alphagenome – skill Khác cho agent AI

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 |

| 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 or mismatched returned allele 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 -o hbb_tf.tsv

One row per variant x track (x gene or splice junction where applicable),

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 scorer-track entries mean something may be 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 = natural-log FC
client.query_interval(genome.Interval("chr11", 5225726, 5226575), requested_scorers=["AVI_SCORE"])

query_interval returns up to 3 SNVs per non-N 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 raises if any submitted lookup fails;

completion order is not input order;

the script makes separate variant requests 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" --scorers RNA_SEQ --sequence-length 500KB -o mouse.tsv
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

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