Workflow for read alignment, sorting, indexing, mapping statistics, and downstream-ready alignment artifacts.
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ATAC-seq processing with assay QC, MACS3 peak calling, consensus peak matrices, differential accessibility, and motif or footprint follow-up.
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설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
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.deeptoolspysam아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.
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Workflow for read alignment, sorting, indexing, mapping statistics, and downstream-ready alignment artifacts.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
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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.
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Workflow for event-level and isoform-level splicing analysis with sashimi-ready outputs and splice QC.
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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.
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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.).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.