---

name: "homer-motif-analysis" description: "De novo and known TF motif enrichment in ChIP-seq/ATAC-seq peaks via HOMER. findMotifsGenome.pl finds over-represented patterns vs background; annotatePeaks.pl assigns context (TSS distance, gene, repeat). Use after MACS3 to identify enriched TFs, annotate peaks with nearest genes, and validate ChIP-seq via the target motif." license: "GPL-3.0"

HOMER — Motif Analysis and Peak Annotation

Overview

HOMER (Hypergeometric Optimization of Motif EnRichment) is a suite of Perl/C++ tools for analyzing genomic regulatory elements. Its two primary commands are findMotifsGenome.pl, which performs de novo motif discovery and known motif enrichment against JASPAR/HOMER databases, and annotatePeaks.pl, which maps each peak to the nearest gene, distance to TSS, and genomic feature class (promoter, intron, intergenic, repeat). HOMER takes BED-format peak files from MACS3 or similar peak callers and a reference genome assembly as input, and outputs HTML/text reports ranking enriched motifs by p-value and fold enrichment over a matched background.

When to Use

• Identifying which transcription factors are bound in a ChIP-seq peak set by enriching their known motifs from JASPAR or the HOMER motif library
• Discovering novel sequence motifs de novo in open chromatin regions from ATAC-seq without prior knowledge of the binding TF
• Comparing motif landscapes between two conditions (e.g., treated vs. untreated peak sets) by running HOMER with one set as target and the other as background
• Annotating genomic peaks with nearest genes and distance to TSS for downstream functional analysis or integration with DESeq2 results
• Validating ChIP-seq experiment quality: a successful pull-down should show the target TF's canonical motif as the top hit
• Use macs3-peak-calling first to generate the peak BED files that serve as input to HOMER
• Use jaspar-database to cross-reference HOMER-discovered motifs with JASPAR IDs and additional TF metadata
• Use MEME-CHIP (web or local) when you need a more probabilistic ZOOPS/TCM model or the MEME Suite ecosystem
• Use AME (part of MEME Suite) as a faster alternative for known motif scanning without de novo discovery

Prerequisites

• Software: HOMER (Perl + compiled binaries), conda or manual install
• Genomes: must download genome sequence via installGenome.pl after HOMER install
• Input: BED file of peaks (at minimum: chr, start, end columns); ideally summit-centered peaks from MACS3
• Python packages (for parsing/visualization): pandas, matplotlib, seaborn

Check before installing: The tool may already be available in the current environment (e.g., inside a pixi / conda env). Run command -v findMotifsGenome.pl first and skip the install commands below if it returns a path. When running inside a pixi project, invoke the tool via pixi run findMotifsGenome.pl rather than bare findMotifsGenome.pl.

# Install HOMER via conda (recommended — handles Perl dependencies)
conda install -c bioconda homer

# Verify installation
findMotifsGenome.pl 2>&1 | head -3
# Usage: findMotifsGenome.pl <peak/BED file> <genome> <output directory> [options]

annotatePeaks.pl 2>&1 | head -3
# Usage: annotatePeaks.pl <peak/BED file> <genome> [options]

# Install reference genomes (downloads 2-way masker + sequence; ~3–10 GB each)
installGenome.pl hg38
installGenome.pl mm10

# Install Python parsing dependencies
pip install pandas matplotlib seaborn

Pre-flight Interview

Settle these with the user before writing any analysis code.

decisions:
  - id: D1
    param: genomeAssembly
    kind: derived
    source: upstream
    ask: "Which assembly are the peak coordinates on?"
    default: "carried from the peak-calling stage"

  - id: D2
    param: searchWindow
    kind: required
    source: user
    ask: "How wide a window around each peak centre should be searched?"
    default: "200 bp for TF ChIP, 150 bp for ATAC"

  - id: D3
    param: backgroundRegions
    kind: required
    source: user
    ask: "Compare peaks against GC-matched random genomic regions, or against a specific control set?"
    default: "auto-generated GC-matched background"

  - id: D4
    param: repeatMasking
    kind: required
    source: user
    ask: "Mask repetitive sequence, which otherwise dominates the enrichment as false positives?"
    default: "masked"

  - id: D5
    param: denovoDiscovery
    kind: required
    source: user
    ask: "Search for previously undescribed motifs, or only test the known-motif library?"
    default: "known motifs only - de novo discovery is roughly ten times slower"

  - id: D6
    param: motifLengths
    kind: optional
    source: user
    depends_on: [D5]
    ask: "Which motif widths should the de novo search try?"
    default: "8, 10, 12"
    skip_if: "de novo discovery disabled"

  - id: D7
    param: numDenovoMotifs
    kind: optional
    source: user
    depends_on: [D5]
    ask: "How many de novo motifs should be reported?"
    default: 25
    skip_if: "de novo discovery disabled"

  - id: D8
    param: knownMotifMismatches
    kind: optional
    source: user
    ask: "How loosely may a sequence match a known motif and still count?"
    default: 2

  - id: D9
    param: threads
    kind: never_ask
    source: data
    reason: "Affects runtime only, not the enrichment"
    default: "min(8, available_cores)"

D1 is derived rather than asked: peak coordinates are meaningless outside the assembly they were called on, so the answer is whatever the upstream stage used, not a preference. D3 is the decision most often left at its default without thought - a background that does not match the peaks in GC content produces enrichment for GC content rather than for biology.

Quick Start

# Run de novo + known motif enrichment on TF ChIP-seq peaks (hg38, 200 bp window)
findMotifsGenome.pl peaks/tf_chip_summits.bed hg38 motif_output/ \
    -size 200 -mask -p 4

# Annotate peaks with nearest genes and genomic features
annotatePeaks.pl peaks/tf_chip_peaks.narrowPeak hg38 > annotated_peaks.txt

echo "Top known motif:"
head -2 motif_output/knownResults.txt | tail -1 | cut -f1-4

echo "Annotated peaks: $(wc -l < annotated_peaks.txt) lines"

Workflow

Step 1: Installation and Genome Setup

Install HOMER and download the reference genome sequence required for motif analysis.

# Activate conda environment (or use existing env)
conda create -n homer_env -c bioconda homer python=3.10 -y
conda activate homer_env

# List available genomes
installGenome.pl list

# Install human (hg38) and mouse (mm10) genomes
# Downloads masked genome sequence and annotation files
installGenome.pl hg38
# Output: Installing hg38... Done. (3-5 min, ~3 GB)

installGenome.pl mm10
# Output: Installing mm10... Done. (3-5 min, ~2.5 GB)

# Verify genome is installed
ls ~/.homer/data/genomes/hg38/
# genome.fa  chrom.sizes  ...

# Check HOMER motif database
ls ~/.homer/data/knownTFs/
# vertebrates.motifs  jaspar.motifs  ...

Step 2: Prepare Input Peak File

Prepare a summit-centered BED file from MACS3 output for optimal motif resolution.

# Option A: Use MACS3 summit file directly (already 1 bp summit positions)
# Expand summits to ±100 bp (200 bp total) centered on summit
awk 'BEGIN{OFS="\t"} {
    start = ($2 - 100 < 0) ? 0 : $2 - 100;
    print $1, start, $2 + 100, $4, $5
}' peaks/tf_chip_summits.bed > peaks/tf_chip_200bp.bed

echo "Summit-centered peaks: $(wc -l < peaks/tf_chip_200bp.bed)"
# Summit-centered peaks: 12453

# Option B: Use narrowPeak file directly (HOMER accepts multi-column BED)
# HOMER uses columns 1-3 (chr, start, end) and centers internally with -size
cp peaks/tf_chip_peaks.narrowPeak peaks/input_peaks.bed

# Option C: Prepare a custom background region file (matched GC content)
# HOMER auto-generates background if not provided, but explicit background
# is recommended when comparing two peak sets
# Use the control peak set or random genomic regions as background:
bedtools shuffle -i peaks/tf_chip_peaks.narrowPeak \
    -g ~/.homer/data/genomes/hg38/chrom.sizes \
    -excl peaks/tf_chip_peaks.narrowPeak > peaks/background_regions.bed

echo "Background regions: $(wc -l < peaks/background_regions.bed)"
# Background regions: 12453

Step 3: De Novo Motif Discovery

Run findMotifsGenome.pl for de novo motif discovery and known motif enrichment simultaneously.

mkdir -p motif_output/

# Full run: de novo + known motif enrichment
# -size 200: use 200 bp window centered on peak midpoint
# -mask: mask repetitive elements (recommended for clean motifs)
# -p 4: use 4 CPU threads
# -S 25: find top 25 de novo motifs (default)
findMotifsGenome.pl peaks/tf_chip_200bp.bed hg38 motif_output/ \
    -size 200 \
    -mask \
    -p 4 \
    -S 25

# Check progress output:
# Reading genome sizes for hg38 ...
# Scanning for motifs...
# Optimizing 25 motifs...
# Done! Output in motif_output/

echo "Known results: $(wc -l < motif_output/knownResults.txt) motifs"
echo "De novo motifs: $(ls motif_output/homerResults/*.motif 2>/dev/null | wc -l) motifs"
# Known results: 392 motifs
# De novo motifs: 25 motifs

# For mouse peaks (mm10)
# findMotifsGenome.pl peaks/atac_peaks.bed mm10 motif_output_mm10/ \
#     -size 200 -mask -p 4

Step 4: Known Motif Enrichment Only

Scan peaks for occurrences of a specific known motif or skip de novo discovery for speed.

# Skip de novo discovery (faster when you only need known motifs)
findMotifsGenome.pl peaks/tf_chip_200bp.bed hg38 motif_output_known/ \
    -size 200 \
    -mask \
    -p 4 \
    -nomotif

echo "Known motif results: $(wc -l < motif_output_known/knownResults.txt)"
# Known motif results: 392

# Find occurrences of a specific motif across peaks (outputs peak-level annotation)
# Extract the motif matrix file for the TF of interest from homerResults/
findMotifsGenome.pl peaks/tf_chip_200bp.bed hg38 motif_scan_out/ \
    -size 200 \
    -mask \
    -find motif_output/homerResults/motif1.motif \
    > peaks_with_motif1.txt

echo "Peaks containing motif1: $(wc -l < peaks_with_motif1.txt)"
# Peaks containing motif1: 8941

# Custom background: compare treated vs. control peak sets
findMotifsGenome.pl peaks/treated_peaks.bed hg38 motif_treated_vs_ctrl/ \
    -size 200 \
    -mask \
    -p 4 \
    -bg peaks/control_peaks.bed

Step 5: Peak Annotation

Use annotatePeaks.pl to assign each peak to a genomic feature and nearest gene.

# Annotate peaks with nearest gene and TSS distance
# Outputs a tab-delimited file with genomic context for each peak
annotatePeaks.pl peaks/tf_chip_peaks.narrowPeak hg38 \
    > annotated_peaks.txt

echo "Annotated peaks: $(($(wc -l < annotated_peaks.txt) - 1)) peaks"
# Annotated peaks: 12453 peaks

# Preview column headers and first peak
head -2 annotated_peaks.txt | cut -f1-10

# Annotate ATAC-seq peaks (same command, different input)
annotatePeaks.pl peaks/atac_sample_peaks.narrowPeak hg38 \
    > annotated_atac.txt

# Generate TSS-distance histogram (for tag density plots)
# annotatePeaks.pl can compute read density around peaks with -d flag
# annotatePeaks.pl tss hg38 -size 4000 -hist 10 \
#     -d chip_tagdir/ > tss_histogram.txt

Step 6: Parse HOMER Results with Python

Read knownResults.txt and de novo motif files into pandas for downstream analysis.

import pandas as pd
import subprocess
import io

# --- Parse known motif enrichment results ---
# knownResults.txt columns:
# Motif Name | Consensus | P-value | Log P-value | q-value | # Target Seqs w/ motif | % Target | # Bg Seqs w/ motif | % Bg
known_cols = [
    "motif_name", "consensus", "pvalue", "log_pvalue",
    "qvalue", "n_target_seqs", "pct_target",
    "n_bg_seqs", "pct_bg"
]
known = pd.read_csv(
    "motif_output/knownResults.txt",
    sep="\t", header=0, names=known_cols
)

# Convert string percentages to floats
known["pct_target"] = known