ngs-dna-germline-variants

# 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: ```bash 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: ```bash 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: ```bash 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.