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ngs-dna-umi-panel-variants

✓ Official4,081

by openai · part of openai/plugins

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.

🧩 One of 7 skills in the openai/plugins package — works on its own, and pairs well with its siblings.

This is the playbook your agent receives when the skill activates — you don't need to read it to use the skill, but it's here to audit before installing.

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