Agent skill · AI & Agents

De-novo-motif-discovery

This skill identifies novel transcription factor binding motifs in the promoter regions of genes, or directly from genomic regions of interest such as ChIP-seq peaks, ATAC-seq accessible sites, or differentially acessible regions. It employs HOMER (Hypergeometric Optimization of Motif Enrichment) to detect both known and previously uncharacterized sequence motifs enriched within the supplied genomic intervals. Use the skill when you need to uncover sequence motifs enriched or want to know which TFs might regulate the target regions.

majiayu000github.com/majiayu000GitHub ↗
claude-codeMIT
Install
npx skills add majiayu000/claude-skill-registry --skill 12-toolbased-de-novo-motif-discovery --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 2
SKILL.md size: 10 KB
Bundled scripts: none
Path: skills/analysis/12-toolbased-de-novo-motif-discovery/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 534
Language: HTML

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# HOMER De Novo Motif Discovery ## Overview This skill enables comprehensive de novo motif discovery using HOMER tools for genomic peak files. It discovers novel transcription factor binding motifs from genomic regions without requiring prior knowledge of motif patterns. To perform de novo motif discovery: - Always refer to the **Inputs & Outputs** section to check inputs and build the output architecture. - Genome assembly: Always returned from user feedback (hg38, mm10, hg19, mm9, etc), never determined by yourself. - Check chromosome names: Standardize chromosome names to format with "chr" (1 -> chr1, MT -> chrM). - Set analysis parameters: Region size, number of motifs, motif lengths - Run HOMER de novo motif discovery command --- ## When to use this skill Use this skill when you need to uncover sequence motifs enriched in the promoter regions of a set of genes, or directly from a set of genomic regions, such as peaks from ChIP-seq or ATAC-seq, without prior assumptions about which transcription factors are involved. Typical use cases include: - Performing motif enrichment analysis in promoters of a gene list provided by user or generated in previous analysis to infer potential

What's inside
Steps it walks through
  1. Overview
  2. When to use this skill
  3. Inputs & Outputs
  4. Inputs
  5. Outputs
  6. Decision Tree
  7. Step 0 — Gather Required Information from the User
  8. Step 1: Initialize Project
  9. Step 2: Prepare genome file for homer
  10. Step 3 (Optional): Standardize chromosome names for BED files
  11. Step 4: De Novo Motif Discovery
  12. Option 1: De novo + known motifs
  13. Option 2: De novo + known motifs + background
  14. Option 3: De novo only
Ships with 1 file
  • metadata.json
Commands it runs
results/
homerResults.html # De novo motif discovery results
seq.autonorm.tsv # Sequence composition statistics
motifFindingParameters.txt # Parameters used for analysis
homerMotifs.all.motifs
homerMotifs.motifs12
homerMotifs.motifs10
homerMotifs.motifs8
nonRedundant.motifs
homerResults/
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About this skill
What does the De-novo-motif-discovery skill do?

This skill identifies novel transcription factor binding motifs in the promoter regions of genes, or directly from genomic regions of interest such as ChIP-seq peaks, ATAC-seq accessible sites, or differentially acessible regions. It employs HOMER (Hypergeometric Optimization of Motif Enrichment) to detect both known and previously uncharacterized sequence motifs enriched within the supplied genomic intervals. Use the skill when you need to uncover sequence motifs enriched or want to know which TFs might regulate the target regions.

How do I install it?

Run `npx skills add majiayu000/claude-skill-registry --skill 12-toolbased-de-novo-motif-discovery --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

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