alphagenome-single-variant-analysis

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Analyzes genetic variant effects on gene expression (RNA-seq), chromatin accessibility (DNASE), histone marks (ChIP), and transcription factors using the AlphaGenome API. Use when the user asks about non-coding variant effects, pathogenicity, clinical significance, disease associations, functional effects, gene expression changes, splicing disruption, or regulatory effects in promoters and enhancers. Also use for resolving biological terms to tissue/cell-type ontologies (UBERON/CL) or analyzing variants in chr:pos:ref>alt format.

6installs

Sourcegoogle-deepmind/science-skills

Added on2026-05-20

NPX Install

npx skill4agent add google-deepmind/science-skills alphagenome-single-variant-analysis

SKILL.md Content

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Variant Analysis using AlphaGenome

Prerequisites

uv
: Read the
```
uv
```
skill and follow its Setup instructions to ensure
```
uv
```
is installed and on PATH.
User Notification: If LICENSE_NOTIFICATION.txt does not already exist in this skill directory then (1) prominently notify the user to check the terms at https://deepmind.google.com/science/alphagenome/, then (2) create the file recording the notification text and timestamp.
.env
file: Make sure the
```
.env
```
file exists in your home directory. Create one if it does not exist.
ALPHAGENOME_API_KEY
: This skill requires an API key to function. You must ask the user for an API key if this skill looks relevant to their request and you do not have one in .env. The user can sign up at https://deepmind.google.com/science/alphagenome/. Do NOT ask the user to paste their key into the chat (this would leak the key into the agent's context). Instead, explain that a key is necessary to use AlphaGenome and give the user this command substituting
ENV_FILE
with the resolved literal path to the
.env
file:
bash
```
printf "Enter AlphaGenome API key (typing hidden): " && read -s key && echo && echo "ALPHAGENOME_API_KEY=$key" >> "ENV_FILE" && echo "Saved."
```
The scripts load credentials automatically via
```
dotenv
```
. NEVER read, print, or inspect the
```
.env
```
file or its variables (e.g. no
```
cat
```
,
```
grep
```
,
```
echo
```
,
```
printenv
```
, or
```
os.environ.get
```
on keys). Credentials must stay out of the agent's context.
When running in sandbox,
```
dotenv.load_dotenv()
```
will be a no-op, and instead the sandbox will read credentials and inject them directly.

Core Rules

NEVER run
python3
or
python3 -c
directly. The system Python does not necessarily have pandas, numpy, and other key dependencies. ALWAYS use
```
uv run
```
to run ALL Python code — including scripts, ad-hoc analysis files, and one-liners. Do not attempt to
```
pip install
```
or create new venvs —
```
uv
```
manages an isolated environment automatically.
Offline Only: NEVER use external APIs (e.g., MyGene.info, Ensembl REST) for gene/transcript lookup. Use
```
lookup_gene_info.py
```
with the local GTF. If it fails, fix the environment/paths, do not switch to external APIs.
API Key is required:
```
ALPHAGENOME_API_KEY
```
must be set before running any script (in sandbox, credentials are injected automatically).
Notification: If this skill is used, ensure this is mentioned in the output.
Report Format: Always use the templates in
```
docs/report-templates.md
```
for generating analysis reports, and ensure to include the table of top hits from the discovery scan.

Environment Setup & Troubleshooting

Python Environment

All scripts must be executed using

uv run

, which manages an isolated virtual environment with the correct dependencies via

uv

bash

uv run <script_name> [args...]

For ad-hoc scripts (e.g., inline analysis code saved to a temp file), pass the full path instead of a short name:

bash

uv run --project $SKILL_DIR /tmp/my_analysis.py --arg1 val1

[!NOTE] The first invocation resolves and installs dependencies (~10s). Subsequent runs use the cached environment and start instantly. The cache lives in
~/.cache/uv/
.

Common Issues

Column Names:
```
tidy_scores
```
and metadata often use
```
gene_name
```
(not
```
gene_symbol
```
) and
```
output_type
```
(not
```
modality
```
). Always inspect
```
df.columns
```
before filtering.
Large Genes: Genes > 500kb (e.g.,
```
USH2A
```
) break the
```
whole_gene
```
view. Use
```
--view detail
```
or manual regional windows instead.
Sashimi Strand Error:
```
plot_components.Sashimi
```
does NOT accept a
```
strand
```
argument directly. Filter input tracks instead.
KeyError: 'ontology_curie': Not all tracks have
```
ontology_curie
```
. Check
```
track.metadata.columns
```
before filtering.

Python Path: If

exec: "python": executable file not found

occurs, ensure you are using

uv run

instead of bare

python

python3

NotImplementedError (pandas): "iLocation based boolean indexing on an integer type is not available". This occurs when using boolean masks with
```
.iloc
```
on integer-indexed DataFrames in newer pandas versions. Fix: Convert boolean masks to integer indices using
```
np.flatnonzero(mask)
```
.
GTF Feather Case Sensitivity: The AlphaGenome GTF Feather file uses Capitalized column names (
```
Feature
```
,
```
Start
```
,
```
End
```
,
```
Strand
```
) unlike standard GTF files. Always check
```
df.columns
```
if getting KeyErrors.
score_variant
ontology filtering:
```
score_variant
```
does NOT accept
```
ontology_terms
```
as an argument. You must filter the returned AnnData objects manually by inspecting
```
adata.var
```
columns. In contrast,
```
predict_variant
```
DOES accept
```
ontology_terms
```
directly.
Sashimi Zoom Logic: To ensure "skipping" arcs are visible, expand the zoom to include the flanking exons rather than relying on junction overlap alone.
Junction Scores: Raw
```
Junction
```
objects from
```
prediction
```
may be simple Intervals. Use
```
junction_data.get_junctions_to_plot(predictions=..., name=...)
```
to retrieve objects with the
```
.k
```
(abundance/score) attribute.
uv
Not Found: If
```
exec: uv: not found
```
, follow the installation instructions in Prerequisites.
Registry Authentication Error (401): If
```
uv
```
fails with 401 Unauthorized for a private registry, set
```
UV_INDEX_URL=https://pypi.org/simple
```
before running the script.

References

alphagenome-api.md — API reference and code patterns
interpretation-guide.md — Interpretation guide, score magnitude rules, ISM, and checklist.
report-templates.md — Full report templates
```
scripts/visualize_variant_effects.py
```
— Single-variant visualization template (Ref/Alt comparisons, Splicing).
- Splicing Zoom Strategy: Uses a Hybrid Approach for optimal visibility:
  1. Base Interval: Variant +/- 1 downstream and upstream exon (Structural Context).
  2. Junction Expansion: Expands to include the full span of any significant splicing junction (e.g., exon skipping events that span multiple exons).
  3. Anchor Enforcement: Ensures the exons anchoring these long junctions are fully visible. Lesson: Simple fixed windows (e.g., 2kb) or nearest-exon logic often fail for skipping events. Always use the observed junction data to drive zoom levels.
```
examples/splicing/
```
— Splicing analysis examples
```
examples/model_limitation_RNU4ATAC/
```
— ncRNA structure limitation case study
```
examples/polyadenylation_HBA2/
```
— 3' UTR / Polyadenylation case study
```
examples/regulatory/
```
— Regulatory variant examples
```
examples/negative_result_GATA4/
```
— Negative results (mathematical artefact)
```
examples/negative_result_TGFB3/
```
— Negative results (proxies)
```
scripts/lookup_gene_info.py
```
— Gene & transcript lookup
```
scripts/resolve_ontology_terms.py
```
— Ontology term resolution (UBERON/CL IDs)

Code Patterns

Broad Discovery Scan

Use

score_variant

across differential scorers only to discover unexpected tissue effects.

python

from alphagenome.models import dna_client
from alphagenome.models import variant_scorers
from alphagenome.data import genome
import os
import pandas as pd

# Setup API Key and Client
dna_model = dna_client.create(api_key=os.environ.get('ALPHAGENOME_API_KEY'),
                              address='dns:///gdmscience.googleapis.com:443')

# Define Variant (example)
variant_str = "chr2:1234:A>C"
chrom, pos_str, ref_alt = variant_str.split(':')
ref, alt = ref_alt.split('>')
pos = int(pos_str)

# Use supported sequence length (e.g., 2**20 for optimal performance)
SEQ_LENGTH = 2**20
interval = genome.Interval(chrom, pos - SEQ_LENGTH // 2, pos + SEQ_LENGTH // 2)
variant = genome.Variant(chrom, pos, ref, alt)

scorers = [
    variant_scorers.RECOMMENDED_VARIANT_SCORERS[m]
    for m in variant_scorers.RECOMMENDED_VARIANT_SCORERS
    if "ACTIVE" not in m and "CAGE" not in m and "PROCAP" not in m
]

print(f"Scoring variant {variant_str}...")
scores_list = dna_model.score_variant(interval=interval, variant=variant, variant_scorers=scorers)

# Process and Display Results
all_dfs = []
for score_adata in scores_list:
    df = variant_scorers.tidy_scores([score_adata], match_gene_strand=True)
    if df is not None:
        all_dfs.append(df)

if all_dfs:
    df = pd.concat(all_dfs)
    significant = df[df['quantile_score'].abs() > 0.995]
    ranked = significant.sort_values('raw_score', key=abs, ascending=False)
    print("Top Significant Hits:")
    print(ranked[['biosample_name', 'gene_name', 'output_type', 'quantile_score', 'raw_score']])

Extended Search for Disease-Relevant Tissues