/bio-chipseq-motif-analysis

name: bio-chipseq-motif-analysis
description: De novo motif discovery and known motif enrichment analysis using HOMER and MEME-ChIP. Identify transcription factor binding motifs in ChIP-seq, ATAC-seq, or other genomic peak data. Use when finding enriched DNA motifs in peak sequences.
tool_type: cli
primary_tool: HOMER

Version Compatibility

Reference examples tested with: BioPython 1.83+, bedtools 2.31+, matplotlib 3.8+, pandas 2.2+

Before using code patterns, verify installed versions match. If versions differ:

• Python: `pip show <package>` then `help(module.function)` to check signatures
• CLI: `<tool> --version` then `<tool> --help` to confirm flags

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

Motif Analysis

**"Find enriched motifs in my ChIP-seq peaks"** → Discover de novo DNA-binding motifs and test for known TF motif enrichment in peak sequences.

• CLI: `findMotifsGenome.pl peaks.bed hg38 output/` (HOMER), `meme-chip -db JASPAR peaks.fa` (MEME)

Identify DNA sequence motifs enriched in ChIP-seq or ATAC-seq peaks to discover transcription factor binding sites.

Tool Comparison

| Tool | Strengths | Use Case | |------|-----------|----------| | HOMER | Fast, comprehensive, built-in databases | General motif analysis | | MEME-ChIP | Multiple algorithms, web interface | Publication-quality | | MEME | De novo discovery only | Simple discovery | | FIMO | Known motif scanning | Genome-wide scanning |

HOMER

Installation

conda install -c bioconda homer

# Configure genome (required once)
perl /path/to/homer/configureHomer.pl -install hg38
perl /path/to/homer/configureHomer.pl -install mm10

De Novo Motif Discovery

**Goal:** Discover enriched DNA-binding motifs directly from ChIP-seq peak sequences.

**Approach:** Run findMotifsGenome.pl on a peak BED file with a specified fragment size, optionally providing background regions and target motif lengths.

# Basic motif finding
findMotifsGenome.pl peaks.bed hg38 output_dir/ -size 200

# With background regions
findMotifsGenome.pl peaks.bed hg38 output_dir/ -size 200 -bg background.bed

# Specify motif lengths to search
findMotifsGenome.pl peaks.bed hg38 output_dir/ -size 200 -len 8,10,12

Key Options

| Option | Description | |--------|-------------| | `-size <#>` | Fragment size for analysis (default 200) | | `-size given` | Use actual peak sizes | | `-bg <file>` | Background regions (BED) | | `-len <#,#,...>` | Motif lengths to search | | `-mask` | Mask repeats | | `-p <#>` | Number of CPUs | | `-S <#>` | Number of motifs to find (default 25) | | `-mis <#>` | Mismatches allowed (default 2) | | `-noweight` | Don't adjust for GC content |

Output Files

output_dir/
├── homerResults.html      # Main results page
├── knownResults.html      # Known motif enrichment
├── homerMotifs.all.motifs # All discovered motifs
├── knownResults.txt       # Known motif statistics
└── motif1.motif           # Individual motif files

Known Motif Enrichment Only

# Skip de novo, only check known motifs
findMotifsGenome.pl peaks.bed hg38 output_dir/ -size 200 -nomotif

Scan for Specific Motifs

# Find instances of motif in peaks
annotatePeaks.pl peaks.bed hg38 -m motif.motif > annotated.txt

# Scan genome for motif occurrences
scanMotifGenomeWide.pl motif.motif hg38 > motif_sites.bed

Motif Comparison

# Compare discovered motifs to known database
compareMotifs.pl motifs.motif output_dir/ -known

Create Custom Motif

# From consensus sequence
seq2profile.pl CACGTG 4 > MYC.motif

# From aligned sequences
cat aligned_seqs.txt | alignAndConvert.pl - > custom.motif

MEME Suite

Installation

conda install -c bioconda meme

Extract Sequences from Peaks

# Get FASTA sequences under peaks
bedtools getfasta -fi genome.fa -bed peaks.bed -fo peaks.fa

# Center peaks and resize
bedtools slop -i peaks.bed -g genome.sizes -b 100 | \
    bedtools getfasta -fi genome.fa -bed - -fo peaks_centered.fa

MEME (De Novo Discovery)

# Basic de novo discovery
meme peaks.fa -dna -oc meme_output -mod zoops -nmotifs 10 -minw 6 -maxw 20

# With Markov background
fasta-get-markov peaks.fa > background.model
meme peaks.fa -dna -oc meme_output -bfile background.model -mod zoops -nmotifs 10

MEME Options

| Option | Description | |--------|-------------| | `-mod zoops` | Zero or one per sequence (default for ChIP) | | `-mod oops` | Exactly one per sequence | | `-mod anr` | Any number of repeats | | `-nmotifs <#>` | Number of motifs to find | | `-minw <#>` | Minimum motif width | | `-maxw <#>` | Maximum motif width | | `-revcomp` | Search both strands | | `-bfile <file>` | Background model file |

MEME-ChIP (Comprehensive Pipeline)

**Goal:** Run a comprehensive motif analysis pipeline combining de novo discovery, central enrichment testing, and database comparison.

**Approach:** Provide peak FASTA sequences and a motif database to MEME-ChIP, which runs MEME, DREME, CentriMo, TOMTOM, and FIMO in a single invocation.

# All-in-one ChIP-seq motif analysis
meme-chip -oc meme_chip_output -db motif_database.meme peaks.fa

MEME-ChIP runs: 1. MEME - De novo discovery (central enrichment) 2. DREME - Short motif discovery 3. CentriMo - Central enrichment analysis 4. TOMTOM - Compare to known motifs 5. FIMO - Find motif instances

DREME (Short Motifs)

# Find short enriched motifs
dreme -oc dreme_output -p peaks.fa -n background.fa

CentriMo (Central Enrichment)

# Test for central enrichment of known motifs
centrimo -oc centrimo_output peaks.fa motif_database.meme

TOMTOM (Motif Comparison)

# Compare discovered motifs to database
tomtom -oc tomtom_output discovered.meme database.meme

FIMO (Motif Scanning)

# Scan sequences fo