Bionty: Curate metadata using ontologies

Access public & custom ontologies with auto-complete. Map synonyms with ease.

If you’d like to maintain in-house along with public ontologies, you can manage them with Bionty’s SQL interface: lnschema-bionty.

Out-of-the-box ontologies

• Gene: Ensembl, NCBI Gene, HGNC, MGI

• Protein: Uniprot

• Species: NCBI Taxonomy, Ensembl Species

• CellLine: Cell Line Ontology

• CellType: Cell Ontology

• CellMarker: CellMarker

• Tissue: Uberon

• Disease: Mondo, Human Disease

• Phenotype: Human Phenotype

• Pathway: Pathway Ontology

• Readout: Experimental Factor Ontology

• BFXPipeline: largely based on nf-core

Check out versions.yaml for details.

Install

Bionty is a Python package available for

pip install bionty

Look up ontology records with auto-complete

import bionty as bt

gene = bt.Gene()
gene.lookup().LNMA

See Look up records of species, gene, protein, cell marker for more.

Curate metadata

import pandas as pd

# Create an example Pandas DataFrame of various cell types.
df = pd.DataFrame(
    index=[
        "placental epithelial cell",
        "capillary",
        "This cell type does not exist",
    ]
)

# The DataFrame can either be curated by ontology ID (id="ontology_id")
# or by ontology term names (id="name").
curated_df = bt.CellType(id="name").curate(df)

# ✅ 2 terms (66.7%) are mapped.
# 🔶 1 terms (33.3%) are not mapped.

See Curate entity identifiers for more.

Track ontology sources

# Display all managed versions
bt.display_available_versions()

# Access to the Mondo ontology
disease = bt.Disease(database="mondo")

# Access to the Human Disease ontology
disease = bt.Disease(database="doid", version="2023-01-30")

Didn’t see your favorite source or version? See how to Extend Bionty.