The DisGeNET knowledge platform for disease genomics: 2019 update

By Janet Piñero González, Juan Manuel Ramírez-Anguita, Josep Saüch-Pitarch, Francesco Ronzano, Emilio Centeno, Ferran Sanz, Laura Inés Furlong. Nucleic Acids Res., 2019

2,560 citations recorded by the source corpus.

Abstract One of the most pressing challenges in genomic medicine is to understand the role played by genetic variation in health and disease. Thanks to the exploration of genomic variants at large scale, hundreds of thousands of disease-associated loci have been uncovered. However, the identification of variants of clinical relevance is a significant challenge that requires comprehensive interrogation of previous knowledge and linkage to new experimental results. To assist in this complex task, we created DisGeNET (http://www.disgenet.org/), a knowledge management platform integrating and standardizing data about disease associated genes and variants from multiple sources, including the scientific literature. DisGeNET covers the full spectrum of human diseases as well as normal and abnormal traits. The current release covers more than 24 000 diseases and traits, 17 000 genes and 117 000 genomic variants. The latest developments of DisGeNET include new sources of data, novel data attributes and prioritization metrics, a redesigned web interface and recently launched APIs. Thanks to the data standardization, the combination of expert curated information with data automatically mined from the scientific literature, and a suite of tools for accessing its publicly available data, DisGeNET is an interoperable resource supporting a variety of applications in genomic medicine and drug R&D.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/152ac06268f9eb385bfc3f3d9a43e89c81db4c5b

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Citations in the corpus

Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development (2021, methodology): “DisGeNET: DisGeNET integrates gene-disease association data from expert curated repositories, GWAS catalogues, animal models and the scientific literature (Piñero et al. 2020).”
Open Graph Benchmark: Datasets for Machine Learning on Graphs (2020, background): “tein-protein association graphs and drug-drug interaction networks typically found in biology and medicine (Szklarczyk et al., 2019; Wishart et al., 2018; Davis et al., 2019; Szklarczyk et al., 2016; Piñero et al., 2020). Very recently, Sinha et al. (2020) proposed a synthetic link prediction benchm”
Ensembl 2021 (2020, methodology): “Other improvements include enriched annotation of potential splicing impact (using scores from SpliceAI (34)), incorporation of phenotype association and variant citation data from DisGeNET (35), and protein annotations from neXtProt (36).”
Open Targets Platform: supporting systematic drug–target identification and prioritisation (2020, neutral): “Several other resources including Pharos (35), Disgenet (36) or CanSAR (37) given their respective areas of expertise (38).”
gprofiler2 -- an R package for gene list functional enrichment analysis and namespace conversion toolset g:Profiler (2020, methodology): “For example, to use the gene-disease association data from the DisGeNET database 27 for enrichment analysis, the user can upload the GMT file in R using the upload_GMT_file function that returns a unique token for the file which can then be used as a value for the organism argument in the gost…”
HERB: a high-throughput experiment- and reference-guided database of traditional Chinese medicine (2020, methodology): “Then, we standardized the disease information to MeSH terms (https://www.nlm.nih.gov/mesh/meshhome.html) and the DisGeNet database (18), and cross-referenced them with the HPO and Disease Ontology (20) databases.”
UniProt: the universal protein knowledgebase in 2021 (2020, methodology): “These include the website REST-ful Application Programming Interface (API), stable URLs that can be bookmarked, linked, and reused, the Proteins extended REST API providing genomic coordinates of UniProtKB sequences and annotations imported and mapped from large-scale data imports (29), and the SPAR”
webTWAS: a resource for disease candidate susceptibility genes identified by transcriptome-wide association study (2021, methodology): “DisGeNET is a popular GWAS resource which collects genes and variants associated to human diseases, and the DisGeNET score reflects the strength of a particular gene-disease association based on current knowledge (47).”