---

name: alphagenome description: "Look 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), score variants or scan windows on demand with the AlphaGenome model for human and mouse (variant scoring, in silico mutagenesis, REF-versus-ALT track prediction), and build 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." license: MIT compatibility: "Python 3.10+ with the alphagenome package (0.9.0 or later for the Atlas client; brings numpy, pandas, anndata, grpcio). Network access to gdmscience.googleapis.com:443 and a free non-commercial AlphaGenome API key in ALPHAGENOME_API_KEY (ALPHA_GENOME_API_KEY also read). Human data is GRCh38 only; mouse is mm10 (model only)." allowed-tools: Read Write Edit Bash metadata: version: "1.0" skill-author: K-Dense Inc. upstream-version: "alphagenome 0.9.0" last-reviewed: "2026-09-13" openclaw: primaryEnv: ALPHAGENOME_API_KEY envVars: - name: ALPHAGENOME_API_KEY required: true description: AlphaGenome API key, free for non-commercial use from deepmind.google.com/science/alphagenome. ALPHA_GENOME_API_KEY is accepted as an alternative spelling.

AlphaGenome and the AlphaGenome Atlas

AlphaGenome is DeepMind's sequence-to-function model: 1 Mb of DNA in, base-pair predictions 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. Outputs must not be used to train other models, and are not for diagnostic procedures or medical decisions.

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

Never put the key on a command line or in a file you commit; the scripts only read it from the environment. An invalid key surfaces as ValueError: API key not valid, not as a permission error.

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 accepted by the scripts and by genome.Variant.from_str.
• 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, so a variant with REF and ALT swapped, or on GRCh37, returns a wrong record silently. Check REF against the FASTA before trusting a lookup.
• 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 | | fi_MERGED_SPLICING ... fi_IS_DELETION | all 18 attributions (keys in references/atlas.md) | | top_track_* (with --with-tracks) | the strongest track behind the top feature: scorer, track, biosample, ontology CURIE, gene, raw score | | atlas_url | deep link to the variant on the portal | | error | per-variant lookup failure (indel, N base, wrong REF) instead of a crash |

The Atlas report's advice: rank, do not threshold, and pick thresholds by region or application. Pathogenic regulatory variants sit in lower AVI bins than protein-truncating or splice-motif variants, so a single genome-wide cut-off under-calls exactly the variants this resource was built for.

Read the attribution before the number. MERGED_SPLICING or ALPHAMISSENSE on top means a splice or coding mechanism; MAX_ABS_DNASE, MAX_ABS_CHIP_TF, MAX_ABS_RNA_SEQ mean a regulatory mechanism you can resolve by track; CACTUS_241_WAY or PHASTCONS_470_WAY on top means conservation is carrying the score and the molecular mechanism is not resolved.

2. Resolve the mechanism by track

python atlas_query.py scorers                                   # what the server serves right now
python atlas_query.py tracks --scorer RNA_SEQ --query colon     # find ontology CURIEs
python atlas_query.py scores --variant chr22:36201698:A>C \
    --scorers RNA_SEQ DNASE SPLICE_SITE_USAGE --ontology UBERON:0001157 -o colon.tsv
python atlas_query.py scores --interval chr11:5225727-5226575 --scorers CHIP_TF --gene HBB -o hbb_tf.tsv

One row per variant x track (x gene for RNA_SEQ, POLYADENYLATION, SPLICE_*), with raw_score and, where served, quantile_score. Track-level scorer names: ATAC, DNASE, CHIP_TF, CHIP_HISTONE, CAGE, PROCAP, RNA_SEQ, POLYADENYLATION, SPLICE_SITES, SPLICE_SITE_USAGE, SPLICE_JUNCTIONS, CONTACT_MAPS, plus *_ACTIVE variants; scorers is the authority on the live list. Filter by the tissue the question is about, not by the genome-wide maximum: 9,440 tracks means something is always extreme somewhere.

3. Send the reader to the portal

python atlas_link.py variant chr22:36201698:A>C --biosample "colon" --modalities RNA_SEQ,DNASE,CHIP_TF
python atlas_link.py locus chr11:5225727-5226575 --tf GATA1
python atlas_link.py gene HBB --markdown

No key, no network. The site shows the AVI track, per-modality heatmaps over every biosample, REF-vs-ALT prediction tracks, and motif instances. Attach a link to every variant you report.

In Python

import os
from alphagenome.atlas import atlas
from alphagenome.data import genome

client = atlas.create(os.environ["ALPHAGENOME_API_KEY"], timeout=30)
scores = client.query_variant(
    genome.Variant.from_str("chr22:36201698:A>C"),
    requested_scorers=["AVI_SCORE", "AVI_SCORE_FEATURE_IMPORTANCE", "RNA_SEQ"],
    ontology_terms=["UBERON:0001157"],          # optional; ignored for scorers without ontology metadata
)
avi = scores["AVI_SCORE"]                        # AnnData: X (1,1) raw; layers['quantiles'] (1,1) cdf
fi = scores["AVI_SCORE_FEATURE_IMPORTANCE"]      # AnnData: X (1,18); var['name'] = feature keys
rna = scores["RNA_SEQ"]                          # AnnData: obs = variant x gene, var = tracks, X = log2 FC
client.query_interval(genome.Interval("chr11", 5225726, 5226575), requested_scorers=["AVI_SCORE"])

query_interval returns all 3 SNVs per base, in 32 bp chunks. Keep windows to about 1 kb (3,000 variants); atlas_query.py refuses more unless --max-window is raised. query_variants stops at the first failed lookup; the script queries one variant at a time so misses become error cells.

Model workflow

Score variants the Atlas does not hold

python score_variants.py --variant chr22:36201698:A>C -o scores.tsv                   # 12 recommended scorers, 1 Mb
python score_variants.py --input indels.vcf --scorers RNA_SEQ SPLICE_SITE_USAGE \
    --ontology UBERON:0001157 --min-abs-quantile 0.99 -o colon.tsv
python score_variants.py --organism mouse --variant chr7:45000000:A>G --sequence-length 500KB
python score_variants.py --list-scorers
python score_variants.py --list-tracks --output-type RNA_SEQ --query liver -o tracks.tsv

Output is the official tidy table from variant_scorers.tidy_scores: one row per variant x scorer x track (x gene) with raw_score and quantile_score, sorted by |raw|. Default scorers are the 12 recommended difference scorers; --include-active adds the seven *_ACTIVE activity scorers. At most 20 scorers per request.

from alphagenome.models import dna_client, variant_scorers
model = dna_client.create(os.environ["ALPHAGENOME_API_KEY"])
variant = genome.Variant.from_str("chr22:36201698:A>C")
interval = variant.reference_interval.resize(dna_client.SEQUENCE_LENGTH_1MB)
adatas = model.score_variant(interval, variant, variant_scorers=[variant_scorers.RECOMMENDED_VARIANT_SCORERS["RNA_SEQ"]])
df = variant_scorers.tidy_scores(adatas)         # filter df.ontology_curie afterwards; score_variant takes no ontology_terms

Predict tracks and mutagenise

vo = model.predict_variant(interval, variant,
                           requested_outputs=[dna_client.OutputType.RNA_SEQ, dna_client.OutputType.DNASE],
                           ontology_terms=["UBERON:0001157"])
vo.reference.rna_seq.values, vo.alternate.rna_seq.values      # (1048576, n_tracks)

window = genome.Interval("chr20", 3_753_000, 3_753_400).resize(dna_client.SEQUENCE_LENGTH_16KB)
ism = model.score_ism_variants(interval=window, ism_interval=window.resize(256),
                               variant_scorers=[variant_scorers.CenterMaskScorer(
                                   requested_output=dna_client.OutputType.DNASE, width=501,
                                   aggregation_type=variant_scorers.AggregationType.DIFF_MEAN)])

Supported windows: 16 kb, 100 kb, 500 kb, 1 Mb (2**14 to 2**20); 1 Mb is the default and is required for distal enhancers and contact maps. Ontology terms are CURIEs (UBERON:0002048 lung, CL:0000084 T cell); discover them with --list-tracks or model.output_metadata(...).concatenate(). Plotting, gene annotation (GENCODE v46 Feather on GCS), splicing and haplotype recipes: references/model-api.md.

Reading the numbers

Always report raw score and quantile or Phred, with the scorer, track, biosample CURIE, and gene. `raw