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).