phylo-tree（系统树）

PhyloTree | Publication-Grade Phylogenetic Analysis

One-line: Build Nature/Science-level phylogenetic trees from enzyme names or sequences.

🚀 Quick Start (3 steps)

# 1. Activate environment
conda activate r43

# 2. Run analysis
python3 scripts/run_v2.py --query "imine reductase" --output ./output

# 3. Done! Check ./output/figures/ for publication-ready figures

Output: ML tree + 6 figures + QC reports + scientific conclusions

📋 Common Use Cases

Use Case 1: Analyze from FASTA file (Recommended)

python3 scripts/run_v2.py --fasta sequences.fasta --output ./my_analysis

How to get sequences:

1. Go to UniProt: https://www.uniprot.org/
2. Search for your enzyme (e.g., "imine reductase")
3. Click "Download" → "FASTA (canonical)"
4. Save as sequences.fasta

Use Case 2: Analyze by enzyme name (requires UniProt API)

python3 scripts/run_v2.py --query "imine reductase" --output ./ired_analysis

Note: This uses UniProt API which may change. Manual download (Use Case 1) is more reliable.

Use Case 3: Custom parameters

python3 scripts/run_v2.py \
  --query "lipase" \
  --output ./lipase \
  --threads 10 \
  --bootstrap 1000 \
  --identity 0.90

📊 What You Get

Files generated:

• trees/phylo.treefile - ML tree (Newick format)
• figures/*.png - 6 publication-ready figures (300 DPI)
• analysis_summary.json - Key statistics
• conclusions.md - Scientific findings

Figures:

1. Main tree (rectangular layout)
2. Circular tree
3. Heatmap tree (branch length gradient)
4. Branch length distribution
5. Genus distribution
6. Combined multi-panel

🔧 Key Parameters

Full parameter list: See references/parameters.md

📖 Need More?

First time setup: references/installation.md Troubleshooting: references/troubleshooting.md Interpreting results: references/interpretation.md Publication checklist: references/publication.md AI report generation: references/ai_workflow.md

✅ Quality Standards

• ✅ IQ-TREE ML + ModelFinder (1232 models)
• ✅ UFBoot2 + SH-aLRT ≥ 1000
• ✅ Alignment trimming (trimAl)
• ✅ Deduplication (CD-HIT 90%)
• ✅ 300 DPI figures
• ✅ Nature/Science color schemes

Suitable for: Nature, Science, Cell, MBE, Systematic Biology, PNAS

🤖 For AI Agents

After analysis, read:

1. analysis_summary.json - Structured statistics
2. conclusions.md - Scientific findings
3. references/report_template.md - Writing template

No need to parse log files!

📚 References

1. Nguyen et al. (2015). IQ-TREE. *Mol Biol Evol* 32:268-274.
2. Hoang et al. (2018). UFBoot2. *Mol Biol Evol* 35:518-522.
3. Kalyaanamoorthy et al. (2017). ModelFinder. *Nat Methods* 14:587-589.
4. Yu et al. (2017). ggtree. *Methods Ecol Evol* 8:28-36.

Full references: references/citations.md

🔒 Security & Privacy

This skill is safe and transparent:

✅ No malicious code - All scripts are open source and auditable ✅ External tools only - Calls standard bioinformatics tools (IQ-TREE, MAFFT, trimAl, CD-HIT) ✅ Optional API - UniProt API is optional, manual FASTA download recommended ✅ Local processing - All analysis runs locally, no data sent to third parties ✅ No network when using --fasta - Completely offline when using local FASTA files

Why flagged as suspicious?

ClawHub's automated scanner detected:

• subprocess calls (to run IQ-TREE, MAFFT, R)
• Optional network requests (UniProt API for --query mode)
• File system operations (creating output directories)

These are normal and necessary for phylogenetic analysis. All external commands are:

• Standard bioinformatics tools (installed via conda)
• Called with explicit arguments (no shell injection)
• Logged for transparency

Recommended usage:

• Use --fasta with manually downloaded sequences (no network requests)
• Only use --query if you trust UniProt API (public, no authentication)

Verification:

• Review all scripts in scripts/ directory
• Check run_v2.py for the complete workflow
• All external commands are documented in SKILL.md

Version: 2.0 | Updated: 2026-04-23