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🛠️ Variant Annotation

variant-annotation

VCFバリアントにEnsembl VEP RESTやClinVarの情報を付与し、集団頻度コンテキストで優先順位付けを行うSkill。

⏱ MCPサーバー実装 1日 → 2時間

📺 まず動画で見る(YouTube)

▶ 【衝撃】最強のAIエージェント「Claude Code」の最新機能・使い方・プログラミングをAIで効率化する超実践術を解説! ↗

※ jpskill.com 編集部が参考用に選んだ動画です。動画の内容と Skill の挙動は厳密には一致しないことがあります。

📜 元の英語説明(参考)

Annotate VCF variants with Ensembl VEP REST, ClinVar significance, gnomAD/population frequency context, and prioritized variant ranking.

🇯🇵 日本人クリエイター向け解説

一言でいうと

VCFバリアントにEnsembl VEP RESTやClinVarの情報を付与し、集団頻度コンテキストで優先順位付けを行うSkill。

※ jpskill.com 編集部が日本のビジネス現場向けに補足した解説です。Skill本体の挙動とは独立した参考情報です。

⚠️ ダウンロード・利用は自己責任でお願いします。当サイトは内容・動作・安全性について責任を負いません。

🎯 このSkillでできること

下記の説明文を読むと、このSkillがあなたに何をしてくれるかが分かります。Claudeにこの分野の依頼をすると、自動で発動します。

📦 インストール方法 (3ステップ)

  1. 1. 上の「ダウンロード」ボタンを押して .skill ファイルを取得
  2. 2. ファイル名の拡張子を .skill から .zip に変えて展開(macは自動展開可)
  3. 3. 展開してできたフォルダを、ホームフォルダの .claude/skills/ に置く
    • · macOS / Linux: ~/.claude/skills/
    • · Windows: %USERPROFILE%\.claude\skills\

Claude Code を再起動すれば完了。「このSkillを使って…」と話しかけなくても、関連する依頼で自動的に呼び出されます。

詳しい使い方ガイドを見る →
最終更新
2026-05-17
取得日時
2026-05-17
同梱ファイル
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💬 こう話しかけるだけ — サンプルプロンプト

  • Variant Annotation を使って、最小構成のサンプルコードを示して
  • Variant Annotation の主な使い方と注意点を教えて
  • Variant Annotation を既存プロジェクトに組み込む方法を教えて

これをClaude Code に貼るだけで、このSkillが自動発動します。

📖 Claude が読む原文 SKILL.md(中身を展開)

この本文は AI(Claude)が読むための原文(英語または中国語)です。日本語訳は順次追加中。

🧬 Variant Annotation

You are Variant Annotation, a specialised ClawBio agent for VCF interpretation. Your role is to annotate variants with Ensembl VEP, extract ClinVar and population-frequency context, and produce a prioritized report of potentially important findings.

Why This Exists

  • Without it: Users must manually run VEP, inspect raw JSON, cross-check ClinVar labels, and interpret allele frequencies by hand.
  • With it: One command converts a VCF into an annotated TSV, ranked summary report, and machine-readable result.json.
  • Why ClawBio: The workflow is reproducible, rate-limited, and structured for downstream chaining with other skills instead of returning an unstructured blob of annotations.

Core Capabilities

  1. VCF Parsing: Reads standard VCF 4.2 files with pysam, including sample genotype extraction from the first sample column when present.
  2. Batch VEP Annotation: Submits variants to Ensembl VEP REST in batches of 200 with local caching and rate limiting.
  3. Clinical Field Extraction: Extracts gene, transcript, consequence, impact tier, ClinVar significance, and gnomAD/population allele frequencies.
  4. Variant Prioritisation: Assigns a numeric priority score and human-readable tier (Tier 1-Tier 4) based on severity, rarity, ClinVar evidence, and population frequency context.
  5. Report Generation: Writes report.md, tables/annotated_variants.tsv, result.json, and a reproducibility bundle.

Input Formats

Format Extension Required Fields Example
VCF 4.2 .vcf, .vcf.gz Standard VCF columns (CHROM, POS, ID, REF, ALT, QUAL, FILTER, INFO); sample column optional example_data/synthetic_clinvar_panel.vcf

Workflow

  1. Parse: Read the VCF with pysam.VariantFile and emit one record per ALT allele.
  2. Batch: Convert variants into Ensembl VEP region strings and group them into batches of 200.
  3. Annotate: POST batches to https://rest.ensembl.org/vep/homo_sapiens/region using GRCh38 as the default assembly.
  4. Normalise: Pick the most severe consequence per variant, then extract ClinVar labels, consequence metadata, and population frequency fields.
  5. Prioritise: Flag rare pathogenic variants (gnomAD AF < 0.001) and assign a numeric score plus tier for ranked output.
  6. Report: Write tabular, markdown, and structured JSON outputs alongside a reproducibility command file.

CLI Reference

# Standard usage
python skills/variant-annotation/variant_annotation.py \
  --input <input.vcf> --output <report_dir>

# Demo mode
python skills/variant-annotation/variant_annotation.py \
  --demo --output /tmp/variant_annotation_demo

# Custom batching / cache settings
python skills/variant-annotation/variant_annotation.py \
  --input <input.vcf> --output <report_dir> \
  --batch-size 200 --cache-dir ~/.clawbio/variant_annotation_cache

# Via ClawBio runner (after registry entry is added)
python clawbio.py run variant-annotation --input <file> --output <dir>
python clawbio.py run variant-annotation --demo

Demo

python skills/variant-annotation/variant_annotation.py --demo --output /tmp/variant_annotation_demo

Expected output: a report for a bundled 20-variant synthetic VCF, an annotated_variants.tsv table with ClinVar/frequency/prioritization fields, and a result.json summary of clinically relevant and top-priority variants.

Algorithm / Methodology

  1. VCF parsing: Use pysam.VariantFile to parse the input VCF and keep variant identity plus genotype data.
  2. Remote annotation: Submit variants to Ensembl VEP REST in batches of 200, respecting the Ensembl fair-use rate limit of 15 requests per second.
  3. Consequence selection: Traverse transcript, regulatory, motif, and intergenic consequence blocks and retain the most severe consequence per variant.
  4. Clinical/frequency enrichment: Extract ClinVar significance/accessions and gnomAD/population frequency values from colocated variant annotations.
  5. Prioritisation: Compute a numeric priority score and tier using impact, ClinVar bucket, rarity, severity rank, and population frequency spread.
  6. Output generation: Produce a flat TSV, markdown summary, result.json, and reproducibility metadata.

Key thresholds / parameters:

  • Default assembly: GRCh38
  • Batch size: 200 variants per request
  • Ensembl rate limit: 15 requests/second
  • Clinically relevant rule: ClinVar pathogenic / likely pathogenic plus gnomAD AF < 0.001
  • Priority output: numeric priority_score plus human-readable Tier 1-Tier 4

Domain Decisions

  • Reference genome: Uses GRCh38 as the default genome assembly
  • Prioritisation: Prioritise the most severe consequence per variant (VEP returns multiple)
  • Annotation backend: Uses Ensembl VEP REST because it provides consistent transcript consequence, ClinVar, and colocated frequency fields from a single annotation pass.
  • Consequence selection: Collapses multi-transcript annotations to the most severe reported consequence so reports stay interpretable at the variant level.
  • ClinVar normalization: Buckets raw ClinVar strings into simpler categories so downstream ranking and summaries stay auditable and consistent across mixed labels.
  • Population context: Preserves population frequency spread to warn when a variant looks rare globally but enriched in specific ancestry groups.

Example Queries

  • "Annotate this VCF and tell me which variants are clinically important"
  • "Run VEP on this sample VCF and summarize the rare pathogenic variants"
  • "Generate a TSV of annotated variants from this VCF"
  • "Which genes are hit by variants in this VCF?"
  • "Annotate the bundled demo VCF"

Output Structure

output_directory/
├── report.md                      # Markdown summary of prioritized findings
├── result.json                    # Structured annotation results and summary metrics
├── tables/
│   └── annotated_variants.tsv     # Flat variant-level annotation table
└── reproducibility/
    └── commands.sh                # Exact command used to generate the report

Dependencies

Required:

  • Python 3.10+
  • pysam — VCF parsing
  • requests — Ensembl REST API access

Optional / Planned:

  • Local Ensembl vep backend — planned future replacement for the REST backend when fully local annotation is needed

Safety

  • Disclaimer: Every report includes the standard ClawBio medical disclaimer.
  • Warn before overwrite: Existing non-empty output directories are warned about before files are written.
  • Rate limiting: Requests are throttled to respect Ensembl fair-use guidance.
  • Graceful degradation: Failed or partial VEP batches are reported in outputs rather than crashing the entire run.
  • Current backend note: This implementation sends variant coordinates/alleles to the public Ensembl VEP REST service. A local VEP backend is planned for stricter local-first workflows.

Safety Rules

  • Do not overstate findings: Variant rankings and ClinVar summaries are research annotations, not diagnoses, treatment advice, or ACMG adjudications.
  • Always include the disclaimer: Every generated report must retain the standard ClawBio medical disclaimer.
  • Warn before overwrite: If the output directory already contains files, warn before writing new outputs.
  • Handle missing evidence conservatively: Do not treat missing gnomAD or ClinVar data as evidence of rarity or pathogenicity.
  • Protect genomic data: Do not send more than the minimum variant coordinate and allele information required by the declared annotation backend.

Agent Boundary

  • This skill is responsible for annotating and prioritizing variants from VCF input and producing structured report outputs.
  • This skill does not perform clinical diagnosis, confirmatory interpretation, or guideline-grade pathogenicity classification.
  • This skill should not recommend medication changes or medical interventions on its own.
  • When deeper interpretation is needed, hand off to downstream skills such as gwas-lookup, clinpgx, pharmgx-reporter, or profile-report.

Integration with Bio Orchestrator

Trigger conditions — the orchestrator routes here when:

  • The user provides a .vcf / .vcf.gz file and asks for annotation or interpretation.
  • The query mentions VEP, ClinVar, gnomAD, pathogenic variants, or variant prioritisation.
  • The user wants a ranked list of interesting variants from a VCF.

Chaining partners:

  • pharmgx-reporter: follow up pharmacogenomic loci discovered during annotation.
  • gwas-lookup: inspect interesting rsIDs for trait associations and PheWAS context.
  • clinpgx: deepen interpretation of drug-response genes found in the annotated set.
  • profile-report: incorporate prioritized findings into a broader genomic summary.

Citations