Bulk RNA-seq (Claude Skill)


Type	Claude Skill
Supplier	K-Dense Inc. (community OSS)
Availability	GA — part of the actively maintained K-Dense `scientific-agent-skills` collection
Pricing	Free / OSS (MIT)
Capabilities	Read/Write — Claude runs the skill’s Python locally (Bash), not as an MCP tool

How to install

Claude Code / Claude.ai — Skills CLI (recommended):
```
npx skills add K-Dense-AI/scientific-agent-skills
```
Installs the K-Dense collection; enable the bulk-rnaseq skill when prompted. Works across Claude Code, Cursor, and Codex via the Agent Skills spec (requires Node ≥ 18).
Claude Code / Claude Desktop — manual clone:
```
git clone https://github.com/K-Dense-AI/scientific-agent-skills
cp -r scientific-agent-skills/skills/bulk-rnaseq ~/.claude/skills/
```
Project-scoped alternative: copy into .claude/skills/ instead of ~/.claude/skills/. The skill declares its own Python dependencies in its SKILL.md; install them (the K-Dense skills generally use uv / pip) when prompted on first use.

What it does

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.

Primary use cases: 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).

Notes

Distributed as a SKILL.md (plus code examples) in the K-Dense collection — Claude executes it locally via Bash/Python rather than as an MCP server. Upstream license: MIT. The skill name to enable after install is bulk-rnaseq.

Sources

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