Imported from openai/plugins (plugins/ngs-analysis/skills/ngs-shotgun-metagenomics/SKILL.md). Install upstream with npx skills add openai/plugins --skill ngs-shotgun-metagenomics. Copyright stays with the author.

Shotgun Metagenomics

Use this skill for shotgun metagenomic FASTQs.

Essential Inputs

Confirm:

• paired-end or single-end reads
• host organism and host-depletion requirement
• target outputs: taxonomic profile, functional profile, assembly, binning, or QC only
• preferred database family, if any
• database paths or permission to download large databases
• sample metadata, batches, and negative controls

Public Defaults

Prefer nf-core/taxprofiler for reproducible taxonomic profiling. Use direct Kraken2/Bracken, MetaPhlAn, or HUMAnN when the user wants a focused path or already has databases installed.

For direct backend execution, prefer the plugin runner over handwritten shell when possible because it validates database bundle contents and records resources/resource_plan.json, resource_manifest.tsv, resource_env.sh, and resource_readiness.md. --run-bracken and --run-humann make those database bundles blocking, not merely optional.

Preflight

python plugins/ngs-analysis/scripts/ngs_preflight.py --pipeline shotgun_metagenomics --emit-install-plan

Local Execution Package

For FASTQ intake/QC before host-depletion, taxonomic profiling, or functional profiling, use:

python plugins/ngs-analysis/scripts/run_fastq_assay_package.py \
  --lane shotgun_metagenomics \
  --sample-sheet shotgun_samples.csv \
  --execute

This validates read paths and structure, runs seqkit stats and FastQC/MultiQC when available, and writes taxonomic_classification_status.json. Add --kraken-db /path/to/db only when a local Kraken2 database is available; otherwise the package records the database/tool blocker explicitly.

For backend taxonomic and functional profiling when databases are available, use:

python plugins/ngs-analysis/scripts/run_shotgun_metagenomics.py \
  --sample-sheet shotgun_samples.csv \
  --kraken-db /db/kraken2/standard \
  --host-reference /refs/human_kneaddata_db \
  --run-bracken \
  --run-humann \
  --humann-db /db/humann \
  --metadata sample_metadata.tsv \
  --execute

For nf-core execution, use plugins/ngs-analysis/scripts/run_nfcore_pipeline.py --pipeline taxprofiler.

When --host-reference is supplied, the backend runner adds a KneadData host-depletion step, requires kneaddata in tool preflight, writes cleaned FASTQs under host_depletion/, and uses those cleaned reads for downstream Kraken2 and HUMAnN steps. Keep the host reference path and host-depletion decision visible because it can change taxonomic and functional abundance conclusions.

The backend runner writes native matrix artifacts when database tools produce outputs:

• tables/bracken_est_reads_matrix.tsv
• tables/bracken_relative_abundance_matrix.tsv
• tables/humann_pathabundance_matrix.tsv
• tables/humann_genefamilies_matrix.tsv
• tables/bracken_summary.json and tables/humann_summary.json
• tables/top_bracken_taxa.tsv, tables/top_humann_pathways.tsv, tables/top_humann_gene_families.tsv, and tables/metagenomics_backend_review.json when normalized backend matrices are available

If Kraken2/Bracken/HUMAnN outputs are absent, the summaries and visualization manifest keep those layers not_available instead of implying taxonomic or functional interpretation succeeded.

Kickoff Pattern

nf-core preflight run:

nextflow run nf-core/taxprofiler \
  -profile test,docker \
  --outdir results/taxprofiler_test

Direct Kraken2 skeleton:

kraken2 \
  --db /path/to/kraken2_db \
  --paired sample_R1.fastq.gz sample_R2.fastq.gz \
  --report results/kraken2/sample.report \
  --output results/kraken2/sample.kraken

Visualization Outputs

The local FASTQ package always writes visualizations/index.html and visualizations/visualization_manifest.json. With only FASTQs, this is a read-QC/readiness bundle. Provide existing --kraken-report, --bracken-table, --humann-pathabundance, or --humann-genefamilies files to generate native taxonomy and functional-profile plots without requiring a Marimo notebook. For full backend runs, run_shotgun_metagenomics.py now also merges generated Bracken/HUMAnN outputs into plugin-native tables for the review bundle and writes visualizations/shotgun_backend_dashboard.html plus SVG plots for top Bracken taxa, HUMAnN pathways, and HUMAnN gene families when the corresponding matrices are present.

Guardrails

• Do not auto-download large databases without confirming size and destination.
• Host depletion choices can change biological conclusions; document the reference and parameters.
• Negative controls should stay visible in QC and interpretation.