# Bulk Rnaseq

End-to-end bulk RNA-seq orchestrator, takes raw FASTQ reads through QC and trimming...

- Repository: https://github.com/synthetic-sciences/openscience (4321 stars)
- Category: Design
- License: MIT
- Last updated: 2026-10-30
- Runs scripts: yes, review before use

## Install

Claude Code: `npx skills add synthetic-sciences/openscience --skill bulk-rnaseq -g -a claude-code -y`
Codex: `npx skills add synthetic-sciences/openscience --skill bulk-rnaseq -g -a codex -y`
Cursor: `npx skills add synthetic-sciences/openscience --skill bulk-rnaseq -g -a cursor -y`
Gemini CLI: `npx skills add synthetic-sciences/openscience --skill bulk-rnaseq -g -a gemini-cli -y`

## Description

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.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# 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

The rest: https://github.com/synthetic-sciences/openscience/blob/HEAD/backend/cli/skills/biology/bulk-rnaseq/SKILL.md