plugin/skills/pipeline-cutandrun

Automates CUT&RUN and CUT&Tag sequencing analysis from raw FASTQ reads to peak calls with Nextjob workflows.

26|5|Updated Mar 8, 2026

Search Tags:#epigenomics #peak calling #cutandrun #spike-in normalization #sequencing data #nexflow

Ready to run

npx skills add https://github.com/ammawla/encode-toolkit --skill plugin-skills-pipeline-cutandrun

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: plugin/skills/pipeline-cutandrun
Source: https://github.com/ammawla/encode-toolkit/tree/main/plugin/skills/pipeline-cutandrun
Command: npx skills add https://github.com/ammawla/encode-toolkit --skill plugin-skills-pipeline-cutandrun

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires bowtie2, samtools, bedtools, picard, seacr, macs2, deeptools, fastqc, multiqc, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill enables efficient processing of CUT&RUN and CUT&Tag sequencing data to identify precise protein-DNA interactions, significantly reducing manual analysis time and improving result accuracy.

Core Features & Use Cases

End-to-End Workflow: From FASTQ input through alignment, spike-in normalization, peak calling, and QC.
Library Preparation Optimization: Recommends parameters based on target type and experimental setup.
Use Case: Researchers analyzing transcription factor binding sites can process their CUT&RUN data to obtain high-confidence peak sets with spike-in calibration, fitting into broader epigenomic studies.

Quick Start

Run the pipeline with your paired-end FASTQ files specified, selecting appropriate parameters for your genome and spike-in reference to generate peak calls and QC metrics.

Frequently Asked Questions about plugin/skills/pipeline-cutandrun

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I analyze CUT&RUN sequencing data from FASTQ files to peak calling?▼

You can analyze CUT&RUN sequencing data by automating the workflow from raw FASTQ reads through alignment, spike-in normalization, and peak identification using Nextflow pipelines. This process generates high-confidence peak sets and quality control metrics.

What is spike-in normalization and when do I need it for CUT&Tag data?▼

Spike-in normalization for CUT&Tag data is a calibration technique used to accurately quantify DNA-protein interactions across samples. You need it when comparing epigenomic profiles between different experimental conditions to ensure precise mapping and reduce manual analysis time.

Can I use SEACR and MACS2 together for CUT&RUN peak calling?▼

Yes, you can use SEACR and MACS2 together for CUT&RUN peak calling. The pipeline integrates multiple peak callers alongside alignment tools like bowtie2 and samtools to process sequencing data and identify precise protein-DNA interactions.

What's the best way to perform end-to-end epigenomics quality control for CUT&Tag?▼

The best way to perform end-to-end epigenomics quality control for CUT&Tag is by running automated QC scripts that utilize fastqc and multiqc. This generates comprehensive QC metrics alongside peak calls and spike-in calibration results.

Do I need Nextflow to run the CUT&RUN analysis pipeline?▼

Yes, you need Nextflow to run this CUT&RUN analysis pipeline. The workflow is built on Nextflow to orchestrate the end-to-end process, coordinating dependencies like bowtie2, picard, and bedtools for robust epigenomic data processing.

Why does my CUT&RUN alignment fail during sequencing data processing?▼

CUT&RUN alignment may fail during sequencing data processing if library preparation parameters are incorrect or if references are mismatched. The pipeline recommends parameters based on target type and experimental setup to optimize alignment and prevent failures.