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AlphaGenome

Google
restricted / Overall score: 2.0

AlphaGenome is Google DeepMind's genomic sequence model for predicting gene expression, splicing, chromatin features, contact maps, and variant effects. It accepts sequences up to one million base pairs and produces most outputs at single-base-pair resolution through an API, a precomputed Atlas, and a gated downloadable checkpoint.

The repository documents both API and local-checkpoint surfaces; the downloadable model remains subject to separate non-commercial terms.

Openness

2 high confidence
2.0
weights
open(gated downloadable checkpoint files)
data
open(training TFRecords for all 12 predicted modalities published in a public, unauthenticated Google Cloud Storage bucket (alphagenome-datasets) that the alphagenome_research data loader reads from)
code
partial(Apache-2.0 API client, examples, and notebook
license
AlphaGenome-Model-Terms(non-commercial use only)

A separate Google DeepMind repository publishes the model's JAX implementation with a loader for its training files, and those files sit in a public Google Cloud Storage bucket that anyone can list and download without signing in. The downloadable checkpoint is still covered by the AlphaGenome Model Terms, which forbid commercial use.

Adoption

2 medium confidence
2.0

The gated Hugging Face endpoint reports zero downloads, so GitHub stars are used as the adoption signal instead.

Capability

2 high confidence
2.0

AlphaGenome's strength is its resolution span: sequences up to one million bases are resolved to single-base outputs. Its only input is a DNA sequence, so it does not also count as accepting multiple sensors, and no adaptation or generative pathway is documented beyond a broad prediction surface.

Verified 2026-09-10