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tooluniverse-epigenomics

Analyze DNA methylation, ChIP-seq, and ATAC-seq data with pandas and ToolUniverse annotation tools.

1.7k|254|Updated Mar 3, 2025
Search Tags:#multi-omics#epigenomics#atac-seq#chip-seq#dna methylation#differential methylation#cpg density
One-click install
npx skills add https://github.com/mims-harvard/ToolUniverse --skill tooluniverse-epigenomics
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: tooluniverse-epigenomics
Source: https://github.com/mims-harvard/ToolUniverse/tree/main/plugins/tooluniverse/skills/tooluniverse-epigenomics
Command: npx skills add https://github.com/mims-harvard/ToolUniverse --skill tooluniverse-epigenomics

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, scipy, and includes scripts (resource) components.

What problem does it solve?

Answering quantitative epigenomics questions—such as counting differentially methylated CpGs, computing chromosome-level CpG density, or annotating ChIP-seq peaks—requires careful statistical handling (FDR correction, genome-build awareness, rows-vs-unique-sites disambiguation) that is easy to get wrong when hand-rolling analysis code.

Core Features & Use Cases

  • Methylation Analysis: Load beta/M-value matrices, filter CpG probes, run differential methylation (t-test/Wilcoxon + FDR), identify age-related CpGs, and compute chromosome density statistics via the bundled methylation_density.py script.
  • ChIP-seq & ATAC-seq Processing: Load BED/narrowPeak files, compute peak statistics, annotate peaks to genes and promoter regions, and detect nucleosome-free regions.
  • Multi-Omics & Clinical Integration: Correlate methylation with expression, analyze missing data across modalities, and annotate genes using ToolUniverse tools (Ensembl, SCREEN, ChIPAtlas, ENCODE, GTEx).
  • Use Case: Given a long-format methylation CSV and chromosome lengths, ask how many sites are removed when filtering extreme methylation values—the skill runs the deterministic script and reports the correct row-level count.

Quick Start

Ask the agent to compute the genome-wide average chromosomal density of filtered CpG sites from your methylation CSV and chromosome-length file using the epigenomics skill.

Frequently Asked Questions about tooluniverse-epigenomics

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I compute CpG density per chromosome from methylation data?▼

Run the bundled methylation_density.py script with your CpG CSV and chromosome-length CSV. It outputs per-chromosome density, genome-wide average density, and total density, handling the rows-versus-unique-positions distinction automatically.

How to find differentially methylated CpGs between two groups?▼

Load the beta-value matrix, define sample groups from clinical data, then run a per-probe t-test or Wilcoxon test with Benjamini-Hochberg FDR correction. Significant DMPs are typically filtered at padj < 0.05 with an absolute delta-beta threshold.

Why does my filtered CpG count differ from expected results?▼

Long-format methylation CSVs contain one row per sample-position pair, so row counts are roughly n_samples times larger than unique-position counts. Check whether the question asks about rows removed or unique sites, and deduplicate by position only when unique sites are requested.

Does this skill support ChIP-seq and ATAC-seq peak files?▼

Yes, it loads BED, narrowPeak, and broadPeak files, computes peak statistics, annotates peaks to genes and promoter regions, and detects nucleosome-free regions under 150 bp for ATAC-seq. It assumes peaks are pre-called and does not perform peak calling.

What are the limitations of this epigenomics analysis approach?▼

It uses pure Python interval operations instead of pybedtools, t-test/Wilcoxon instead of limma, and is Illumina-centric for 450K/EPIC arrays. ToolUniverse API annotation is rate-limited to roughly 20 genes per batch.

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