Abstract
Phylogenetic networks are a generalization of evolutionary or phylogenetic trees that are used to represent the evolution of species which have undergone reticulate evolution. In this paper we consider spaces of such networks defined by some novel local operations that we introduce for converting one phylogenetic network into another. These operations are modeled on the well-studied nearest-neighbor interchange (NNI) operations on phylogenetic trees, and lead to natural generalizations of the tree spaces that have been previously associated to such operations. We present several results on spaces of some relatively simple networks, called level-1 networks, including the size of the neighborhood of a fixed network, and bounds on the diameter of the metric defined by taking the smallest number of operations required to convert one network into another.We expect that our results will be useful in the development of methods for systematically searching for optimal phylogenetic networks using, for example, likelihood and Bayesian approaches.
Original language | English |
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Pages (from-to) | 699-725 |
Number of pages | 27 |
Journal | Journal of Mathematical Biology |
Volume | 72 |
Issue number | 3 |
Early online date | 3 Jun 2015 |
DOIs | |
Publication status | Published - Feb 2016 |
Keywords
- Phylogenetic networks
- Spaces of phylogenetic trees
- Phylogenetic tree metrics
- Nearest-neighbor interchange (NNI)
Profiles
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Katharina Huber
- School of Computing Sciences - Associate Professor
- Computational Biology - Member
Person: Research Group Member, Academic, Teaching & Research
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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
Person: Research Group Member, Research Centre Member, Academic, Teaching & Research