Shotgun metagenomics workflow with host-depletion-aware QC, taxonomic profiling, functional profiling, AMR follow-up, and reproducible community output tables.
Scanned 9/4/2026
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---
name: metagenomics
description: Shotgun metagenomics workflow with host-depletion-aware QC, taxonomic profiling, functional profiling, AMR follow-up, and reproducible community output tables.
tool_type: mixed
primary_tool: Kraken2
---
# Metagenomics
## Version Compatibility
Reference examples assume:
- `fastp` 0.23+
- `kraken2` 2.1+
- `bracken` 2.8+
- `metaphlan` 4+
- `humann` 3.9+
Verify the environment first:
- CLI: `kraken2 --version`, `bracken -v`, `metaphlan --version`, `humann --version`
## Overview
Use this skill for shotgun metagenomics when the user needs:
- QC and host depletion review
- taxonomic abundance tables
- functional pathway profiles
- AMR or strain-level follow-up
## When To Use This Skill
- the data are shotgun metagenomics rather than amplicon sequencing
- the user wants species or genus abundances, function, or resistance summaries
- multiple samples need cohort-level comparison
## Quick Route
- host-associated samples: perform host depletion before interpretation
- taxonomy only: `kraken2 + bracken` is a common pragmatic route
- function only or plus taxonomy: add `humann`
- strain claims require more evidence than top-level taxonomy calls
## Progressive Disclosure
- Read [technical_reference.md](technical_reference.md) for database choice, host contamination review, and functional profiling caveats.
- Read [commands_and_thresholds.md](commands_and_thresholds.md) for command-line patterns, thresholds, and output layout.
## Expected Inputs
- paired or single-end metagenomic FASTQ
- sample metadata
- taxonomy and optional function databases
## Expected Outputs
- `results/taxonomy/bracken_species.tsv`
- `results/taxonomy/bracken_genus.tsv`
- `results/function/pathabundance.tsv`
- `results/amr/amr_summary.tsv`
- `qc/read_processing_summary.tsv`
## Starter Pattern
```bash
fastp \
-i sample_R1.fastq.gz \
-I sample_R2.fastq.gz \
-o qc/sample.clean.R1.fastq.gz \
-O qc/sample.clean.R2.fastq.gz \
--html qc/sample.fastp.html \
--json qc/sample.fastp.json
kraken2 \
--db $KRAKEN_DB \
--paired qc/sample.clean.R1.fastq.gz qc/sample.clean.R2.fastq.gz \
--report results/taxonomy/sample.kraken.report \
--output results/taxonomy/sample.kraken.out \
--confidence 0.1
```
## Workflow
### 1. Run read QC and optional host depletion
At minimum, inspect read quality, adapter content, and retained reads. For host-associated samples, remove host reads before community interpretation.
### 2. Profile taxonomy
Use a k-mer or marker-based profiler. Document the database and version because abundance results depend strongly on the reference.
### 3. Refine abundance tables
Convert raw classification to species or genus abundance tables suitable for cohort comparison.
### 4. Add function or AMR when requested
Run pathway or AMR profiling only after confirming taxonomic QC and read retention are reasonable.
### 5. Export cohort-ready outputs
Save per-sample tables and merged matrices with clear metadata joins.
## Output Artifacts
```text
results/
├── taxonomy/
│ ├── sample.kraken.report
│ ├── bracken_species.tsv
│ └── bracken_genus.tsv
├── function/
│ └── pathabundance.tsv
└── amr/
└── amr_summary.tsv
qc/
├── read_processing_summary.tsv
└── sample.fastp.html
```
## Quality Review
- retained reads after QC should be reported explicitly
- host-associated samples with large host contamination need a clear host depletion statement
- avoid over-interpreting taxa with extremely low abundance
- abundance comparisons should state whether values are relative abundance, counts, or normalized function estimates
## Anti-Patterns
- comparing outputs from different databases as if they were directly interchangeable
- making strain-level claims from genus-level evidence
- ignoring host contamination in human-associated or plant-associated samples
- mixing taxonomy-only and pathway outputs without clarifying what each table means
## Related Skills
- Microbiome Amplicon
- Pathogen Epidemiological Genomics
- Phylogenetics
## Optional Supplements
- `scikit-bio`
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