bio-workflows-rnaseq-to-de

npx skills add majiayu000/claude-skill-registry --skill rnaseq-to-de-gptomics-bioskills --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

# 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

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 \