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atac-seq
ATAC-seq processing with assay QC, MACS3 peak calling, consensus peak matrices, differential accessibility, and motif or footprint follow-up.
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
菜单
ATAC-seq processing with assay QC, MACS3 peak calling, consensus peak matrices, differential accessibility, and motif or footprint follow-up.
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
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Standard scRNA-seq preprocessing and clustering with Scanpy. Use for QC, normalization, HVG selection, PCA, neighbor graph construction, UMAP, Leiden clustering, and export of an analysis-ready AnnData object.
| name | atac-seq |
| description | ATAC-seq processing with assay QC, MACS3 peak calling, consensus peak matrices, differential accessibility, and motif or footprint follow-up. |
| tool_type | mixed |
| primary_tool | MACS3 |
Reference examples assume:
macs3 3.0+samtools 1.18+deepTools 3.5+Verify the runtime first:
macs3 --version, samtools --version, bamCoverage --versionUse this skill when the user needs:
BAMPE| Check | Guidance |
|---|---|
| uniquely mapped reads | >= 20M preferred for strong bulk ATAC |
| TSS enrichment | > 7 acceptable, > 10 strong |
| FRiP | > 0.2 often strong for good bulk ATAC |
results/peaks/sample_peaks.narrowPeakresults/matrix/consensus_peak_counts.tsvresults/diff_accessibility.tsvfigures/tss_enrichment.pdffigures/fragment_size_distribution.pdfmacs3 callpeak \
-t atac.bam \
-f BAMPE \
-g hs \
-n sample \
--nomodel \
--shift -100 \
--extsize 200 \
-q 0.01 \
--outdir results/peaks
| Parameter | Typical value | Notes |
|---|---|---|
-f | BAMPE | paired-end ATAC should use fragment-aware mode |
--nomodel | on | standard for ATAC |
--shift | -100 | common Tn5 offset convention |
--extsize | 200 | common first-pass extension |
-q | 0.01 | starting FDR threshold |
Review:
Use fragment-aware paired-end mode and Tn5-aware shifting or equivalent settings.
Merge peaks across samples, count fragments into consensus intervals, then produce a peak-by-sample matrix.
Use replicate-aware statistics and report both effect size and adjusted significance.
Only after peak quality and read depth support it.
results/
├── peaks/
│ ├── sample_peaks.narrowPeak
│ └── sample_summits.bed
├── matrix/
│ └── consensus_peak_counts.tsv
└── diff_accessibility.tsv
qc/
├── tss_enrichment.tsv
└── fragment_metrics.tsv
figures/
├── tss_enrichment.pdf
└── fragment_size_distribution.pdf
7 should trigger caution.0.1 is usually weak and needs scrutiny.deeptoolspysam