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 -> Trim -> Bowtie2 align (genome) -> Filter/dedup -> SEACR peaks
                     |                          |              |
              Bowtie2 align (spike-in)   Spike-in normalize  Signal tracks
                     |
              Scale factor calculation

ENCODE Repository

• GitHub: ENCODE-DCC/cutandrun-pipeline
• Container: encodedcc/cutandrun-pipeline
• This skill: Nextflow DSL2 reimplementation for portability

Core Tools and Versions

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 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 main.nf \
    -profile slurm \
    --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)

nextflow run main.nf \
    -profile gcp \
    --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 'gs://bucket/results/' \
    -resume

Resource Requirements

Pipeline Parameters

Output Files

results/
  fastqc/                           # Raw read quality
  alignment/
    {sample}.filtered.bam           # Filtered, deduplicated BAM
    {sample}.filtered.bam.bai
  spikein/
    {sample}.spikein_counts.txt     # Spike-in read counts
    {sample}.scale_factor.txt       # Computed scale factor
  peaks/
    {sample}.seacr.stringent.bed    # SEACR stringent peaks
    {sample}.seacr.relaxed.bed      # SEACR relaxed peaks
    {sample}.macs2_peaks.narrowPeak # MACS2 peaks (if requested)
  signal/
    {sample}.normalized.bw          # Spike-in normalized signal
    {sample}.fragments.bed          # Fragment BED file
  qc/
    {sample}.flagstat.txt
    {sample}.fragment_sizes.txt
    {sample}.frip.txt
  multiqc/
    multiqc_report.html

QC Thresholds

Fragment Size Distribution

CUT&RUN produces a characteristic nucleosomal ladder:

• <120 bp: Sub-nucleosomal (TF binding)
• ~150 bp: Mononucleosomal (histone marks)
• ~300 bp: Dinucleosomal
• Absence of nucleosomal pattern suggests protocol issues

Spike-in Normalization

Spike-in normalization is CRITICAL for CUT&RUN quantitative comparison.

How It Works

1. E. coli DNA is carried over from pA-MNase/pA-Tn5 production
2. Each sample has a different amount of spike-in reads
3. Samples with more target cleavage have fewer spike-in reads (proportionally)
4. Scale factor = 1 / (spike-in reads / minimum spike-in reads across samples)

Scale Factor Calculation

Sample A: 200,000 spike-in reads -> scale = 1.0 (minimum)
Sample B: 400,000 spike-in reads -> scale = 0.5
Sample C: 100,000 spike-in reads -> scale = 2.0

Higher spike-in counts = less target enrichment = lower scale factor.

SEACR vs MACS2

SEACR is specifically designed for the sparse, low-background signal profile of CUT&RUN data. MACS2 may overcall peaks due to the low background.

Critical Pitfalls

Spike-in Calibration is CRITICAL

Without spike-in normalization, quantitative comparisons between samples are unreliable. The amount of pA-MNase (or pA-Tn5) varies between experiments, and spike-in reads provide the internal calibration standard.

IgG Control vs No-Antibody Control

• IgG control: Non-specific antibody, captures background binding
• No-antibody: No antibody, c