Claude-skill-registry differential-methylation

This skill performs differential DNA methylation analysis (DMRs and DMCs) between experimental conditions using WGBS methylation tracks (BED/BedGraph). It standardizes input files into per-sample four-column Metilene tables, constructs a merged methylation matrix, runs Metilene for DMR detection, filters the results, and generates quick visualizations.

install

source · Clone the upstream repo

git clone https://github.com/majiayu000/claude-skill-registry

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/majiayu000/claude-skill-registry "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/data/21-differential-methylation" ~/.claude/skills/majiayu000-claude-skill-registry-differential-methylation && rm -rf "$T"

manifest: skills/data/21-differential-methylation/SKILL.md

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))

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.