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Machine learning51k · #1830

alphagenome

Looks up precomputed genomic effects for genetic variants. It provides scores and explanations to understand the impact of variants on the genome.

What it does

AlphaGenome is a tool that predicts the impact of small changes in human and mouse DNA. It analyzes these changes and indicates how likely they are to affect important functions, such as gene expression.

When to use it

  • ✓To check the impact of a known genetic mutation.
  • ✓To understand how a DNA variation might affect health.
  • ✓To research effects of genetic variants in scientific studies.

Example of what to ask your AI

“What is the impact of the variant chr22:36201698:A>C?”

Want more? Open the sections below.

How to use

The easiest way: copy the message below and paste it into Claude, ChatGPT, Cursor or any AI. It reads the skill and follows the instructions.

Skill link

https://github.com/K-Dense-AI/scientific-agent-skills/blob/main/skills/alphagenome/SKILL.md

Ready-to-paste message

Use this skill to help me: https://github.com/K-Dense-AI/scientific-agent-skills/blob/main/skills/alphagenome/SKILL.md Read the SKILL.md file at that link and follow its instructions for my task.
How it works

AlphaGenome uses an AI model trained on extensive genetic data. It analyzes the DNA sequence and predicts the effects of specific changes across various biological assay types.

Installation

Easiest way: copy the skill link and paste it into Claude, ChatGPT or Cursor asking it to follow the instructions.

In the terminal (Claude Code, Codex, Cursor), in your project folder:

npx skills add K-Dense-AI/scientific-agent-skills --skill alphagenome

Or download just the file:

curl -L https://raw.githubusercontent.com/K-Dense-AI/scientific-agent-skills/main/skills/alphagenome/SKILL.md -o SKILL.md
Technical content (original SKILL.md)

Original text by the author, straight from GitHub.

AlphaGenome and the AlphaGenome Atlas

AlphaGenome is DeepMind's sequence-to-function model: 1 Mb of DNA in, predictions at modality-specific resolutions for eleven assay types across thousands of human and mouse tracks out. The AlphaGenome Atlas (released 2026-09-08) is that model run once over every possible single-nucleotide change in GRCh38, about 9 billion variants, stored with a single ranking number, the AlphaGenome Variant Impact (AVI) score, its genome-wide percentile, and an 18-way attribution of what drives it. Both are reached through one pip install alphagenome and one API key.

Research and theoretical modelling only. API outputs must not be used to train other models, and are not for diagnostic procedures or medical decisions. Permissive-use downloads have separate terms; check the artifact licence.

When to use which

You have Use Why
hg38 SNVs (a VCF, a credible set, a region up to ~1 kb) Atlas via scripts/atlas_query.py precomputed, higher quota, includes AVI and attributions
indels, mouse variants, a non-reference background, a custom scorer or window model via scripts/score_variants.py or Python the Atlas is SNV-only and hg38-only
a hypothesis to explain (which motif, which tissue, REF vs ALT tracks) model predict_variant + plots, Atlas track scores, portal link mechanism, not just rank
GRCh37 coordinates, rsIDs, unnormalised indels genomic-coordinates first, then come back wrong build or swapped REF gives a plausible wrong answer
ClinVar assertions, gene-disease validity, ACMG framing folklore-variant-evidence, database-lookup AlphaGenome is one evidence line, never the verdict
promoter/enhancer/expression predictions without a DeepMind key genomic-intelligence different provider, keyless demo tier

Setup

uv pip install alphagenome                     # PyPI; tested on Python 3.12 and 3.13, alphagenome 0.9.0
export ALPHAGENOME_API_KEY="..."               # https://deepmind.google.com/science/alphagenome
cd skills/alphagenome/scripts
python atlas_query.py scorers                  # proves key + network in one call

Shell examples quote variant strings because > otherwise redirects output. Network examples below are illustrative and were checked against SDK 0.9.0 contracts, without authenticated prediction/Atlas calls during this review.

Never put the key on a command line or in a file you commit; the scripts only read it from the environment. Authentication failures may surface as ValueError or PermissionError, depending on the gRPC status returned by the service.

The coordinate contract

  • A variant is 1-based chr:pos:ref>alt (chr22:36201698:A>C). gnomAD (22-36201698-A-C), GTEx (chr22_36201698_A_C_b38), and Open Targets spellings are autodetected by the scripts. In the SDK, pass the matching variant_format=genome.VariantFormat.GNOMAD (or GTEX, OPEN_TARGETS, OPEN_TARGETS_BIGQUERY); from_str defaults to chr:pos:ref>alt.
  • An interval on the command line is 1-based closed chr:start-end; the SDK's genome.Interval is 0-based half-open. The scripts convert.
  • Human is GRCh38 only. The Atlas key is chr:pos:alt; REF is implied by the reference. Swapping REF/ALT can cause a miss; a wrong REF or build can misidentify a record. The scripts reject a returned variant that differs from the request, but cannot validate the build. Check REF against GRCh38 FASTA.
  • rsIDs are not accepted by the API or the portal. Resolve them to coordinates.
  • Use the chr prefix; MT becomes chrM.

Atlas workflow

1. Rank with AVI

python atlas_query.py avi --variant "chr22:36201698:A>C" "chr9:128225994:G>A"
python atlas_query.py avi --input candidates.vcf --min-phred 20 -o avi.tsv
python atlas_query.py avi --interval chr11:5225727-5226575 --top-k 25 -o hbb_window.tsv
python atlas_query.py avi --input credible_set.tsv --with-tracks -o avi_tracks.tsv

Output, one row per variant:

Column Meaning
avi_raw composite model output (the 18 attributions sum to it)
avi_cdf_quantile cumulative quantile against all genome-wide SNVs, as served
avi_tail_quantile, avi_phred, avi_top_percent tail = 1 - cdf, phred = -10 log10(tail); Phred 20 = top 1 %, 30 = top 0.1 %
top_feature, top_feature_value largest absolute SHAP attribution and its value

…

FAQ

What is the AlphaGenome Atlas?

It's a collection of pre-computed predictions for almost every possible single-nucleotide change in the human GRCh38 reference genome. It provides an impact score (AVI) and details about what drives that impact.

Can I use this for medical diagnosis?

No. AlphaGenome is for research and theoretical modeling only. Outputs must not be used for diagnostic procedures or medical decisions.

Does it work for genomes of animals other than humans and mice?

Currently, AlphaGenome only supports human (GRCh38) and mouse genomes.

Details

Original description by the author

Looks up precomputed AlphaGenome Atlas effects for any GRCh38 single-nucleotide variant (AVI score with Phred and 18 SHAP feature attributions, plus raw and quantile scores for RNA-seq, DNase, ATAC, ChIP-TF, ChIP-histone, CAGE, PRO-cap, splicing, polyadenylation and contact-map tracks), scores variants or scans windows on demand with the AlphaGenome model for human and mouse (variant scoring, in s

repo: K-Dense-AI/scientific-agent-skills

Popularity

GitHub stars
51k
Ranking position
#1830
License
MIT
Updated
10/15/2026

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