bulk-rnaseq

1# Bulk RNA-seq
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3## Overview
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5This 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.
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7"Defensible" means three things, applied throughout:
8- **Reproducible** — pinned pipeline/tool versions, containers where possible, recorded parameters, fixed random seeds.
9- **Quality-gated** — QC is inspected and acted on before, during, and after quantification, not skipped.
10- **Statistically sound** — adequate replication, a design that matches the biology, counts handled correctly, and FDR-controlled testing.
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12The pipeline is: **FastQC/trim → align/quant (STAR/Salmon) → counts → DE (pydeseq2) → enrichment (pathway-enrichment) → figures**.
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14## When to Use This Skill
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16Use this skill when the user wants to:
17- Go from FASTQ files (or a sequencing run) to differentially expressed genes and pathways.
18- Run or configure `nf-core/rnaseq`, or align/quantify with STAR, Salmon, or featureCounts.
19- Turn Salmon/STAR/featureCounts output into a counts matrix ready for DESeq2/PyDESeq2.
20- Design or sanity-check a bulk RNA-seq experiment (replicates, batch, strandedness) before committing compute.
21- Scope an end-to-end RNA-seq analysis and decide which tools and skills to chain.
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