THE ACCURACY OF PHYLOGENETIC ESTIMATION USING THE NEIGHBOR-JOINING METHOD.

Area of Science:

• Phylogenetics
• Computational Biology
• Evolutionary Biology

Background:

• Phylogenetic trees are crucial for understanding evolutionary relationships.
• The neighbor-joining (NJ) method is a widely used algorithm for phylogenetic tree reconstruction.
• Factors influencing the accuracy of NJ phylogenetic estimation require thorough investigation.

Purpose of the Study:

• To investigate factors affecting the accuracy of the neighbor-joining (NJ) method in phylogenetic tree estimation.
• To compare the performance of NJ with UPGMA and Maximum Parsimony (MP) under various evolutionary scenarios.
• To identify key evolutionary parameters impacting phylogenetic accuracy.

Main Methods:

• Simulated character evolution under diverse evolutionary models (rates and contexts).
• Applied NJ, UPGMA, and MP methods to 8-OTU tree topologies with varying imbalance and stemminess.
• Assessed accuracy using the strict consensus index and analyzed results with ANOVA.

Main Results:

• Evolutionary context and tree imbalance were the most significant factors influencing NJ accuracy.
• NJ generally outperformed UPGMA and MP based on the average strict consensus index.
• Higher absolute rates of change tended to improve accuracy across all tested methods.

Conclusions:

• Evolutionary context and tree topology characteristics are critical for accurate NJ phylogenetic inference.
• While NJ showed superior average performance, no single method was universally best.
• Understanding these factors is essential for selecting appropriate phylogenetic methods and interpreting results.