Tidy R interface to the Open Targets Platform GraphQL API.
Query genes, diseases, drugs, variants, and genetic evidence directly from R and receive analysis-ready tibbles — no manual JSON wrangling required.
Python users: see the sister package otargenpy.
Installation
GitHub (recommended — latest features and bug fixes)
# install.packages("devtools")
devtools::install_github("amirfeizi/otargen")CRAN (stable release)
install.packages("otargen")Tip: The GitHub version often includes new functions and fixes ahead of the CRAN release. We recommend installing from GitHub to get the latest updates.
Quick start
Every function takes a single identifier (gene, disease, drug, or variant) and returns a tidy tibble.
Drug safety & mechanisms (by ChEMBL ID)
library(otargen)
# Adverse events reported for imatinib
adverseEventsQuery(chemblId = "CHEMBL941")
# Mechanism of action for imatinib
mechanismsOfActionQuery(chemblId = "CHEMBL941")
# Drug indications with clinical stage info
indicationsQuery(chemblId = "CHEMBL941")Gene-level queries (by Ensembl ID)
# Known drugs and clinical candidates targeting TP53
knownDrugsGeneQuery(ensgId = "ENSG00000141510")
# Cancer hallmarks for TP53
hallmarksQuery(ensgId = "ENSG00000141510")
# Protein-protein interactions for TP53 from IntAct
interactionsQuery(ensgId = "ENSG00000141510", sourceDatabase = "intact", size = 25)
# DepMap cancer cell-line essentiality for EGFR
depMapQuery(ensgId = "ENSG00000146648")
# Target safety liabilities for EGFR
safetyQuery(ensgId = "ENSG00000146648")Gene + disease evidence (by Ensembl ID + EFO ID)
# ChEMBL evidence linking PARP1 to breast cancer
chemblQuery(ensemblId = "ENSG00000143799", efoId = "EFO_0000305")
# GWAS credible sets for PCSK9 and hyperlipidemia
gwasCredibleSetsQuery(ensemblId = "ENSG00000169174", efoId = "EFO_0004911")
# ClinVar evidence for BRCA1 and ovarian cancer
clinVarQuery(ensemblId = "ENSG00000012048", efoId = "EFO_0001075")
# Literature evidence from Europe PMC
europePMCQuery(ensemblId = "ENSG00000012048", efoId = "EFO_0001075")Pharmacogenomics & variants
# Pharmacogenomics data for a drug
pharmacogenomicsChemblQuery(chemblId = "CHEMBL1016")
# UniProt variants
uniProtVariantsQuery(variantId = "4_1804392_G_A")
# Variant effect predictions
variantEffectPredictorQuery(variantId = "1_154453788_C_T")Genetics & colocalisation
# Locus-to-gene predictions for a credible set
locus2GeneQuery(studyLocusId = "fa375739ca2a6b825ce5cc69d117e84b")
# GWAS colocalisation analysis
gwasColocalisation(study_locus_id = "5a86bfd40d2ebecf6ce97bbe8a737512")Visualization
Built-in plotting functions turn query results into publication-ready figures with a single call.
# Adverse events lollipop chart for imatinib
ae <- adverseEventsQuery(chemblId = "CHEMBL941")
plot_adverse_events(ae)
# Protein interaction network for TP53
int <- interactionsQuery(ensgId = "ENSG00000141510", sourceDatabase = "intact", size = 25)
plot_interactions(int)
# Locus-to-gene candidate ranking
l2g <- locus2GeneQuery(studyLocusId = "fa375739ca2a6b825ce5cc69d117e84b")
plot_l2g(l2g)
# GWAS colocalisation scatter plot
coloc <- gwasColocalisation(study_locus_id = "5a86bfd40d2ebecf6ce97bbe8a737512")
plot_colocalisation(coloc)
# Drug indications by clinical trial stage
ind <- indicationsQuery(chemblId = "CHEMBL941")
plot_indications(ind)| Function | Input | Plot type |
|---|---|---|
plot_adverse_events |
adverseEventsQuery |
Lollipop chart with significance threshold |
plot_interactions |
interactionsQuery |
Circular network graph |
plot_l2g |
locus2GeneQuery |
Ranked bar chart of L2G scores |
plot_colocalisation |
gwasColocalisation |
H4 vs variant count scatter |
plot_indications |
indicationsQuery |
Clinical stage bar chart |
Available functions (40)
| Category | Functions |
|---|---|
| Drug queries |
adverseEventsQuery, indicationsQuery, knownDrugsChemblQuery, mechanismsOfActionQuery, pharmacogenomicsChemblQuery
|
| Gene / target queries |
compGenomicsQuery, depMapQuery, geneOntologyQuery, geneticConstraintQuery, hallmarksQuery, interactionsQuery, knownDrugsGeneQuery, mousePhenotypesQuery, pathwaysQuery, pharmacogenomicsGeneQuery, safetyQuery
|
| Gene + disease evidence |
chemblQuery, clinVarQuery, europePMCQuery, geneBurdenQuery, genomicsEnglandQuery, orphanetQuery, uniprotLiteratureQuery
|
| Variant queries |
pharmacogenomicsVariantQuery, uniProtVariantsQuery, variantEffectPredictorQuery, variantEffectQuery, variantsQuery
|
| Genetics / GWAS |
gwasColocalisation, gwasCredibleSet, gwasCredibleSetsQuery, locus2GeneQuery, overlapInfoForStudy, qtlCredibleSetsQuery, sharedTraitStudiesQuery
|
| Visualization |
plot_adverse_events, plot_colocalisation, plot_indications, plot_interactions, plot_l2g
|
Full documentation: https://amirfeizi.github.io/otargen/
What’s new in 2.0.1
Bug fixes for HTTP 400 errors caused by upstream Open Targets API schema changes. Nine functions updated: chemblQuery, clinVarQuery, geneBurdenQuery, orphanetQuery, indicationsQuery, knownDrugsChemblQuery, knownDrugsGeneQuery, geneOntologyQuery, and interactionsQuery. See NEWS.md for details.
Citation
If you use otargen in your research, please cite:
Feizi A, Ray D (2023). otargen: an R package for accessing and visualizing Open Targets Genetics data. Bioinformatics, 39(7). https://doi.org/10.1093/bioinformatics/btad441
Contributing
Bug reports and feature requests: GitHub Issues
Contributions are welcome via pull requests on GitHub.