variant-annotation

Files (6)

• SKILL.md
• synthetic_clinvar_panel.vcf · example_data/
• variant_annotation_summary.png · figures/
• requirements.txt
• test_variant_annotation.py · tests/
• variant_annotation.py

SKILL.md (first lines)

# 🧬 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

```bash
# 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

```bash
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

From ClawBio/ClawBio, MIT.

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