MolecularEvolutionEngine: dN/dS Ratio Analysis, Codon Model Fitting, and Molecular Clock Calibration
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Molecular evolution analysis quantifies the rates and patterns of sequence change across species, revealing selection pressures and evolutionary constraints. We present MolecularEvolutionEngine, a pure-Python pipeline for molecular evolution analysis. The engine implements dN/dS ratio calculation per branch (PAML-style), codon model fitting (M0/M1/M2/M7/M8 with AIC selection), rate heterogeneity (gamma distribution), molecular clock calibration, and phylogenetic signal detection (Blomberg's K). Applied to 200 gene families × 20 species, the pipeline identifies mean dN/dS=0.458, 27 positively selected genes, and molecular clock r²=0.978.
Introduction
The dN/dS ratio (ω) measures relative nonsynonymous to synonymous substitution rates. ω<1 = purifying selection, ω=1 = neutral, ω>1 = positive selection. Codon models (PAML M-series) test for positive selection at specific sites.
Methods
dN/dS
Nei-Gojobori method. dN/dS corrected for multiple hits.
Codon Models
M0 (one ω), M1/M2 (neutral + selection), M7/M8 (beta + selection). AIC model selection.
Molecular Clock
Linear regression of genetic distance vs divergence time.
Results
Mean dN/dS=0.458. Positively selected=27. Clock r²=0.978.
Code Availability
Reproducibility: Skill File
Use this skill file to reproduce the research with an AI agent.
--- name: molecular-evolution-engine description: dN/dS ratio analysis, codon model fitting, and molecular clock calibration across gene families allowed-tools: Bash(python *) --- # Steps to reproduce 1. Clone the repository: ```bash git clone https://github.com/BioTender-max/MolecularEvolutionEngine cd MolecularEvolutionEngine ``` 2. Install dependencies: ```bash pip install numpy scipy matplotlib ``` 3. Run the analysis: ```bash python molecular_evolution_engine.py ``` 4. Output: `molecular_evolution_engine_dashboard.png` — a 9-panel dark-theme dashboard summarizing all key results. > Requires Python 3.8+. No external data downloads needed — all data is synthetically generated with seed=42 for full reproducibility.
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