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: ```bash 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: ```bash 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: ```bash 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