# google-deepmind/alphagenome

This API provides programmatic access to the AlphaGenome model developed by Google DeepMind.

Repository: https://github.com/google-deepmind/alphagenome
Canonical: https://ross.abutalabs.com/products/alphagenome
Homepage: https://www.alphagenomedocs.com
Language: Python
License: Apache-2.0
License Family: permissive
Last push: 2026-08-26T12:08:25+00:00

## Health v2 (maintenance only)
Score: 89/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 99, release rhythm 98, longevity 49
- inputs: {"age_days": 686, "days_push": 7, "days_rel": 16, "gap_med": 28, "n_releases_24m": 12}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1979, forks 272 (observed 2026-08-28T04:06:01.521077+00:00)

## What it is
A Python SDK providing programmatic access to Google DeepMind's AlphaGenome model, which predicts genomic regulatory outputs such as gene expression, splicing, chromatin features, and contact maps from DNA sequences up to 1 million base pairs. It includes client code, tutorials, variant scoring strategies, and matplotlib-based visualization utilities, offered free for non-commercial use via a hosted API.

## Use cases
- predict gene expression from a DNA sequence
- score the effect of a genetic variant on splicing
- analyze chromatin features at single base-pair resolution
- predict genomic contact maps for a region
- interpret regulatory code of human or mouse genome intervals
- visualize genomic model predictions with matplotlib

## When to choose
- you need state-of-the-art predictions of regulatory genomic outputs for human (hg38) or mouse (mm10) sequences
- you are running small to medium-scale analyses requiring thousands of variant effect predictions
- you want a Python client with tutorials and plotting utilities for a hosted genomic model

## When to avoid
- you need large-scale analyses exceeding roughly 1 million predictions
- your use is commercial, since the API is free only for non-commercial use
- you need fully offline or self-hosted model inference

## Facets
- artifact type: library
- maturity: active
- function: machine-learning, sdk, data-visualization, api-framework
- domain: bioinformatics, artificial-intelligence, deep-learning, healthcare
- platform: python, cloud, cross-platform
- tags: genomics, dna, variant-effect-prediction, gene-expression, splicing, chromatin, deepmind, regulatory-code, api-client

## Member repositories
- google-deepmind/alphagenome (main) score 89

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:01.521077+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T03:04:42.234788+00:00, confidence not recorded.
  - readme: https://github.com/google-deepmind/alphagenome (fetched 2026-08-28T04:06:01.521077+00:00, sha 858f54494da9)
  - homepage: https://www.alphagenomedocs.com (fetched 2026-08-29T10:43:39.893472+00:00, sha 743fbf083dae)
  - site_page: https://www.alphagenomedocs.com/installation.html (fetched 2026-08-29T10:43:39.902152+00:00, sha e531668604cc)
  - site_page: https://www.alphagenomedocs.com/faqs.html (fetched 2026-08-29T10:43:39.903886+00:00, sha 56d41c1080ce)
  - registry_pypi: https://pypi.org/pypi/alphagenome/json (fetched 2026-08-29T10:43:39.906553+00:00, sha 9eebe775ae69)
- Data as of 2026-08-30T08:39:29.467469+00:00.