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>thenhelp(module.function)to check signatures - CLI:
<tool> --versionthen<tool> --helpto 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
bashconda 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.
bash# 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
bash# Skip de novo, only check known motifs findMotifsGenome.pl peaks.bed hg38 output_dir/ -size 200 -nomotif
Scan for Specific Motifs
bash# 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
bash# Compare discovered motifs to known database compareMotifs.pl motifs.motif output_dir/ -known
Create Custom Motif
bash# From consensus sequence seq2profile.pl CACGTG 4 > MYC.motif # From aligned sequences cat aligned_seqs.txt | alignAndConvert.pl - > custom.motif
MEME Suite
Installation
bashconda install -c bioconda meme
Extract Sequences from Peaks
bash# 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)
bash# 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.
bash# All-in-one ChIP-seq motif analysis meme-chip -oc meme_chip_output -db motif_database.meme peaks.fa
MEME-ChIP runs:
- MEME - De novo discovery (central enrichment)
- DREME - Short motif discovery
- CentriMo - Central enrichment analysis
- TOMTOM - Compare to known motifs
- FIMO - Find motif instances
DREME (Short Motifs)
bash# Find short enriched motifs dreme -oc dreme_output -p peaks.fa -n background.fa
CentriMo (Central Enrichment)
bash# Test for central enrichment of known motifs centrimo -oc centrimo_output peaks.fa motif_database.meme
TOMTOM (Motif Comparison)
bash# Compare discovered motifs to database tomtom -oc tomtom_output discovered.meme database.meme
FIMO (Motif Scanning)
bash# Scan sequences for motif matches fimo --oc fimo_output motif.meme sequences.fa # Scan genome fimo --oc fimo_output --max-stored-scores 1000000 motif.meme genome.fa
Motif Databases
HOMER Built-in
bash# List available motif sets ls /path/to/homer/data/knownTFs/ # Vertebrate, known motifs (default) findMotifsGenome.pl peaks.bed hg38 output/ -mknown vertebrates/known.motifs
JASPAR
bash# Download JASPAR motifs wget https://jaspar.genereg.net/download/data/2024/CORE/JASPAR2024_CORE_vertebrates_non-redundant_pfms_meme.txt # Use with MEME suite meme-chip -db JASPAR2024_CORE_vertebrates_non-redundant_pfms_meme.txt peaks.fa
HOCOMOCO
bash# Download HOCOMOCO wget https://hocomoco11.autosome.org/final_bundle/hocomoco11/core/HUMAN/mono/HOCOMOCOv11_core_HUMAN_mono_meme_format.meme # Use with MEME suite tomtom discovered.meme HOCOMOCOv11_core_HUMAN_mono_meme_format.meme
Python: Parse HOMER Results
pythonimport pandas as pd def parse_homer_known(results_file): '''Parse HOMER knownResults.txt.''' df = pd.read_csv(results_file, sep='\t') df.columns = ['Motif', 'Consensus', 'P-value', 'Log P-value', 'q-value', 'Targets', 'Target%', 'Background', 'Background%'] df['P-value'] = df['P-value'].astype(float) return df.sort_values('P-value') known = parse_homer_known('output_dir/knownResults.txt') print(known[['Motif', 'P-value', 'Target%']].head(20))
Python: Parse MEME Results
pythonfrom Bio import motifs def parse_meme_file(meme_file): '''Parse MEME output file.''' with open(meme_file) as f: record = motifs.parse(f, 'meme') return record record = parse_meme_file('meme_output/meme.txt') for m in record: print(f'{m.name}: {m.consensus}') print(m.counts)
Complete Workflows
ChIP-seq Motif Analysis
Goal: Run a complete motif analysis workflow combining HOMER and MEME-ChIP on ChIP-seq peaks.
Approach: Run HOMER findMotifsGenome.pl for fast de novo and known motif discovery, then extract centered peak sequences and run MEME-ChIP for a complementary analysis.
bash#!/bin/bash set -euo pipefail PEAKS=$1 # narrowPeak or BED file GENOME=$2 # hg38, mm10, etc. OUTDIR=$3 mkdir -p $OUTDIR # HOMER analysis echo "Running HOMER..." findMotifsGenome.pl $PEAKS $GENOME ${OUTDIR}/homer \ -size 200 -p 8 -mask # Extract sequences for MEME echo "Extracting sequences..." bedtools slop -i $PEAKS -g ${GENOME}.chrom.sizes -b 0 | \ awk 'BEGIN{OFS="\t"} {center=int(($2+$3)/2); print $1,center-100,center+100}' | \ bedtools getfasta -fi ${GENOME}.fa -bed - -fo ${OUTDIR}/peaks.fa # MEME-ChIP analysis echo "Running MEME-ChIP..." meme-chip -oc ${OUTDIR}/meme_chip \ -db /path/to/JASPAR.meme \ ${OUTDIR}/peaks.fa echo "Done. Results in ${OUTDIR}/"
ATAC-seq Footprint Motifs
bash# Analyze motifs in footprint regions findMotifsGenome.pl footprints.bed hg38 footprint_motifs/ \ -size given -mask -p 8 # Compare to accessible regions background findMotifsGenome.pl footprints.bed hg38 footprint_motifs/ \ -size given -bg accessible_peaks.bed -mask -p 8
Visualization
HOMER Logo
bash# Generate sequence logo motif2Logo.pl motif.motif > logo.eps
Plot with Python
pythonimport logomaker import pandas as pd import matplotlib.pyplot as plt def plot_motif(pwm_file): '''Plot sequence logo from HOMER PWM.''' pwm = pd.read_csv(pwm_file, sep='\t', skiprows=1, header=None) pwm.columns = ['A', 'C', 'G', 'T'] logo = logomaker.Logo(pwm, shade_below=0.5, fade_below=0.5) plt.show()
Quality Metrics
| Metric | Good | Concerning |
|---|---|---|
| P-value | < 1e-10 | > 1e-5 |
| Target % | > 20% | < 5% |
| Background % | < Target/2 | Similar to Target |
| Bit score | > 10 | < 5 |
Common Issues
No Significant Motifs
- Check peak quality (too few peaks?)
- Try different peak sizes (
-size) - Ensure genome build matches
- Check for repeat masking issues
Too Many Motifs
- Increase significance threshold
- Use
-Sto limit number of motifs - Filter by target percentage
Wrong Background
- Use matched GC content background
- Consider using input/control peaks
- Try shuffled sequences
Related Skills
- peak-calling - Generate input peaks
- peak-annotation - Annotate peaks with genes
- atac-seq/footprinting - TF footprint analysis
- genome-intervals - BED file operations
