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ngs-shotgun-metagenomics

Start shotgun metagenomics workflows for mixed-community DNA reads in FASTQ files. Check read quality, remove host sequences, and profile organisms and biological functions using supported tools and databases, producing tables and visual summaries when results are available.

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원문원문 설명 보기

Kick off public shotgun metagenomics QC, host-depletion, taxonomic profiling, and functional profiling workflows using nf-core/taxprofiler, Kraken2, Bracken, MetaPhlAn, and HUMAnN.

설치 방법 보기

이럴 때 유용합니다

  • Checking FASTQ quality and analysis readiness
  • Removing host-derived sequences before profiling
  • Summarizing microbial composition across samples
  • Visualizing pathways and gene-family profiles

포함된 파일(2)

  • SKILL.md4.8 KB
  • agents/openai.yaml288 B

SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

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.

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