bulk-rnaseqlisted

End-to-end bulk RNA-seq orchestrator — takes raw FASTQ reads through QC and trimming (FastQC, fastp/Trim Galore), alignment and quantification (STAR, Salmon, featureCounts), assembles a gene-level counts matrix, then hands off to differential expression (pydeseq2), pathway/GSEA enrichment (pathway-enrichment), and publication figures (scientific-visualization). Use whenever the user has bulk RNA-seq reads or quant output and wants a complete, reproducible differential-expression workflow — e.g. "analyze my RNA-seq", "FASTQ to DESeq2", "run nf-core/rnaseq", "STAR/Salmon quantification", "build a counts matrix for DESeq2", or "go from reads to differentially expressed genes and enriched pathways". Routes between an nf-core/rnaseq (Nextflow) path and a standalone STAR/Salmon path, and covers experimental design, strandedness, and QC gates. For single-cell RNA-seq use the scanpy skill instead.
tassiovale/claude-code-kit · ★ 10 · Web & Frontend · score 72

Install: claude install-skill tassiovale/claude-code-kit

# Bulk RNA-seq ## Overview This skill orchestrates a complete, **defensible** bulk RNA-seq differential-expression study, from raw sequencing reads to enriched pathways and figures. It is a router, not a reimplementation: most stages already have dedicated skills in this repo, and this skill connects them in the right order, fills the one real gap (raw reads → a gene-level counts matrix), and enforces the design and QC decisions that determine whether the final result is trustworthy. "Defensible" means three things, applied throughout: - **Reproducible** — pinned pipeline/tool versions, containers where possible, recorded parameters, fixed random seeds. - **Quality-gated** — QC is inspected and acted on before, during, and after quantification, not skipped. - **Statistically sound** — adequate replication, a design that matches the biology, counts handled correctly, and FDR-controlled testing. The pipeline is: **FastQC/trim → align/quant (STAR/Salmon) → counts → DE (pydeseq2) → enrichment (pathway-enrichment) → figures**. ## When to Use This Skill Use this skill when the user wants to: - Go from FASTQ files (or a sequencing run) to differentially expressed genes and pathways. - Run or configure `nf-core/rnaseq`, or align/quantify with STAR, Salmon, or featureCounts. - Turn Salmon/STAR/featureCounts output into a counts matrix ready for DESeq2/PyDESeq2. - Design or sanity-check a bulk RNA-seq experiment (replicates, batch, strandedness) before committing compute. - Scope