Ngs Atacseq Peaks Qc

Others

ngs-atacseq-peaks-qc

Ngs Atacseq Peaks Qc is an Agent Skill for general use published in the openai/plugins repository on GitHub: a SKILL.md instruction file an AI agent can load to do this task.

Run or plan ATAC-seq QC, alignment, TSS enrichment, fragment-size, blacklist, peak-calling, consensus peak, and differential accessibility workflows.

View on GitHub ↗

QUICK START

How to use this skill

Bring this guide into your coding agent with a prompt tailored to the tool you use.

Open your project in Codex.
Copy and paste the prompt below.
Review files and risks before approving any action.

Prompt to paste

Inspect the Agent Skill at https://github.com/openai/plugins/blob/HEAD/plugins/ngs-analysis/skills/ngs-atacseq-peaks-qc/SKILL.md and all supporting files. Report provenance, permissions, scripts, dependencies, and security risks. Propose an exact installation into .agents/skills/ngs-atacseq-peaks-qc, but wait for my approval before writing files or running scripts.

Copying this prompt does not install or run the skill. Review third-party files before use.

ATAC-seq Peaks QC

Use this skill for ATAC-seq accessibility analysis from FASTQ or BAM. If the assay is ChIP-seq, CUT&RUN, CUT&Tag, or antibody-targeted enrichment, use ngs-chip-cutrun-peaks-qc.

Essential Inputs

Confirm:

FASTQ/BAM inputs and paired-end status
organism, genome build, blacklist, and mitochondrial contig names
biological replicates, conditions, batches, and sample metadata
whether the target is QC only, peaks, consensus peaks, bigWigs, or differential accessibility
whether Tn5 shifting is handled by the chosen workflow
desired peak caller and downstream matrix generation

Route

Prefer nf-core/atacseq for full reproducible processing. Use direct MACS2 only when BAMs are already aligned, duplicate/blacklist handling is known, and the user wants focused peak calling.

Preflight command:

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

For compact read-level intake/QC, use the shared epigenomics execution package:

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

For local-light ATAC alignment, peaks, FRiP, TSS, bigWig tracks, and consensus peaks from FASTQ or prepared BAMs, use the dedicated ATAC runner:

python plugins/ngs-analysis/scripts/run_atacseq_peaks_qc.py \
  --sample-sheet atac_samples.csv \
  --bowtie2-index /refs/GRCh38/bowtie2/genome \
  --genome-size hs \
  --blacklist-bed /refs/GRCh38/blacklists/encode_blacklist.bed \
  --tss-bed /refs/GRCh38/tss.bed \
  --execute

This runner emits qc/atacseq_qc_summary.{tsv,json}, qc/atacseq_qc_dashboard.html, native SVG FRiP/peak and insert-size plots, browser-track handoff files under tracks/, and TSS profile/heatmap commands when --tss-bed is supplied. Add --run-motifs --motif-genome <genome> when HOMER motif enrichment should be part of the backend run.

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 atacseq.

QC Gates

Review before biological interpretation:

read depth, alignment rate, duplicate rate, and mitochondrial fraction
insert-size periodicity/nucleosome pattern
TSS enrichment and FRiP score when available
blacklist overlap and peak count per sample
replicate concordance and consensus peak support

Do not proceed to differential accessibility if replicate quality or metadata is insufficient.

Outputs

Produce:

sample sheet and workflow command/profile
QC summary and failed-sample flags
narrowPeak/BED peak sets, consensus peaks, bigWigs, browser-track manifests, browser-track preview HTML, native QC dashboard/SVG plots, TSS plots, and peak-count matrix when requested
motif summary files when a motif backend is requested
differential-accessibility design and contrasts if applicable
caveats for low TSS enrichment, high mitochondrial reads, weak replicate concordance, or poor FRiP