Workflow for read alignment, sorting, indexing, mapping statistics, and downstream-ready alignment artifacts.
日本語の概要は準備中です。原文の説明を表示しています。
ChIP-seq peak calling and downstream interpretation with MACS3, signal track export, annotation, motif analysis, and differential binding review.
インストールする前に、エージェントに与えられる指示の中身を確認できます。
Reference examples assume:
macs3 3.0+samtools 1.18+deepTools 3.5+Before using commands, verify the installed environment:
macs3 --version, samtools --version, bamCoverage --version--help and adapt rather than forcing the example unchanged.Use this skill for:
--broad.-f BAMPE.| Requirement | Narrow TF-style | Broad histone-style |
|---|---|---|
| usable uniquely mapped reads | >= 10M | >= 20M |
| matched input recommended | yes | yes |
| biological replicates recommended | >= 2 | >= 2 |
chip.baminput.bam when availableresults/peaks/sample_peaks.narrowPeak or .broadPeakresults/peaks/sample_summits.bedresults/tracks/sample_treat_pileup.bwresults/annotation/peak_annotation.tsvqc/chip_qc_summary.tsvmacs3 callpeak \
-t chip.bam \
-c input.bam \
-f BAMPE \
-g hs \
-n sample \
-q 0.01 \
--outdir results/peaks
| Parameter | Typical value | Meaning |
|---|---|---|
-f | BAM or BAMPE | paired-end should use BAMPE |
-g | hs, mm, or numeric | effective genome size |
-q | 0.01 or 0.05 | FDR cutoff for narrow peaks |
--broad | broad marks only | broad peak mode |
--broad-cutoff | 0.1 | broad-peak FDR cutoff |
-B --SPMR | enabled for tracks | bedGraph for normalized signal |
Check:
-q 0.01 is a good starting point--broad --broad-cutoff 0.1-f BAMPEUse -B --SPMR, sort the resulting bedGraph, then convert to bigWig for browser use.
Map peaks to promoters, gene bodies, or distal intervals and review top loci in a genome browser or track plot.
Only after peak quality looks credible and replicate structure supports the downstream question.
results/
├── peaks/
│ ├── sample_peaks.narrowPeak
│ ├── sample_summits.bed
│ └── sample_model.r
├── tracks/
│ ├── sample_treat_pileup.bdg
│ └── sample_treat_pileup.bw
└── annotation/
└── peak_annotation.tsv
qc/
└── chip_qc_summary.tsv
< 0.01 poor0.01-0.05 usable but weak> 0.05 generally soliddeeptoolspysamまだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
Workflow for read alignment, sorting, indexing, mapping statistics, and downstream-ready alignment artifacts.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
Workflow for event-level and isoform-level splicing analysis with sashimi-ready outputs and splice QC.
日本語の概要は準備中です。原文の説明を表示しています。
ATAC-seq processing with assay QC, MACS3 peak calling, consensus peak matrices, differential accessibility, and motif or footprint follow-up.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。