Skills

Alphagenome

Looks up precomputed AlphaGenome Atlas effects for any GRCh38 single-nucleotide variant...

Install

npx skills add K-Dense-AI/scientific-agent-skills --skill alphagenome -g -a claude-code -y
Category
Design
Updated
9d ago
License
MIT
Context cost
Mediumabout 3,000 tokens
Runs scripts
Yes, review before use

What it does

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 silico mutagenesis, REF-versus-ALT track prediction), and builds Atlas website deep links. Use when the user mentions AlphaGenome, AlphaGenome Atlas, AVI or AlphaGenome Variant Impact, DeepMind variant effect prediction, or wants to prioritise or mechanistically interpret non-coding, regulatory, splicing, enhancer, promoter, or chromatin-accessibility effects of SNVs from a VCF, credible set, or region. Research use only; not a clinical tool.

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 haveUseWhy
hg38 SNVs (a VCF, a credible set, a region up to ~1 kb)Atlas via scripts/atlas_query.pyprecomputed, higher quota, includes AVI and attributions
indels, mouse variants, a non-reference background, a custom scorer or windowmodel via scripts/score_variants.py or Pythonthe 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 linkmechanism, not just rank
GRCh37 coordinates, rsIDs, unnormalised indelsgenomic-coordinates first, then come backwrong build or swapped REF gives a plausible wrong answer
ClinVar assertions, gene-disease validity, ACMG framingfolklore-variant-evidence, database-lookupAlphaGenome is one evidence line, never the verdict
promoter/enhancer/expression predictions without a DeepMind keygenomic-intelligencedifferent provider, keyless demo tier

Setup

Read the whole SKILL.md on GitHub

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