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Network models for sequence evolution

A von Haeseler1, G A Churchill

  • 1Department of Zoology, University of Munich, Federal Republic of Germany.

Journal of Molecular Evolution
|July 1, 1993
PubMed
Summary

This study introduces network phylogenies for modeling sequence evolution with multiple lineage contributions. The new approach allows for non-treelike evolutionary relationships, improving accuracy in phylogenetic inference.

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Area of Science:

  • Computational Biology
  • Evolutionary Biology
  • Bioinformatics

Background:

  • Traditional phylogenetic models assume strictly tree-like evolutionary histories.
  • Real biological data, such as viral evolution, can involve reticulate events like recombination or horizontal gene transfer, which are not captured by tree-like models.
  • There is a need for more flexible models to accurately represent complex evolutionary histories.

Purpose of the Study:

  • To introduce a general class of models for sequence evolution that incorporates network phylogenies.
  • To develop computational and statistical methods for inferring these network-based evolutionary relationships.
  • To apply the developed methods to real biological data, such as viral gene sequences.

Main Methods:

  • Development of an algorithm to compute probability distributions for binary character-state configurations under network models.
  • Implementation of statistical inference within a likelihood framework.
  • A stepwise model selection procedure using likelihood ratios, starting from a star phylogeny and successively adding splits, allowing for non-treelike relationships.

Main Results:

  • A novel method for phylogenetic inference that accommodates network structures, generalizing traditional tree-based approaches.
  • Simultaneous estimation of the fraction of invariable sites and other model parameters using maximum likelihood, crucial for data fitting.
  • Demonstration of the method's applicability using VP1 gene sequences from foot and mouth disease viruses (FMDV), revealing both treelike and network evolutionary patterns among serotypes.

Conclusions:

  • Network phylogenies offer a more general and accurate framework for modeling sequence evolution when reticulate events are present.
  • The developed statistical inference methods provide a robust approach to exploring and fitting complex evolutionary models.
  • The study highlights the utility of network phylogenies in understanding the evolutionary dynamics of viruses like FMDV.

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