is 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.

Morphine disrupts macrophage functions even during HIV infection

“…By this method, there were 60 up‐regulated and 73 down‐regulated proteins (Figure 4A). Using the latter set of differentially expressed proteins, we performed downstream analysis by KEGG pathways, Gene Ontology keywords, WikiPathways, UniProt keywords, and Diseases pathways 39–43 . We also used the STRING database to create an interactome (Figure S3).…”

“…Up‐regulated and down‐regulated proteins in response to MOR were assessed by Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) ifor significant interactions (String Consortium, Switzerland). Using these outputs, we identified cellular and disease related processes enriched with MOR, as determined by Kyoto Encyclopedia of Genes and Genomes (KEGG pathway, Kyoto, Japan), Gene Ontology, WikiPathway, Diseases database (Novo Nordisk Foundation Center for Protein Research, Denmark), and UniProt keyword 38–43 …”

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“…Next, using the taxonomy ID of MPXV, we collected reviewed protein entries (Swiss-Prot) from UniProt (Apweiler et al, 2004). For human proteins, we queried DISEASES, a human disease database, with DOID:3292 (Grissa et al, 2022). Lastly, Open Targets Platform was used to fetch information about “druggability” of proteins reported from studies (Ochoa et al, 2021).…”

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“…Given the curated phenotype lists described above, human phenotype-gene associations were retrieved from multiple sources, including Online Mendelian Inheritance in Man (OMIM) [26], Orphanet [27], ClinVar [28], DISEASES [29], DatabasE of genomiC varIation and Phenotype in Humans using Ensembl Resources (DECIPHER) [30], the American Heart Association (AHA) [31], and Geneshot [32]. From OMIM and Orphanet human phenotype-gene associations were obtained from the Jackson Laboratory HPO database (hpo.jax.org, October 2021 release), providing curated links between HPO terms and human genes.…”

“…The ClinVar-based HPO-gene associations were compiled for the human abnormal morphology of the great vessels, heart, and CNS phenotypes. Literature-based human disease-gene associations were obtained from the DISEASES portal [29]. This dataset contains disease-gene associations text-mined from literature and genome-wide association studies.…”