Analyzes CUT&RUN (Skene Henikoff 2017) and CUT&Tag (Kaya-Okur 2019) chromatin profiling data. Handles SEACR vs MACS2 peak calling (with the btaf375 2025 benchmark guidance), pA-MNase vs pA-Tn5 vs pAG-Tn5 chimera differences, E. coli spike-in carryover normalization, IgG-only control logic (no input), characteristic fragment-size signatures (25-75 bp for CUT&Tag), and lower depth requirements (5M reads typical vs 25M for ChIP). Use when calling peaks from CUT&RUN/CUT&Tag, scaling by E. coli spike-in carryover, choosing SEACR norm mode, or comparing CUT&RUN/Tag results to traditional ChIP.
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Analyzes CUT&RUN (Skene Henikoff 2017) and CUT&Tag (Kaya-Okur 2019) chromatin profiling data. Handles SEACR vs MACS2 peak calling (with the btaf375 2025 benchmark guidance), pA-MNase vs pA-Tn5 vs pAG-Tn5 chimera differences, E. coli spike-in carryover normalization, IgG-only control logic (no input), characteristic fragment-size signatures (25-75 bp for CUT&Tag), and lower depth requirements (5M reads typical vs 25M for ChIP). Use when calling peaks from CUT&RUN/CUT&Tag, scaling by E. coli spike-in carryover, choosing SEACR norm mode, or comparing CUT&RUN/Tag results to traditional ChIP.
"Analyze CUT&RUN or CUT&Tag chromatin profiling data" -> Use the lower-background, lower-input alternatives to traditional ChIP. CUT&RUN tethers MNase to an antibody via Protein A; CUT&Tag tethers Tn5 via Protein A/G. Both bypass cross-linking, fragmentation, and IP washes — producing 10-100× lower background, allowing 100-1000× lower cell input, and shifting the peak-calling problem from "find signal in noise" to "find signal in near-zero background."
Aligner: bowtie2 (CUT&RUN/Tag standard) or bwa-mem; chromap optional
Peak calling (CUT&RUN/Tag): SEACR (Meers 2019), MACS2 with -f BAMPE --keep-dup all, or both for consensus
Spike-in: E. coli carryover from bacterially-produced pA-MNase/Tn5 (automatic, variable)
Control: IgG-only (no input control; native chromatin has no meaningful "input")
CUT&RUN/CUT&Tag has different QC thresholds, different peak calling defaults, different spike-in protocols, and different antibody requirements than traditional ChIP. Treating it as ChIP fails silently.
Most conservative; may miss true peaks at marginal regions
2025 benchmark (Bioinformatics 41:btaf375, Nooranikhojasteh et al): benchmarked MACS2, SEACR, GoPeaks and LanceOtron on CUT&RUN.
MACS2 better for sharp peaks (H3K4me3, TFs)
SEACR gave the highest signal-to-noise across marks
No single caller is universally optimal; the study favors careful parameter tuning or ensemble/consensus approaches over any single tool
SEACR Workflow (Canonical CUT&RUN/Tag Caller)
Goal: Call CUT&RUN/CUT&Tag peaks from aligned BAMs using SEACR with IgG-aware threshold.
Approach: Align with Henikoff parameters, convert BAM to fragment bedGraph via bamtobed-bedpe, then invoke SEACR with norm stringent mode and IgG control.
# 1. Align with bowtie2 (Henikoff lab standard parameters)
bowtie2 --local --very-sensitive --no-mixed --no-discordant \
--phred33 -I 10 -X 700 \
-x hg38 -1 reads_R1.fq -2 reads_R2.fq \
-S aln.sam
# 2. Convert SAM to BAM, sort, index
samtools view -bS aln.sam | samtools sort -o aln.bam
samtools index aln.bam
# 3. Generate bedGraph for SEACR (paired-end fragments)
samtools view -bS -F 0x04 aln.bam | bedtools bamtobed -bedpe -i - > aln.bedpe
awk '$1==$4 && $6-$2 < 1000 {print $0}' aln.bedpe > aln.clean.bedpe
cut -f 1,2,6 aln.clean.bedpe | sort -k1,1 -k2,2n -k3,3n > aln.fragments.bed
bedtools genomecov -bg -i aln.fragments.bed -g hg38.chrom.sizes > aln.bedgraph
# 4. Same for IgG control# (... produce igg.bedgraph similarly ...)# 5. SEACR with stringent + norm + IgG control (recommended default).# Final argument is the OUTPUT PREFIX; SEACR appends ".stringent.bed" / ".relaxed.bed".
bash SEACR_1.3.sh aln.bedgraph igg.bedgraph norm stringent target_peaks
# Output file: target_peaks.stringent.bed# Alternative: no IgG control, use top 1% of peaks# bash SEACR_1.3.sh aln.bedgraph 0.01 non stringent target_peaks
SEACR mode selection:
norm (recommended): scales target to IgG distribution
non: use ONLY if upstream spike-in normalization was applied; otherwise use norm
stringent: top-half of signal blocks (recommended default)
relaxed: full distribution (use only for very sparse signal)
Top-X% without IgG: bash SEACR_1.3.sh target.bg 0.01 non stringent out_prefix -> writes out_prefix.stringent.bed
E. coli Spike-In Carryover (Automatic Spike-In)
CUT&RUN/CUT&Tag spike-in is "free" because the pA-MNase or pA-Tn5 carries E. coli DNA from bacterial production. Carryover is variable across batches but stable within a batch.
# Align reads to combined hg38 + E. coli genome (or sequential)
bowtie2 -x hg38_ecoli_combined -1 R1.fq -2 R2.fq -S aln.sam
# Count E. coli reads per sample
ECOLI_READS=$(samtools view -c -F 4 aln.bam ecoli_chr1)
# Scaling factor: smallest E. coli read count / per-sample E. coli reads# Apply BEFORE peak calling for cross-condition comparison
Expected E. coli alignment fractions:
Target ChIP samples: 0.5-2% of total reads
IgG control: 2-5% (higher because no target chromatin to dilute)
Below 0.1% E. coli: spike-in carryover lost; cross-condition normalization unreliable
Above 10%: target ChIP failed (mostly E. coli)
The carryover is variable between batches of bacterial production; single-experiment carryover spike-in is noisier than deliberate Drosophila spike-in (ChIP-Rx). For high-stakes cross-condition claims, add deliberate Drosophila spike-in despite the E. coli carryover. See chip-seq/spike-in-normalization.
QC Differences from Traditional ChIP
Metric
Traditional ChIP
CUT&RUN/CUT&Tag
FRiP (TF)
> 0.05
> 0.10 (often > 0.25)
FRiP (histone)
> 0.10
> 0.25
Library size requirement
20-50M
3-10M (often sufficient)
Input control
Required
IgG only (no input meaningful)
Fragment size (CUT&Tag)
Sub-nucleosomal or mono-nucleosomal
Sharp peak at 25-75 bp (Tn5 staggered cuts)
Fragment size (CUT&RUN)
Variable
Mono- + di-nucleosomal pattern
Duplicates
Remove (MarkDuplicates)
Keep for CUT&Tag (low PCR cycles, dups have biology)
Spike-in alignment
Deliberate (Drosophila); 0.5-5%
Automatic E. coli; 0.5-5%
Cell input
1-10M
5,000-100,000
Critical: --keep-dup all in MACS for CUT&Tag. PCR cycles are 12-15 (vs 5-8 for ChIP); duplicates at high-coverage TF binding sites contain biology. The MACS default --keep-dup 1 (keeps one read per position) will over-deduplicate CUT&Tag data.
Fragment Size as Diagnostic (Critical for CUT&Tag)
Sharp peak at 25-75 bp (Tn5 staggered insertion = ~9 bp + protein-DNA-protein interaction)
Secondary peak at ~150-200 bp (mono-nucleosomal CUT&Tag from H3K4me3 etc)
< 25 bp: Tn5 self-tagmentation noise; high abundance indicates over-tagmentation
Flat distribution above 200 bp: poor enzyme activity or over-amplification
Expected for CUT&RUN:
Mono-nucleosomal (~150 bp) for histone marks
Sub-nucleosomal (~50-100 bp) for TFs (rare; CUT&RUN better for histones)
Di-nucleosomal (~300 bp) secondary peak common
Per-Tool Failure Modes
SEACR -- Wrong mode for context
Trigger: Using non mode without prior spike-in normalization; using relaxed mode on standard CUT&Tag.
Mechanism:non assumes target is already scaled to IgG (typical for ChIP-Rx-style spike-in); on raw counts, it inflates false positives. relaxed includes the full distribution; appropriate only for sparse signal.
Fix: Default to norm stringent with IgG control. Use non only when upstream spike-in scaling has been applied.
Trigger: Using MACS2 default dedup settings on CUT&Tag.
Mechanism: Low PCR cycles (12-15) in CUT&Tag mean PCR duplicates contain real biology at high-coverage sites; auto-dedup over-filters.
Symptom: Peak counts much lower than published for same antibody / cell line.
Fix:macs2 callpeak --keep-dup all -f BAMPE for CUT&Tag. For CUT&RUN, dedup behavior depends on PCR cycles — verify with library complexity (NRF).
pA-Tn5 vs pAG-Tn5 -- Antibody species mismatch
Trigger: Using pA-Tn5 (Henikoff original) with mouse primary antibody.
Mechanism: Protein A binds rabbit IgG much better than mouse IgG; mouse antibodies give weak signal with pA-Tn5.
Fix: Use pAG-Tn5 (binds both); or switch to a rabbit primary antibody for the same target.
Digitonin permeabilization -- Wrong concentration
Trigger: Default 0.05% digitonin on all cell lines.
Mechanism: Optimal digitonin varies by cell type (some need 0.02%, some 0.1%); over-permeabilization releases chromatin into supernatant; under-permeabilization prevents antibody access.
Symptom: Inconsistent signal across cell lines; high IgG signal (under-permeabilized) or low target signal (over-permeabilized).
Fix: Titrate digitonin per cell line using a known-positive H3K4me3 antibody as control.
ConA bead vs sepharose -- Volume / sample mismatch
Trigger: Switching bead type between protocols without adjusting volume.
Mechanism: ConA magnetic beads (e.g., Bangs) and sepharose ConA have different binding capacities; protocols designed for one give wrong cell loading for the other.
Fix: Follow Henikoff lab protocol exactly for the chosen bead; or titrate cell number per bead volume.
Adapter readthrough in short fragments
Trigger: 100-150 bp reads on 25-75 bp CUT&Tag fragments.
Mechanism: Reads longer than fragments read through both adapters; downstream alignment loses the fragment.
Symptom: Many reads with adapter sequence at 3' end; alignment rate drops.
Fix: Aggressive adapter trimming with cutadapt: -e 0.1 -O 5 --minimum-length 25. Use 50 bp paired-end sequencing for CUT&Tag instead of 150 bp.
MACS2 fragment-size modeling failure on CUT&Tag
Trigger: Running MACS2 without -f BAMPE on CUT&Tag PE data.
Mechanism: MACS2 in -f BAM mode tries to model fragment size from cross-correlation; CUT&Tag fragments are 25-75 bp, not the 200 bp ChIP expects; modeling fails or produces wrong estimate.
Fix: Always use -f BAMPE for CUT&Tag; MACS uses actual fragment spans from mate pairs.
Reconciliation: When CUT&RUN/Tag Disagrees with ChIP
Pattern
Likely cause
Action
Peak count much lower in CUT&Tag vs ChIP
Lower background reveals signal vs noise; CUT&Tag often has FEWER but cleaner peaks
Both correct; CUT&Tag specificity > sensitivity
Peak count much higher in CUT&Tag vs ChIP
--keep-dup all retained PCR duplicates as peaks
Verify NRF; if low, consider deduplicating with caution
Same antibody, different signal
Native chromatin vs cross-linked accessibility differs
Native CUT&RUN may miss DSG-dependent cofactors (BRD4); add brief fixation
FRiP very high (>50%)
Likely real for CUT&Tag (low background); confirm with motif enrichment
Verify motif enrichment at peaks; if missing, suspect technical artifact
H3K4me3 CUT&Tag peak count differs from ChIP
Expected; CUT&Tag has higher specificity
Trust CUT&Tag for sharp marks
H3K27me3 CUT&RUN/Tag misses regions
Broad domains require deeper sequencing; CUT&Tag was designed for sharp marks
Use CUT&RUN or traditional ChIP for very broad marks
Common Errors
Error / symptom
Cause
Solution
SEACR "input file not bedgraph"
Wrong file format
Use bedtools genomecov -bg; not bedGraphToBigWig output
MACS2 modeling fails on CUT&Tag
Default -f BAM on PE
-f BAMPE
Peak count for mouse antibody
Used pA-Tn5 not pAG-Tn5
Switch to pAG-Tn5 OR use rabbit primary
Very high adapter content in FASTQ
Read length > fragment length
Trim aggressively; consider 50 bp PE for CUT&Tag
Sample-to-sample carryover variability >5x
E. coli carryover variable between bacterial production batches
Add deliberate Drosophila spike-in for cross-condition
IgG signal as strong as target
Failed antibody / over-permeabilization
Validate antibody on positive control; titrate digitonin
References
Skene PJ & Henikoff S 2017 eLife 6:e21856 (CUT&RUN)
