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

--reads

--bowtie2_index

--spikein_index

--chrom_sizes

--blacklist

--outdir

./results

--seacr_mode

stringent

stringent

relaxed

--seacr_norm

norm

norm

non

--control

null

--peak_caller

seacr

seacr

macs2

both

--skip_spikein

false

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, captures MNase accessibility background
• IgG is preferred but not always available
• SEACR can work without control (uses top 1% of signal as threshold)

SEACR Stringent vs Relaxed Mode

• Stringent: Returns only the most enriched peaks (fewer, higher confidence)
• Relaxed: Returns a broader set including weaker peaks
• For initial analysis, use stringent mode
• For comprehensive catalogs, use relaxed mode with downstream filtering

CUT&RUN Suspect List (Nordin 2023)

In addition to the ENCODE blacklist, filter CUT&RUN peaks against the suspect list (Nordin et al. 2023), which identifies regions with artifactual signal specific to CUT&RUN/CUT&Tag protocols:

# Download suspect list
wget https://github.com/Boyle-Lab/Blacklist/raw/master/lists/CUTandRUN.suspectlist.hg38.bed.gz

# Filter peaks
bedtools intersect \
    -a peaks.bed \
    -b hg38-blacklist.v2.bed CUTandRUN.suspectlist.hg38.bed \
    -v \
    > peaks_filtered.bed

CUT&RUN vs CUT&Tag

Both protocols are supported by this pipeline. Differences:

• CUT&RUN: Uses pA-MNase, E. coli spike-in from MNase production
• CUT&Tag: Uses pA-Tn5, E. coli spike-in from Tn5 production
• CUT&Tag has higher background from Tn5 insertion preference
• CUT&Tag may work better for histone marks; CUT&RUN for TFs

Provenance Integration

After pipeline completion, log all outputs:

encode_log_derived_file(
    file_path="/results/peaks/sample1.seacr.stringent.bed",
    source_accessions=["ENCSR...", "ENCFF..."],
    description="CUT&RUN peaks from ENCODE CUT&RUN pipeline",
    file_type="CUT&RUN_peaks",
    tool_used="Bowtie2 2.5.3 + SEACR 1.3",
    parameters="stringent mode, spike-in normalized, blacklist + suspect list filtered"
)

Reference Files

Detailed step-by-step documentation is provided in the

references/

1. 01-qc-trimming.md

   -- Read QC and adapter trimming for CUT&RUN

2. 02-bowtie2-alignment.md

   -- Bowtie2 alignment to genome and spike-in

3. 03-filtering-spikein.md

   -- Filtering, dedup, and spike-in normalization

4. 04-seacr-peaks.md

   -- SEACR peak calling and MACS2 alternative

5. 05-qc-metrics.md

   -- Fragment sizes, FRiP, spike-in QC

Walkthrough: Processing ENCODE CUT&RUN from FASTQ to Peaks

Goal: Process CUT&RUN/CUT&Tag FASTQ files through the ENCODE-compatible pipeline to generate peak calls with spike-in normalization. Context: CUT&RUN uses targeted MNase digestion (lower background than ChIP-seq) but requires different peak calling (SEACR instead of MACS2) and spike-in normalization for quantitative comparisons.

Step 1: Find CUT&RUN experiment

encode_search_experiments(assay_title="CUT&RUN", organism="Homo sapiens")

Expected output:

{
  "total": 35,
  "results": [
    {"accession": "ENCSR900CUR", "assay_title": "CUT&RUN", "target": "H3K27me3", "biosample_summary": "K562", "status": "released"}
  ]
}

Step 2: List FASTQ files

encode_list_files(accession="ENCSR900CUR", file_format="fastq")

Expected output:

{
  "files": [
    {"accession": "ENCFF900CR1", "output_type": "reads", "paired_end": "1", "file_size_mb": 800},
    {"accession": "ENCFF901CR2", "output_type": "reads", "paired_end": "2", "file_size_mb": 850}
  ]
}

Interpretation: CUT&RUN yields smaller files than ChIP-seq (~800MB vs ~2.5GB) due to lower background.

Step 3: Run the CUT&RUN pipeline

nextflow run pipeline-cutandrun/main.nf \
  --fastq_r1 ENCFF900CR1.fastq.gz \
  --fastq_r2 ENCFF901CR2.fastq.gz \
  --genome GRCh38 \
  --spike_in_genome dm6 \
  --target H3K27me3 \
  --peak_caller seacr \
  -profile docker

Key pipeline steps:

1. Adapter trimming (Trim Galore)
2. Bowtie2 alignment (very-sensitive-local)
3. Spike-in alignment (E. coli or Drosophila)
4. Spike-in normalization (scale factor)
5. SEACR peak calling (stringent mode)
6. Signal track generation with spike-in scaling

Step 4: Validate output quality

Key difference from ChIP-seq: CUT&RUN has inherently lower background, so peak callers like MACS2 overfit. Use SEACR (Meers et al. 2019) instead.

Step 5: Compare with ChIP-seq for the same target

encode_search_experiments(assay_title="Histone ChIP-seq", biosample_term_name="K562", target="H3K27me3", organism="Homo sapiens")

Interpretation: CUT&RUN typically identifies fewer but higher-confidence peaks than ChIP-seq. Concordant peaks between both methods are the highest confidence.

Integration with downstream skills

• SEACR peaks feed into -> histone-aggregation for cross-experiment comparison
• Spike-in normalized signals feed into -> visualization-workflow
• Peak regions feed into -> regulatory-elements for chromatin state classification
• QC uses different thresholds than ChIP-seq -> quality-assessment (see suspect list)
• Pipeline provenance logged by -> data-provenance

Code Examples

1. Survey CUT&RUN/CUT&Tag availability

encode_get_facets(assay_title="CUT&RUN", facet_field="target.label", organism="Homo sapiens")

Expected output:

{
  "facets": {
    "target.label": {"H3K27me3": 15, "H3K4me3": 12, "H3K27ac": 8, "CTCF": 5}
  }
}

2. Find matching ChIP-seq for comparison

encode_search_experiments(assay_title="Histone ChIP-seq", biosample_term_name="K562", target="H3K27me3", organism="Homo sapiens")

Expected output:

{
  "total": 5,
  "results": [
    {"accession": "ENCSR000CHI", "assay_title": "Histone ChIP-seq", "target": "H3K27me3", "biosample_summary": "K562"}
  ]
}

3. Track CUT&RUN experiments

encode_track_experiment(accession="ENCSR900CUR", notes="K562 H3K27me3 CUT&RUN - SEACR peaks for comparison with ChIP-seq")

Expected output:

{
  "status": "tracked",
  "accession": "ENCSR900CUR",
  "notes": "K562 H3K27me3 CUT&RUN - SEACR peaks for comparison with ChIP-seq"
}

Integration

Related Skills

• pipeline-guide

  -- Parent skill with compute resource assessment and cloud setup

• histone-aggregation

  -- Aggregate histone mark data across samples

• quality-assessment

  -- Evaluate pipeline output quality metrics

• data-provenance

  -- Track all pipeline inputs, outputs, and parameters

• download-encode

  -- Download ENCODE CUT&RUN FASTQ files for pipeline input

• publication-trust

  -- Verify literature claims backing analytical decisions

Presenting Results

When reporting CUT&RUN pipeline results:

• SEACR peak counts: Report peak counts for both stringent and relaxed modes. If MACS2 was also run, include those counts for comparison
• Spike-in normalization factor: Report the computed scale factor per sample and the spike-in read fraction (ideal 1-10% of total reads). Explain that higher spike-in counts indicate less target enrichment
• FRiP: Report the fraction of reads in peaks (>10% pass, 5-10% warning, <5% fail). Note that CUT&RUN FRiP thresholds differ from ChIP-seq
• Signal track paths: Provide paths to spike-in normalized bigWig files for genome browser visualization
• Fragment size distribution: Confirm the expected nucleosomal ladder pattern and note the dominant fragment class (sub-nucleosomal for TFs, mononucleosomal for histone marks)
• Key QC metrics: Present mapping rate (>80%), duplication rate (<20%), and spike-in calibration status in a summary table
• Suspect list filtering: Note whether peaks were filtered against both the ENCODE blacklist and the CUT&RUN suspect list (Nordin 2023)
• Next steps: Suggest

  peak-annotation

  for gene association of peaks, or

  visualization-workflow

  for genome browser session generation

For the request: "$ARGUMENTS"