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ngs-dna-umi-panel-variants

Plan or run targeted DNA panel analysis using UMIs, barcodes that identify DNA molecules. Organize consensus reads, coverage checks, and low-frequency variant results with molecular support, filtering reasons, and explicit quality limitations.

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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.

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适用场景

  • Plan a targeted DNA panel workflow using UMIs
  • Review molecular depth and target coverage
  • Check evidence supporting low-frequency variants
  • Summarize duplex consensus quality

包含的文件(2)

  • SKILL.md3.7 KB
  • agents/openai.yaml291 B

SKILL.md(原文)

安装前可以查看交给 AI 代理的指令内容。

UMI Panel DNA Variants

Use 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.

Essential Inputs

Confirm:

  • panel/capture kit name and target BED
  • UMI layout: inline read, index read, single UMI, duplex UMI, or unknown
  • whether consensus reads have already been generated
  • FASTQ/BAM input and pairing convention
  • reference build and panel-specific annotation requirements
  • minimum allele fraction goal and intended use: screening, research, validation, or exploratory
  • positive/negative controls and expected spike-ins when available

Route

Use 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.

Preflight command:

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

For compact local checks from prepared consensus or alignment BAM/CRAM files, use the dedicated UMI panel runner:

python plugins/ngs-analysis/scripts/run_dna_umi_panel_variants.py \
  --sample-sheet umi_panel_samples.tsv \
  --reference-fasta reference.fa \
  --target-bed panel_targets.bed \
  --umi-mode duplex \
  --umi-tag RX \
  --execute

This 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.

The 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.

Decision Points

  • Do not trim or discard UMI bases until their layout and destination are known.
  • Separate raw read depth from unique molecular depth and consensus depth.
  • Track on-target rate, coverage uniformity, family size distribution, strand/duplex support, and per-target dropout.
  • Low allele fraction calls require stronger artifact review than ordinary germline calls.
  • Use panel-specific hotspot/blacklist rules only when their provenance is known.

Outputs

Produce:

  • UMI layout and consensus strategy
  • target BED/resource manifest
  • raw-depth, molecular-depth, and consensus-depth QC summary
  • qc/umi_postrun_summary.tsv for consensus reads, target coverage, variant counts, family size, and duplex fraction
  • qc/umi_molecular_evidence_contract.tsv for low-AF evidence readiness, hotspot review, and duplex review expectations
  • variant calls with allele fraction, depth, strand/duplex support, and filtering rationale
  • limitations around sensitivity, panel dropout, molecule count, and non-validated interpretation

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