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      A 3-factor approximation algorithm for a Maximum Acyclic Agreement Forest on k rooted, binary phylogenetic trees

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          Abstract

          Phylogenetic trees are leaf-labelled trees, where the leaves correspond to extant species (taxa), and the internal vertices represent ancestral species. The evolutionary history of a set of species can be explained by more than one phylogenetic tree, giving rise to the problem of comparing phylogenetic trees for similarity. Various distance metrics, like the subtree prune-and-regraft (SPR), tree bisection reconnection (TBR) and nearest neighbour interchange (NNI) have been proposed to capture this similarity. The distance between two phylogenetic trees can also be measured by the size of a Maximum Agreement Forest (MAF) on these trees, as it has been shown that the rooted subtree prune-and-regraft distance is 1 less than the size of a MAF. Since computing a MAF of minimum size is an NP-hard problem, approximation algorithms are of interest. Recently, it has been shown that the MAF on k(>=2) trees can be approximated to within a factor of 8. In this paper, we improve this ratio to 3. For certain species, however, the evolutionary history is not completely tree-like. Due to reticulate evolution two gene trees, though related, appear different, making a phylogenetic network a more appropriate representation of reticulate evolution. A phylogenetic network contains hybrid nodes for the species evolved from two parents. The number of such nodes is its hybridization number. It has been shown that this number is 1 less than the size of a Maximum Acyclic Agreement Forest (MAAF). We show that the MAAF for k(>= 2) phylogenetic trees can be approximated to within a factor of 3.

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          Author and article information

          Journal
          2014-07-26
          2016-09-06
          Article
          1407.7125
          25f7f963-eb81-4605-b8e6-2f7af0b610cb

          http://arxiv.org/licenses/nonexclusive-distrib/1.0/

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          14 pages, 8 figures
          cs.DS

          Data structures & Algorithms
          Data structures & Algorithms

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