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genomics-vcf-operations

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Load when summarising / filtering a VCF — variant classification (SNP / MNP / INS / DEL / COMPLEX), Ti/Tv ratio, QUAL / DP threshold filtering, INFO-field parsing. Skip when the input is a BAM (use `genomics-variant-calling` upstream first) or when adding functional annotations (use `genomics-variant-annotation`).

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Source SKILL.md: https://github.com/TianGzlab/OmicsClaw/blob/HEAD/skills/genomics/genomics-vcf-operations/SKILL.md

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genomics-vcf-operations

When to use

The user has a VCF (cohort, single-sample, or merged) and wants: classify variants by type (SNP / MNP / INS / DEL / COMPLEX), compute Ti/Tv on biallelic SNPs, optionally apply hard QUAL / DP filters, and emit per-chromosome counts. This skill mirrors a subset of bcftools stats + a simple QUAL/DP filter pass — pure Python, no bcftools required.

For variant calling itself (BAM → VCF) see genomics-variant-calling; for functional impact (gene / consequence / impact) use genomics-variant-annotation.

Inputs & Outputs

InputFormatRequired
Variants.vcf (uncompressed; .vcf.gz not auto-decompressed)yes (unless --demo)
OutputPathNotes
Variant tabletables/variants.csvper-variant CHROM/POS/REF/ALT/QUAL/DP/type
Filtered VCFoutput_dir/filtered.vcfonly when --min-qual > 0 or --min-dp > 0
Reportreport.md + result.jsonalways; result.json["data"]["variants_per_chrom"] mirrors per-chrom counts

Flow

  1. Load VCF (--input <file.vcf>) or generate a demo VCF at output_dir/demo.vcf (genomics_vcf_operations.py:305).
  2. Parse records; classify each ALT into SNP / MNP / INS / DEL / COMPLEX.
  3. Apply --min-qual and --min-dp filters; write filtered.vcf if either threshold is active (:329).
  4. Compute Ti/Tv on biallelic SNPs; aggregate per-chromosome counts.
  5. Write tables/variants.csv (genomics_vcf_operations.py:325) + report.md + result.json (:341).

Gotchas

  • --input REQUIRED unless --demo. genomics_vcf_operations.py:310 raises ValueError("--input required when not using --demo"); non-existent paths raise FileNotFoundError at :313.
  • .vcf.gz is not auto-decompressed. The script reads plain text; gzipped VCFs raise an unintelligible parse error rather than a clean bgzip hint. Pre-decompress with bgzip -d (or gunzip -k) first.
  • Filtered VCF only emitted when a filter is active. --min-qual 0 and --min-dp 0 (defaults at :296-297) keep every record and SKIP the filtered.vcf write. Pass at least one threshold > 0 to get the filtered file.
  • Multi-allelic rows are scored per-ALT but counted as one VCF line. Per-allele Ti/Tv is computed correctly, but downstream tools that count "rows" will under-count vs bcftools view. Pre-normalise (bcftools norm -m -) for row-by-allele math.
  • DP is read from INFO/DP only. Per-sample FORMAT/DP (genotype-level) is ignored — single-sample VCFs that only put DP in FORMAT will see DP=NA, and --min-dp will drop them all.
  • Demo VCF is a minimal SNV+indel set with random QUAL/DP. Useful for orchestrator smoke tests; not biologically meaningful.

Key CLI

# Demo
python omicsclaw.py run genomics-vcf-operations --demo --output /tmp/vcf_demo

# Filter at QUAL>=30 and DP>=10
python omicsclaw.py run genomics-vcf-operations \
  --input cohort.vcf --output results/ \
  --min-qual 30 --min-dp 10

See also

  • references/parameters.md — every CLI flag
  • references/methodology.md — variant-type rules, Ti/Tv interpretation
  • references/output_contract.md — tables/variants.csv + filtered.vcf
  • Adjacent skills: genomics-variant-calling (upstream — produces the VCF), genomics-variant-annotation (downstream — adds gene / consequence / impact), genomics-sv-detection (parallel — SVs instead of small variants), genomics-phasing (parallel — phased VCF analysis)