openai/ngs-chip-cutrun-peaks-qc

ChIP/CUT&RUN Peaks QC

Use this skill for antibody-targeted enrichment workflows: ChIP-seq, CUT&RUN, or CUT&Tag. Use ngs-atacseq-peaks-qc for ATAC-seq.

Essential Inputs

Confirm:

• assay: ChIP-seq, CUT&RUN, or CUT&Tag
• target class: transcription factor, histone mark, chromatin regulator, or custom target
• FASTQ/BAM inputs and paired-end status
• input DNA, IgG, no-antibody, or spike-in controls
• organism, genome build, blacklist, and spike-in genome if used
• biological replicates, conditions, batches, and sample metadata
• desired endpoint: QC, peaks, bigWigs, consensus peaks, or differential binding

Route

Use nf-core/chipseq for ChIP-seq and nf-core/cutandrun for CUT&RUN/CUT&Tag when they fit the assay. Use direct MACS2 only for prepared BAMs with known control and duplicate policy.

Preflight command:

python plugins/ngs-analysis/scripts/ngs_preflight.py --pipeline chip_cutrun_peaks_qc --emit-install-plan

For compact FASTQ intake/QC, use the shared epigenomics execution package:

python plugins/ngs-analysis/scripts/run_fastq_assay_package.py \
  --lane epigenomics_peaks \
  --sample-sheet chip_or_cutrun_samples.csv \
  --execute

It records FASTQ-level QC and peak-calling readiness.

For local-light alignment, control-aware MACS2 peak calling, FRiP, bigWig tracks, consensus peaks, and motif-handoff artifacts, use the dedicated ChIP/CUT&RUN runner:

python plugins/ngs-analysis/scripts/run_chip_cutrun_peaks_qc.py \
  --sample-sheet chip_or_cutrun_samples.csv \
  --assay chipseq \
  --target-class tf \
  --peak-mode narrow \
  --bowtie2-index /refs/GRCh38/bowtie2/genome \
  --genome-size hs \
  --blacklist-bed /refs/GRCh38/blacklists/encode_blacklist.bed \
  --execute

This runner emits qc/chip_cutrun_qc_summary.{tsv,json}, qc/chip_cutrun_qc_dashboard.html, native SVG FRiP/peak and insert-size plots, browser-track handoff files under tracks/, and motifs/motif_summary.tsv. Add --run-motifs --motif-genome <genome> when HOMER motif enrichment should be executed instead of only planned.

It also emits resources/resource_plan.json, resource_manifest.tsv, resource_env.sh, and resource_readiness.md. The resource check is advisory by default for local-light runs; add --genome-build, --bundle-root <bundle>=<path>, and --require-resource-plan when missing registered reference bundles should block readiness.

For nf-core execution, use plugins/ngs-analysis/scripts/run_nfcore_pipeline.py --pipeline chipseq or --pipeline cutandrun.

Decision Points

• Choose narrow versus broad peak mode from target biology, not from convenience.
• Preserve control pairing and spike-in metadata through sample sheets.
• For histone marks, expect broad or domain-like signal for many marks; for TFs, expect sharper peaks and stronger replicate checks.
• Review alignment rate, duplicate rate, fragment size, FRiP/peak signal, blacklist overlap, and replicate concordance.
• Keep consensus peak generation and differential binding design separate from raw peak calling.

Outputs

Produce:

• assay/target/control manifest
• command/profile and sample sheet
• QC summary with replicate/control status
• peaks, bigWigs, browser-track manifests, browser-track preview HTML, native QC dashboard/SVG plots, consensus peaks, and count matrix when requested
• motif summary files when a motif backend is requested
• differential binding design and caveats for missing controls, weak enrichment, or poor replicate concordance