otargenpy 2.0.1

Tidy Python interface to the Open Targets Platform GraphQL API

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).

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Installation

PyPI (stable)

pip install otargenpy

GitHub (recommended — latest features and bug fixes)

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

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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")

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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)

Function	Input	Plot type
plot_adverse_events	adverse_events_query	Lollipop chart with significance threshold
plot_interactions	interactions_query	Circular network graph
plot_l2g	locus2gene_query	Ranked bar chart of L2G scores
plot_colocalisation	gwas_colocalisation	H4 vs variant count scatter
plot_indications	indications_query	Faceted clinical stage chart

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Available functions (40)

Category	Functions
Drug queries	adverse_events_query, indications_query, known_drugs_chembl_query, mechanisms_of_action_query, pharmacogenomics_chembl_query
Gene queries	comp_genomics_query, depmap_query, gene_ontology_query, genetic_constraint_query, hallmarks_query, interactions_query, known_drugs_gene_query, mouse_phenotypes_query, pathways_query, pharmacogenomics_gene_query, safety_query
Gene + disease	chembl_query, clinvar_query, europe_pmc_query, gene_burden_query, genomics_england_query, gwas_credible_sets_query, orphanet_query, uniprot_literature_query
Variant queries	pharmacogenomics_variant_query, qtl_credible_sets_query, uniprot_variants_query, variant_effect_predictor_query, variant_effect_query
Genetics / GWAS	gwas_colocalisation, gwas_credible_set, locus2gene_query, overlap_info_for_study, shared_trait_studies_query, variants_query
Visualization	plot_adverse_events, plot_colocalisation, plot_indications, plot_interactions, plot_l2g

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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

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Contributing

Bug reports and feature requests: GitHub Issues

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License

MIT