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Bulk RNA-seq Preparation

Turns raw RNA-seq reads or quantification output into a validated gene counts matrix ready for differential expression.

Source Download skill
Repository
K-Dense-AI/scientific-agent-skills
Stars
55,780
Licence
MIT
Files
8 files

What it does

Once installed, the agent follows a structured bulk RNA-seq workflow: it checks sample sheets for missing or inconsistent FASTQ files, strandedness and replicate problems, then guides the path from raw reads (via nf-core/rnaseq or standalone STAR/Salmon/featureCounts recipes) to a gene-level integer counts matrix plus metadata template. Bundled Python scripts validate the samplesheet and assemble the counts matrix, rejecting ambiguous inputs, and hand the results to downstream statistics skills such as PyDESeq2. It does not perform the statistical testing itself.

Great for

  • Researchers preparing bulk RNA-seq data for a differential expression analysis
  • Bioinformatics students learning the FASTQ-to-counts workflow and its pitfalls
  • Teams that want a samplesheet and counts-matrix check before spending compute on an nf-core run

Skip it if

  • Single-cell or nuclei RNA-seq data, which needs a different workflow (the skill points to scanpy)
  • Clinical or diagnostic decisions: outputs are research analysis aids and depend on correct wet-lab design and reference choices
  • Anyone expecting a turnkey end-to-end run: the full Nextflow, STAR and trimming recipes are illustrative and were not validated end to end

Try asking

  • “Check my samplesheet.csv for paired-end and strandedness errors before I run nf-core/rnaseq”
  • “Build a counts matrix from my Salmon quant directories using tx2gene.tsv”
  • “Help me design a bulk RNA-seq experiment with enough biological replicates for DESeq2”
  • “Turn my featureCounts output into a counts.csv and metadata template for PyDESeq2”
Step-by-step guide: how to use Bulk RNA-seq PreparationInstall, first run, examples, tips and FAQs

Install in 3 steps

  1. 1

    Download the skill

    The ZIP contains the 8 original files, the MIT licence and an attribution note.

    bulk-rnaseq.zip
  2. 2

    Add it to your agent

    Claude Code (all projects)

    mkdir -p ~/.claude/skills && unzip bulk-rnaseq.zip -d ~/.claude/skills/

    Claude Code / Agent SDK (this project only)

    mkdir -p .claude/skills && unzip bulk-rnaseq.zip -d .claude/skills/

    Claude apps (web/desktop): Settings → Capabilities → Skills → Upload skill, and choose the ZIP.

  3. 3

    Ask for the task

    No special command needed — the agent reads the skill's description and loads it when your request matches. Try one of the prompts above.

Get better results

  • Run validate_samplesheet.py first; it catches most common failures before compute is spent
  • Pick one quantification route (nf-core or standalone) for the whole comparison, since STAR and Salmon counts are not interchangeable
  • Record exact pipeline, tool and reference annotation versions for your methods section

What's inside

  • references/counts-and-handoff.md
  • references/design-and-qc.md
  • references/upstream-manual.md
  • references/upstream-nfcore.md
  • scripts/_tabular.py
  • scripts/build_counts_matrix.py
  • scripts/validate_samplesheet.py
  • SKILL.md

Redistributed unmodified from K-Dense-AI/scientific-agent-skills under the MIT licence. The assessment was drafted with AI from the skill's files on 2026-10-29. Skills can instruct your agent to run code — read SKILL.md and any scripts before installing.