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

# Extract methylation calls from Bismark BAM
bismark_methylation_extractor --gzip --bedGraph \
    sample_bismark_bt2.bam

Paired-End Extraction

bismark_methylation_extractor --paired-end --gzip --bedGraph \
    sample_bismark_bt2_pe.bam

Common Options

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

# 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

# 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

# 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

# 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

# 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

# bedGraph to BigWig (requires UCSC tools)
bedGraphToBigWig sample.bedGraph.gz chrom.sizes sample.bw

M-Bias Plot

# 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

# 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

# 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

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

Output Formats

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