genomics-variant-calling

Documents

Load when summarising small variants (SNVs / indels) from a VCF or computing demo-pattern variant statistics (Ti/Tv ratio, per-chromosome distribution, SNP / indel split). Skip when filtering / merging VCFs (use `genomics-vcf-operations`), when calling structural variants (use `genomics-sv-detection`), 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-variant-calling/SKILL.md

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genomics-variant-calling

When to use

The user has a VCF (or a BAM intended for calling) and wants small-variant summary statistics: total variant count, SNP / indel split, Ti/Tv ratio, per-chromosome distribution. The script does not invoke an external caller (GATK HaplotypeCaller, Mutect2, DeepVariant, FreeBayes, etc.) — it summarises an existing VCF or generates a demo VCF for downstream-skill smoke tests.

For variant filtering / normalisation use genomics-vcf-operations; for SVs use genomics-sv-detection; for functional annotation use genomics-variant-annotation.

Inputs & Outputs

Input	Format	Required
Variants	`.vcf` or `.bam` (BAM only used as a placeholder; no calling is run)	yes (unless `--demo`)

Output	Path	Notes
Variant table	`tables/variants.csv`	per-variant CHROM/POS/REF/ALT/QUAL
Per-chromosome	`tables/variants_per_chrom.csv`	counts per chromosome
Report	`report.md` + `result.json`	always; `result.json["data"]["variants_per_chrom"]` mirrors the table

Flow

Load VCF (--input <file.vcf>) or generate a demo VCF at output_dir/demo_variants.vcf with --n-variants records (genomics_variant_calling.py:94).
Parse records; classify SNP vs indel; compute Ti/Tv on biallelic SNPs.
Aggregate per-chromosome counts.
Write tables/variants.csv (genomics_variant_calling.py:300) + tables/variants_per_chrom.csv (:308) + report.md + result.json envelope (:314).

Gotchas

No external caller is invoked. This skill does NOT run GATK / Mutect2 / DeepVariant / FreeBayes — it ingests a VCF and summarises it. To actually CALL variants, run an external pipeline first; this skill consumes the resulting VCF.
--input REQUIRED unless --demo. genomics_variant_calling.py:289 raises ValueError("--input required when not using --demo"); non-existent paths raise FileNotFoundError at :292.
--n-variants only affects --demo (genomics_variant_calling.py:278, default 500). It is silently ignored when --input is set.
Multi-allelic VCF rows ARE split per-ALT. genomics_variant_calling.py:181-182 iterates for a in alt.split(","): and emits one CSV row per ALT allele. Output row counts therefore exceed input VCF line counts on multi-allelic data — no need to pre-normalise unless your downstream consumer requires one row per VCF line.
Demo VCF is a minimal SNV set (no indels, no structural variants, no genotype fields). Useful for orchestrator smoke tests; do NOT use for biological inference.

Key CLI

# Demo (500 synthetic SNVs)
python omicsclaw.py run genomics-variant-calling --demo --output /tmp/var_demo

# Custom demo size
python omicsclaw.py run genomics-variant-calling --demo --n-variants 2000 \
  --output /tmp/var_demo_large

# Real VCF
python omicsclaw.py run genomics-variant-calling \
  --input cohort.vcf --output results/

See also

references/parameters.md — every CLI flag
references/methodology.md — SNP / indel / Ti-Tv definitions
references/output_contract.md — tables/variants.csv schema
Adjacent skills: genomics-alignment (upstream — produces the BAM that calling consumes), genomics-vcf-operations (downstream — VCF filtering / merging), genomics-variant-annotation (downstream — functional impact), genomics-sv-detection (parallel — SVs instead of small variants)