---
id: bionty
version: "2.4.3"
license: unclear
license_treatment: unclear
maintenance: active
---

# bionty — Basic biological entities, coupled to public ontologies [`source <https://github.com/laminlabs/bionty/blob/main/bionty/models.py>`__].

License: unclear · Maintenance: active · Downloads: 81.1K/mo

## What it is and what it does

Bionty is a registry layer for biological ontologies, providing standardized access to public biological entity vocabularies (genes, proteins, cell types, tissues, experimental factors, and others) and tools to create, search, and manage records against those sources. It wraps ontology data from public sources and allows you to create local records that reference standardized terms, reducing typos and duplications in biological datasets.

The package is built on lamindb-core and designed for bioinformatics workflows where consistent entity naming and hierarchical relationships matter. You can extend public ontologies with custom in-house terms, manage multiple versions of the same ontology, and use synonyms and abbreviations to handle naming variations. It's most useful in contexts where you're building or curating biological datasets and need to enforce consistency against established standards.

Use it for:

- Standardize gene and protein names in a genomics analysis pipeline by looking up canonical identifiers against public gene registries.
- Build a cell-type annotation system for single-cell RNA-seq data by creating records tied to standardized CellType and CellMarker ontologies.
- Create an in-house tissue or experimental-factor taxonomy by extending public ontologies with custom hierarchical relationships.
- Prevent duplicate or misspelled biological entity entries in a research database by validating against public ontology sources.
- Manage multiple versions of a biological ontology within a single application.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Bionty provides access to and management of biological ontologies—registries of standardized terms for genes, proteins, cell types, tissues, and other biological entities—with tools to create records, search public sources, and build custom in-house ontologies.

Yes, if you are building bioinformatics or biomedical data pipelines where standardized biological entity naming and ontology management are requirements. The low install friction, active maintenance, and lack of known vulnerabilities make it safe to adopt. However, verify the license terms before use in proprietary projects, as the metadata does not clearly declare one. If you don't need ontology standardization, it adds unnecessary complexity.

## Install

pip install bionty
uv add bionty
poetry add bionty

## Installing bionty

Before you install: Low install friction with a pure-Python wheel and only three runtime dependencies (lamindb-core, requests, pyyaml). The package is actively maintained with a recent release and steady commit activity.

License in practice: License status is unclear—no SPDX identifier or raw license text is available in the package metadata. Verify the actual license before use in proprietary or restricted contexts.

Quickstart:

pip install bionty

import bionty as bt

# Access a public ontology
gene_registry = bt.Gene.public()
results = gene_registry.search('BRCA')

Requires Python >=3.10.

Verify before relying:

- Actual license text and terms—metadata shows no SPDX or raw license field despite active development.
- Whether lamindb-core introduces additional transitive dependencies or system requirements beyond what's documented.
- Scope and coverage of the public ontologies claimed in the description—exact list and update frequency.

## Package facts

- License: not declared (unclear)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 81.1K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

biological ontology registry, gene protein cell type database, public ontology access, biological entity standardization, ontology version management, cell marker tissue annotation, biological taxonomy lookup, ontology-registry, bioinformatics, data-standardization

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