Metagenomic Krona Chart

Source: https://github.com/aipoch/medical-research-skills

Metagenomic Krona Chart

When to Use

• Use this skill when the task is to Generate interactive Krona charts (sunburst plots) for metagenomic samples.
• Use this skill for data analysis tasks that require explicit assumptions, bounded scope, and a reproducible output format.
• Use this skill when you need a documented fallback path for missing inputs, execution errors, or partial evidence.

Key Features

• Scope-focused workflow aligned to: Analyze data with metagenomic-krona-chart using a reproducible workflow, explicit validation, and structured outputs for review-ready interpretation.
• Packaged executable path(s): scripts/main.py.
• Reference material available in references/ for task-specific guidance.
• Structured execution path designed to keep outputs consistent and reviewable.

Dependencies

See ## Prerequisites above for related details.

• Python: 3.10+. Repository baseline for current packaged skills.
• pandas: unspecified. Declared in requirements.txt.
• plotly: unspecified. Declared in requirements.txt.

Example Usage

See ## Usage above for related details.

cd "20260318/scientific-skills/Data Analytics/metagenomic-krona-chart"
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.

• Execution model: validate the request, choose the packaged workflow, and produce a bounded deliverable.
• Input controls: confirm the source files, scope limits, output format, and acceptance criteria before running any script.
• Primary implementation surface: scripts/main.py.
• Reference guidance: references/ contains supporting rules, prompts, or checklists.
• Parameters to clarify first: input path, output path, scope filters, thresholds, and any domain-specific constraints.
• Output discipline: keep results reproducible, identify assumptions explicitly, and avoid undocumented side effects.

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

# Example invocation: python scripts/main.py --help

# Example invocation: python scripts/main.py --input "Audit validation sample with explicit symptoms, history, assessment, and next-step plan."

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.

Function Description

Generate interactive sunburst charts (Krona Chart) to display taxonomic abundance hierarchies in metagenomic samples. Supports parsing data from common classification tool outputs such as Kraken2, Bracken, and Centrifuge, and generates interactive HTML visualization charts.

Output Example

skills/metagenomic-krona-chart/
├── SKILL.md
├── scripts/
│   └── main.py
├── example/
│   ├── input.tsv
│   └── output.html
└── README.md

Usage

Basic Usage

# Example invocation: python scripts/main.py -i input.tsv -o krona_chart.html

Parameter Description

Input Format

Kraken2/Bracken Report Format

100.00  1000000 0   U   0   unclassified
 99.00  990000  0   R   1   root
 95.00  950000  0   D   2   Bacteria
 50.00  500000  0   P   1234    Proteobacteria
...

Custom Format (TSV)

taxon_id	name	rank	parent_id	reads	percent
2	Bacteria	domain	1	950000	95.0
1234	Proteobacteria	phylum	2	500000	50.0

Dependency Requirements

• Python 3.8+
• plotly >= 5.0.0
• pandas >= 1.3.0

pip install plotly pandas

Output Features

• Interactive sunburst chart with zoom and click support
• Color-coded different taxonomic levels
• Hover to display detailed information (reads, percentage)
• Center displays total reads
• Responsive design, adapts to different screens

Notes

1. Input files need to contain taxonomic hierarchy information
2. For large datasets, use --min-percent to filter low-abundance taxa
3. Output is a standalone HTML file that can be viewed offline

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