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Oldman, James, Wu, Taoyang 
ORCID: https://orcid.org/0000-0002-2663-2001, van Iersel, Leo and Moulton, Vincent ORCID: https://orcid.org/0000-0001-9371-6435
(2016)
TriLoNet: Piecing together small networks to reconstruct reticulate evolutionary histories.
Molecular Biology and Evolution, 33 (8).
pp. 2151-2162.
ISSN 0737-4038
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Abstract
Phylogenetic networks are a generalisation of evolutionary trees that can be used to represent reticulate processes such as hybridisation and recombination. Here we introduce a new approach called TriLoNet 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.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | phylogenetic network,reticulate evolution,networks reconstruction,supernetwork |
| Faculty \ School: | Faculty of Science > School of Computing Sciences |
| UEA Research Groups: | Faculty of Science > Research Groups > Computational Biology Faculty of Science > Research Groups > Norwich Epidemiology Centre Faculty of Medicine and Health Sciences > Research Groups > Norwich Epidemiology Centre Faculty of Science > Research Centres > Centre for Ecology, Evolution and Conservation Faculty of Science > Research Groups > Data Science and AI |
| Related URLs: | |
| Depositing User: | Pure Connector |
| Date Deposited: | 15 Apr 2016 15:00 |
| Last Modified: | 10 Dec 2024 01:27 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/58283 |
| DOI: | 10.1093/molbev/msw068 |
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