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ngs-epigenomics-peaks — Codex skill: ATAC-seq, ChIP-seq, CUT&RUN

Routes ATAC-seq, ChIP-seq, CUT&RUN and CUT&Tag work to the right pipeline: QC, alignment, signal tracks, peak calling, consensus and differential peaks.

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

What ngs-epigenomics-peaks does

This skill acts as a dispatcher. It first pins down the assay, the input files, organism and genome build, control samples, replicates and the peak type you want. Once the assay is clear, the request goes to a deep skill for that protocol: ATAC-seq to ngs-atacseq-peaks-qc with nf-core/atacseq, ChIP-seq to ngs-chip-cutrun-peaks-qc with nf-core/chipseq, and CUT&RUN or CUT&Tag to the same skill with nf-core/cutandrun. Direct MACS2 stays for narrow peak-calling jobs on BAMs you already have.

An environment check runs first: python plugins/ngs-analysis/scripts/ngs_preflight.py --pipeline epigenomics_peaks --emit-install-plan. For FASTQ intake and QC there is a shared package: python plugins/ngs-analysis/scripts/run_fastq_assay_package.py --lane epigenomics_peaks --sample-sheet assay_samples.csv --execute. It checks sample-sheet paths and read structure, runs seqkit stats and FastQC/MultiQC when present, and writes peak_calling_readiness.json. Alignment, signal tracks, TSS/FRiP, consensus and differential peaks still go through the assay-specific workflow.

The ATAC and ChIP/CUT&RUN runners also drop review files next to the TSV/JSON summaries: qc/*_dashboard.html, SVG plots for FRiP and peaks, insert-size SVGs, a browser-track preview HTML, UCSC track lines and IGV session files. Replicate and control metadata has to be carried through the sample sheet before you run the real analysis, or the pipeline will not line up the comparisons.

What it helps with

  • pick the right pipeline for a given epigenomics assay
  • validate sample-sheet paths and read structure before a run
  • produce a FASTQ QC report ahead of alignment
  • get consensus and differential peaks
  • open signal tracks in IGV or UCSC

Example prompts for the agent

  • Run the epigenomics preflight: python plugins/ngs-analysis/scripts/ngs_preflight.py --pipeline epigenomics_peaks --emit-install-plan
  • I have ATAC-seq FASTQ, human, hg38, two replicates — what do I need for peak calling?
  • Run FASTQ QC with python plugins/ngs-analysis/scripts/run_fastq_assay_package.py --lane epigenomics_peaks --sample-sheet assay_samples.csv --execute

How to install ngs-epigenomics-peaks

1Skills CLI (Vercel)

npx skills add openai/plugins --skill ngs-epigenomics-peaks -a codex

2Manually: put the files in place

.agents/skills/ngs-epigenomics-peaks/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-epigenomics-peaks

How do I install ngs-epigenomics-peaks in Claude?

Run npx skills add openai/plugins --skill ngs-epigenomics-peaks, then npx skills add openai/plugins --skill ngs-epigenomics-peaks -a codex. The skill then sits at .agents/skills/ngs-epigenomics-peaks/SKILL.md.

What does ngs-epigenomics-peaks do?

It dispatches ATAC-seq, ChIP-seq, CUT&RUN and CUT&Tag requests. It collects the analysis parameters and hands off to the skill for that protocol, covering QC, alignment, signal tracks, peak calling, consensus and differential analysis.

Which pipelines does it use by default?

nf-core/atacseq for ATAC-seq, nf-core/chipseq for ChIP-seq, and nf-core/cutandrun for CUT&RUN or CUT&Tag. Direct MACS2 is only for peak calling on prepared BAMs.

Source text

SKILL.md · MIT
---
name: ngs-epigenomics-peaks
description: Dispatch ATAC-seq, ChIP-seq, CUT&RUN, or CUT&Tag requests to assay-specific QC, alignment, signal-track, peak-calling, consensus, and differential peak workflows.
---

# Epigenomics Peaks

Use this skill as the epigenomics dispatcher for ATAC-seq, ChIP-seq, CUT&RUN, or CUT&Tag analysis. Hand off to the assay-specific deep skill once the assay type is known.

## Essential Inputs

Confirm:

- assay type
- FASTQ or BAM input
- organism and genome build
- blacklist file, if available
- control samples: input DNA, IgG, or spike-in
- biological replicates
- peak type: narrow, broad, accessibility, or protocol-specific
- desired outputs: QC report, peaks, consensus peaks, bigWigs, differential peaks

## Public Defaults

Choose the workflow by assay:

- ATAC-seq: `ngs-atacseq-peaks-qc` using `nf-core/atacseq` by default
- ChIP-seq: `ngs-chip-cutrun-peaks-qc` using `nf-core/chipseq` by default
- CUT&RUN or CUT&Tag: `ngs-chip-cutrun-peaks-qc` using `nf-core/cutandrun` by default

Use direct MACS2 only for focused peak-calling tasks from prepared BAMs.

## Preflight

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

## Local Execution Package

For FASTQ intake/QC over ATAC-seq, ChIP-seq, CUT&RUN, or CUT&Tag data, use the shared FASTQ assay package:

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

This validates sample-sheet paths and read structure, runs seqkit stats and FastQC/MultiQC when available, and writes `peak_calling_readiness.json`. Full alignment, signal tracks, TSS/FRiP, consensus peaks, and differential analyses still route through the assay-specific workflow.

Assay-specific ATAC and ChIP/CUT&RUN runners now also emit native review files alongside TSV/JSON summaries: `qc/*_dashboard.html`, FRiP/peak SVG plots, insert-size SVG plots, browser-track preview HTML, UCSC track lines, and IGV session files.

## Kickoff Pattern

ATAC-seq preflight run:

```bash
nextflow run nf-core/atacseq \
  -profile test,docker \
  --outdir results/atacseq_test
```

ChIP-seq preflight run:

```bash
nextflow run nf-core/chipseq \
  -profile test,docker \
  --outdir results/chipseq_test
```

CUT&RUN/CUT&Tag preflight run:

```bash
nextflow run nf-core/cutandrun \
  -profile test,docker \
  --outdir results/cutandrun_test
```

Carry replicate and control metadata through the sample sheet before running real analysis.

© OpenAI · MIT · openai/plugins

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