ngs-bulk-rnaseq

# 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: ```bash 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: ```bash 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 ```bash 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: ```bash nextflow run nf-core/rnaseq \ -profile test,docker \ --outdir results/rnaseq_test ``` Real run skeleton: ```bash 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: ```bash 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.