TriLoNet: Piecing Together Small Networks to Reconstruct Reticulate Evolutionary Histories

James Oldman, Taoyang Wu, Leo van Iersel, Vincent Lynmore Moulton

Research output: Contribution to journalArticleScientificpeer-review

12 Citations (Scopus)
18 Downloads (Pure)

Abstract

Phylogenetic networks are a generalization of evolutionary trees that can be used to represent reticulate processes such as hybridization and recombination. Here, we introduce a new approach called TriLoNet (Trinet Level- one Network algorithm) to construct such networks directly from sequence alignments which works by piecing together smaller phylogenetic networks. More specifically, using a bottom up approach similar to Neighbor-Joining, TriLoNet constructs level-1 networks (networks that are somewhat more general than trees) from smaller level-1 networks on three taxa. In simulations, we show that TriLoNet compares well with Lev1athan, a method for reconstructing level-1 networks from three-leaved trees. In particular, in simulations we find that Lev1athan tends to generate networks that overestimate the number of reticulate events as compared with those generated by TriLoNet. We also illustrate TriLoNet’s applicability using simulated and real sequence data involving recombination, demonstrating that it has the potential to reconstruct informative reticulate evolutionary histories. TriLoNet has been implemented in JAVA and is freely available at https://www.uea.ac.uk/computing/TriLoNet.
Original languageEnglish
Pages (from-to)2151-2162
Number of pages12
JournalMolecular Biology and Evolution
Volume33
Issue number8
DOIs
Publication statusPublished - 2016

Keywords

  • phylogenetic network
  • reticulate evolution
  • networks reconstruction
  • supernetwork

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