Update to Life Sciences NGS Analysis

{
  "name": "ngs-bulk-rnaseq",
  "description": "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.",
  "included_files": [
    {
      "relative_path": "agents/openai.yaml",
      "size_in_bytes": 296
    }
  ],
  "skill_md_contents": "---\nname: ngs-bulk-rnaseq\ndescription: 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.\n---\n\n# Bulk RNA-seq\n\nUse 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.\n\n## Essential Inputs\n\nConfirm:\n\n- organism and genome build\n- FASTA and GTF, or supported nf-core genome key\n- paired-end or single-end reads\n- strandedness, or whether to infer strandedness\n- sample sheet and metadata\n- counts-only vs differential expression\n- contrasts, covariates, and batch terms for differential expression\n\n## Dispatch\n\n- FASTQ or aligned reads to raw counts, transcript estimates, or MultiQC summaries: `ngs-bulk-rnaseq-counts-qc`\n- Raw count matrix plus sample metadata to contrasts, plots, and DE result tables: `ngs-bulk-rnaseq-differential-expression`\n\nIf 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.\n\n## Public Default\n\nPrefer `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.\n\n## Plugin-Owned Local Paths\n\nUse the counts/QC runner for local FASTQ-to-matrix execution:\n\n```bash\npython plugins/ngs-analysis/scripts/run_bulk_rnaseq_counts_qc.py \\\n  --sample-sheet samplesheet.csv \\\n  --fastq-root path/to/fastqs \\\n  --transcriptome-fasta reference/transcriptome.fasta \\\n  --genome-fasta reference/genome.fa \\\n  --annotation-gtf reference/genes.gtf \\\n  --execute\n```\n\nUse the differential-expression runner when the user already has a count or expression matrix:\n\n```bash\npython plugins/ngs-analysis/scripts/run_bulk_rnaseq_de.py \\\n  --count-matrix count_matrix.tsv \\\n  --sample-metadata sample_metadata.tsv \\\n  --contrasts contrasts.tsv \\\n  --execute\n```\n\n## Preflight\n\n```bash\npython plugins/ngs-analysis/scripts/ngs_preflight.py --pipeline bulk_rnaseq --emit-install-plan\npython plugins/ngs-analysis/scripts/ngs_preflight.py --pipeline bulk_rnaseq_counts_qc --emit-install-plan\npython plugins/ngs-analysis/scripts/ngs_preflight.py --pipeline bulk_rnaseq_differential_expression --emit-install-plan\npython plugins/ngs-analysis/scripts/ngs_preflight.py --profile local_light --emit-install-plan\n```\n\n## Kickoff Pattern\n\nPreflight run:\n\n```bash\nnextflow run nf-core/rnaseq \\\n  -profile test,docker \\\n  --outdir results/rnaseq_test\n```\n\nReal run skeleton:\n\n```bash\nnextflow run nf-core/rnaseq \\\n  -profile docker \\\n  --input samplesheet.csv \\\n  --outdir results/rnaseq \\\n  --genome GRCh38 \\\n  --aligner star_salmon\n```\n\nIf strandedness is unknown, run inference or use the pipeline's strandedness detection before committing to final counts.\n\nLocal execution run:\n\n```bash\npython plugins/ngs-analysis/scripts/run_bulk_rnaseq_counts_qc.py \\\n  --sample-sheet samplesheet.csv \\\n  --fastq-root path/to/fastqs \\\n  --transcriptome-fasta reference/transcriptome.fasta\n```\n\nThe 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.\n\n## Downstream\n\nOnly start DESeq2/edgeR/limma analysis after confirming biological replicates, design formula, and contrasts. Preserve the raw count matrix and sample metadata.\n"
}