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
npx skills add K-Dense-AI/scientific-agent-skills --skill bulk-rnaseq --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.
# 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
- Overview
- When to Use This Skill
- The Pipeline at a Glance
- Two Upstream Paths — Pick One
- Setup
- Quick Start
- Path A — nf-core/rnaseq (recommended)
- Path B — standalone STAR/Salmon (abbreviated)
- Counts → DE → enrichment (both paths)
- Stage-by-Stage Workflow
- The counts → DE bridge (the key glue)
- Common Pitfalls
- Integration with Other Skills
- Reference Files
This skill's glue (bridge + handoffs) — Python uv pip install pytximport pandas Downstream skills install their own deps: pydeseq2 skill -> uv pip install pydeseq2 pathway-enrichment skill -> uv pip install gseapy gprofiler-official Path A (nf-core): only Nextflow + a container engine are needed — see the `nextflow` skill. Path B (standalone tools): install via bioconda. Pin versions for reproducibility. conda create -n rnaseq -c bioconda -c conda-forge \ fastqc fastp trim-galore "star=2.7.11b" "salmon=1.10.3" subread multiqc python scripts/validate_samplesheet.py --samplesheet samplesheet.csv
What does the bulk-rnaseq skill do?
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
How do I install it?
Run `npx skills add K-Dense-AI/scientific-agent-skills --skill bulk-rnaseq --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 K-Dense-AI/scientific-agent-skills, a repository with 32,619 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.