Workflow for read alignment, sorting, indexing, mapping statistics, and downstream-ready alignment artifacts.
中文简介正在准备中。当前显示原文说明。
ATAC-seq processing with assay QC, MACS3 peak calling, consensus peak matrices, differential accessibility, and motif or footprint follow-up.
中文简介正在准备中。当前显示原文说明。
查看安装方法安装前可以查看交给 AI 代理的指令内容。
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还没有评价。欢迎分享你的使用感受。
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还没有评论。来写第一条吧。
Workflow for read alignment, sorting, indexing, mapping statistics, and downstream-ready alignment artifacts.
中文简介正在准备中。当前显示原文说明。
Validate protein designs using AlphaFold2 structure prediction. Use this skill when: (1) Validating designed sequences fold correctly, (2) Predicting binder-target complex structures, (3) Calculating confidence metrics (pLDDT, pTM, ipTM), (4) Self-consistency validation of designs, (5) Multi-chain complex prediction with AlphaFold-Multimer. For faster single-chain prediction, use esm. For QC thresholds, use protein-qc.
中文简介正在准备中。当前显示原文说明。
AlphaFold2 / AlphaFold-Multimer structure prediction for validation and confidence scoring. Use this skill when: (1) Validating designed sequences fold correctly, (2) Predicting binder-target complex structures, (3) Calculating confidence metrics (pLDDT, pTM, ipTM), (4) Self-consistency validation of designs, (5) Multi-chain complex prediction with AlphaFold-Multimer. For faster single-chain prediction, use esm2-sequence-scoring. For QC thresholds, use protein-design-qc.
中文简介正在准备中。当前显示原文说明。
Workflow for event-level and isoform-level splicing analysis with sashimi-ready outputs and splice QC.
中文简介正在准备中。当前显示原文说明。
End-to-end binder design using BindCraft hallucination. Use this skill when: (1) Designing protein binders with built-in AF2 validation, (2) Running production-quality binder campaigns, (3) Using different design protocols (fast, default, slow), (4) Need joint backbone and sequence optimization, (5) Want high experimental success rate. For backbone-only generation, use rfdiffusion. For QC thresholds, use protein-qc. For tool selection guidance, use binder-design.
中文简介正在准备中。当前显示原文说明。
Guidance for choosing the right protein binder design tool. Use this skill when: (1) Deciding between BoltzGen, BindCraft, or RFdiffusion, (2) Planning a binder design campaign, (3) Understanding trade-offs between different approaches, (4) Selecting tools for specific target types. For specific tool parameters, use the individual tool skills (boltzgen, bindcraft, rfdiffusion, etc.).
中文简介正在准备中。当前显示原文说明。