git:20260920.e127735 to git:20260920.ce7840d

---

name: pipeline-wgbs

description: "Execute ENCODE Whole Genome Bisulfite Sequencing (WGBS) pipeline from FASTQ to methylation calls. Child of pipeline-guide. Provides Nextflow execution with Docker and cloud deployment. Use when processing WGBS/bisulfite-seq data, calling methylation levels, generating bedMethyl files. Trigger on: WGBS pipeline, bisulfite sequencing, methylation calling, DNA methylation pipeline, bismark, bwa-meth, bedMethyl."

---

# ENCODE WGBS Pipeline: FASTQ to Methylation Calls

## When to Use

- User wants to run a WGBS/bisulfite sequencing pipeline from FASTQ to methylation calls

- User asks about "WGBS pipeline", "bisulfite sequencing", "methylation calling", "Bismark", or "bedMethyl"

- User needs to process whole-genome bisulfite sequencing data following ENCODE standards

- Example queries: "process my WGBS FASTQs", "call methylation levels from bisulfite-seq", "run Bismark on my WGBS data"

Execute the ENCODE DNA methylation pipeline for Whole Genome Bisulfite Sequencing data,

producing per-CpG methylation levels in bedMethyl format.

## Pipeline Overview

```

```

### ENCODE Repository

- **GitHub**: `ENCODE-DCC/dna-me-pipeline`

- **Container**: built from `scripts/Dockerfile` in this skill (`docker build -t encode-toolkit/pipeline-wgbs:1.0.0 scripts/`); override with `--container`

- **WDL**: Available for Cromwell execution

- **This skill**: Nextflow DSL2 reimplementation for portability

## Core Tools and Versions

| Tool | Version | Purpose | Citation |

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

| Trim Galore | 0.6.10 | Adapter + quality trimming (bisulfite-aware) | Krueger (Babraham) |

| MethylDackel | 0.6.1 | Methylation extraction from BAM | Ryan (GitHub) |

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

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

## Key Literature

1. **Krueger & Andrews 2011** - "Bismark: a flexible aligner and methylation caller for

Bisulfite-Seq applications" (Bioinformatics, ~4,000 citations)

DOI: 10.1093/bioinformatics/btr167

2. **Lister et al. 2009** - "Human DNA methylomes at base resolution show widespread

epigenomic differences" (Nature, ~5,000 citations)

DOI: 10.1038/nature08514

3. **Schultz et al. 2015** - "Human body epigenome maps reveal noncanonical DNA

methylation variation" (Nature, ~1,500 citations)

DOI: 10.1038/nature14248

4. **Pedersen et al. 2014** - "Fast and accurate alignment of long bisulfite-seq reads"

arXiv:1401.1129 (bwa-meth)

5. **Amemiya et al. 2019** - "The ENCODE Blacklist" (Scientific Reports, ~1,372 citations)

DOI: 10.1038/s41598-019-45839-z

## Execution

```bash

--reads '/data/fastq/*_R{1,2}.fastq.gz' \

--outdir results/ \

-resume

```

### SLURM HPC

```bash

--reads '/data/fastq/*_R{1,2}.fastq.gz' \

--outdir results/ \

-resume

```

```bash

```

## Resource Requirements

| Step | CPUs | RAM | Time (30x human) |

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

| Trim Galore | 4 | 4 GB | 1-2 hours |

| Bismark align | 8 | 48 GB | 8-16 hours |

| Deduplication | 2 | 16 GB | 1-2 hours |

| **Total** | **8** | **48 GB** | **12-24 hours** |

## Pipeline Parameters

| Parameter | Default | Description |

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

| `--merge_context` | `true` | Merge the two strands of each CpG/CHG into one record (`MethylDackel --mergeContext`) |

Notes on what the workflow does and does not do:

- **Aligner**: Bismark with Bowtie2. bwa-meth is described in `references/02-bismark-alignment.md`

as a manual alternative; it is not an option of this workflow and is not in the image.

- **Overlapping mates**: MethylDackel never counts both mates of an overlapping read pair, so

there is no switch for it. `--merge_context` is a separate choice about per-CpG versus

per-cytosine output.

- **Bisulfite conversion rate**: not computed by the workflow. Estimate it from the CHH

methylation percentage in the Bismark alignment report, or align a lambda/pUC19 spike-in

separately when the sample carries real non-CpG methylation (ESCs, neurons).

## Output Files

```

results/

bismark/

coverage/

{sample}.coverage_stats.txt

multiqc/

multiqc_report.html

```

### bedMethyl Format

```

```

## QC Thresholds (ENCODE Standards)

## Critical Pitfalls

### RRBS vs WGBS

RRBS (Reduced Representation) uses MspI digestion and covers ~10% of CpGs.

WGBS covers the full genome. These are DIFFERENT protocols:

- Never mix RRBS and WGBS data in the same analysis

### Strand-Specific vs Merged CpG

Bismark reports methylation per strand by default. For most analyses, merge

complementary CpG strands:

- Forward C at position N and reverse G at position N+1 are the same CpG

- MethylDackel `--mergeContext` handles this automatically

### Incomplete Bisulfite Conversion

Conversion artifacts produce false methylation calls:

- Always include lambda phage or pUC19 spike-in DNA

- Unmethylated spike-in should show ≥98% conversion

### M-bias Plots

### Low Coverage Regions

Regions with <5x coverage have unreliable methylation estimates:

- For differential methylation analysis, consider `--min_coverage 10`

## Provenance Integration

After pipeline completion, log all outputs:

```python

# Log derived bedMethyl files

encode_log_derived_file(

file_path="/results/bismark/methylation/sample1.CpG.bedMethyl.gz",

source_accessions=["ENCSR...", "ENCFF..."],

description="CpG methylation calls from ENCODE WGBS pipeline",

file_type="bedMethyl",

tool_used="Bismark 0.24.2 + MethylDackel 0.6.1",

)

```

## Reference Files

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

1. `01-qc-trimming.md` -- Bisulfite-specific adapter trimming with Trim Galore

4. `04-methylation-calling.md` -- MethylDackel extraction and bedMethyl generation

## Walkthrough: Processing ENCODE WGBS from FASTQ to Methylation Calls

**Goal**: Process whole-genome bisulfite sequencing FASTQ files through the ENCODE pipeline to generate per-CpG methylation calls for epigenomic analysis.

### Step 1: Find WGBS experiment

```

encode_get_experiment(accession="ENCSR765JPC")

```

Expected output:

```json

{

"accession": "ENCSR765JPC",

"assay_title": "WGBS",

"biosample_summary": "liver",

"replicates": 2,

"status": "released"

}

```

### Step 2: List FASTQ files

```

```

Expected output:

```json

{

"files": [

{"accession": "ENCFF300BS1", "output_type": "reads", "paired_end": "1", "file_size_mb": 45000},

{"accession": "ENCFF301BS2", "output_type": "reads", "paired_end": "2", "file_size_mb": 46000}

]

}

```

**Interpretation**: WGBS files are very large (~45GB per read file). Ensure adequate storage (>500GB for processing).

### Step 3: Run the WGBS pipeline

```bash

```

Key pipeline steps:

### Step 4: Validate output quality

| Metric | Threshold | Purpose |

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

### Step 5: Identify differentially methylated regions

Feed per-CpG methylation into -> **methylation-aggregation** for cross-tissue comparison and HMR/UMR/PMD identification.

### Integration with downstream skills

- Per-CpG methylation files feed into -> **methylation-aggregation** for cross-tissue atlas

- DMRs feed into -> **peak-annotation** for nearest gene assignment

- Methylation at regulatory elements connects to -> **regulatory-elements**

- CpG variant methylation integrates with -> **variant-annotation**

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

## Code Examples

### 1. Find WGBS data for methylation analysis

```

encode_search_experiments(

assay_title="WGBS",

organ="brain"

)

```

Expected output:

```json

{

"total": 6,

"experiments": [

{

"accession": "ENCSR321BRN",

"assay_title": "WGBS",

"biosample_summary": "brain tissue female adult (53 years)",

"status": "released"

}

]

}

```

### 2. Download processed methylation files

```

encode_search_files(

assay_title="WGBS",

organ="brain",

file_format="bed",

output_type="methylation state at CpG",

assembly="GRCh38"

)

```

Expected output:

```json

{

"total": 3,

"files": [

{

"accession": "ENCFF567MET",

"output_type": "methylation state at CpG",

"assembly": "GRCh38",

"file_size_mb": 845.2

}

]

}

```

## Integration

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

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

| Per-CpG methylation (bedMethyl) | **methylation-aggregation** | Cross-tissue methylation atlas |

| Differentially methylated regions | **peak-annotation** | Assign DMRs to nearest genes |

| Methylation at regulatory sites | **regulatory-elements** | Correlate methylation with cCRE activity |

| CpG methylation near variants | **variant-annotation** | Annotate variants affecting CpG methylation |

| Pipeline parameters | **data-provenance** | Record Bismark/MethylDackel versions |

| Methylation at promoters | **gtex-expression** | Correlate promoter methylation with gene expression |

## Related Skills

- `pipeline-guide` -- Parent skill with compute resource assessment and cloud setup

- `methylation-aggregation` -- Aggregate methylation data across samples/tissues

- `quality-assessment` -- Evaluate pipeline output quality metrics

- `data-provenance` -- Track all pipeline inputs, outputs, and parameters

- `download-encode` -- Download ENCODE WGBS FASTQ files for pipeline input

- `publication-trust` -- Verify literature claims backing analytical decisions

## Presenting Results

When reporting WGBS pipeline results:

- **Next steps**: Suggest `methylation-aggregation` for cross-sample averaging and HMR/UMR/PMD identification

## For the request: "$ARGUMENTS"