/pipeline-cutandrun

name: pipeline-cutandrun
description: "Execute CUT&RUN processing pipeline from FASTQ to peaks and signal tracks. Child of pipeline-guide. Provides Nextflow execution with Docker and cloud deployment. Use when processing CUT&RUN or CUT&Tag data, an alternative to ChIP-seq with lower background. Trigger on: CUT&RUN pipeline, CUT&Tag, SEACR, Henikoff, targeted chromatin, pA-MNase, process CUT&RUN."

ENCODE CUT&RUN Pipeline: FASTQ to Peaks and Signal Tracks

When to Use

• User wants to run a CUT&RUN or CUT&Tag processing pipeline from FASTQ to peaks
• User asks about "CUT&RUN pipeline", "CUT&Tag", "SEACR", "spike-in normalization", or "targeted chromatin"
• User needs to process CUT&RUN/CUT&Tag data with spike-in calibration and SEACR peak calling
• Example queries: "process my CUT&RUN FASTQs", "run SEACR on CUT&Tag data", "normalize CUT&RUN with spike-in controls"

Execute the CUT&RUN/CUT&Tag processing pipeline for targeted chromatin profiling, producing peak calls with SEACR and spike-in normalized signal tracks.

Pipeline Overview

FASTQ
  |-> FastQC (raw reads)
  +-> Trim Galore -> Bowtie2 (genome) -> {sample}.sorted.bam
        |
        |-> unmapped read pairs -> Bowtie2 (spike-in) -> counts -> scale_factors.txt
        |                                                                |
        +-> filter (MAPQ 10, proper pairs) -> Picard MarkDuplicates      |
            (removed) -> blacklist filter -> {sample}.filtered.bam       |
                 |-> fragment BED -> fragment bedGraph -> SEACR peaks    |
                 |-> MACS2 peaks (with --peak_caller macs2|both)         |
                 |-> FRiP vs every peak set -> {sample}.frip_mqc.tsv     |
                 +-> bamCoverage -> {sample}.normalized.bw <-- factor ---+

Not run by this workflow

• **Peak-level filtering**: `--blacklist` is applied to the BAM only. Peak

files are never filtered afterwards, and there is no separate suspect-list input. Pass a pre-merged blacklist + suspect-list BED as `--blacklist`, or filter the peak files yourself.

• **Spike-in scaling of the SEACR input**: only the bigWig is scaled

(`bamCoverage --scaleFactor`). The fragment bedGraph given to SEACR is unscaled.

ENCODE Repository

• ENCODE does not publish an official CUT&RUN pipeline. This workflow follows the published

CUT&RUN/CUT&Tag processing protocol (Bowtie2, fragment bedGraphs, SEACR) and applies ENCODE conventions for filtering, blacklisting, and QC.

• **Container**: built from `scripts/Dockerfile` in this skill (`docker build -t encode-toolkit/pipeline-cutandrun:1.0.0 scripts/`); override with `--container`
• **This skill**: Nextflow DSL2 reimplementation for portability

Core Tools and Versions

Versions are those installed by `scripts/Dockerfile`, which is what the workflow runs.

| Tool | Version | Purpose | Citation | |------|---------|---------|----------| | Bowtie2 | 2.5.4 | Alignment (genome + spike-in) | Langmead & Salzberg 2012 | | SEACR | 1.3 | Peak calling (CUT&RUN-specific) | Meers et al. 2019 | | MACS2 | 2.2.9.1 | Alternative peak caller | Zhang et al. 2008 | | Picard | 3.1.1 | Duplicate marking and removal | Broad Institute | | samtools | 1.19 | BAM operations | Li et al. 2009 | | bedtools | 2.31.0 | Genomic arithmetic | Quinlan & Hall 2010 | | deepTools | 3.5.5 | Signal track generation | Ramirez et al. 2016 | | Trim Galore | 0.6.10 | Adapter trimming | Krueger (Babraham) | | FastQC | 0.12.1 | Read quality | Andrews (Babraham) | | MultiQC | 1.21 | Aggregated QC | Ewels et al. 2016 |

The conda alternative (`cutandrun-env.yml`) pins the same version of every tool in the table above, but installs no SEACR (only `r-base`): SEACR is not a conda package, so on that route `SEACR_1.3.sh` and `SEACR_1.3.R` must be fetched separately from the SEACR repository.

Key Literature

1. **Skene & Henikoff 2017** - "An efficient targeted nuclease strategy for high-resolution mapping of DNA binding sites" (eLife, ~1,500 citations) DOI: 10.7554/eLife.21856

2. **Meers et al. 2019** - "Peak calling by Sparse Enrichment Analysis for CUT&RUN chromatin profiling" (Epigenetics & Chromatin, ~800 citations) DOI: 10.1186/s13072-019-0287-4

3. **Kaya-Okur et al. 2019** - "CUT&Tag for efficient epigenomic profiling of small samples and single cells" (Nature Communications, ~1,200 citations) DOI: 10.1038/s41467-019-09982-5

4. **Nordin et al. 2023** - "The CUT&RUN suspect list of problematic regions" (Genome Biology) DOI: 10.1186/s13059-023-02960-3

5. **Amemiya et al. 2019** - "The ENCODE Blacklist" (Scientific Reports, ~1,372 citations) DOI: 10.1038/s41598-019-45839-z

Execution

Quick Start (Local)

nextflow run scripts/main.nf \
    -profile local \
    --reads '/data/fastq/*_R{1,2}.fastq.gz' \
    --bowtie2_index '/ref/bowtie2_index/genome' \
    --spikein_index '/ref/bowtie2_ecoli/ecoli' \
    --chrom_sizes '/ref/hg38.chrom.sizes' \
    --blacklist '/ref/hg38-blacklist.v2.bed' \
    --outdir results/ \
    -resume

SLURM HPC

nextflow run scripts/main.nf \
    -profile slurm \
    --container /path/to/pipeline-cutandrun.sif \
    --reads '/data/fastq/*_R{1,2}.fastq.gz' \
    --bowtie2_index '/ref/bowtie2_index/genome' \
    --spikein_index '/ref/bowtie2_ecoli/ecoli' \
    --chrom_sizes '/ref/hg38.chrom.sizes' \
    --blacklist '/ref/hg38-blacklist.v2.bed' \
    --outdir results/ \
    -resume

Cloud (GCP / AWS)

# Google Cloud Batch
nextflow run scripts/main.nf -profile gcp \
    --container us-docker.pkg.dev/<project>/<repo>/pipeline-cutandrun:1.0.0 \
    --gcp_project <project> \
    --gcp_workdir gs://<bucket>/work \
    --reads 'gs://<bucket>/fastq/*_R{1,2}.fastq.gz' \
    --bowtie2_index gs://<bucket>/ref/bowtie2_index/genome \
    --spikein_index gs://<bucket>/ref/bowtie2_ecoli/ecoli \
    --chrom_sizes gs://<bucket>/ref/hg38.chrom.sizes \
    --blacklist gs://<bucket>/ref/hg38-blacklist.v2.bed \
    --outdir g