Individual skill
bulk-rnaseq
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.
skills/bulk-rnaseq/SKILL.md
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SKILL.mdcore · 16,890 bytesSKILL.mdcore · 16,890 bytesSKILL.mdcore · 16,890 bytesSKILL.mdcore · 16,890 bytesSKILL.mdcore · 16,890 bytesreferences/counts-and-handoff.mddoc · 8,483 bytesreferences/design-and-qc.mddoc · 6,388 bytesreferences/upstream-manual.mddoc · 7,119 bytesreferences/upstream-nfcore.mddoc · 7,473 bytesscripts/_tabular.pyruns · 975 bytesscripts/build_counts_matrix.pyruns · 14,423 bytesscripts/validate_samplesheet.pyruns · 10,518 bytes
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# 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