motif-analysis
Guide for de novo and known motif enrichment analysis of ENCODE ChIP-seq and ATAC-seq peaks using HOMER and MEME Suite. Use when users need to discover TF binding motifs in peaks, validate ChIP-seq targets, or find co-binding partners. Trigger on: motif analysis, HOMER, MEME, de novo motif, motif enrichment, findMotifsGenome, AME, MEME-ChIP, known motif, TF binding motif, co-factor, motif discovery.
npx skills add majiayu000/claude-skill-registry --skill motif-analysis-ammawla-encode-toolkit --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
What it does
Guides de novo and known motif enrichment analysis of ENCODE ChIP-seq and ATAC-seq peaks using two tool suites: HOMER and MEME Suite. It covers input preparation (peaks to sequences, summit-centered windows for TF ChIP-seq, blacklists, and optional subsampling), running commands for motif discovery and enrichment, and result interpretation (validation of the target motif, co-factor discovery, and centrality analysis).
How it works
The skill describes practical steps and commands for:
- Preparing input: obtain ENCODE peaks, extract summit-centered sequences for TF ChIP-seq, remove blacklisted regions, and optionally subsample top peaks; convert to FASTA.
- Running HOMER: use findMotifsGenome.pl with -size, -mask, -p, and optional -preparsedDir; run on peaks or ATAC peaks with -size given for ATAC. Output includes homerResults.html (de novo) and knownResults.html (known motifs).
- Running MEME-ChIP: run meme-chip with parameters (e.g., -meme-maxw 30, -meme-nmotifs 10, -meme-minw 6, -db JASPAR2024_CORE_vertebrates.meme, -o), using summits.fa as input. Also describes AME, FIMO, and CentriMo usage with corresponding commands.
- Interpreting results: validate primary motif against the ChIP target, identify co-regulatory motifs, use CentriMo for centrality, and understand p-value semantics between HOMER and MEME.
- Complete workflows and code examples for full HOMER and MEME-ChIP pipelines, including peak processing, extraction, and result logging steps.
When to use it
Use when: you need to discover TF binding motifs in ChIP-seq or ATAC-seq peaks, validate ChIP targets by motif enrichment, or identify co-binding partners and co-factor motifs in peak regions.
What it can touch
The skill references tools and files including:
- HOMER: findMotifsGenome.pl, homer_output, -size, -mask, -p, -preparsedDir, -bg, -mknown, -len
- MEME-ChIP: meme-chip, summits.fa, -meme-maxw, -meme-nmotifs, -meme-minw, -db, -o, -meme-mod
- AME, FIMO, CentriMo with corresponding options
- JASPAR2024_CORE_vertebrates.meme, hg38.fa, summits.fa, summits_200bp.bed, top0_summits.bed, top5k_summits.fa, etc.
Caveats
- Peak summit vs full peak region guidance; TF ChIP-seq typically uses summit-centered windows (150-250bp) while histone ChIP-seq uses full peaks.
- Background modeling guidance (GC-matched background by default in HOMER; shuffle-based background in MEME-ChIP) and cautions about GC bias.
- Requires input FASTA sequences (summits.fa or similar) and appropriate genome files; ATAC-seq should use full peak regions with -size given.
- No outcomes promised beyond described steps and expected outputs (HTML reports, PWMs, motif lists).
# Motif Analysis of ENCODE Peak Data ## When to Use - User wants to discover transcription factor binding motifs in ChIP-seq or ATAC-seq peaks - User asks about "motif enrichment", "HOMER", "MEME", or "de novo motif discovery" - User needs to validate ChIP-seq targets by checking if the expected motif is enriched - User wants to find co-binding partners or co-factor motifs in peak regions - Example queries: "find motifs in my CTCF peaks", "run HOMER on ATAC-seq peaks", "what TFs co-bind with p300 in liver?" Help the user perform de novo and known motif enrichment analysis on ENCODE ChIP-seq and ATAC-seq peaks. Motif analysis serves two critical purposes: (1) validating that ChIP-seq experiments pulled down the expected transcription factor, and (2) discovering co-regulatory partners that co-bind with the target factor. This skill covers the two major tool suites -- HOMER and MEME Suite -- from input preparation through result interpretation. ## Literature Foundation | Reference | Journal | Key Contribution | DOI | Citations | |-----------|---------|-----------------|-----|-----------| | Heinz et al. (2010) | Molecular Cell | HOMER: Simple combinations of lineage-determining TFs pri
- When to Use
- Literature Foundation
- Prerequisites: Input Preparation
- Obtaining ENCODE Peaks
- Preparing Sequences from Peaks
- Subsampling Large Peak Sets
- Part 1: HOMER findMotifsGenome
- 1a. Basic Usage
- 1b. Output Structure
- 1c. HOMER for ATAC-seq Peaks
- Part 2: MEME-ChIP Suite
- 2a. MEME-ChIP: All-in-One Pipeline
- 2b. Individual MEME Suite Tools
- 2c. Obtaining the JASPAR Database
For TF ChIP-seq: extract summit +/- 100bp (200bp window)
awk 'BEGIN{OFS="\t"} {summit=$2+$10; print $1, summit-100, summit+100, $4, $5}' \
peaks.narrowPeak > summits_200bp.bed
Remove blacklisted regions (Amemiya et al. 2019)
bedtools intersect -a summits_200bp.bed \
Extract FASTA sequences (requires genome FASTA)
bedtools getfasta -fi hg38.fa -bed summits_clean.bed -fo summits.fa
For ATAC-seq: use full peak regions (typically 200-500bp)
bedtools getfasta -fi hg38.fa -bed atac_peaks_clean.bed -fo atac_peaks.fa
Sort by signalValue (column 7) descending, take top 10,000What does the motif-analysis skill do?
Guide for de novo and known motif enrichment analysis of ENCODE ChIP-seq and ATAC-seq peaks using HOMER and MEME Suite. Use when users need to discover TF binding motifs in peaks, validate ChIP-seq targets, or find co-binding partners. Trigger on: motif analysis, HOMER, MEME, de novo motif, motif enrichment, findMotifsGenome, AME, MEME-ChIP, known motif, TF binding motif, co-factor, motif discovery.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill motif-analysis-ammawla-encode-toolkit --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.