Microbiome amplicon

Microbiome Amplicon

Version Compatibility

Reference examples assume recent stable releases of the preferred tools, especially QIIME2-style and the other tools listed below.

Before using code or command patterns, verify installed versions match the environment:

• Python: python -c "import <module>; print(<module>.__version__)"
• CLI: <tool> --version
• If signatures differ, inspect the installed help or API and adapt the pattern instead of retrying unchanged.

Overview

Workflow for amplicon microbiome analysis including denoising, taxonomy assignment, diversity analysis, and differential abundance.

When To Use This Skill

• use when the task is 16S, ITS, or other amplicon-based microbiome profiling
• use when denoising, taxonomy assignment, and diversity metrics are required
• use when the user needs cohort-level differential abundance or community structure summaries

Quick Route

• If the input is raw or minimally processed data, start with validation and QC before any modeling.
• If the input is already processed, skip directly to the first workflow step that matches the user goal.
• If the user asks for a biological conclusion, always produce at least one QC or confidence artifact alongside the final result.

Progressive Disclosure

• Read references/technical_reference.md when you need deeper tool-selection rules, environment adaptation notes, or extra validation guidance.
• Keep SKILL.md as the main execution path and load the reference file only when the task or failure mode needs the extra detail.

Default Rules

• Prefer Python-first workflows unless the task explicitly requires something else.
• Keep intermediate and final outputs separated.
• Record software versions, reference builds, and key parameters when they affect interpretation.
• Favor reproducible tables and figures over one-off interactive-only outputs.

Expected Inputs

• amplicon FASTQ files
• sample metadata
• taxonomy database

Expected Outputs

• ASV or OTU tables
• taxonomy assignments
• diversity and differential abundance summaries

Preferred Tools

• QIIME2-style workflows
• pandas
• scikit-bio
• seaborn

Starter Pattern

Preferred starting point: QIIME2-style
Inputs: amplicon FASTQ files, sample metadata, taxonomy database
Outputs: ASV or OTU tables, taxonomy assignments, diversity and differential abundance summaries

Workflow

1. Preprocess reads

Trim primers or adapters and denoise reads into ASVs or OTUs.

2. Assign taxonomy

Use a suitable taxonomy model or reference database for the marker type.

3. Compute diversity

Calculate alpha and beta diversity with metadata-aware comparisons.

4. Compare groups

Run differential abundance with methods matched to compositional data constraints.

5. Export community reports

Save tables, ordinations, and taxonomy summaries.

Output Artifacts

• Recommended output layout:
  • results/ for final tables and serialized objects
  • figures/ for plots and static visual exports
  • qc/ for checks that justify downstream interpretation
• Minimum expected outputs for this skill:
• ASV or OTU tables
• taxonomy assignments
• diversity and differential abundance summaries

Quality Review

• Confirm identifiers and metadata join correctly before modeling or summarizing.
• Generate at least one QC artifact before final biological interpretation.
• Keep raw or minimally processed inputs separate from transformed outputs.
• Review sample contamination, depth differences, and database choice before comparing communities.
• State clearly whether outputs are relative abundance, counts, or derived functions.

Anti-Patterns

• treating relative abundance changes as absolute shifts without context
• using a taxonomy database mismatched to the marker region
• running differential abundance without accounting for compositional effects

Related Skills

• Metagenomics
• Pathogen Epidemiological Genomics
• Phylogenetics

Optional Supplements

• scikit-bio