Bio Chipseq Cut And Run Tag

Version Compatibility

Reference examples tested with: SEACR 1.3+, MACS2 2.2.9+, MACS3 3.0.4+, samtools 1.19+, bowtie2 2.5+, bedtools 2.31+, deepTools 3.5+, GoPeaks 1.0+, LanceOtron (pip).

CUT&RUN / CUT&Tag

"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.

Protocol Variant Taxonomy

Algorithmic Taxonomy

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)
• IgG control: bash SEACR_1.3.sh target.bg igg.bg norm stringent out_prefix -> writes out_prefix.stringent.bed
• 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

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)

samtools view -f 0x2 sample.bam | awk '{print $9}' | awk '$1>0' \
    | sort -n | uniq -c | awk '{print $2, $1}' > frag_sizes.tsv

Expected 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.

CUT&Tag MACS2 -- Default --keep-dup 1 removes biology

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

Common Errors

References

• Skene PJ & Henikoff S 2017 eLife 6:e21856 (CUT&RUN)
• Skene PJ Henikoff JG & Henikoff S 2018 Nat Protoc 13:1006 (CUT&RUN protocol)
• Kaya-Okur HS et al 2019 Nat Commun 10:1930 (CUT&Tag)
• Kaya-Okur HS et al 2020 Nat Protoc 15:3264 (CUT&Tag protocol)
• Meers MP et al 2019 Epigenetics Chromatin 12:42 (SEACR)
• Nooranikhojasteh A et al 2025 Bioinformatics 41:btaf375 (CUT&RUN peak-caller benchmark)
• Yashar WM et al 2022 Genome Biol 23:144 (GoPeaks)
• Hentges LD et al 2022 Bioinformatics 38:4255 (LanceOtron)
• Bartosovic M Kabbe M & Castelo-Branco G 2021 Nat Biotechnol 39:825 (scCUT&Tag)
• Janssens DH et al 2021 Nat Genet 53:1586 (AutoCUT&Tag)
• Li Q et al 2024 Nat Methods 21:2044 (scNanoSeq-CUT&Tag, long-read single-cell CUT&Tag)

Related Skills

• chip-seq/peak-calling - Traditional ChIP peak calling (MACS3 + IDR vs naive overlap)
• chip-seq/chipseq-qc - QC battery (different thresholds for CUT&RUN/Tag)
• chip-seq/spike-in-normalization - Deliberate Drosophila spike-in beyond E. coli carryover
• chip-seq/differential-binding - DiffBind / csaw differential on CUT&RUN/Tag
• chip-seq/peak-annotation - Annotate CUT&RUN/Tag peaks (same tools as ChIP)
• chip-seq/super-enhancers - SE calling on CUT&Tag H3K27ac
• alignment-files/sam-bam-basics - BAM preparation
• read-qc/adapter-trimming - Aggressive trimming for short fragments