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Tooluniverse Epigenomics

Checked
mims-harvard·vlatest·data·Apache-2.0

Tooluniverse Epigenomics analyzes genomic data to predict epigenetic modifications, aiding in understanding gene regulation and disease mechanisms.

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add mims-harvard/tooluniverse --skill tooluniverse-epigenomics -a claude-code
  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

mims-harvard

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Install target

Live repository

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No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

This skill passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.

  • Declares what it does: passed

    SKILL.md parses and declares a name and a description.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

  • No undeclared network calls: passed

    Makes no network calls.

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  • Pinned to a commit: passed

    Scanned mims-harvard/tooluniverse at f5c37eebeb.

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Scanned
19 files, 6,889 lines
Copies in repo
3 — all of them scanned
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About this skill

What it does

This skill, Tooluniverse Epigenomics, analyzes genomic data related to epigenetics using Python and integrated ToolUniverse annotation tools. It processes various data types including methylation data, ChIP-seq peaks, and ATAC-seq signals to predict epigenetic modifications and understand gene regulation. The tool prioritizes data inspection and statistical rigor in its analysis, always verifying scientific facts through database searches.

When to use it

This skill is useful for:

  • Analyzing methylation data (e.g., beta values or M-values).
  • Performing ChIP-seq peak analysis.
  • Investigating ATAC-seq data and identifying Nucleosome Free Regions (NFRs).
  • Integrating multiple omics datasets to understand relationships between epigenetic modifications and gene expression.
  • Calculating genome-wide epigenomic statistics.

Key capabilities

  • Data Processing: Loads and filters methylation, ChIP-seq, and ATAC-seq data in various formats (CSV, TSV, Parquet, HDF5, BED).
  • Statistical Analysis: Performs differential methylation analysis (T-test/Wilcoxon), correlation analyses (Pearson/Spearman), and FDR correction.
  • Annotation: Uses ToolUniverse tools to annotate genomic regions with information from ENCODE, GEO, and GTEx databases.
  • Coordinate System Awareness: Tracks genome build (hg19/hg38/mm10) and chromosome prefixes.
  • Multi-Omics Integration: Correlates methylation patterns with gene expression and ChIP-seq data.

Example prompts

  • "Perform differential methylation analysis between group A and group B on my methylation beta value file."
  • "Analyze the ChIP-seq peaks in my BED file and annotate them to genes using ENCODE tools."
  • "What is the median gene expression for BRCA1 in whole blood according to GTEx?"

Tips & gotchas

  • Data Format: The skill requires data in specific formats (CSV, TSV, Parquet, HDF5, BED). Ensure your data is properly formatted before analysis.
  • Genome Build: Be mindful of the genome build used for your data (hg19/hg38/mm10) and specify it when necessary.
  • ToolUniverse Dependencies: The skill relies on ToolUniverse annotation tools, so ensure these are accessible and functioning correctly.