ngs-dna-umi-panel-variants — Codex skill: DNA panel analysis
Plan or run targeted DNA panel analysis with UMIs, consensus reads and low-frequency variant calling. Useful when molecular evidence and panel-specific QC matter.
npx skills add openai/plugins --skill ngs-dna-umi-panel-variants -a codexWhat ngs-dna-umi-panel-variants does
The skill lays out a workflow covering FASTQ checks, UMI extraction and consensus generation, alignment, target coverage review, and variant calling. For prepared consensus or aligned BAM/CRAM files, run_dna_umi_panel_variants.py can produce QC summaries and a checklist of evidence to review for low-allele-fraction calls.
Before starting, confirm the panel, target BED, UMI layout, reference build, and intended use. The local runner relies on an available fgbio/samtools/bcftools backend; without it, consensus-BAM VCF outputs are not produced. Low-frequency calls need review of molecular support, strand or duplex evidence, and target dropout. Ordinary germline analysis without molecular consensus belongs to a different skill.
What it helps with
- plan a UMI-aware targeted DNA panel workflow
- review target coverage and family-size distribution
- separate raw, molecular, and consensus depth
- prepare an evidence checklist for low-frequency calls
Example prompts for the agent
- Plan a UMI-based DNA panel workflow and list the inputs that need confirmation.
- Review prepared consensus BAM files for this panel and summarize target coverage.
- Draft a low-frequency variant review plan that includes duplex support and panel QC.
How to install ngs-dna-umi-panel-variants
1Skills CLI (Vercel)
npx skills add openai/plugins --skill ngs-dna-umi-panel-variants -a codex2Manually: put the files in place
.agents/skills/ngs-dna-umi-panel-variants/SKILL.mdFor all projects use ~/.agents/skills/
1Skills CLI (Vercel)
npx skills add openai/plugins --skill ngs-dna-umi-panel-variants -a claude-code2Manually: put the files in place
~/.claude/skills/ngs-dna-umi-panel-variants/SKILL.mdCopy the whole skill folder; for one project use .claude/skills/ngs-dna-umi-panel-variants/
Manually: put the files in place
.cursor/skills/ngs-dna-umi-panel-variants/SKILL.mdCursor also reads .agents/skills/ and .claude/skills/
1GitHub CLI
gh skill install openai/plugins ngs-dna-umi-panel-variants2Manually: put the files in place
.github/skills/ngs-dna-umi-panel-variants/SKILL.mdPlatform CLI
gemini skills install https://github.com/openai/plugins --path plugins/ngs-analysis/skills/ngs-dna-umi-panel-variantsManually: put the files in place
.devin/skills/ngs-dna-umi-panel-variants/SKILL.mdThe legacy .windsurf/skills/ path still works
Claude.ai
Zip the folder and upload it in Settings → Capabilities → Skills
Step by step, with commands for each platform: Codex skills: how to install, where they live, how to call them
Questions about ngs-dna-umi-panel-variants
How do I install ngs-dna-umi-panel-variants in Codex?
Run npx skills add openai/plugins --skill ngs-dna-umi-panel-variants -a codex. The command selects Codex for installation.
What does ngs-dna-umi-panel-variants do?
It helps plan or run targeted DNA panel workflows using UMIs, from consensus and coverage checks through variant calling and QC.
What is needed for a local run?
Provide a sample sheet, reference FASTA, target BED, and UMI settings. The local fgbio/samtools/bcftools backend must be available to produce VCF outputs.
Source text
SKILL.md · MIT
---
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.
---
# 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:
```bash
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:
```bash
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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