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ngs-shotgun-metagenomics — Codex skill: setup

This skill starts shotgun metagenomics QC, host removal, and taxonomic or functional profiling. Use it to structure an analysis from FASTQ files.

Install into Codex · Skills CLI (Vercel) All methods and platforms ↓
npx skills add openai/plugins --skill ngs-shotgun-metagenomics -a codex
Open on GitHub

What ngs-shotgun-metagenomics does

The skill starts a shotgun metagenomics workflow from FASTQ data. It checks paired-end or single-end reads, sample metadata, batches, and controls, then can run seqkit stats, FastQC, and MultiQC. For profiling, it supports nf-core/taxprofiler, Kraken2, Bracken, MetaPhlAn, and HUMAnN, with native tables, summaries, SVG plots, and HTML dashboards when the relevant outputs exist.

Its scripts include ngs_preflight.py, run_fastq_assay_package.py, run_shotgun_metagenomics.py, and run_nfcore_pipeline.py. Supplying --host-reference adds a KneadData host-depletion step; cleaned reads go into host_depletion/ before downstream Kraken2 and HUMAnN runs. Database bundles are required for the matching profiling steps, and large databases need approval for their size and destination before download. Negative controls remain visible during QC and review.

What it helps with

  • validate shotgun FASTQ inputs
  • remove host reads with KneadData
  • build taxonomic tables with Kraken2 and Bracken
  • produce HUMAnN pathway and gene-family profiles
  • create review dashboards from profiling matrices

Example prompts for the agent

  • Inspect shotgun_samples.csv, run the FASTQ QC checks, and prepare the execution plan.
  • Run taxonomic and functional profiling with /db/kraken2/standard, /db/humann, and sample_metadata.tsv.
  • Use the nf-core/taxprofiler test run and place its results in results/taxprofiler_test.

How to install ngs-shotgun-metagenomics

1Skills CLI (Vercel)

npx skills add openai/plugins --skill ngs-shotgun-metagenomics -a codex

2Manually: put the files in place

.agents/skills/ngs-shotgun-metagenomics/SKILL.md

For all projects use ~/.agents/skills/

Step by step, with commands for each platform: Codex skills: how to install, where they live, how to call them

Questions about ngs-shotgun-metagenomics

How do I install ngs-shotgun-metagenomics in Codex?

Run npx skills add openai/plugins --skill ngs-shotgun-metagenomics, then run npx skills add openai/plugins --skill ngs-shotgun-metagenomics -a codex. The skill file is placed at .agents/skills/ngs-shotgun-metagenomics/SKILL.md.

What does ngs-shotgun-metagenomics do?

It prepares shotgun FASTQ data for QC, host depletion, taxonomic profiling, and functional profiling. The workflow can use nf-core/taxprofiler, Kraken2, Bracken, MetaPhlAn, and HUMAnN.

What happens when --host-reference is supplied?

The runner adds KneadData, stores cleaned reads under host_depletion/, and feeds them into Kraken2 and HUMAnN. The reference and depletion choice need to stay documented because they affect abundance conclusions.

Source text

SKILL.md · MIT
---
name: ngs-shotgun-metagenomics
description: Kick off public shotgun metagenomics QC, host-depletion, taxonomic profiling, and functional profiling workflows using nf-core/taxprofiler, Kraken2, Bracken, MetaPhlAn, and HUMAnN.
---

# 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

```bash
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:

```bash
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:

```bash
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:

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

Direct Kraken2 skeleton:

```bash
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.

© OpenAI · MIT · openai/plugins

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