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Scientific-ToolingScientific-Tooling/structured-intelligence/skills/genome-variant-filtering/SKILL.md

genome-variant-filtering

Skill4 starsChanged 12 days agoMITView on GitHub

Filter raw variant calls using GATK VQSR, GATK hard filters, or bcftools expression-based filtering.

The file

We split this skill into the rules it gives an agent

3 rules. About 0 are common, 0 are distinctive, and 3 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈0

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 3

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    If hard-filter: split VCF into SNPs and indels with gatk SelectVariants.

    line 28
  2. Very specific:

    If VQSR: run gatk VariantRecalibrator + gatk ApplyVQSR for SNPs then indels with resource annotations.

    line 32
  3. Very specific:

    If bcftools: apply user-provided filter expressions via bcftools filter.

    line 33

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Files
SKILL.md

Skill: genome-variant-filtering

Use When

  • User wants to filter a raw VCF to remove low-confidence variants.
  • User needs to apply GATK best-practice hard filters or VQSR.
  • User wants to use custom bcftools filter expressions.
  • User needs to separate SNPs from indels for different filter strategies.

Inputs

  • Required:
    • Raw VCF file (.vcf.gz).
    • Reference FASTA (for GATK operations).
  • Optional:
    • Filter strategy: vqsr, hard, or bcftools (default: hard).
    • Resource VCFs for VQSR: HapMap, Omni, 1000 Genomes, dbSNP.
    • Custom filter expressions (for bcftools mode).
    • Truth sensitivity level for VQSR (default: 99.5 for SNPs, 99.0 for indels).

Workflow

  1. If hard-filter: split VCF into SNPs and indels with gatk SelectVariants.
  2. Apply GATK recommended hard filters:
    • SNPs: QD < 2.0, FS > 60.0, MQ < 40.0, MQRankSum < -12.5, ReadPosRankSum < -8.0.
    • Indels: QD < 2.0, FS > 200.0, ReadPosRankSum < -20.0.
  3. If VQSR: run gatk VariantRecalibrator + gatk ApplyVQSR for SNPs then indels with resource annotations.
  4. If bcftools: apply user-provided filter expressions via bcftools filter.
  5. Merge filtered SNPs and indels back into a single VCF.
  6. Index the output VCF.
  7. Report: variants before and after filtering, pass rate, filter category breakdown.

Output Contract

  • Filtered VCF file (.vcf.gz).
  • Filter summary statistics: total variants, PASS count, filtered count by category.

Limits

  • VQSR requires a sufficiently large call set to train the model (at least ~30 exomes or 1 WGS); use hard filters for small sample sets.
  • Resource VCFs must match the reference genome build (e.g., GRCh38).
  • GATK is required for VQSR and hard-filter modes; bcftools mode only requires bcftools.
  • Hard filters are recommended when VQSR training data is insufficient.
  • Common failure cases:
    • VQSR failing due to too few variants in the training set for model convergence.
    • Resource VCFs on a different genome build than the input VCF (e.g., GRCh37 vs GRCh38).
    • bcftools filter expression syntax errors causing silent empty output.

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