bio-workflows-rnaseq-to-de
End-to-end RNA-seq workflow from FASTQ files to differential expression results. Covers QC, quantification (Salmon or STAR+featureCounts), and DESeq2 analysis with visualization. Use when running RNA-seq from FASTQ to DE results.
npx skills add majiayu000/claude-skill-registry --skill rnaseq-to-de-gptomics-bioskills-276fb1a5 --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# RNA-seq to Differential Expression Workflow Complete pipeline from raw FASTQ files to differential expression results. ## Workflow Overview ``` FASTQ files | v [1. QC & Trimming] -----> fastp | v [2. Quantification] ----> Salmon (recommended) or STAR + featureCounts | v [3. Import to R] -------> tximport (for Salmon) or direct counts | v [4. DE Analysis] -------> DESeq2 | v [5. Visualization] -----> Volcano, MA, heatmaps | v Significant gene list ``` ## Primary Path: Salmon + DESeq2 ### Step 1: Quality Control with fastp ```bash # Single sample fastp -i sample_R1.fastq.gz -I sample_R2.fastq.gz \ -o sample_R1.trimmed.fq.gz -O sample_R2.trimmed.fq.gz \ --detect_adapter_for_pe \ --qualified_quality_phred 20 \ --length_required 35 \ --html sample_fastp.html # Batch processing for sample in sample1 sample2 sample3; do fastp -i ${sample}_R1.fastq.gz -I ${sample}_R2.fastq.gz \ -o trimmed/${sample}_R1.fq.gz -O trimmed/${sample}_R2.fq.gz \ --detect_adapter_for_pe \ --html qc/${sample}_fastp.html done ``` **QC Checkpoint 1:** Check fastp reports - Q30 bases >80% - Adapter content <5% - Duplication rate reasonable for library type ### Step 2: Salmon Quantification ```bash # Build index (onc
- Workflow Overview
- Primary Path: Salmon + DESeq2
- Step 1: Quality Control with fastp
- Step 2: Salmon Quantification
- Step 3: Import with tximport
- Step 4: DESeq2 Analysis
- Step 5: Visualization and Export
- Alternative Path: STAR + featureCounts + DESeq2
- Step 2 Alternative: STAR Alignment
- Step 3 Alternative: featureCounts
- Step 4 Alternative: Load Counts Directly
- Parameter Recommendations
- Troubleshooting
- Complete Bash Pipeline Script
Single sample
fastp -i sample_R1.fastq.gz -I sample_R2.fastq.gz \
Batch processing
for sample in sample1 sample2 sample3; do
fastp -i ${sample}_R1.fastq.gz -I ${sample}_R2.fastq.gz \
done
Build index (once per transcriptome)
salmon index -t transcriptome.fa -i salmon_index -k 31
Quantify each sample
salmon quant -i salmon_index \What does the bio-workflows-rnaseq-to-de skill do?
End-to-end RNA-seq workflow from FASTQ files to differential expression results. Covers QC, quantification (Salmon or STAR+featureCounts), and DESeq2 analysis with visualization. Use when running RNA-seq from FASTQ to DE results.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill rnaseq-to-de-gptomics-bioskills-276fb1a5 --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.