Abstract
The reconstruction of the evolutionary history of a set of species is an important problem in classification and phylogenetics. Phylogenetic networks are a generalization of evolutionary trees that are used to represent histories for species that have undergone reticulate evolution, an important evolutionary force for many organisms (e.g. plants or viruses). In this paper, we present a novel approach to understanding the structure of networks that are not necessarily binary. More specifically, we define the concept of a closed set and show that the collection of closed sets of a network forms a hierarchy, and that this hierarchy can be deduced from either the subtrees or subnetworks on all 3-subsets. This allows us to also show that closed sets generalize the concept of the SN-sets of a binary network, sets which have proven very useful in elucidating the structure of binary networks. We also characterize the minimal closed sets (under set inclusion) for a special class of networks (2-terminal networks). Taken together, we anticipate that our results should be useful for the development of new phylogenetic network reconstruction algorithms.
Original language | English |
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Pages (from-to) | 200-231 |
Number of pages | 32 |
Journal | Journal of Classification |
Volume | 36 |
Issue number | 2 |
Early online date | 16 Nov 2018 |
DOIs | |
Publication status | Published - Jul 2019 |
Keywords
- Hierarchy
- Lower Stable Ancestor
- Nonbinary network
- Phylogenetic network
Profiles
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Katharina Huber
- School of Computing Sciences - Associate Professor
- Computational Biology - Member
Person: Research Group Member, Academic, Teaching & Research
-
Vincent Moulton
- School of Computing Sciences - Professor in Computational Biology
- Norwich Epidemiology Centre - Member
- Computational Biology - Member
Person: Research Group Member, Academic, Teaching & Research
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Taoyang Wu
- School of Computing Sciences - Lecturer in Computing Sciences
- Centre for Ecology, Evolution and Conservation - Member
- Computational Biology - Member
- Data Science and AI - Member
Person: Research Group Member, Research Centre Member, Academic, Teaching & Research