Individual skill
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 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.
backend/cli/skills/biology/bulk-rnaseq/SKILL.md
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- Last checked
- Nov 9, 2026
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- Nov 8, 2026
- License
- Apache-2.0
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SKILL.mdcore · 16,636 bytesSKILL.mdcore · 16,636 bytesSKILL.mdcore · 16,636 bytesSKILL.mdcore · 16,636 bytesSKILL.mdcore · 16,636 bytesreferences/counts-and-handoff.mddoc · 6,133 bytesreferences/design-and-qc.mddoc · 5,117 bytesreferences/upstream-manual.mddoc · 5,487 bytesreferences/upstream-nfcore.mddoc · 6,186 bytesscripts/build_counts_matrix.pyruns · 8,081 bytesscripts/validate_samplesheet.pyruns · 8,130 bytes
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# 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