otargenpy

Tidy Python interface to the Open Targets Platform GraphQL API.

Query genes, diseases, drugs, variants, and genetic evidence directly from Python and receive analysis-ready pandas DataFrames — no manual JSON wrangling required.

Sister package of otargen (R).

Installation

PyPI (stable)

pip install otargenpy

GitHub (recommended — latest features and bug fixes)

pip install git+https://github.com/amirfeizi/otargenpy.git

Quick start

Every function takes a single identifier and returns a pandas DataFrame.

Drug queries (by ChEMBL ID)

import otargenpy as ot

# Adverse events for imatinib
ae = ot.adverse_events_query("CHEMBL941")

# Mechanism of action
moa = ot.mechanisms_of_action_query("CHEMBL941")

# Drug indications with clinical stage
ind = ot.indications_query("CHEMBL941")

Gene queries (by Ensembl ID)

# Known drugs targeting TP53
drugs = ot.known_drugs_gene_query("ENSG00000141510")

# Cancer hallmarks for TP53
hall = ot.hallmarks_query("ENSG00000141510")

# Protein-protein interactions from IntAct
inter = ot.interactions_query("ENSG00000141510", source_database="intact", size=25)

# DepMap essentiality for EGFR
dep = ot.depmap_query("ENSG00000146648")

# Safety liabilities
safe = ot.safety_query("ENSG00000146648")

Gene + disease evidence (by Ensembl ID + EFO ID)

# ChEMBL evidence linking PARP1 to breast cancer
ev = ot.chembl_query("ENSG00000143799", "EFO_0000305")

# GWAS credible sets for PCSK9 and hyperlipidemia
gwas = ot.gwas_credible_sets_query("ENSG00000169174", "EFO_0004911")

# ClinVar evidence for BRCA1 and ovarian cancer
cv = ot.clinvar_query("ENSG00000012048", "EFO_0001075")

Pharmacogenomics & variants

# Pharmacogenomics for a drug
pgx = ot.pharmacogenomics_chembl_query("CHEMBL1016")

# Variant effect predictions
vep = ot.variant_effect_predictor_query("1_154453788_C_T")

Genetics & colocalisation

# Locus-to-gene predictions
l2g = ot.locus2gene_query("fa375739ca2a6b825ce5cc69d117e84b")

# GWAS colocalisation
coloc = ot.gwas_colocalisation("5a86bfd40d2ebecf6ce97bbe8a737512")

Visualization

Built-in plotting functions turn query results into publication-ready figures.

import otargenpy as ot

ae = ot.adverse_events_query("CHEMBL941")
ot.plot_adverse_events(ae)

inter = ot.interactions_query("ENSG00000141510", source_database="intact", size=25)
ot.plot_interactions(inter)

l2g = ot.locus2gene_query("fa375739ca2a6b825ce5cc69d117e84b")
ot.plot_l2g(l2g)

coloc = ot.gwas_colocalisation("5a86bfd40d2ebecf6ce97bbe8a737512")
ot.plot_colocalisation(coloc)

ind = ot.indications_query("CHEMBL941")
ot.plot_indications(ind)

Available functions (40)

Citation

If you use otargenpy 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

License

MIT