openai/ngs-dna-variant-calling

DNA Variant Calling

Use this skill as the DNA variant-calling dispatcher for WGS, WES, or targeted DNA panel analysis from FASTQ, BAM, or CRAM. Once the sample model is clear, hand off to the narrow subtype skill.

Essential Inputs

Confirm:

• data type: WGS, WES, or panel
• sample model: germline single sample, cohort, trio, tumor-only, or tumor-normal
• input type: FASTQ, BAM, or CRAM
• organism and reference genome
• known-sites resources for BQSR, if required
• target BED for WES or panels
• UMI or duplex handling
• desired callers and annotation outputs

Dispatch

Route by biological/sample model:

• Germline singleton, cohort, family, trio, WGS, WES, or ordinary inherited panel: ngs-dna-germline-variants
• Tumor-normal, tumor-only, relapse-baseline, or other cancer somatic calling: ngs-dna-somatic-variants
• UMI, duplex, molecular-barcode, or low-frequency targeted panel calling: ngs-dna-umi-panel-variants

If the request is ambiguous, ask only for the missing sample model and assay design needed to choose among these three. Do not run one generic variant workflow when the request needs subtype-specific assumptions.

Public Default

Prefer nf-core/sarek for an end-to-end public workflow. Use direct GATK4, DeepVariant, samtools, or bcftools commands only for smaller, focused tasks or when the user explicitly wants a custom pipeline.

Preflight

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

Local Execution Package

For a compact BAM/CRAM-to-VCF run with a matching reference FASTA, use the plugin-owned samtools/bcftools runner:

python plugins/ngs-analysis/scripts/run_dna_variant_calling.py \
  --sample-sheet dna_samples.tsv \
  --reference-fasta reference.fa \
  --region chr20:1-100000 \
  --filter-min-qual 30 \
  --filter-min-site-dp 10 \
  --execute

The sample sheet should include sample and bam or cram columns. When --region is provided the runner also emits per-base depth plus a callable-loci summary for that interval, and when filter thresholds are provided it emits a soft-filtered VCF alongside the raw calls. This package is suitable for focused local checks and run-envelope generation; subtype skills still own germline, somatic, UMI, reference-resource, cohort, annotation, and workflow assumptions.

This compact runner now writes advisory resources/resource_plan.json, resource_manifest.tsv, resource_env.sh, and resource_readiness.md artifacts for the selected genome bundle. Use --require-resource-plan when missing registered reference resources should block readiness; otherwise the explicit --reference-fasta remains enough for focused local checks.

Kickoff Pattern

Preflight-first nf-core pattern:

nextflow run nf-core/sarek \
  -profile test,docker \
  --outdir results/sarek_test

Real run skeleton:

nextflow run nf-core/sarek \
  -profile docker \
  --input samplesheet.csv \
  --outdir results/sarek \
  --genome GRCh38 \
  --tools haplotypecaller,vep

For WES/panel data, include the target BED. For tumor-normal data, verify pair metadata before execution. For UMI panels, preserve barcode handling and molecule-level QC.

Guardrails

• Do not mix genome builds across FASTA, GTF/BED, known sites, and VEP cache.
• Do not download large references without confirming disk space and target path.
• Treat clinical interpretation as out of scope unless the user has a validated clinical workflow.