Bulk Rnaseq
Prepares bulk RNA-seq FASTQ, Salmon, STAR or featureCounts output for gene-level...
Install
npx skills add K-Dense-AI/scientific-agent-skills --skill bulk-rnaseq -g -a claude-code -y- Repository
- K-Dense-AI/scientific-agent-skills55.7k
- Category
- Engineering
- Updated
- 9d ago
- License
- MIT
- Context cost
- Mediumabout 3,000 tokens
- Runs scripts
- Yes, review before use
What it does
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.
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.
