Alphagenome Single Variant Analysis

What it does

When your agent uses it

• The user asks about non-coding variant effects
• Clinical significance
• Disease associations
• Functional effects

Example prompts

• “Use the alphagenome-single-variant-analysis skill to analyz genetic variant effects on gene expression (RNA-seq), chromatin accessibility (DNASE)…”
• “/alphagenome-single-variant-analysis”

Requirements

• Python 3
• A credential in ALPHAGENOME_API_KEY

Workflow steps

5 steps, taken from the first numbered list in SKILL.md.

1. 1uv: Read the uv skill and follow its Setup instructions to ensure
2. 2User Notification: If
3. 3.env file: Make sure the .env file exists in your home directory.
4. 4ALPHAGENOME_API_KEY: This skill requires an API key to function.
5. 5ALPHAGENOME_GTF_PATH (Optional): Accelerate gene/transcript lookup by

What it can do on your machine

Read from SKILL.md and the folder at commit 6883275. It shows what the files ask for, not the result of running them.

• Tool permissions

  Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

  From allowed-tools in the SKILL.md frontmatter.

• Runs code

  Ships 1 file in scripts/, which the agent can run.

  Shell commands in SKILL.md call:

  • uv
  • python3
  • pip

  From the folder's file list and the shell code blocks in SKILL.md.

• Network

  Hosts in commands or code, which the agent is likely to contact:

  • pypi.org

  Also links to:

  • deepmind.google.com

  From URLs in SKILL.md, links to its own repository left out.

• Credentials

  Names these keys or tokens, usually read from environment variables:

  • ALPHAGENOME_API_KEY

  From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Alphagenome Single Variant Analysis loads about 3k tokens when it runs, and up to ~3.2k if it reads all its reference files. Until then it costs about 143 tokens; SKILL.md has 850 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it

~143

When it runs · the whole SKILL.md, loaded when a task matches

~3k

With references · SKILL.md plus every file in references/, read only if the agent opens them

~3.2k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

• NoteMentions a .env fileSKILL.md:25

  3.  **`.env` file**: Make sure the `.env` file exists in your home directory.

• NoteMentions a .env fileSKILL.md:37

  code.v46.annotation.gtf.gz.feather" >> ~/.env

• NoteMentions a .env fileSKILL.md:169

  # Load environment variables from ~/.env

• NoteMentions a .env fileSKILL.md:170

  dotenv.load_dotenv(os.path.expanduser('~/.env'))

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from google-deepmind/science-skills at commit 6883275, republished under its Apache-2.0 licence (© google-deepmind). 850 words, ~2,987 tokens.

name

alphagenome-single-variant-analysis

description

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.

uv run <script_name> [args...]

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

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']])

# 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']])

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`

© google-deepmind, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 53 other files (scripts, references) in skills/alphagenome_single_variant_analysis of google-deepmind/science-skills.

• SKILL.md
• docs/alphagenome-api.md
• docs/examples/model_limitation_RNU4ATAC/README.md
• docs/examples/model_limitation_RNU4ATAC/ism_skeletal_muscle_RNA_SEQ.png
• docs/examples/model_limitation_RNU4ATAC/plot_skeletal_muscle_RNU4ATAC_detail.png
• docs/examples/model_limitation_RNU4ATAC/plot_skeletal_muscle_RNU4ATAC_effects.png
• docs/examples/model_limitation_RNU4ATAC/plot_skeletal_muscle_RNU4ATAC_wholegene.png
• docs/examples/model_limitation_RNU4ATAC/report.md
• docs/examples/negative_result_GATA4/README.md
• docs/examples/negative_result_GATA4/ism_heart_DNASE.png
• docs/examples/negative_result_GATA4/plot_heart_GATA4_detail.png
• docs/examples/negative_result_GATA4/plot_heart_GATA4_effects.png
• docs/examples/negative_result_GATA4/plot_heart_GATA4_wholegene.png
• docs/examples/negative_result_GATA4/report.md
• docs/examples/negative_result_TGFB3/README.md
• docs/examples/negative_result_TGFB3/ism_heart_ATAC.png
• … and 38 more

Open the folder on GitHubat commit 6883275

Used in 2 other repositories

We found 2 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 2 other GitHub owners. This page covers the copy in google-deepmind/science-skills, which our catalogue first saw on October 7, 2026.

• NeuroAIHub/BrainPilotpackages/skills/skills/18_Genetics_Genomics/alphagenome-single-variant-analysis
• franklee16/academic-research-skillsscience-skills/alphagenome_single_variant_analysis