Alphagenome Single Variant Analysis

Variant Analysis using AlphaGenome

Prerequisites

1. uv: Read the uv skill and follow its Setup instructions to ensure uv is installed and on PATH.

2. User Notification: If .licenses/alphagenome_single_variant_analysis_LICENSE.txt does not already exist in the workspace root 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.

3. .env file: Make sure the .env file exists in your home directory. Create one if it does not exist.

4. ALPHAGENOME_API_KEY: This skill requires an API key to function.

   You can register for a key at https://deepmind.google.com/science/alphagenome/. You MUST use the safe credentials protocol in the credentials skill to check for and request this key if this skill looks relevant to the user's request.

5. ALPHAGENOME_GTF_PATH (Optional): Accelerate gene/transcript lookup by pointing to a local copy of the GTF feather file instead of downloading from GCS:

   Copy

   echo "ALPHAGENOME_GTF_PATH=/path/to/local/gencode.v46.annotation.gtf.gz.feather" >> ~/.env

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.

• 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.

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:

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.

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

# Load environment variables from ~/.env
dotenv.load_dotenv(os.path.expanduser('~/.env'))

# 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

# Define keywords based on disease context
disease_keywords = ["liver", "hepatocyte"]

# Filter for any match
mask = df['biosample_name'].str.contains('|'.join(disease_keywords), case=False, na=False)

relevant_hits = df[mask].sort_values('raw_score', key=abs, ascending=False)
print(f"\n--- Extended Analysis (Keywords: {disease_keywords}) ---")
print(relevant_hits.head(20)[['biosample_name', 'output_type', 'raw_score', 'quantile_score']])

Workflow Checklist

Variant Analysis Progress:
- [ ] Step 0: Review Golden Examples (MANDATORY)
- [ ] Step 1: Create Output Folder and Setup
- [ ] Step 2: Parse User Query & Research
- [ ] Step 3: Resolve Tissues & Modalities
- [ ] Step 4: Visualize & Save Plots
- [ ] Step 5: Analyze Predictions (view plots, no code). MANDATORY: Read [interpretation-guide.md](docs/interpretation-guide.md) before interpreting results.
- [ ] Step 6: Write Report, save it as `report.md` (MANDATORY)
- [ ] Step 7: Self-Critique (view `report.md` to verify links & claims)
- [ ] Step 8: Make artifact out of `report.md`

Multi-Variant Workflow

If multiple variants are specified, spawn sub-agents to run each variant analysis and then synthesize each report.md into a single report.

Script Reference