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ngs-chip-cutrun-peaks-qc — Codex skill: what it does, install

Plans and runs QC for ChIP-seq, CUT&RUN, and CUT&Tag, covering controls, peak calls, bigWigs, replicates, and differential binding.

Install into Codex · Skills CLI (Vercel) All methods and platforms ↓
npx skills add openai/plugins --skill ngs-chip-cutrun-peaks-qc -a codex
Open on GitHub

What ngs-chip-cutrun-peaks-qc does

This skill works from FASTQ or BAM inputs plus assay, target class, paired-end status, controls, genome build, blacklist, spike-in, replicate, and sample metadata. It helps route the analysis, select narrow or broad peaks, and review alignment, duplication, fragment size, FRiP, blacklist overlap, and replicate concordance.

For ChIP-seq, it can route to nf-core/chipseq; CUT&RUN and CUT&Tag can use nf-core/cutandrun. The dedicated runner produces QC summaries, an HTML dashboard, native SVG plots, browser-track handoff files, bigWigs, consensus peaks, and motif outputs when requested. Direct MACS2 use is limited to prepared BAM files with a known control and duplicate policy.

Resource manifests and readiness files are produced as well. The local-light resource check is advisory by default; use --genome-build, --bundle-root <bundle>=<path>, and --require-resource-plan when absent reference bundles must stop readiness.

What it helps with

  • run QC across ChIP-seq, CUT&RUN, or CUT&Tag
  • select narrow or broad peaks from target biology
  • preserve control and spike-in pairing
  • generate bigWigs and consensus peaks
  • design a differential binding analysis

Example prompts for the agent

  • Run ChIP-seq QC for a transcription-factor target and assess replicate concordance.
  • Process chip_or_cutrun_samples.csv and produce bigWigs plus consensus peaks.
  • Plan differential binding for CUT&Tag with its control and spike-in metadata.

How to install ngs-chip-cutrun-peaks-qc

1Skills CLI (Vercel)

npx skills add openai/plugins --skill ngs-chip-cutrun-peaks-qc -a codex

2Manually: put the files in place

.agents/skills/ngs-chip-cutrun-peaks-qc/SKILL.md

For all projects use ~/.agents/skills/

Step by step, with commands for each platform: Codex skills: how to install, where they live, how to call them

Questions about ngs-chip-cutrun-peaks-qc

How do I install ngs-chip-cutrun-peaks-qc in Codex?

Run npx skills add openai/plugins --skill ngs-chip-cutrun-peaks-qc, then run npx skills add openai/plugins --skill ngs-chip-cutrun-peaks-qc -a codex. The installed skill is available at .agents/skills/ngs-chip-cutrun-peaks-qc/SKILL.md.

What does ngs-chip-cutrun-peaks-qc do?

It plans or runs QC for ChIP-seq, CUT&RUN, and CUT&Tag, handles controls and spike-in metadata, calls peaks, and prepares bigWigs, consensus peaks, and differential binding materials.

How should I choose narrow versus broad peak mode?

Choose the mode from the target biology. Transcription factors usually call for sharper peaks, while many histone marks produce broad or domain-like signal.

Source text

SKILL.md · MIT
---
name: ngs-chip-cutrun-peaks-qc
description: Run or plan ChIP-seq, CUT&RUN, or CUT&Tag QC, control handling, spike-in, peak calling, broad-vs-narrow target selection, replicate, bigWig, and differential binding workflows.
---

# ChIP/CUT&RUN Peaks QC

Use this skill for antibody-targeted enrichment workflows: ChIP-seq, CUT&RUN, or CUT&Tag. Use `ngs-atacseq-peaks-qc` for ATAC-seq.

## Essential Inputs

Confirm:

- assay: ChIP-seq, CUT&RUN, or CUT&Tag
- target class: transcription factor, histone mark, chromatin regulator, or custom target
- FASTQ/BAM inputs and paired-end status
- input DNA, IgG, no-antibody, or spike-in controls
- organism, genome build, blacklist, and spike-in genome if used
- biological replicates, conditions, batches, and sample metadata
- desired endpoint: QC, peaks, bigWigs, consensus peaks, or differential binding

## Route

Use `nf-core/chipseq` for ChIP-seq and `nf-core/cutandrun` for CUT&RUN/CUT&Tag when they fit the assay. Use direct MACS2 only for prepared BAMs with known control and duplicate policy.

Preflight command:

```bash
python plugins/ngs-analysis/scripts/ngs_preflight.py --pipeline chip_cutrun_peaks_qc --emit-install-plan
```

For compact FASTQ intake/QC, use the shared epigenomics execution package:

```bash
python plugins/ngs-analysis/scripts/run_fastq_assay_package.py \
  --lane epigenomics_peaks \
  --sample-sheet chip_or_cutrun_samples.csv \
  --execute
```

It records FASTQ-level QC and peak-calling readiness.

For local-light alignment, control-aware MACS2 peak calling, FRiP, bigWig tracks, consensus peaks, and motif-handoff artifacts, use the dedicated ChIP/CUT&RUN runner:

```bash
python plugins/ngs-analysis/scripts/run_chip_cutrun_peaks_qc.py \
  --sample-sheet chip_or_cutrun_samples.csv \
  --assay chipseq \
  --target-class tf \
  --peak-mode narrow \
  --bowtie2-index /refs/GRCh38/bowtie2/genome \
  --genome-size hs \
  --blacklist-bed /refs/GRCh38/blacklists/encode_blacklist.bed \
  --execute
```

This runner emits `qc/chip_cutrun_qc_summary.{tsv,json}`, `qc/chip_cutrun_qc_dashboard.html`, native SVG FRiP/peak and insert-size plots, browser-track handoff files under `tracks/`, and `motifs/motif_summary.tsv`. Add `--run-motifs --motif-genome <genome>` when HOMER motif enrichment should be executed instead of only planned.

It also emits `resources/resource_plan.json`, `resource_manifest.tsv`, `resource_env.sh`, and `resource_readiness.md`. The resource check is advisory by default for local-light runs; add `--genome-build`, `--bundle-root <bundle>=<path>`, and `--require-resource-plan` when missing registered reference bundles should block readiness.

For nf-core execution, use `plugins/ngs-analysis/scripts/run_nfcore_pipeline.py --pipeline chipseq` or `--pipeline cutandrun`.

## Decision Points

- Choose narrow versus broad peak mode from target biology, not from convenience.
- Preserve control pairing and spike-in metadata through sample sheets.
- For histone marks, expect broad or domain-like signal for many marks; for TFs, expect sharper peaks and stronger replicate checks.
- Review alignment rate, duplicate rate, fragment size, FRiP/peak signal, blacklist overlap, and replicate concordance.
- Keep consensus peak generation and differential binding design separate from raw peak calling.

## Outputs

Produce:

- assay/target/control manifest
- command/profile and sample sheet
- QC summary with replicate/control status
- peaks, bigWigs, browser-track manifests, browser-track preview HTML, native QC dashboard/SVG plots, consensus peaks, and count matrix when requested
- motif summary files when a motif backend is requested
- differential binding design and caveats for missing controls, weak enrichment, or poor replicate concordance

© OpenAI · MIT · openai/plugins

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