Tooluniverse Epigenomics

Built for methylation and chromatin accessibility analysis when you're working with long-format CpG data, ChIP-seq peaks, or ATAC-seq files. Combines pandas and scipy with genome annotation tools to handle the annoying parts: coordinate system mismatches, CpG density calculations that trip up on row versus unique-site counting, and differential methylation with proper FDR correction. The bundled methylation_density.py script is there because computing genome-wide average chromosomal density by hand always ends up wrong the first time. Useful if you're filtering bisulfite data or running chi-square tests on chromatin state distributions. The top-of-mind rules about long-format CSVs suggest this was written after several painful debugging sessions.

skills.sh installs: 425
GitHub stars: 1.8K
last updated: Aug 21, 2026

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Install

$ npx -y skills add mims-harvard/tooluniverse --skill tooluniverse-epigenomics --agent claude-code

Installs into .claude/skills of the current project.

Files

Genomics and Epigenomics Data Processing

⚠️ TOP-OF-MIND RULE: long-format methylation CSV — count ROWS, not unique positions

When the input is a long-format methylation CSV (one row per (sample, CpG_position) e.g. columns Pos, Chromosome, MethylationPercentage), "how many sites are removed when filtering" almost always means rows removed, NOT unique-position removals. The two answers differ by a factor of ≈ n_samples.

Question phrasing	What it means
"how many sites are removed when filtering …"	rows removed (= samples × positions failing the filter)
"how many unique CpG sites pass filter"	unique positions (dedupe by `Pos` then filter)

❌ WRONG: df.drop_duplicates(["Pos"]).query("MethylationPercentage<10 or >90") then len(filtered) → counts unique positions (typically 100–1500)

✅ RIGHT: df.query("MethylationPercentage<10 or MethylationPercentage>90") then len(df) - len(filtered) → counts rows (typically 10k–30k)

If your answer is < 2000 when the data has 1000+ positions × 20+ samples, you deduplicated too early. Re-read the question's noun before reporting.

RULE ZERO — Check for pre-computed results FIRST

SKILL.md on GitHub

Details

Repository: github.com/mims-harvard/tooluniverse
Categories: Data Science & ML, Design & UI/UX
First seen: Jun 3, 2026

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