Microbiome Diversity Reporter

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name: microbiome-diversity-reporter

description: Interpret Alpha and Beta diversity metrics from 16S rRNA sequencing results.

license: MIT

skill-author: AIPOCH

---

# Microbiome Diversity Reporter

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When to Use

Key Features

Dependencies

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Example Usage

See `## Usage` above for related details.

cd "20260318/scientific-skills/Academic Writing/microbiome-diversity-reporter"
python -m py_compile scripts/main.py
python scripts/main.py --help

Example run plan:

1. Confirm the user input, output path, and any required config values.

2. Edit the in-file `CONFIG` block or documented parameters if the script uses fixed settings.

3. Run `python scripts/main.py` with the validated inputs.

4. Review the generated output and return the final artifact with any assumptions called out.

Implementation Details

See `## Workflow` above for related details.

Quick Check

Use this command to verify that the packaged script entry point can be parsed before deeper execution.

python -m py_compile scripts/main.py

Audit-Ready Commands

Use these concrete commands for validation. They are intentionally self-contained and avoid placeholder paths.

python -m py_compile scripts/main.py
python scripts/main.py --help
python scripts/main.py -h

Workflow

1. Confirm the user objective, required inputs, and non-negotiable constraints before doing detailed work.

2. Validate that the request matches the documented scope and stop early if the task would require unsupported assumptions.

3. Use the packaged script path or the documented reasoning path with only the inputs that are actually available.

4. Return a structured result that separates assumptions, deliverables, risks, and unresolved items.

5. If execution fails or inputs are incomplete, switch to the fallback path and state exactly what blocked full completion.

Overview

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

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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

| Parameter | Description | Required |

|------|------|------|

| `--input` | OTU/ASV table path (TSV format) | Yes |

| `--metadata` | Sample metadata (TSV format) | Required for Beta diversity |

| `--metric` | Alpha diversity metric: shannon, simpson, chao1, observed_otus | No (default: shannon) |

| `--alpha` | Calculate Alpha diversity only | No |

| `--beta` | Calculate Beta diversity only | No |

| `--full` | Generate full report (Alpha + Beta) | No |

| `--output` | Output report path | No (default: stdout) |

| `--format` | Output format: html, json, markdown | No (default: html) |

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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

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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

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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

| Risk Indicator | Assessment | Level |

|----------------|------------|-------|

| Code Execution | Python/R scripts executed locally | Medium |

| Network Access | No external API calls | Low |

| File System Access | Read input files, write output files | Medium |

| Instruction Tampering | Standard prompt guidelines | Low |

| Data Exposure | Output files saved to workspace | Low |

Security Checklist

Prerequisites

# Python dependencies
pip install -r requirements.txt

Evaluation Criteria

Success Metrics

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

- Performance optimization

- Additional feature support

Output Requirements

Every final response should make these items explicit when they are relevant:

Error Handling

Input Validation

This skill accepts requests that match the documented purpose of `microbiome-diversity-reporter` and include enough context to complete the workflow safely.

Do not continue the workflow when the request is out of scope, missing a critical input, or would require unsupported assumptions. Instead respond:

> `microbiome-diversity-reporter` only handles its documented workflow. Please provide the missing required inputs or switch to a more suitable skill.

References

Response Template

Use the following fixed structure for non-trivial requests:

1. Objective

2. Inputs Received

3. Assumptions

4. Workflow

5. Deliverable

6. Risks and Limits

7. Next Checks

If the request is simple, you may compress the structure, but still keep assumptions and limits explicit when they affect correctness.