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Metagenomics

skill-cheatthegod-biohermes-metagenomics · by cheatthegod

Shotgun metagenomics workflow with host-depletion-aware QC, taxonomic profiling, functional profiling, AMR follow-up, and reproducible community output tables.

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Install

$ agentstack add skill-cheatthegod-biohermes-metagenomics

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Security review

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No issues found. Passed automated security review. · v0.1.0 How review works →

  • Prompt-injection patterns
  • Secret / credential exfiltration
  • Dangerous shell & filesystem operations
  • Untrusted network calls
  • Known-malicious package signatures

What it can access

  • Network access No
  • Filesystem access No
  • Shell / process execution No
  • Environment & secrets No
  • Dynamic code execution No

From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.

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About

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 [technicalreference.md](technicalreference.md) for database choice, host contamination review, and functional profiling caveats.
  • Read [commandsandthresholds.md](commandsandthresholds.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

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

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

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

Install and usage instructions live in the source repository linked above.

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Versions

  • v0.1.0 Imported from the upstream source.