Microbiome Diversity Reporter

Overview

This tool is used to analyze and interpret diversity metrics in microbiome 16S rRNA sequencing data, including:

• Alpha Diversity: Species diversity within a single sample
• Beta Diversity: Species composition differences between samples

Usage

Command Line

# Analyze Alpha diversity for a single sample
python scripts/main.py --input otu_table.tsv --metric shannon --output alpha_report.html

# Analyze Beta diversity (PCoA)
python scripts/main.py --input otu_table.tsv --beta --metadata metadata.tsv --output beta_report.html

# Generate full report (Alpha + Beta)
python scripts/main.py --input otu_table.tsv --full --metadata metadata.tsv --output diversity_report.html

Parameter Description

Input Format

OTU Table (TSV)

#OTU ID	Sample1	Sample2	Sample3
OTU_1	100	50	200
OTU_2	50	100	0
OTU_3	25	25	50

Metadata (TSV)

SampleID	Group	Age	Gender
Sample1	Control	25	M
Sample2	Treatment	30	F
Sample3	Treatment	28	M

Output

Generates HTML/JSON/Markdown reports containing:

1. Alpha Diversity Results

   • Diversity index values
   • Rarefaction curves
   • Box plots (by group)

2. Beta Diversity Results

   • PCoA scatter plots
   • NMDS plots
   • Distance matrix heatmaps
   • PERMANOVA statistical tests

3. Statistical Summary

   • Sample information statistics
   • Species richness
   • Diversity index distribution

Dependencies

• Python 3.8+
• numpy
• pandas
• scipy
• scikit-bio
• matplotlib
• seaborn
• plotly (for interactive charts)

Example Output

{
  "alpha_diversity": {
    "shannon": {
      "Sample1": 2.45,
      "Sample2": 1.89,
      "Sample3": 2.12
    },
    "statistics": {
      "mean": 2.15,
      "std": 0.28
    }
  },
  "beta_diversity": {
    "method": "braycurtis",
    "pcoa": {
      "variance_explained": [0.45, 0.25, 0.15]
    }
  }
}

References

1. Shannon, C.E. (1948) A mathematical theory of communication
2. Simpson, E.H. (1949) Measurement of diversity
3. Chao, A. (1984) Non-parametric estimation of classes
4. Lozupone et al. (2005) UniFrac: a phylogenetic metric

Risk Assessment

Security Checklist

• No hardcoded credentials or API keys
• No unauthorized file system access (../)
• Output does not expose sensitive information
• Prompt injection protections in place
• Input file paths validated (no ../ traversal)
• Output directory restricted to workspace
• Script execution in sandboxed environment
• Error messages sanitized (no stack traces exposed)
• Dependencies audited

Prerequisites

# Python dependencies
pip install -r requirements.txt

Evaluation Criteria

Success Metrics

• Successfully executes main functionality
• Output meets quality standards
• Handles edge cases gracefully
• Performance is acceptable

Test Cases

1. Basic Functionality: Standard input → Expected output
2. Edge Case: Invalid input → Graceful error handling
3. Performance: Large dataset → Acceptable processing time

Lifecycle Status

• Current Stage: Draft
• Next Review Date: 2026-03-06
• Known Issues: None
• Planned Improvements:
  • Performance optimization
  • Additional feature support