Claw Metagenomics

Shotgun Metagenomics Profiler

[!note] Vault audit 2026-07-24 — USE-15 Use this as the single-command shotgun runner emphasizing WHO-critical antimicrobial-resistance (resistome) profiling alongside Bracken/HUMAnN; when you need host-read depletion before profiling use metagenomics. Single-command AMR/resistome-focused runner vs host-depletion-aware workflow is the distinguishing axis (these overlap but are not duplicates).

Comprehensive shotgun metagenomics analysis combining taxonomic classification, antimicrobial resistance gene detection, and functional pathway profiling from paired-end FASTQ files.

What it does

1. Takes paired-end FASTQ files (R1, R2) or a single concatenated FASTQ as input
2. Runs Kraken2 taxonomic classification against a standard database (e.g., Standard-8, PlusPF)
3. Refines abundances with Bracken at species level (read re-estimation)
4. Detects antimicrobial resistance genes with RGI against the CARD database
5. Classifies detected ARGs by WHO critical priority pathogen association
6. Optionally runs HUMAnN3 for functional pathway profiling (MetaCyc + UniRef)
7. Calculates alpha diversity metrics from Bracken-adjusted species abundances:
   • Shannon diversity index: H = -sum(p_i * ln(p_i)), where p_i is the proportion of classified reads assigned to species i
   • Simpson diversity index: D = 1 - sum(p_i^2)
   • Pielou evenness: J = H / ln(S), where S is the number of species detected
   • Species richness: S = number of distinct species with at least 1 assigned read
8. Generates four publication-quality figures:
   • Figure 1: Taxonomy bar chart, top 20 species by relative abundance
   • Figure 2: Resistome heatmap, ARG families by drug class with abundance
   • Figure 3: WHO-critical ARG summary, priority-tier breakdown of detected resistance genes
   • Figure 4: Alpha diversity summary (Shannon, Simpson, Pielou in a panel)
9. Produces a full reproducibility bundle (commands.sh, environment.yml, checksums.sha256)

Why this exists

If you ask a general AI to "analyse a metagenome," it will:

• Not know which Kraken2 database to use or how to set confidence thresholds
• Hallucinate Bracken parameters for read-length and taxonomic level
• Miss the connection between detected ARGs and WHO priority pathogen lists
• Skip HUMAnN3 entirely (or misconfigure its database paths)
• Produce a single bar chart with no resistance context
• Skip diversity metric calculations (Shannon, Simpson, Pielou)
• Not provide a reproducibility bundle

This skill encodes the correct methodological decisions:

• Kraken2 confidence threshold of 0.2 (reduces false positives in environmental samples)
• Bracken re-estimation at species level with minimum 10 reads
• RGI bwt read mapping against CARD with --include_wildcard (Perfect/Strict cut-offs belong to rgi main and do not apply to read mapping)
• WHO Bacterial Priority Pathogens List 2024 mapped to detected ARG families and drug classes
• HUMAnN3 with MetaCyc stratification for pathway-level functional context
• Thread count auto-detected from available CPUs
• Full reproducibility bundle for every run

Validated On

The skill works with any shotgun metagenome but has been validated on:

• Peru sewage metagenomics study (6 samples, 3 collection sites: Lima, Cusco, Iquitos)
• Environmental sewage samples with mixed microbial communities
• Read depths ranging from 2M to 15M paired-end reads per sample

WHO-Critical ARG Detection

Detected resistance genes are classified by WHO priority tier. The list edition lives in one place — WHO_BPPL_EDITION in metagenomics_profiler.py — and the report Methods section reads it from there. Current edition: WHO Bacterial Priority Pathogens List 2024 (published 17 May 2024).

Classification matches on ARG family and drug class, not on pathogen, so a carbapenemase is tagged Critical even though 2024 places carbapenem-resistant P. aeruginosa in High.

Usage

# Full pipeline (taxonomy + resistome + functional)
python metagenomics_profiler.py \
    --r1 sample_R1.fastq.gz \
    --r2 sample_R2.fastq.gz \
    --output metagenomics_report

# Skip HUMAnN3 (faster — taxonomy + resistome only)
python metagenomics_profiler.py \
    --r1 sample_R1.fastq.gz \
    --r2 sample_R2.fastq.gz \
    --output metagenomics_report \
    --skip-functional

# Single concatenated FASTQ
python metagenomics_profiler.py \
    --input combined.fastq.gz \
    --output metagenomics_report

# Specify Kraken2 database path
python metagenomics_profiler.py \
    --r1 sample_R1.fastq.gz \
    --r2 sample_R2.fastq.gz \
    --output metagenomics_report \
    --kraken2-db /path/to/kraken2_db \
    --read-length 150

Demo (works out of the box)

python metagenomics_profiler.py --demo --output demo_report

The demo uses pre-computed results from the Peru sewage metagenomics study (6 samples, 3 sites) and generates all figures and reports instantly without requiring external tools.

Example Output

Verbatim from --demo:

Metagenomics Profiler -- ClawBio
========================================
Mode: demo (pre-computed Peru sewage data)
Samples: 6 (3 sites: Lima, Cusco, Iquitos)

Generating taxonomy data...
  Total classified: 94.2%
  Top species: Escherichia coli (Lima: 12.3%, Cusco: 8.1%, Iquitos: 15.6%)
Generating resistome data...
  Total ARG hits: 24 (Perfect: 8, Strict: 16)
  Drug classes: 12
  WHO-Critical ARGs detected: 7
    - NDM-1, OXA-48, KPC-3, CTX-M-15, CTX-M-27, TEM-1, SHV-12
Generating pathway data...
  Total pathways: 10
  Top: PWY-7219: adenosine ribonucleotides de novo biosynthesis

Generating figures...
  Saved: taxonomy_barplot.png
  Saved: resistome_heatmap.png
  Saved: who_critical_args.png

Generating report...
  Saved: report.md
  Saved: reproducibility/ (commands.sh, environment.yml, checksums.sha256)

The Perfect/Strict counts above come from the demo table's own synthetic criteria column. A real run uses rgi bwt, whose output has no Cut_Off column, and prints ARG hits: N (WHO-Critical: M) instead.

Pipeline Architecture

FASTQ R1 + R2
     |
     v
[Kraken2] --> kraken2_report.txt
     |
     v
[Bracken] --> bracken_species.tsv   --> Figure 1: Taxonomy bar chart
     |
     v
[RGI bwt]  --> *.allele_mapping_data.txt --> Figure 2: Resistome heatmap
     |                                --> Figure 3: WHO-critical ARG summary
     v
[HUMAnN3] --> pathabundance.tsv     (optional, --skip-functional to omit)
     |
     v
[Report] --> report.md + figures/ + reproducibility/

Database Requirements

Citations

If you use this skill in a publication, please cite:

• Wood, D.E., Lu, J. & Langmead, B. (2019). Improved metagenomic analysis with Kraken 2. Genome Biology, 20, 257.
• Lu, J. et al. (2017). Bracken: estimating species abundance in metagenomics data. PeerJ Computer Science, 3, e104.
• Alcock, B.P. et al. (2023). CARD 2023: expanded curation, support for machine learning, and resistome prediction at the Comprehensive Antibiotic Resistance Database. Nucleic Acids Research, 51(D1), D419-D430.
• Beghini, F. et al. (2021). Integrating taxonomic, functional, and strain-level profiling of diverse microbial communities with bioBakery 3. eLife, 10, e65088.
• Corpas, M. (2026). ClawBio. https://github.com/ClawBio/ClawBio