Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/85123
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Type: Journal article
Title: A practical algorithm for reconstructing level-1 phylogenetic networks
Author: Huber, K.T.
van Iersel, L.
Kelk, S.
Suchecki, R.
Citation: IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2011; 8(3):635-649
Publisher: IEEE
Issue Date: 2011
ISSN: 1545-5963
1557-9964
Statement of
Responsibility: 
Katharina T. Huber, Leo van Iersel, Steven Kelk, and Radosław Suchecki
Abstract: Recently, much attention has been devoted to the construction of phylogenetic networks which generalize phylogenetic trees in order to accommodate complex evolutionary processes. Here, we present an efficient, practical algorithm for reconstructing level-1 phylogenetic networks-a type of network slightly more general than a phylogenetic tree-from triplets. Our algorithm has been made publicly available as the program Lev1athan. It combines ideas from several known theoretical algorithms for phylogenetic tree and network reconstruction with two novel subroutines. Namely, an exponential-time exact and a greedy algorithm both of which are of independent theoretical interest. Most importantly, Lev1athan runs in polynomial time and always constructs a level-1 network. If the data are consistent with a phylogenetic tree, then the algorithm constructs such a tree. Moreover, if the input triplet set is dense and, in addition, is fully consistent with some level-1 network, it will find such a network. The potential of Lev1athan is explored by means of an extensive simulation study and a biological data set. One of our conclusions is that Lev1athan is able to construct networks consistent with a high percentage of input triplets, even when these input triplets are affected by a low to moderate level of noise.
Keywords: Phylogenetic networks; level-1; triplets; polynomial time
Rights: © 2011 IEEE
RMID: 0030007396
DOI: 10.1109/TCBB.2010.17
Appears in Collections:Australian Centre for Plant Functional Genomics publications

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