Phylogenetics

Phylogenetics

Version Compatibility

Reference examples assume recent stable releases of the preferred tools, especially IQ-TREE 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 multiple sequence alignment, tree inference, annotated tree visualization, and distance-based evolutionary comparison.

When To Use This Skill

• use when the task is tree building or evolutionary relationship analysis
• use when aligned sequences or genomes must be compared in phylogenetic context
• use when the user needs annotated trees or support metrics

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

• aligned sequences
• optional metadata
• model assumptions

Expected Outputs

• phylogenetic trees
• annotated tree figures
• distance or support summaries

Preferred Tools

• alignment tools
• tree inference tools
• ete toolkit-style plotting
• matplotlib

Starter Pattern

mafft --auto input.fasta > aligned.fasta
iqtree2 -s aligned.fasta -m MFP -B 1000

Workflow

1. Prepare alignment

Trim or mask poorly aligned regions and confirm sequence comparability.

2. Choose an inference strategy

Pick distance, maximum likelihood, or other tree approaches matched to the problem.

3. Assess support

Include bootstrap or comparable support metrics where relevant.

4. Annotate with metadata

Overlay sample metadata on trees for interpretation.

5. Export publishable trees

Save tree files plus readable static figures.

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:
• phylogenetic trees
• annotated tree figures
• distance or support 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

• building trees from poor-quality or incompatible alignments
• omitting support metrics on uncertain topologies
• over-interpreting branch differences without scale context

Related Skills

• Metagenomics
• Microbiome Amplicon
• Pathogen Epidemiological Genomics

Optional Supplements

• etetoolkit