Bio Workflows Methylation Pipeline

Version Compatibility

Reference examples tested with: Bismark 0.24+, Bowtie2 2.5.3+, FastQC 0.12+, Trim Galore 0.6.10+, fastp 0.23+, methylKit 1.28+

Before using code patterns, verify installed versions match. If versions differ:

• R: packageVersion('<pkg>') then ?function_name to verify parameters
• 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 Pipeline

"Analyze my bisulfite sequencing data from FASTQ to DMRs" → Orchestrate Bismark alignment, methylation calling, methylKit analysis, DMR detection, annotation with genomic features, and visualization of methylation patterns.

Complete workflow from bisulfite sequencing FASTQ to differentially methylated regions.

Workflow Overview

FASTQ files
    |
    v
[1. QC & Trimming] -----> fastp/Trim Galore
    |
    v
[2. Alignment] ---------> Bismark
    |
    v
[3. Deduplication] -----> deduplicate_bismark
    |
    v
[4. Methylation Calling] -> bismark_methylation_extractor
    |
    v
[5. Per-CpG Analysis] ---> methylKit (R) or scipy (Python)
    |
    v
[6. DMR Detection] ------> methylKit/DSS
    |
    v
Differentially methylated regions

Primary Path: Bismark + methylKit

Step 1: Quality Control

# Trim Galore recommended for bisulfite data (handles adapter bias)
trim_galore --paired --fastqc \
    -o trimmed/ \
    sample_R1.fastq.gz sample_R2.fastq.gz

# Or fastp with conservative settings
fastp -i sample_R1.fastq.gz -I sample_R2.fastq.gz \
    -o trimmed/sample_R1.fq.gz -O trimmed/sample_R2.fq.gz \
    --detect_adapter_for_pe \
    --qualified_quality_phred 20 \
    --length_required 35 \
    --html qc/sample_fastp.html

Step 2: Bismark Alignment

# Prepare genome (once)
bismark_genome_preparation --bowtie2 genome/

# Align
bismark --genome genome/ \
    -1 trimmed/sample_R1_val_1.fq.gz \
    -2 trimmed/sample_R2_val_2.fq.gz \
    -o aligned/ \
    --parallel 4 \
    --temp_dir tmp/

# Output: sample_R1_val_1_bismark_bt2_pe.bam

QC Checkpoint: Check Bismark report

• Mapping efficiency >50% (BS-seq has lower rates)
• Bisulfite conversion rate >99%

Step 3: Deduplication

deduplicate_bismark \
    --bam \
    -p \
    -o deduplicated/ \
    aligned/sample_R1_val_1_bismark_bt2_pe.bam

Step 4: Methylation Calling

bismark_methylation_extractor \
    --paired-end \
    --comprehensive \
    --bedGraph \
    --cytosine_report \
    --genome_folder genome/ \
    -o methylation/ \
    deduplicated/sample_R1_val_1_bismark_bt2_pe.deduplicated.bam

# Generate summary report
bismark2report
bismark2summary

Step 5: Analysis with methylKit

library(methylKit)

# Read methylation calls
files <- list(
    'methylation/control_1.CpG_report.txt',
    'methylation/control_2.CpG_report.txt',
    'methylation/treated_1.CpG_report.txt',
    'methylation/treated_2.CpG_report.txt'
)

sample_ids <- c('control_1', 'control_2', 'treated_1', 'treated_2')
treatment <- c(0, 0, 1, 1)

# Read cytosine reports
meth_obj <- methRead(
    location = as.list(files),
    sample.id = as.list(sample_ids),
    assembly = 'hg38',
    treatment = treatment,
    context = 'CpG',
    pipeline = 'bismarkCytosineReport'
)

# Filter by coverage
meth_filtered <- filterByCoverage(meth_obj, lo.count = 10, hi.perc = 99.9)

# Normalize coverage
meth_norm <- normalizeCoverage(meth_filtered)

# Merge samples (keep sites covered in all)
meth_merged <- unite(meth_norm, destrand = TRUE)

# Sample statistics
getMethylationStats(meth_obj[[1]], plot = TRUE)
getCoverageStats(meth_obj[[1]], plot = TRUE)

Step 5b: Python Alternative for Per-CpG Testing

When methylKit is unavailable or a Python-only workflow is preferred, per-CpG testing can be performed with scipy and statsmodels on beta values computed from the coverage files.

import pandas as pd
from scipy.stats import ttest_ind
from statsmodels.stats.multitest import multipletests
import numpy as np

# Read Bismark coverage files and compute beta values
# beta = count_methylated / (count_methylated + count_unmethylated)
# Filter CpGs with < 10x coverage in any sample
# Run per-CpG Welch's t-test between groups
# Apply BH FDR correction: multipletests(pvals, method='fdr_bh')
# See methylation-analysis/differential-cpg-testing for full pipeline

This approach is appropriate for large sample sizes (>10 per group). For small sample sizes (3-5 per group), use limma on M-values instead (also covered in the differential-cpg-testing skill).

Step 6: DMR Detection

# Calculate differential methylation (per CpG)
diff_meth <- calculateDiffMeth(meth_merged)

# Get significant DMCs
dmc <- getMethylDiff(diff_meth, difference = 25, qvalue = 0.01)

# Tile into regions (DMRs)
tiles <- tileMethylCounts(meth_merged, win.size = 1000, step.size = 1000)
diff_tiles <- calculateDiffMeth(tiles)
dmr <- getMethylDiff(diff_tiles, difference = 25, qvalue = 0.01)

# Export
write.csv(as.data.frame(dmc), 'dmc_results.csv')
write.csv(as.data.frame(dmr), 'dmr_results.csv')

# Annotate with genomic features
library(genomation)
gene_obj <- readTranscriptFeatures('genes.bed')
annotateWithGeneParts(as(dmr, 'GRanges'), gene_obj)

Parameter Recommendations

Troubleshooting

Complete Pipeline Script

#!/bin/bash
set -e

THREADS=4
GENOME="genome/"
SAMPLES="control_1 control_2 treated_1 treated_2"
OUTDIR="methylation_results"

mkdir -p ${OUTDIR}/{trimmed,aligned,deduplicated,methylation,qc}

# Step 1: QC
for sample in $SAMPLES; do
    trim_galore --paired --fastqc -o ${OUTDIR}/trimmed/ \
        ${sample}_R1.fastq.gz ${sample}_R2.fastq.gz
done

# Step 2: Alignment
for sample in $SAMPLES; do
    bismark --genome ${GENOME} \
        -1 ${OUTDIR}/trimmed/${sample}_R1_val_1.fq.gz \
        -2 ${OUTDIR}/trimmed/${sample}_R2_val_2.fq.gz \
        -o ${OUTDIR}/aligned/ \
        --parallel ${THREADS} --temp_dir tmp/
done

# Step 3: Deduplication
for sample in $SAMPLES; do
    deduplicate_bismark --bam -p \
        -o ${OUTDIR}/deduplicated/ \
        ${OUTDIR}/aligned/${sample}_R1_val_1_bismark_bt2_pe.bam
done

# Step 4: Methylation calling
for sample in $SAMPLES; do
    bismark_methylation_extractor --paired-end --comprehensive \
        --bedGraph --cytosine_report \
        --genome_folder ${GENOME} \
        -o ${OUTDIR}/methylation/ \
        ${OUTDIR}/deduplicated/${sample}_R1_val_1_bismark_bt2_pe.deduplicated.bam
done

bismark2report
echo "Pipeline complete. Run R script for DMR analysis."

Related Skills

• methylation-analysis/bismark-alignment - Bismark parameters
• methylation-analysis/methylation-calling - Calling details
• methylation-analysis/methylkit-analysis - methylKit functions
• methylation-analysis/differential-cpg-testing - Per-CpG testing (Python/R alternatives)
• methylation-analysis/dmr-detection - DMR algorithms