# Bulk Rnaseq

Prepares bulk RNA-seq FASTQ, Salmon, STAR or featureCounts output for gene-level...

- Repository: https://github.com/K-Dense-AI/scientific-agent-skills (55651 stars)
- Category: Engineering
- License: MIT
- Last updated: 2026-10-26
- Runs scripts: yes, review before use

## Install

Claude Code: `npx skills add K-Dense-AI/scientific-agent-skills --skill bulk-rnaseq -g -a claude-code -y`
Codex: `npx skills add K-Dense-AI/scientific-agent-skills --skill bulk-rnaseq -g -a codex -y`
Cursor: `npx skills add K-Dense-AI/scientific-agent-skills --skill bulk-rnaseq -g -a cursor -y`
Gemini CLI: `npx skills add K-Dense-AI/scientific-agent-skills --skill bulk-rnaseq -g -a gemini-cli -y`

## Description

Prepares bulk RNA-seq FASTQ, Salmon, STAR or featureCounts output for gene-level differential expression. Covers nf-core/rnaseq and standalone quantification, biological replication, strandedness, reference provenance, validated count assembly and a PyDESeq2 handoff. Use for FASTQ-to-counts analysis, nf-core/rnaseq configuration, STAR/Salmon quantification, or building a counts matrix for DESeq2. For single-cell data use scanpy; for statistical fitting alone use pydeseq2.

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

# Bulk RNA-seq

## Overview

This skill prepares bulk RNA-seq reads and quantification output for a reproducible gene-level comparison. It owns sample validation, reads-to-counts recipes, and the count/metadata handoff; downstream statistical fitting and enrichment remain in their specialist skills.

"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/K-Dense-AI/scientific-agent-skills/blob/HEAD/skills/bulk-rnaseq/SKILL.md