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ngs-bulk-rnaseq

Routes bulk RNA-seq requests to workflows for generating expression counts and checking quality, or comparing gene expression between groups. Checks input data, sample information, and analysis requirements before directing the next step.

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Dispatch bulk RNA-seq requests to FASTQ-to-count QC or count-matrix differential-expression skills using nf-core/rnaseq, STAR, Salmon, featureCounts, MultiQC, and R/Bioconductor workflows.

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适用场景

  • Generate expression counts from FASTQ files
  • Compare gene expression between sample groups
  • Plan quality checks and statistical analysis together
  • Choose a processing route for the available environment

包含的文件(2)

  • SKILL.md3.6 KB
  • agents/openai.yaml296 B

SKILL.md(原文)

安装前可以查看交给 AI 代理的指令内容。

Bulk RNA-seq

Use this skill as the bulk RNA-seq dispatcher. Route FASTQ/BAM processing to count-generation QC, and route count-matrix statistical analysis to differential-expression guidance.

Essential Inputs

Confirm:

  • organism and genome build
  • FASTA and GTF, or supported nf-core genome key
  • paired-end or single-end reads
  • strandedness, or whether to infer strandedness
  • sample sheet and metadata
  • counts-only vs differential expression
  • contrasts, covariates, and batch terms for differential expression

Dispatch

  • FASTQ or aligned reads to raw counts, transcript estimates, or MultiQC summaries: ngs-bulk-rnaseq-counts-qc
  • Raw count matrix plus sample metadata to contrasts, plots, and DE result tables: ngs-bulk-rnaseq-differential-expression

If the user asks for both, run count-generation planning first and start differential expression only after the raw count matrix, sample metadata, replicates, design formula, and contrasts are confirmed.

Public Default

Prefer nf-core/rnaseq for standardized processing when a stable container or HPC runtime is available. Use the local_light Snakemake/Salmon path when Docker, registry egress, or Nextflow process containers are unavailable and a compact local run is appropriate.

Plugin-Owned Local Paths

Use the counts/QC runner for local FASTQ-to-matrix execution:

python plugins/ngs-analysis/scripts/run_bulk_rnaseq_counts_qc.py \
  --sample-sheet samplesheet.csv \
  --fastq-root path/to/fastqs \
  --transcriptome-fasta reference/transcriptome.fasta \
  --genome-fasta reference/genome.fa \
  --annotation-gtf reference/genes.gtf \
  --execute

Use the differential-expression runner when the user already has a count or expression matrix:

python plugins/ngs-analysis/scripts/run_bulk_rnaseq_de.py \
  --count-matrix count_matrix.tsv \
  --sample-metadata sample_metadata.tsv \
  --contrasts contrasts.tsv \
  --execute

Preflight

python plugins/ngs-analysis/scripts/ngs_preflight.py --pipeline bulk_rnaseq --emit-install-plan
python plugins/ngs-analysis/scripts/ngs_preflight.py --pipeline bulk_rnaseq_counts_qc --emit-install-plan
python plugins/ngs-analysis/scripts/ngs_preflight.py --pipeline bulk_rnaseq_differential_expression --emit-install-plan
python plugins/ngs-analysis/scripts/ngs_preflight.py --profile local_light --emit-install-plan

Kickoff Pattern

Preflight run:

nextflow run nf-core/rnaseq \
  -profile test,docker \
  --outdir results/rnaseq_test

Real run skeleton:

nextflow run nf-core/rnaseq \
  -profile docker \
  --input samplesheet.csv \
  --outdir results/rnaseq \
  --genome GRCh38 \
  --aligner star_salmon

If strandedness is unknown, run inference or use the pipeline's strandedness detection before committing to final counts.

Local execution run:

python plugins/ngs-analysis/scripts/run_bulk_rnaseq_counts_qc.py \
  --sample-sheet samplesheet.csv \
  --fastq-root path/to/fastqs \
  --transcriptome-fasta reference/transcriptome.fasta

The local runners create a standard run envelope with run_manifest.json, config.json, validation/, logs/, versions/, artifact_index.json, and summary.md. Do not depend on development-only eval harness paths in a shared package.

Downstream

Only start DESeq2/edgeR/limma analysis after confirming biological replicates, design formula, and contrasts. Preserve the raw count matrix and sample metadata.

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