» Articles » PMID: 29156001

DGIdb 3.0: a Redesign and Expansion of the Drug-gene Interaction Database

Overview
Specialty Biochemistry
Date 2017 Nov 21
PMID 29156001
Citations 452
Authors
Affiliations
Soon will be listed here.
Abstract

The drug-gene interaction database (DGIdb, www.dgidb.org) consolidates, organizes and presents drug-gene interactions and gene druggability information from papers, databases and web resources. DGIdb normalizes content from 30 disparate sources and allows for user-friendly advanced browsing, searching and filtering for ease of access through an intuitive web user interface, application programming interface (API) and public cloud-based server image. DGIdb v3.0 represents a major update of the database. Nine of the previously included 24 sources were updated. Six new resources were added, bringing the total number of sources to 30. These updates and additions of sources have cumulatively resulted in 56 309 interaction claims. This has also substantially expanded the comprehensive catalogue of druggable genes and anti-neoplastic drug-gene interactions included in the DGIdb. Along with these content updates, v3.0 has received a major overhaul of its codebase, including an updated user interface, preset interaction search filters, consolidation of interaction information into interaction groups, greatly improved search response times and upgrading the underlying web application framework. In addition, the expanded API features new endpoints which allow users to extract more detailed information about queried drugs, genes and drug-gene interactions, including listings of PubMed IDs, interaction type and other interaction metadata.

Citing Articles

Genetically supported targets and drug repurposing for brain aging: A systematic study in the UK Biobank.

Yi F, Yuan J, Somekh J, Peleg M, Zhu Y, Jia Z Sci Adv. 2025; 11(11):eadr3757.

PMID: 40073132 PMC: 11900869. DOI: 10.1126/sciadv.adr3757.


Harnessing the power of genomics in hypertension: tip of the iceberg?.

Naderi H, Warren H, Munroe P Camb Prism Precis Med. 2025; 3:e2.

PMID: 40071139 PMC: 11894416. DOI: 10.1017/pcm.2025.1.


Integrating transcriptomic data with a novel drug efficacy prediction model for TCM active compound discovery.

Li Y, Shen Y, Cai Y, Zhang Y, Gao J, Huang L Sci Rep. 2025; 15(1):7688.

PMID: 40044718 PMC: 11882833. DOI: 10.1038/s41598-024-82498-1.


Mendelian randomization reveals plasminogen as a common therapeutic target for myocardial infarction and atrial fibrillation.

Charati H, Hamta A J Cardiovasc Thorac Res. 2025; 16(4):249-257.

PMID: 40027362 PMC: 11866770. DOI: 10.34172/jcvtr.33269.


Integrative systems biology framework discovers common gene regulatory signatures in mechanistically distinct inflammatory skin diseases.

Mishra B, Gou Y, Tan Z, Wang Y, Hu G, Athar M NPJ Syst Biol Appl. 2025; 11(1):21.

PMID: 40016271 PMC: 11868562. DOI: 10.1038/s41540-025-00498-x.


References
1.
Kim S, Thiessen P, Bolton E, Chen J, Fu G, Gindulyte A . PubChem Substance and Compound databases. Nucleic Acids Res. 2015; 44(D1):D1202-13. PMC: 4702940. DOI: 10.1093/nar/gkv951. View

2.
Fisch K, Meissner T, Gioia L, Ducom J, Carland T, Loguercio S . Omics Pipe: a community-based framework for reproducible multi-omics data analysis. Bioinformatics. 2015; 31(11):1724-8. PMC: 4443682. DOI: 10.1093/bioinformatics/btv061. View

3.
Mock A, Murphy S, Morris J, Marass F, Rosenfeld N, Massie C . CVE: an R package for interactive variant prioritisation in precision oncology. BMC Med Genomics. 2017; 10(1):37. PMC: 5445311. DOI: 10.1186/s12920-017-0261-6. View

4.
Chakravarty D, Gao J, Phillips S, Kundra R, Zhang H, Wang J . OncoKB: A Precision Oncology Knowledge Base. JCO Precis Oncol. 2017; 2017. PMC: 5586540. DOI: 10.1200/PO.17.00011. View

5.
Law V, Knox C, Djoumbou Y, Jewison T, Guo A, Liu Y . DrugBank 4.0: shedding new light on drug metabolism. Nucleic Acids Res. 2013; 42(Database issue):D1091-7. PMC: 3965102. DOI: 10.1093/nar/gkt1068. View