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A Practical Guide to Phylogenetics for Nonexperts

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Maximum Parsimony on Phylogenetic networks.

Lavanya Kannan¹, Ward C Wheeler

¹Division of Invertebrate Zoology and Richard Gilder Graduate School, American Museum of Natural History, New York, NY - 10024, USA. lkannan@amnh.org.

Algorithms for Molecular Biology : AMB

|May 4, 2012

Summary

This summary is machine-generated.

This study extends phylogenetic tree parsimony algorithms to phylogenetic networks, developing heuristics to efficiently calculate evolutionary costs. These methods accurately estimate optimal parsimony scores for complex evolutionary histories, aiding in the search for the most parsimonious network.

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Area of Science:

Evolutionary Biology
Computational Biology
Phylogenetics

Background:

Phylogenetic networks generalize phylogenetic trees to model complex evolutionary events.
Maximum Parsimony is a character-based method for inferring evolutionary trees by minimizing evolutionary steps.
Existing methods for optimizing parsimony scores on trees are well-established.

Purpose of the Study:

To define and extend the concept of parsimony score to phylogenetic networks.
To adapt existing algorithms (Sankoff, Fitch) for calculating parsimony scores on networks.
To develop heuristic algorithms for efficiently finding optimal parsimony scores on networks.

Main Methods:

Defined parsimony score on networks as the sum of substitution costs along all edges.
Extended Sankoff and Fitch algorithms to networks, addressing conflicting assignments at reticulate vertices.
Developed and applied heuristic algorithms for computing parsimony scores on networks with unequal substitution costs.

Main Results:

Demonstrated that Sankoff and Fitch algorithms can be naturally extended to phylogenetic networks.
Developed heuristics that provide accurate estimations of optimum parsimony scores for networks.
Validated heuristics against exhaustively determined scores for networks with up to 10 leaves and 2 reticulations.

Conclusions:

The defined parsimony score on networks incorporates costs for reticulate vertices, aiding in selecting simpler evolutionary structures.
Heuristic algorithms are crucial for computationally hard problems of finding the most parsimonious network.
The developed score provides a comparable criterion for evaluating evolutionary networks.