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Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

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Evaluating variations on the STAR algorithm for relative efficiency and sample sizes needed to reconstruct species

James H Degnan¹

¹Department of Mathematics and Statistics, University of Canterbury, Christchurch, 8140, New Zealand. j.degnan@math.canterbury.ac.nz

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing

|February 21, 2013

Summary

This study evaluates different node numbering schemes for the STAR method, which infers species trees from gene trees. Analytical methods show variations can improve efficiency and accuracy in phylogenetic analysis.

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A Practical Guide to Phylogenetics for Nonexperts

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Area of Science:

Phylogenetics
Computational Biology
Evolutionary Biology

Background:

Incongruence among gene trees, often due to incomplete lineage sorting, necessitates robust species tree inference methods.
The STAR (Species Tree Analysis) method offers an efficient approach using topological information from gene trees to construct a distance matrix for phylogenetic analysis.

Purpose of the Study:

To analytically evaluate the efficiency of alternative node numbering schemes for the STAR algorithm.
To assess how different numbering strategies impact the accuracy and computational performance of species tree inference.

Main Methods:

Utilized expected values and variances of average pairwise distances derived from the STAR algorithm's distance matrix.
Performed analytical evaluations on variations of the original STAR node numbering scheme for small tree topologies.

Main Results:

Demonstrated that specific alternative node numbering schemes can be analytically evaluated for their efficiency.
Identified potential improvements in species tree inference accuracy and computational efficiency through modified numbering strategies.

Conclusions:

The choice of node numbering in gene trees significantly influences the performance of the STAR method for species tree inference.
Analytical evaluation provides a framework for optimizing phylogenetic inference algorithms like STAR by exploring variations in their underlying methodologies.