Inferring Phylogenetic Networks Using PhyloNet.

Dingqiao Wen, Yun Yu, Jiafan Zhu, Luay Nakhleh
Author Information
  1. Dingqiao Wen: Computer Science.
  2. Yun Yu: Computer Science.
  3. Jiafan Zhu: Computer Science.
  4. Luay Nakhleh: Computer Science.

Abstract

PhyloNet was released in 2008 as a software package for representing and analyzing phylogenetic networks. At the time of its release, the main functionalities in PhyloNet consisted of measures for comparing network topologies and a single heuristic for reconciling gene trees with a species tree. Since then, PhyloNet has grown significantly. The software package now includes a wide array of methods for inferring phylogenetic networks from data sets of unlinked loci while accounting for both reticulation (e.g., hybridization) and incomplete lineage sorting. In particular, PhyloNet now allows for maximum parsimony, maximum likelihood, and Bayesian inference of phylogenetic networks from gene tree estimates. Furthermore, Bayesian inference directly from sequence data (sequence alignments or biallelic markers) is implemented. Maximum parsimony is based on an extension of the "minimizing deep coalescences" criterion to phylogenetic networks, whereas maximum likelihood and Bayesian inference are based on the multispecies network coalescent. All methods allow for multiple individuals per species. As computing the likelihood of a phylogenetic network is computationally hard, PhyloNet allows for evaluation and inference of networks using a pseudolikelihood measure. PhyloNet summarizes the results of the various analyzes and generates phylogenetic networks in the extended Newick format that is readily viewable by existing visualization software.

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Grants

  1. R01 LM009494/NLM NIH HHS

MeSH Term

Bayes Theorem
Evolution, Molecular
Hybridization, Genetic
Phylogeny
Sequence Alignment
Software

Word Cloud

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