Discovers de novo motifs and tests known motif enrichment in ChIP-seq, ATAC-seq, or other peak sequences using HOMER, MEME-ChIP (STREME, CentriMo, TOMTOM, FIMO), monaLisa, and AME. Handles background selection (GC-matched, dinucleotide-shuffled, Markov order-2, peak-flanks), motif databases (JASPAR 2024 CORE PWMs, JASPAR 2026 deep-learning collection, HOCOMOCO v12, HOMER built-in), centrally-enriched motif testing, and differential motif analysis. Use when identifying TF binding motifs in peaks, testing for known TF enrichment, scanning for motif instances, comparing motif content between conditions, or interpreting motifs from deep learning models.
Discovers de novo motifs and tests known motif enrichment in ChIP-seq, ATAC-seq, or other peak sequences using HOMER, MEME-ChIP (STREME, CentriMo, TOMTOM, FIMO), monaLisa, and AME. Handles background selection (GC-matched, dinucleotide-shuffled, Markov order-2, peak-flanks), motif databases (JASPAR 2024 CORE PWMs, JASPAR 2026 deep-learning collection, HOCOMOCO v12, HOMER built-in), centrally-enriched motif testing, and differential motif analysis. Use when identifying TF binding motifs in peaks, testing for known TF enrichment, scanning for motif instances, comparing motif content between conditions, or interpreting motifs from deep learning models.
tool_type
cli
primary_tool
HOMER
Version Compatibility
Reference examples tested with: HOMER 4.11+, MEME suite 5.5+ (STREME replaces DREME from 5.4+), monaLisa 1.10+, JASPAR 2024 CORE, HOCOMOCO v12, BioPython 1.83+, bedtools 2.31+.
DREME was removed from MEME suite 5.4+; use STREME instead. Some tutorials still reference DREME — verify the installed version via meme --version. JASPAR 2026 (released late 2025) integrates 1259 BPNet ChIP models in a Deep Learning collection; the CORE collection remains the standard PWM source.
Motif Analysis on ChIP-seq Peaks
"Find enriched DNA binding motifs in my ChIP-seq peaks" -> Discover de novo motif patterns and test for known TF motif enrichment in peak sequences, with appropriate background to control for compositional and positional biases.
R (regression-based, selective enrichment): monaLisa::calcBinnedMotifEnrR(seqs, bins, pwms)
CLI (deep-learning-derived motifs): TF-MoDISco on BPNet attribution scores (see chip-deep-learning)
Motif discovery is sensitive to background choice and peak quality. Hyper-ChIPable artifacts at rRNA / housekeeping loci often produce false-positive motifs (GC-rich or A-T-rich biases of those regions). Filter peaks against blacklists and inspect peak distribution before running motif discovery.
Tool Taxonomy
Tool
Discovery type
Background handling
Strength
Fails when
HOMER findMotifsGenome.pl
De novo + known
GC-matched genomic regions (auto)
Fast (multi-core); integrated vertebrate/insect/plant DBs; one-command full report
Background can include unmasked repeats producing motif artifacts; -size given slow; auto background may include peaks themselves
MEME-ChIP
De novo (STREME, MEME) + central enrichment (CentriMo) + DB comparison (TOMTOM) + scanning (FIMO)
Markov order-2 from input; shuffled (preserves dinucleotide)
Comprehensive single command; rigorous statistics; HTML report
Slower; sequences must be 100-500 bp; central enrichment requires summit-centered peaks
STREME (MEME 5.4+)
De novo (replaced DREME)
Markov order-2
Bailey 2021 benchmark: more accurate than DREME/HOMER/MEME/Peak-motifs; handles 3-30 bp; scales to 100k+ sequences
Memory-hungry for very long sequences (>1 kb)
MEME (classical)
De novo (long, gapped)
Markov
Long motifs; gapped motifs
Slow (no parallel); replaced by STREME for short motifs
DREME
De novo (short)
Shuffled
Historical; small fast
Removed from MEME 5.4+; use STREME
monaLisa (Stadler lab)
Binned enrichment regression
Native (binned scoring)
Modern; regression-based; selectivity (TF-specific in differential peaks)
R-only; less integrated with browsers
AME (MEME suite)
Known motif differential
Matched background set required
Designed for two-set comparison (e.g., peaks vs. control regions)
Requires user-provided background set
CentriMo
Known motif central enrichment
Auto from input
Tests positional enrichment relative to peak center
Requires summit-centered peaks (200-500 bp)
FIMO
Motif scanning
Markov model
Genome-wide scanning at user-set p-value
Many false positives at p ≤ 1e-4; tighten to 1e-5 for whole-genome
HOMER scanMotifGenomeWide.pl
Motif scanning
None
Genome-wide scanning at fixed score threshold
Less calibrated than FIMO; HOMER's PWM format
RSAT peak-motifs
De novo + known
k-mer comparison
Web-server; multi-tool ensemble
Web limits; less reproducible from CLI
TF-MoDISco
DL attribution-based
Implicit in model
Motifs from BPNet/chromBPNet attribution scores; captures soft motif syntax
Requires trained DL model; see chip-deep-learning
Background Selection — The Biggest Source of Error
Motif enrichment p-values are conditional on the background distribution. Wrong background produces wrong motifs.
Background
What it preserves
Use case
Limitation
GC-matched genomic regions
Mononucleotide composition; chromatin context
TF motifs; avoid GC-bias artifacts
Doesn't preserve dinucleotide (CpG, TpA)
Dinucleotide-shuffled
CpG and TpA frequencies
Short motifs; avoiding repeat-derived artifacts
Doesn't capture genomic position context
Markov order-2 (trinucleotide)
Trinucleotide context
Compositional control; STREME/MEME default
Doesn't capture chromatin context
Peak-flanking sequences (±500 bp upstream/downstream of peak)
Local genomic context
When peak GC differs from genome
May contain shared regulatory motifs if peaks cluster
Repeat-masked input
Sequence with repeats replaced by N
Avoid TE-derived motif artifacts
Loses motifs in evolved-from-repeat regulatory elements
Input control peaks
Open-chromatin / artifact regions
TF discrimination from generic chromatin
Hard to obtain; controversial
Differential set (AME)
Treatment-condition-specific peaks vs ctrl peaks
Differential motif enrichment
Requires a control peak set
Practical default: STREME / MEME-ChIP with Markov order-2 (built-in default); HOMER with -mask flag (mask repeats); always inspect for repeat-derived motifs (e.g., Alu-derived AluY consensus, LINE motifs).
Window Around Summit Matters
Motif enrichment improves dramatically when sequences are summit-centered:
Window
When
±100 bp (200 bp total)
Sharp TFs (CTCF, p53); summit reliably reflects motif position
±150-250 bp (300-500 bp)
Most TFs and sharp histones; balance of motif coverage and noise
For ChIP-seq broad histone marks (H3K27me3, H3K9me3) motif analysis is generally NOT informative — these marks reflect Polycomb / heterochromatin domains without sequence-specific binding. Motif analysis applies to TFs and to histone marks deposited by sequence-specific cofactors (H3K27ac partial, since BRD4 reads acetyl).
HOMER Workflow
# De novo + known motif discovery, repeat-masked, GC-matched background
findMotifsGenome.pl peaks.narrowPeak hg38 homer_out/ \
-size 200 \
-mask \
-p 8
# With user-supplied background (e.g., control peaks or random genomic)
findMotifsGenome.pl peaks.narrowPeak hg38 homer_out/ \
-size 200 -mask -p 8 \
-bg background_peaks.bed
# Known motifs only (skip de novo; faster)
findMotifsGenome.pl peaks.narrowPeak hg38 homer_known_only/ \
-size 200 -mask -nomotif
# Differential motif analysis: peaks gained in condition A vs condition B
findMotifsGenome.pl gained_in_A.bed hg38 differential_motifs/ \
-size 200 -mask -bg gained_in_B.bed
HOMER output files:
homerResults.html — de novo motifs ranked by significance
knownResults.html — known motif enrichment
homerMotifs.all.motifs — all de novo motifs (PWM format)
knownResults.txt — tab-separated known motif stats
MEME-ChIP Workflow
# Center peaks to ±100 bp around summit (column 10 in narrowPeak)
awk 'BEGIN{OFS="\t"} {summit = $2 + $10; print $1, summit - 100, summit + 100, $4, $5, $6}' \
peaks.narrowPeak > peaks_centered.bed
bedtools getfasta -fi hg38.fa -bed peaks_centered.bed -fo peaks_centered.fa
# Full MEME-ChIP analysis
meme-chip \
-oc meme_chip_out/ \
-db JASPAR2024_CORE_vertebrates_non-redundant_pfms_meme.txt \
-meme-nmotifs 5 \
-streme-nmotifs 10 \
-minw 6 -maxw 20 \
peaks_centered.fa
# MEME-ChIP runs: STREME (replaces DREME) + MEME + CentriMo + TOMTOM + FIMO# CentriMo tests known motifs for central enrichment — strongest signal of# direct binding vs. tethered/indirect binding
monaLisa Workflow (R, Regression-Based)
Goal: Test which TF motifs are enriched in specific bins of peaks (e.g., bins of differential log2FC, or bins of accessibility).
Approach: monaLisa builds a per-motif regression of peak signal on motif occurrence, controlling for GC content. Selective for TFs that discriminate between bins.
library(monaLisa)
library(JASPAR2024)
library(TFBSTools)
library(Biostrings)
library(BSgenome.Hsapiens.UCSC.hg38)# Load peaks and split into bins (e.g., quintiles of log2FC)
peaks <- rtracklayer::import('peaks.bed')
peaks$log2FC <- ... # from differential analysis
bins <- bin(peaks$log2FC, binmode ='equalN', nElements =200)# Get sequences around peak centers
seqs <- getSeq(BSgenome.Hsapiens.UCSC.hg38, resize(peaks, width =500, fix ='center'))# Load JASPAR PWMs
pwms <- getMatrixSet(RSQLite::dbConnect(RSQLite::SQLite(), db(JASPAR2024())),list(species =9606, collection ='CORE'))# Compute binned motif enrichment with GC control
res <- calcBinnedMotifEnrR(seqs = seqs, bins = bins, pwmL = pwms, BPPARAM = MulticoreParam(8))# Plot heatmap of motif enrichment vs bins
plotMotifHeatmaps(x = res, which.plots =c('log2enr','negLog10P'),
width =1.8, maxEnr =2, maxSig =10)
Per-Tool Failure Modes
HOMER -- Background includes peaks themselves
Trigger: Running findMotifsGenome.pl without -bg on a large peak set covering >5% of genome.
Mechanism: HOMER auto-samples GC-matched genomic regions for background, which can overlap the peak set itself.
Symptom: Even known TF motif p-values are weak (>1e-3); de novo motifs less enriched than expected.
Fix: Supply explicit -bg background (e.g., random genomic intervals matching peak count and width) OR use MEME-ChIP with internal shuffled background.
HOMER / MEME -- Repeat-derived false-positive motifs
Trigger: Running on unmasked peaks; peaks cover transposable elements (Alu, LINE, LTR).
Mechanism: TEs contain over-represented k-mers that motif algorithms mistake for biology.
Symptom: Top de novo motif matches AluY consensus (~280 bp), LINE/L1, or LTR families.
Fix: Use -mask (HOMER) or pre-mask peak sequences with RepeatMasker; verify TOMTOM matches against legitimate TF databases.
STREME -- Memory failure on long sequences
Trigger: Running STREME on full-peak sequences (>1 kb each) with > 50k peaks.
Mechanism: STREME holds suffix structures in memory; long sequences explode RAM.
Fix: Resize peaks to ±100-250 bp summit-centered before STREME; or downsample peak count.
CentriMo -- No central enrichment due to wrong centering
Trigger: Using full peak coordinates (BED start, end) without recentering on summit.
Mechanism: Peak start coordinate is the left edge, not the summit; motif may be enriched near the summit but appears unenriched relative to the start.
Symptom: Known TF motifs show no central enrichment despite being clearly enriched overall.
Fix: Recenter sequences on summit: summit = start + summit_offset (narrowPeak column 10) before extracting FASTA.
FIMO -- Massive false positives at default p ≤ 1e-4
Trigger: Genome-wide scanning at FIMO default p-value threshold.
Mechanism: At p ≤ 1e-4, expect ~3M random matches in a 3 Gb genome; most are false positives.
Symptom: FIMO output has millions of motif "hits"; can't distinguish real binding.
Fix: Tighten to --thresh 1e-5 or stricter for genome-wide scans; or restrict scan to peaks: fimo --bgfile motif_bg motif.meme peaks.fa.
monaLisa -- GC bins not respected
Trigger: Running calcBinnedMotifEnrR without GC binning when peaks have systematic GC differences (e.g., promoters vs distal enhancers).
Mechanism: GC-rich motifs are over-enriched in GC-rich bins simply by chance.
Symptom: Top motifs are CpG-rich families (e.g., E2F, NRF1) regardless of biology.
Fix: Use background = 'genome' with GC-matched genomic background; or background = 'otherBins' with stratified GC.
Motif analysis on hyper-ChIPable regions
Trigger: Running motifs on peaks dominated by hyper-ChIPable artifacts (rRNA, tRNA, housekeeping).
Mechanism: These regions have systematic compositional biases (GC-rich, A-T-rich, repeat-derived); motifs from artifact peaks reflect compositional biology, not TF binding.
Symptom: Top de novo motif matches no known TF; high-GC or low-complexity consensus.
Fix: Apply blacklist + custom hyper-ChIPable filter (top-1% input signal) before motif analysis. See chipseq-qc.
Reconciliation: When Motif Methods Disagree
Pattern
Likely cause
Action
HOMER finds motif X; MEME-ChIP misses
Different background; HOMER may have permissive background
Run MEME-ChIP with explicit GC-matched background; check
MEME finds long gapped motif; STREME doesn't
MEME captures variable-length structure; STREME limited to 30 bp
Both are correct; report MEME for long motifs
Top de novo motif doesn't match TOMTOM databases
Novel motif OR repeat artifact OR compositional artifact
Inspect peaks for repeats; check input control; could be genuine novel TF
Known motif enriched but no de novo recovery
Insufficient enrichment for de novo; or motif is degenerate
Trust known motif enrichment; de novo needs strong signal
Differential motif gained in treatment but TF expression unchanged
TF post-translational regulation (binding mode change without expression change)
Check ChIP signal at known TF target genes; not a contradiction
