Imported from openai/plugins (plugins/ngs-analysis/skills/ngs-dna-germline-variants/SKILL.md). Install upstream with npx skills add openai/plugins --skill ngs-dna-germline-variants. Copyright stays with the author.

Germline DNA Variants

Use this skill for germline WGS, WES, or inherited-disease panel analysis from FASTQ, BAM, or CRAM. If the request is tumor-only, tumor-normal, or low-frequency molecular-barcode panel calling, use a somatic or UMI-panel skill instead.

Essential Inputs

Confirm:

• data type: WGS, WES, or targeted panel
• sample model: singleton, cohort, duo, trio, family, or case/control
• input type: FASTQ, BAM, or CRAM
• organism, reference build, FASTA, indexes, and contig naming
• known-sites resources for BQSR, contamination, and annotation
• target BED and bait BED for WES/panel data
• sex/ploidy assumptions and mitochondrial/sex-chromosome requirements
• desired callers, annotation outputs, and final VCF/gVCF expectations

Route

Prefer nf-core/sarek for full FASTQ/BAM-to-VCF workflows. Use direct GATK4, DeepVariant, samtools, or bcftools only for focused tasks or a custom workflow.

Preflight command:

python plugins/ngs-analysis/scripts/ngs_preflight.py --pipeline dna_germline_variants --emit-install-plan

For compact local checks from prepared BAM/CRAM files, use the shared DNA execution package:

python plugins/ngs-analysis/scripts/run_dna_variant_calling.py \
  --sample-sheet dna_samples.tsv \
  --reference-fasta reference.fa \
  --execute

Treat this as a focused samtools/bcftools run envelope, not as a substitute for full cohort, trio, gVCF, BQSR, or annotation workflows.

For a higher-fidelity local germline run that owns BQSR, per-sample gVCFs, and joint genotyping assumptions, use the germline-specific runner:

python plugins/ngs-analysis/scripts/run_dna_germline_variants.py \
  --sample-sheet dna_samples.tsv \
  --reference-fasta reference.fa \
  --known-sites dbsnp.vcf.gz \
  --known-sites mills.vcf.gz \
  --emit-gvcf \
  --joint-call \
  --execute

This runner still expects reference-matched resources and an available GATK toolchain. It packages the validation state and generated artifacts even when execution is blocked by missing tools or resources.

It also writes advisory resources/resource_plan.json, resource_manifest.tsv, resource_env.sh, and resource_readiness.md artifacts by default. Add --genome-build, --bundle-root <bundle>=<path>, and --require-resource-plan when complete registered reference and known-sites bundles should be mandatory for readiness.

Decision Points

• For cohorts or families, decide whether the endpoint is per-sample VCFs, gVCFs for joint genotyping, or a jointly called cohort VCF.
• For WES/panels, carry the target BED through alignment metrics, calling, and coverage reports; do not call off-target regions by accident.
• Use BQSR only when reference-matched known-sites resources exist. Do not mix GRCh37, hg19, GRCh38, or T2T resources.
• Check sample identity, sex concordance, contamination, coverage, duplication, insert size, and transition/transversion where feasible.
• For trios, preserve pedigree metadata and report Mendelian/QC checks separately from variant interpretation.

Outputs

Produce:

• command or workflow profile and sample sheet
• reference/resource manifest with versions and checksums when available
• QC summary: coverage, duplication, insert size, contamination, sex/relatedness checks when run
• VCF/gVCF path, index path, and annotation path
• limitations: low coverage, missing known-sites, target design gaps, or build mismatches

Clinical interpretation, pathogenicity classification, and report signing are out of scope unless the user provides a validated clinical workflow.