git:20260920.3246884 to git:20260920.ce7840d

---

name: pipeline-chipseq

description: "Execute ENCODE ChIP-seq processing pipeline from FASTQ to peaks and signal tracks. Child of pipeline-guide. Provides stage-by-stage Nextflow execution with Docker containers and cloud deployment. Use when users need to process ChIP-seq data following ENCODE standards, run peak calling with MACS2, perform IDR analysis, or generate signal tracks. Trigger on: ChIP-seq pipeline, run ChIP-seq, process ChIP-seq, MACS2 peak calling, IDR analysis, ChIP-seq FASTQ processing."

---

# ENCODE ChIP-seq Pipeline

## When to Use

- User wants to run a ChIP-seq processing pipeline from FASTQ to peaks and signal tracks

- User asks about "ChIP-seq pipeline", "MACS2", "peak calling", "BWA alignment for ChIP", or "IDR"

- User needs to process histone or TF ChIP-seq data following ENCODE standards

- Example queries: "process my ChIP-seq FASTQs", "run the ENCODE ChIP-seq pipeline", "call peaks from ChIP-seq with MACS2 and IDR"

Execute the ENCODE ChIP-seq processing pipeline from raw FASTQ files through peak calling,

implementation following ENCODE uniform analysis standards.

## Overview

## Key Literature

| Reference | Journal | Year | DOI | Relevance |

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

| Landt et al. "ChIP-seq guidelines and practices" | Genome Research | 2012 | 10.1101/gr.136184.111 | ENCODE ChIP-seq standards (~4,000 citations) |

| ENCODE Project Consortium "Expanded encyclopaedias" | Nature | 2020 | 10.1038/s41586-020-2493-4 | ENCODE Phase 3 standards |

| Zhang et al. "Model-based Analysis of ChIP-Seq (MACS)" | Genome Biology | 2008 | 10.1186/gb-2008-9-9-r137 | Peak caller (~7,000 citations) |

| Li et al. "Measuring reproducibility (IDR)" | Annals of Applied Statistics | 2011 | 10.1214/11-AOAS466 | Replicate consistency (~1,500 citations) |

| Amemiya et al. "ENCODE Blacklist" | Scientific Reports | 2019 | 10.1038/s41598-019-45839-z | Artifact regions (~1,372 citations) |

## Pipeline Stages

```

│ │

│ ┌───────────────────────────────────────────────────────────┘

│ v

v

```

### Stage Summary

| Stage | Tool | Input | Output | Reference |

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

## Input Requirements

### Narrow vs Broad Peak Mode Decision

## QC Thresholds

These thresholds follow ENCODE standards established by Landt et al. 2012 and the

### Interpreting QC: Traffic Light System

| Color | Meaning | Action |

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

| Green | All metrics pass | Proceed to analysis |

| Yellow | 1-2 metrics marginal | Review library prep, may be usable |

| Red | Multiple failures | Do not use; re-do experiment |

**Important**: No single metric is sufficient. Interpret QC collectively. A sample with

borderline NRF but excellent FRiP may still be usable.

## Execution

### Quick Start (Local Docker)

```bash

nextflow run scripts/main.nf \

-profile local \

--genome GRCh38 \

--peak_type narrow \

--outdir results/

```

### SLURM HPC

```bash

nextflow run scripts/main.nf \

-profile slurm \

--genome GRCh38 \

--peak_type narrow \

--outdir results/

```

```bash

```

## Cloud Cost Estimates

| Platform | Instance | Cost/Sample | Time/Sample | Notes |

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

| AWS | m5.2xlarge | ~$2-5 | 2-4 hours | Spot instances recommended |

| Local | 8 cores, 32GB | $0 | 3-6 hours | Docker required |

## Output Directory Structure

```

results/

peaks/

qc/

```

## Common Pitfalls

Using narrow peak calling for broad marks (H3K27me3, H3K36me3) fragments the signal

## Pipeline Scripts

## ENCODE Data Integration

After running this pipeline on your own data, compare results with ENCODE:

```python

# Find matching ENCODE experiments

encode_search_experiments(

assay_title="Histone ChIP-seq",

target="H3K27ac",

organ="pancreas",

biosample_type="tissue"

)

# Download ENCODE peaks for comparison

encode_batch_download(

download_dir="/data/encode_reference/",

output_type="IDR thresholded peaks",

target="H3K27ac",

organ="pancreas",

assembly="GRCh38"

)

```

## Pitfalls & Edge Cases

## Walkthrough: Processing ENCODE H3K27ac ChIP-seq from FASTQ to Peaks

### Step 1: Find the experiment and download FASTQs

```

encode_get_experiment(accession="ENCSR000AKA")

```

Expected output:

```json

{

"accession": "ENCSR000AKA",

"assay_title": "Histone ChIP-seq",

"target": "H3K27ac",

"biosample_summary": "GM12878",

"replicates": 2,

"status": "released"

}

```

### Step 2: List FASTQ files

```

```

Expected output:

```json

{

"files": [

{"accession": "ENCFF001FQ1", "output_type": "reads", "paired_end": "1", "biological_replicates": [1], "file_size_mb": 2400},

{"accession": "ENCFF002FQ2", "output_type": "reads", "paired_end": "2", "biological_replicates": [1], "file_size_mb": 2500},

{"accession": "ENCFF003FQ3", "output_type": "reads", "paired_end": "1", "biological_replicates": [2], "file_size_mb": 2200},

{"accession": "ENCFF004FQ4", "output_type": "reads", "paired_end": "2", "biological_replicates": [2], "file_size_mb": 2300}

]

}

```

### Step 3: Download FASTQs

```

```

```bash

--genome GRCh38 \

```

```

encode_log_derived_file(

source_accessions=["ENCFF001FQ1", "ENCFF002FQ2", "ENCFF003FQ3", "ENCFF004FQ4"],

)

```

### Integration with downstream skills

- IDR peaks feed into -> **peak-annotation** for gene assignment

- Signal tracks (bigWig) feed into -> **visualization-workflow** for genome browser display

- Peak coordinates feed into -> **histone-aggregation** for cross-experiment union merge

- QC metrics evaluated by -> **quality-assessment** against ENCODE standards

- Pipeline provenance logged by -> **data-provenance**

## Code Examples

### 1. Find ChIP-seq data to process

```

encode_search_experiments(

assay_title="Histone ChIP-seq",

organ="liver",

target="H3K4me3"

)

```

Expected output:

```json

{

"total": 15,

"experiments": [

{

"accession": "ENCSR456LIV",

"target": "H3K4me3-human",

"biosample_summary": "liver tissue male adult (54 years)",

"status": "released"

}

]

}

```

### 2. Download FASTQ files for pipeline input

```

encode_download_files(

download_dir="/data/chipseq/liver_h3k4me3"

)

```

Expected output:

```json

{

"files": [

{"accession": "ENCFF001REP1", "md5_verified": true, "paired_end": "1"},

{"accession": "ENCFF002REP1", "md5_verified": true, "paired_end": "2"}

]

}

```

## Integration

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

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

| Signal tracks (bigWig) | **visualization-workflow** | Genome browser visualization |

| Peak coordinates (BED) | **motif-analysis** | De novo motif discovery in peak regions |

| Filtered peaks | **regulatory-elements** | Classify as enhancers, promoters, insulators |

| QC metrics | **quality-assessment** | Validate against ENCODE ChIP-seq standards |

| Peak files | **variant-annotation** | Identify variants in ChIP-seq peaks |

## Related Skills

- **pipeline-guide** (parent): General pipeline selection and resource assessment

- **histone-aggregation**: Merge peaks across samples/replicates after peak calling

- **quality-assessment**: Deep-dive QC analysis beyond basic metrics

- **regulatory-elements**: Annotate peaks with regulatory element classifications

- **peak-annotation**: Annotate peaks with gene associations

- **compare-biosamples**: Compare ChIP-seq profiles across cell types

- **publication-trust**: Verify literature claims backing analytical decisions

## Presenting Results

When reporting ChIP-seq pipeline results:

## For the request: "$ARGUMENTS"