Update to Life Sciences NGS Analysis

{
  "name": "ngs-dna-umi-panel-variants",
  "description": "Run or plan targeted DNA panel variant workflows that use UMIs, duplex consensus reads, molecular barcodes, low-frequency calling, target coverage, and panel-specific QC.",
  "included_files": [
    {
      "relative_path": "agents/openai.yaml",
      "size_in_bytes": 291
    }
  ],
  "skill_md_contents": "---\nname: ngs-dna-umi-panel-variants\ndescription: Run or plan targeted DNA panel variant workflows that use UMIs, duplex consensus reads, molecular barcodes, low-frequency calling, target coverage, and panel-specific QC.\n---\n\n# UMI Panel DNA Variants\n\nUse this skill for targeted DNA panels where molecular barcodes, UMIs, duplex consensus, or low-frequency allele detection are central to the analysis. If the panel is ordinary germline calling without molecular consensus, use `ngs-dna-germline-variants`.\n\n## Essential Inputs\n\nConfirm:\n\n- panel/capture kit name and target BED\n- UMI layout: inline read, index read, single UMI, duplex UMI, or unknown\n- whether consensus reads have already been generated\n- FASTQ/BAM input and pairing convention\n- reference build and panel-specific annotation requirements\n- minimum allele fraction goal and intended use: screening, research, validation, or exploratory\n- positive/negative controls and expected spike-ins when available\n\n## Route\n\nUse a lab-validated panel workflow when provided. For public-tool planning, combine FASTQ QC, UMI extraction/consensus generation, alignment, target coverage QC, and variant calling as separate audited stages.\n\nPreflight command:\n\n```bash\npython plugins/ngs-analysis/scripts/ngs_preflight.py --pipeline dna_umi_panel_variants --emit-install-plan\n```\n\nFor compact local checks from prepared consensus or alignment BAM/CRAM files, use the dedicated UMI panel runner:\n\n```bash\npython plugins/ngs-analysis/scripts/run_dna_umi_panel_variants.py \\\n  --sample-sheet umi_panel_samples.tsv \\\n  --reference-fasta reference.fa \\\n  --target-bed panel_targets.bed \\\n  --umi-mode duplex \\\n  --umi-tag RX \\\n  --execute\n```\n\nThis writes the consensus/variant command plan, molecular-consensus state, low-frequency calling settings, visualization index, `qc/umi_postrun_summary.{tsv,json}`, `qc/umi_molecular_evidence_contract.{tsv,json}`, and consensus-BAM VCF outputs when the local fgbio/samtools/bcftools backend is available. The post-run summary parses consensus flagstat, target coverage, bcftools stats, and family-size/duplex files when present; missing metrics stay explicit in the notes column. The molecular evidence contract keeps the low-AF review requirements visible per sample: consensus BAM, family-size or molecule-support metrics, variant stats, hotspot review, and duplex review.\n\nThe direct runner also emits `resources/resource_plan.json`, `resource_manifest.tsv`, `resource_env.sh`, and `resource_readiness.md`. The resource check is advisory by default so custom or reduced references can still be planned; add `--genome-build`, `--bundle-root <bundle>=<path>`, and `--require-resource-plan` when missing registered reference bundles should block readiness.\n\n## Decision Points\n\n- Do not trim or discard UMI bases until their layout and destination are known.\n- Separate raw read depth from unique molecular depth and consensus depth.\n- Track on-target rate, coverage uniformity, family size distribution, strand/duplex support, and per-target dropout.\n- Low allele fraction calls require stronger artifact review than ordinary germline calls.\n- Use panel-specific hotspot/blacklist rules only when their provenance is known.\n\n## Outputs\n\nProduce:\n\n- UMI layout and consensus strategy\n- target BED/resource manifest\n- raw-depth, molecular-depth, and consensus-depth QC summary\n- `qc/umi_postrun_summary.tsv` for consensus reads, target coverage, variant counts, family size, and duplex fraction\n- `qc/umi_molecular_evidence_contract.tsv` for low-AF evidence readiness, hotspot review, and duplex review expectations\n- variant calls with allele fraction, depth, strand/duplex support, and filtering rationale\n- limitations around sensitivity, panel dropout, molecule count, and non-validated interpretation\n"
}