---
name: methylation-calling
description: Extract methylation calls from Bismark BAM files using bismark_methylation_extractor. Generates per-cytosine reports for CpG, CHG, and CHH contexts. Use when extracting methylation levels from aligned bisulfite sequencing data for downstream analysis.
tool_type: cli
primary_tool: bismark
---
## Version Compatibility
Reference examples tested with: pandas 2.2+
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
- CLI: `<tool> --version` then `<tool> --help` to confirm flags
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
# Methylation Calling
**"Extract methylation calls from my Bismark BAM"** -> Generate per-cytosine methylation reports (CpG, CHG, CHH contexts) from aligned bisulfite sequencing data.
- CLI: `bismark_methylation_extractor --bedGraph --cytosine_report sample.bam`
## Basic Extraction
```bash
# Extract methylation calls from Bismark BAM
bismark_methylation_extractor --gzip --bedGraph \
sample_bismark_bt2.bam
```
## Paired-End Extraction
```bash
bismark_methylation_extractor --paired-end --gzip --bedGraph \
sample_bismark_bt2_pe.bam
```
## Common Options
```bash
bismark_methylation_extractor \
--paired-end \ # For paired-end data
--gzip \ # Compress output
--bedGraph \ # Generate bedGraph file
--cytosine_report \ # Genome-wide cytosine report
--genome_folder /path/to/genome/ \ # Required for cytosine_report
--buffer_size 10G \ # Memory buffer
--parallel 4 \ # Parallel extraction
-o output_dir/ \
sample.bam
```
## CpG Context Only
```bash
# Most common - extract only CpG methylation
bismark_methylation_extractor \
--paired-end \
--no_overlap \ # Avoid double counting overlapping reads
--gzip \
--bedGraph \
--CX \ # Also extract CHG/CHH (optional)
sample.bam
```
## Genome-Wide Cytosine Report
```bash
# Comprehensive report with all CpGs in genome
bismark_methylation_extractor \
--paired-end \
--gzip \
--bedGraph \
--cytosine_report \
--genome_folder /path/to/genome/ \
sample.bam
```
## Strand-Specific Output
```bash
# Default: strand-specific output
# CpG_OT_sample.txt - Original Top strand
# CpG_OB_sample.txt - Original Bottom strand
# CpG_CTOT_sample.txt - Complementary to OT
# CpG_CTOB_sample.txt - Complementary to OB
# Merge strands (CpG methylation is usually symmetric)
bismark_methylation_extractor --merge_non_CpG --gzip sample.bam
```
## Avoid Double-Counting Overlapping Reads
```bash
# For paired-end data with overlapping reads
bismark_methylation_extractor \
--paired-end \
--no_overlap \ # Ignore overlapping portion of read 2
--gzip \
sample_pe.bam
```
## Generate Coverage File
```bash
# bismark2bedGraph creates coverage file
bismark_methylation_extractor --bedGraph --gzip sample.bam
# Or run separately
bismark2bedGraph -o sample CpG_context_sample.txt.gz
# Coverage format: chr start end methylation_percentage count_meth count_unmeth
```
## Convert to BigWig for Visualization
```bash
# bedGraph to BigWig (requires UCSC tools)
bedGraphToBigWig sample.bedGraph.gz chrom.sizes sample.bw
```
## M-Bias Plot
```bash
# Check for methylation bias across read positions
bismark_methylation_extractor --paired-end \
--mbias_only \ # Only generate M-bias plot
sample.bam
# Generates sample.M-bias.txt and sample.M-bias_R1.png, sample.M-bias_R2.png
```
## Ignore End Bias
```bash
# Ignore positions with systematic bias (found from M-bias plot)
bismark_methylation_extractor \
--paired-end \
--ignore 2 \ # Ignore first 2 bp of read 1
--ignore_r2 2 \ # Ignore first 2 bp of read 2
--ignore_3prime 2 \ # Ignore last 2 bp of read 1
--ignore_3prime_r2 2 \ # Ignore last 2 bp of read 2
sample.bam
```
## Output Files
```bash
# Main output files:
# CpG_context_sample.txt.gz - Per-read CpG methylation
# sample.bismark.cov.gz - Coverage file
# sample.bedGraph.gz - bedGraph for visualization
# sample.CpG_report.txt.gz - Genome-wide CpG report (with --cytosine_report)
# Coverage file format:
# chr start end methylation% count_methylated count_unmethylated
```
## Parse Output in Python
```python
import pandas as pd
cov = pd.read_csv('sample.bismark.cov.gz', sep='\t', header=None,
names=['chr', 'start', 'end', 'meth_pct', 'count_meth', 'count_unmeth'])
cov['coverage'] = cov['count_meth'] + cov['count_unmeth']
cov_filtered = cov[cov['coverage'] >= 10]
```
## Key Parameters
| Parameter | Description |
|-----------|-------------|
| --paired-end | Paired-end mode |
| --gzip | Compress output |
| --bedGraph | Generate bedGraph |
| --cytosine_report | Full genome cytosine report |
| --genome_folder | Path to genome (for cytosine_report) |
| --CX | Report CHG/CHH contexts |
| --no_overlap | Avoid counting overlapping reads twice |
| --parallel | Parallel extraction threads |
| --mbias_only | Only M-bias analysis |
| --ignore N | Ignore first N bp of read 1 |
| --ignore_r2 N | Ignore first N bp of read 2 |
## Output Formats
| Format | Description | Use Case |
|--------|-------------|----------|
| CpG_context | Per-read methylation calls | Detailed analysis |
| .bismark.cov | Per-CpG coverage summary | methylKit input |
| .bedGraph | Methylation track | Genome browser |
| .CpG_report | All genome CpGs | Comprehensive analysis |
## Related Skills
- bismark-alignment - Generate input BAM files
- methylkit-analysis - Import coverage files to R
- differential-cpg-testing - Statistical testing on per-CpG count data
- dmr-detection - Find differentially methylated regions