git:20260920.767027e to git:20260920.ce7840d

---

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

```

```

### 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

| Tool | Version | Purpose | Citation |

|------|---------|---------|----------|

| Bowtie2 | 2.5.3 | 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 |

| samtools | 1.19 | BAM operations | Li et al. 2009 |

| bedtools | 2.31.0 | Genomic arithmetic | Quinlan & Hall 2010 |

| FastQC | 0.12.1 | Read quality | Andrews (Babraham) |

| MultiQC | 1.21 | Aggregated QC | Ewels et al. 2016 |

## 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)

```bash

-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

```bash

-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)

```bash

```

## Resource Requirements

| Step | CPUs | RAM | Time (per sample) |

|------|------|-----|-------------------|

| Bowtie2 align (genome) | 8 | 8 GB | 30-60 min |

| Bowtie2 align (spike-in) | 4 | 4 GB | 10-20 min |

| Filter/dedup | 4 | 8 GB | 15-30 min |

| SEACR peaks | 2 | 4 GB | 10-20 min |

| Signal tracks | 4 | 8 GB | 15-30 min |

| **Total** | **8** | **8 GB** | **1.5-3 hours** |

## Pipeline Parameters

| Parameter | Default | Description |

|-----------|---------|-------------|

| `--reads` | required | Glob pattern to paired FASTQ files |

| `--chrom_sizes` | required | Chromosome sizes file |

| `--outdir` | `./results` | Output directory |

| `--seacr_mode` | `stringent` | SEACR mode: `stringent`, `relaxed`, or `both` |

| `--seacr_threshold` | `0.01` | Top fraction of signal kept by SEACR when no `--control` is given |

| `--control` | `null` | IgG control BAM, already filtered and deduplicated. Converted to a fragment bedGraph for SEACR and passed as `-c` to MACS2 |

| `--macs2_gsize` | `hs` | MACS2 effective genome size (`hs`, `mm`, or a number) |

| `--skip_spikein` | `false` | Skip spike-in calibration; signal tracks are then RPKM-normalized |

## Output Files

```

results/

alignment/

{sample}.filtered.bam.bai

peaks/

signal/

qc/

{sample}.fragment_sizes.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)

### Scale Factor Calculation

```

```

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

## SEACR vs MACS2

| Feature | SEACR | MACS2 |

|---------|-------|-------|

| Designed for | CUT&RUN/CUT&Tag | ChIP-seq |

| Background model | Sparse enrichment | Dynamic Poisson |

| Control required | Optional (IgG) | Recommended |

| Low background | Handles well | May overcall |

| Stringent mode | Very conservative | Via q-value |

| ENCODE recommendation | Primary for CUT&RUN | Alternative |

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,

### 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 Stringent vs Relaxed Mode

- **Stringent**: Returns only the most enriched peaks (fewer, higher confidence)

- **Relaxed**: Returns a broader set including weaker peaks

### CUT&RUN Suspect List (Nordin 2023)

```bash

# Download suspect list

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

bedtools intersect \

-v \

```

### 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:

```python

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",

)

```

## Reference Files

Detailed step-by-step documentation is provided in the `references/` directory:

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

## 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:

```json

{

"total": 35,

"results": [

{"accession": "ENCSR900CUR", "assay_title": "CUT&RUN", "target": "H3K27me3", "biosample_summary": "K562", "status": "released"}

]

}

```

### Step 2: List FASTQ files

```

```

Expected output:

```json

{

"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

```bash

--peak_caller seacr \

```

Key pipeline steps:

### 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

```

```

Expected output:

```json

{

"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:

```json

{

"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:

```json

{

"status": "tracked",

"accession": "ENCSR900CUR",

"notes": "K562 H3K27me3 CUT&RUN - SEACR peaks for comparison with ChIP-seq"

}

```

## Integration

| This skill produces... | Feed into... | Purpose |

|---|---|---|

| SEACR peaks | **histone-aggregation** | Cross-experiment comparison (note: different caller than ChIP-seq) |

| Spike-in normalized signal | **visualization-workflow** | Quantitatively comparable browser tracks |

| Peak regions | **regulatory-elements** | Chromatin state classification |

| CUT&RUN-specific QC | **quality-assessment** | Validate with CUT&RUN-appropriate thresholds |

| Peak coordinates | **motif-analysis** | TF motif discovery at CUT&RUN peaks |

| Pipeline parameters | **data-provenance** | Record SEACR/spike-in normalization details |

| Peak files | **variant-annotation** | Identify variants in CUT&RUN peaks |

| Comparison with ChIP-seq | **compare-biosamples** | Cross-assay concordance analysis |

## 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:

- **Key QC metrics**: Present mapping rate (>80%), duplication rate (<20%), and spike-in calibration status in a summary table

- **Next steps**: Suggest `peak-annotation` for gene association of peaks, or `visualization-workflow` for genome browser session generation

## For the request: "$ARGUMENTS"