WGBS Differential Methylation with metilene

Overview

• Refer to the Inputs & Outputs section to check available inputs and design the output structure.
• Always prompt user for which columns in the BED files are methylation fraction/percent. Never decide by yourself.
• Convert heterogeneous inputs to a per‑sample 4‑column Metilene table (chrom, start, end, methylation_fraction). Sort the BED files after conversion.
• Generate the merged bed file as the input of metilene.
• Run metilene: call DMRs and DMCs with tunable parameters
• Visualize: quick plots (Δmethylation vs –log10(q), length histograms).

Inputs & Outputs

Inputs

sample1.bed # raw methylation BED files, standardize it according to the following steps
sample2.bed

Assumptions: All samples share the same reference genome build and chromosome naming scheme.

Outputs

DMR_DMC_detection/
  stats/
    dmr_results.txt # raw metilene output.
    dmc_results.txt
    significant_dmrs.txt # filtered significant DMRs (TSV).
    significant_dmrs.bed # BED for genome browser.
    significant_dmcs.txt
    significant_dmcs.bed
    dmr_summary.txt # counts and length statistics.
  plots/
    volcano.pdf
    length_hist.pdf
  temp/
    sample1.sorted.bed
    ... # other sorted BED files
    merged_input.bed

Decision Tree

Step 1: Standardize BED file

• extract information from input BED files into per‑sample 4‑column Metilene table and sort

for sample in samples;do
  awk -F'\t' 'BEGIN {OFS="\t"} {print $1, $2, $3, $<n>/100}' sample.bed | sort -V -k1,1 -k2,2n # n is provide by user, devided by 100 if is percentage
done

Step 2: Build the merged methylation matrix (fractions per sample)

Call:

• mcp__methyl-tools__generate_metilene_input

with:

• group1_files: Comma-separated group 1 bedGraph/BED files (from Step 1, must be sorted)
• group1_files: Comma-separated group 2 bedGraph/BED files (from Step 1, must be sorted)
• output_path: Output file path for generated metilene input
• group1_name: Identifier of group 1
• group2_name: Identifier of group 2

This tool will:

• Generate a input file for metilene

Step 3: Run metilene (DMR mode)

Call:

• mcp__methyl-tools__run_metilene

with:

• merged_bed_path: file path for metilene input
• group_a_name: name of group A (e.g. "case")
• group_b_name: name of group B (e.g. "control")
• mode: Mode for metilene CLI (e.g. 1: de-novo, 2: pre-defined regions, 3: DMCs), assign 1 for DMR analysis
• threads: Always use 1 threads to avoid error
• output_results_path: Output path for the DMR results

Step 4: Run metilene (DMC mode)

Call:

• mcp__methyl-tools__run_metilene

with:

• merged_bed_path: file path for metilene input
• group_a_name: name of group A (e.g. "case")
• group_b_name: name of group B (e.g. "control")
• mode: Mode for metilene CLI (e.g. 1: de-novo, 2: pre-defined regions, 3: DMCs), assign 3 for DMR analysis
• output_results_path: Output path for the DMC results

Step 5: Filter significant DMRs and export BED

Call:

• mcp__methyl-tools__filter_dmrs with:
• metilene_results_path: DMR results from Step 3
• significant_tsv_path: Output path for the DMR results (e.g. significant_dmrs.tsv)
• significant_bed_path: Output path for the DMR results (e.g. significant_dmrs.bed)
• q_threshold, delta_threshold as agreed.

Step 6: Filter significant DMCs and export BED

Call:

• mcp__methyl-tools__filter_dmrs with:
• metilene_results_path: DMC results from Step 4
• significant_tsv_path: Output path for the DMC results (e.g. significant_dmcs.tsv)
• significant_bed_path: Output path for the DMC results (e.g. significant_dmcs.bed)
• q_threshold, delta_threshold as agreed.

Step 6: Visualization (quick, optional)

Volcano-like plot (Δmethylation vs –log10(q))

1. Call:

• mcp__methyl-tools__plot_dmr_volcano with:
• metilene_results_path: DMR results from Step 3
• output_pdf_path
• q_threshold, delta_threshold as agreed.
• Optional tuning of point_size, alpha as needed.

DMR length histogram Call:

• mcp__methyl-tools__plot_dmr_length_hist

with:

• significant_bed_path: Path for the signimicant DMRs (BED format from Step 5)
• output_pdf_path

Troubleshooting

• Chromosome naming mismatches: standardize to a single scheme (chr1 vs 1) across all samples.