bio-chipseq-cut-and-run-tag
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.
npx skills add BioTender-max/awesome-bio-agent-skills --skill cut-and-run-tag --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
## Version Compatibility Reference examples tested with: SEACR 1.4+, 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. Treati
- Version Compatibility
- Protocol Variant Taxonomy
- Algorithmic Taxonomy
- SEACR Workflow (Canonical CUT&RUN/Tag Caller)
- E. coli Spike-In Carryover (Automatic Spike-In)
- QC Differences from Traditional ChIP
- Fragment Size as Diagnostic (Critical for CUT&Tag)
- Per-Tool Failure Modes
- SEACR -- Wrong mode for context
- CUT&Tag MACS2 -- Default --keep-dup auto removes biology
- pA-Tn5 vs pAG-Tn5 -- Antibody species mismatch
- Digitonin permeabilization -- Wrong concentration
- ConA bead vs sepharose -- Volume / sample mismatch
- Adapter readthrough in short fragments
bowtie2 --local --very-sensitive --no-mixed --no-discordant \
samtools view -bS aln.sam | samtools sort -o aln.bam
samtools index aln.bam
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
Final argument is the OUTPUT PREFIX; SEACR appends ".stringent.bed" / ".relaxed.bed".
bash SEACR_1.4.sh aln.bedgraph igg.bedgraph norm stringent target_peaks
Output file: target_peaks.stringent.bedWhat does the bio-chipseq-cut-and-run-tag skill do?
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.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill cut-and-run-tag --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BioTender-max/awesome-bio-agent-skills, a repository with 135 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.