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PHYLOGENIES FROM RESTRICTION SITES: A MAXIMUM-LIKELIHOOD APPROACH.

Joseph Felsenstein¹

¹Department of Genetics SK-50, University of Washington, Seattle, WA, 98195, USA.

Evolution; International Journal of Organic Evolution

|June 1, 2017

Summary

Maximum likelihood analysis of restriction sites provides phylogenetic estimates. This method accounts for multiple gains and losses, offering a more comprehensive evolutionary analysis than parsimony methods.

Keywords:

Maximum likelihood phylogenies restriction sites

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Area of Science:

Computational Biology
Phylogenetics
Molecular Evolution

Background:

Restriction site data is crucial for inferring evolutionary relationships.
Existing methods like parsimony have limitations in accounting for evolutionary events.
Maximum likelihood methods offer a statistically robust framework for phylogenetic inference.

Purpose of the Study:

To extend maximum likelihood methods for phylogenetic analysis of restriction site data to any number of species.
To develop computational algorithms that fully account for multiple gains and losses of restriction sites.
To evaluate the performance of the extended method using hominoid and simulated data.

Main Methods:

Numerical extension of likelihood computations for restriction site data.
Development of algorithms to handle multiple gains and losses of restriction sites.
Application of the method to empirical (hominoid) and simulated datasets.

Main Results:

The extended maximum likelihood method can analyze restriction site data for any number of species.
The algorithms account for multiple gains and losses, and the absence of potential sites.
Analysis of hominoid data suggests a chimpanzee-gorilla clade, though not statistically significant.
Simulated data confirms the method's proper functioning.

Conclusions:

Maximum likelihood analysis of restriction sites is a viable method for phylogenetic inference.
The developed computational methods provide a more complete evolutionary picture than parsimony.
Further extensions are possible to incorporate varying rates of base substitutions, albeit with increased computational cost.