📝 Variant Annotation

Variant annotation and functional effect prediction. Supports VEP, snpEff, and ANNOVAR.

CLI Reference

python omicsclaw.py run genomics-variant-annotation --demo
python omicsclaw.py run genomics-variant-annotation --input <data.vcf> --output <dir>

Why This Exists

• Without it: Variants lack biological context, remaining as simple coordinate tuples
• With it: Transforms structural variation into biological impact and transcript-level consequences
• Why OmicsClaw: Unified framework for multiple ontology backends like VEP or ANNOVAR without custom parsing

Workflow

1. Calculate: Prepare genome indices and transcript boundary maps.
2. Execute: Run annotation search across known consequence states.
3. Assess: Filter variants by putative pathological score.
4. Generate: Save annotated VCFs with strict ontologies.
5. Report: Tabulate key functionally relevant variants.

Example Queries

• "Annotate this vcf file using VEP"
• "Run snpEff and summarize high impact variants"

Output Structure

output_directory/
├── report.md
├── result.json
├── annotated.vcf.gz
├── figures/
│   └── impact_distribution.png
├── tables/
│   └── top_variants.csv
└── reproducibility/
    ├── commands.sh
    ├── environment.yml
    └── checksums.sha256

Safety

• Local-first: Strict offline processing without external upload.
• Disclaimer: Requires OmicsClaw reporting structures and disclaimers.
• Audit trail: Hyperparameters and operational flow states are logged fully.

Integration with Orchestrator

Trigger conditions:

• Automatically invoked dynamically based on tool metadata and user intent matching.

Chaining partners:

• variant-call

  — Upstream raw variation

• vcf-ops

  — Upstream filtering steps

Citations

• VEP — Variant Effect Predictor
• snpEff
• ANNOVAR