Summary This source did not publish a separate summary. Review SKILL.md before using the skill.
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
Takes paired-end FASTQ files (R1, R2) or a single concatenated FASTQ as input
Runs Kraken2 taxonomic classification against a standard database (e.g., Standard-8, PlusPF)
Refines abundances with Bracken at species level (read re-estimation)
Detects antimicrobial resistance genes with RGI against the CARD database
Classifies detected ARGs by WHO critical priority pathogen association
Optionally runs HUMAnN3 for functional pathway profiling (MetaCyc + UniRef)
Generates three publication-quality figures:
Figure 1 : Taxonomy bar chart — top 20 species by relative abundanceFigure 2 : Resistome heatmap — ARG families by drug class with abundanceFigure 3 : WHO-critical ARG summary — priority-tier breakdown of detected resistance genes
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
