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Cited by 413Pharmacogenetics and Drug MetabolismComputational Drug Discovery MethodsBiomedical Text Mining and Ontologies

DGIdb 2.0: mining clinically relevant drug–gene interactions

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Wagner, Alex H., Coffman, Adam C., Ainscough, Benjamin J., Spies, Nicholas C., Skidmore, Zachary L., Campbell, Katie M., Krysiak, Kilannin, Pan, Deng +7 more·2016·Nucleic Acids Research
DOI 10.1093/nar/gkv1165PMC PMC4702839
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Abstract

The Drug-Gene Interaction Database (DGIdb, www.dgidb.org) is a web resource that consolidates disparate data sources describing drug-gene interactions and gene druggability. It provides an intuitive graphical user interface and a documented application programming interface (API) for querying these data. DGIdb was assembled through an extensive manual curation effort, reflecting the combined information of twenty-seven sources. For DGIdb 2.0, substantial updates have been made to increase content and improve its usefulness as a resource for mining clinically actionable drug targets. Specifically, nine new sources of drug-gene interactions have been added, including seven resources specifically focused on interactions linked to clinical trials. These additions have more than doubled the overall count of drug-gene interactions. The total number of druggable gene claims has also increased by 30%. Importantly, a majority of the unrestricted, publicly-accessible sources used in DGIdb are now automatically updated on a weekly basis, providing the most current information for these sources. Finally, a new web view and API have been developed to allow searching for interactions by drug identifiers to complement existing gene-based search functionality. With these updates, DGIdb represents a comprehensive and user friendly tool for mining the druggable genome for precision medicine hypothesis generation.

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  • Top enriched pathways for hypertrophic cardiomyopathy pointed to candidate cardiomyocyte-specific therapeutic targets including ADCY5 and druggable targets including SRPK3.

    “Top enriched pathways for hypertrophic cardiomyopathy pointed to candidate cardiomyocyte-specific therapeutic targets including ADCY5, disruption of which is associated with longevity and protection against cardiomyopathy in mouse models40, as well as druggable targets41 including SRPK3, a downstream effector of MEF242 which is known to play a critical role in myocardial cell hypertrophy43.”

    From Transfer learning enables predictions in network biology (2023) · cites this paper as [41]

    The Introduction (page unavailable) describes DGIdb as a resource for checking whether candidate genes belong to potentially druggable categories, supporting the citation’s use for identifying druggable targets. Our copy does not identify SRPK3 as druggable or link it to hypertrophic cardiomyopathy, so it does not support that specific example. (LLM, conf=0.83)

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Research works printed but not linked: 28

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Cited by

Sorted by citations

Authors
Wagner, A. H., Coffman, A. C., Ainscough, B. J., Spies, N. C., Skidmore, Z. L., Campbell, K. M., Krysiak, K., Pan, D., McMichael, J. F., Eldred, J. M., Walker, J. R., Wilson, R. K., Mardis, E. R., Griffith, M., & Griffith, O. L.
Affiliations
Washington University in St. Louis (US)
Corresponding author
Griffith, Obi L.
ORCID
Venue
Nucleic Acids Research
Publisher
Oxford University Press (OUP)
Published
2015-11-03
Volume
44
Issue
D1
Pages
D1036-D1044
ISSN
  • 0305-1048 (ISSN-L)
  • 1362-4962
DOI
10.1093/nar/gkv1165
PMC
PMC4702839
OpenAlex
W2202954603
Cited by
413
References
2
Citations by year
2026: 18 · 2025: 39 · 2024: 41 · 2023: 37 · 2022: 40 · 2021: 28 · 2020: 44 · 2019: 32 · 2018: 52 · 2017: 59 · 2016: 23
FWCI
52.11
Type
journal article
Language
en
Open access
Licence
Locations
  • Nucleic Acids Research (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Digital Commons@Becker (Washington University School of Medicine) (submittedVersion, other-oa)
  • HighWire Press Open Archive (submittedVersion)
Funders
  • National Institutes of Health
  • National Human Genome Research Institute (U54HG003079, R00HG007940, K99HG007940)
  • National Cancer Institute (K22CA188163, T32CA113275)
Topics
  • Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Pharmacology › Pharmacogenetics and Drug Metabolism
  • Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
Keywords
drug-gene interactions, druggable genome, precision medicine, drug target prioritization, database curation, web API
MeSH terms
Genes, Ligands, Drug Discovery, Data Mining, Databases, Pharmaceutical

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