Agent skill · research science · k-dense-ai
bulk-rnaseq
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.
Why this skill is useful
Adds a complete, executable RNA-seq analysis pipeline that integrates multiple tools and ensures reproducibility and quality control.
What it needs
Requires fastp, fastqc, featurecounts, pathway-enrichment, pydeseq2, salmon, scientific-visualization, star installed locally. About 8k tokens when loaded. Last updated 2026-08-03. 32,849 stars on the source repository.
What this skill does
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 an end-to-end RNA-seq analysis and decide which tools and skills to chain. This is bulk RNA-seq (samples = biological specimens). For single-cell/nuclei data use scanpy; for the DE statistics alone use pydeseq2; for enrichment alone use pathway-enrichment. The Pipeline at a Glance Two Upstream Paths — Pick One The reads → counts stage can be run two ways. They produce equivalent gene counts; choose by context, then stay on that path. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills k-dense-ai/bulk-rnaseq