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数据与复现
BI
bio-rnaseq-qc
维护者 FreedomIntelligence · 最近更新 2026年4月1日
RNA-seq specific quality control including rRNA contamination detection, strandedness verification, gene body coverage, and transcript integrity metrics. Use when validating RNA-seq libraries before differential expression analysis.
原始来源
FreedomIntelligence/OpenClaw-Medical-Skills
https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bio-rnaseq-qc
- 维护者
- FreedomIntelligence
- 许可
- MIT
- 最近更新
- 2026年4月1日
技能摘要
来自 SKILL.md 的关键信息
核心说明
- CLI：infer_experiment.py，read_distribution.py (RSeQC)。
- CLI：picard CollectRnaSeqMetrics。
- RNA-seq specific QC metrics beyond general read quality。
- Check RNA-seq alignment quality" → Assess gene body coverage，read distribution (exonic/intronic/intergenic)，strand specificity，、 rRNA contamination rate. CLI：infer_experiment.py，read_distribution.py (RSeQC) CLI：picard CollectRnaSeqMetrics。
原始文档
SKILL.md 摘录
rRNA Contamination Detection
High rRNA content indicates failed rRNA depletion or polyA selection.
Expected rRNA Levels
| Library Type | Expected rRNA |
|---|---|
| PolyA selected | < 5% |
| rRNA depleted | < 10% |
| Total RNA | 50-80% |
Strand Inference
| Tool Setting | 1++,1--,2+-,2-+ | 1+-,1-+,2++,2-- |
|---|---|---|
| Forward (dUTP) | ~0 | ~1 |
| Reverse (Illumina) | ~1 | ~0 |
| Unstranded | ~0.5 | ~0.5 |
适用场景
- 适合在validating RNA-seq libraries before differential expression analysis时使用。
不适用场景
- Do not rely on this catalog entry alone ，用于 installation 或 maintenance details。
上游相关技能
- quality-reports - General FastQC
- fastp-workflow - Read trimming
- alignment-files/alignment-validation - General BAM QC
- rna-quantification/featurecounts-counting - Quantification after QC
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