Claw metagenomics

Shotgun Metagenomics Profiler

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. Generates three 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
8. 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
• 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 MAIN with "Perfect" and "Strict" hit criteria only (no "Loose" hits)
• WHO Critical Priority Pathogen list mapped to detected ARG families
• 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

A key feature is the classification of detected resistance genes by WHO priority tier:

Priority	Pathogen	Resistance
Critical	Acinetobacter baumannii	Carbapenem-resistant
Critical	Pseudomonas aeruginosa	Carbapenem-resistant
Critical	Enterobacteriaceae	Carbapenem-resistant, 3rd-gen cephalosporin-resistant
High	Enterococcus faecium	Vancomycin-resistant
High	Staphylococcus aureus	Methicillin-resistant, vancomycin-resistant
High	Helicobacter pylori	Clarithromycin-resistant
High	Campylobacter	Fluoroquinolone-resistant
High	Salmonella spp.	Fluoroquinolone-resistant
High	Neisseria gonorrhoeae	3rd-gen cephalosporin-resistant, fluoroquinolone-resistant
Medium	Streptococcus pneumoniae	Penicillin-non-susceptible
Medium	Haemophilus influenzae	Ampicillin-resistant
Medium	Shigella spp.	Fluoroquinolone-resistant

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

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

Taxonomy (Kraken2 + Bracken):
  Total classified: 94.2%
  Top species: Escherichia coli (12.3%), Klebsiella pneumoniae (8.7%),
               Pseudomonas aeruginosa (5.1%), Acinetobacter baumannii (3.9%)

Resistome (RGI/CARD):
  Total ARG hits: 247 (Perfect: 89, Strict: 158)
  Drug classes: 14
  WHO-Critical ARGs detected: 23
    - Carbapenem resistance: NDM-1, OXA-48, KPC-3
    - 3rd-gen cephalosporin resistance: CTX-M-15, CTX-M-27

Functional Pathways (HUMAnN3):
  Total pathways: 312
  Top: PWY-7219 (adenosine ribonucleotides de novo biosynthesis)

Figures saved to: demo_report/figures/
  taxonomy_barplot.png (300 dpi)
  resistome_heatmap.png (300 dpi)
  who_critical_args.png (300 dpi)

Reproducibility:
  commands.sh | environment.yml | checksums.sha256

Pipeline Architecture

FASTQ R1 + R2
     |
     v
[Kraken2] --> kraken2_report.txt
     |
     v
[Bracken] --> bracken_species.tsv   --> Figure 1: Taxonomy bar chart
     |
     v
[RGI MAIN] --> rgi_results.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

Tool	Database	Size	Notes
Kraken2	Standard-8 or PlusPF	8-70 GB	Set via --kraken2-db or $KRAKEN2_DB
Bracken	(built from Kraken2 DB)	included	Read-length specific (default: 150 bp)
RGI	CARD	~500 MB	Auto-downloaded via rgi auto_load
HUMAnN3	ChocoPhlAn + UniRef90	~15 GB	Set via --humann-db or $HUMANN_DB

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