Who is Who in Phylogenetic Networks

Publications related to 'Program LEV1ATHAN' : LEV1ATHAN is a Java program for generating level-1 networks from sets of rooted triplets. It implements a heuristic to find a level-1 network consistent with as many of the input triplets as possible. Available at http://skelk.sdf-eu.org/lev1athan/.   Order by:   Type | Year

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Associated keywords

explicit-network from-subnetworks from-trinets from-triplets galled-tree generation heuristic phylogenetic-network phylogeny Program-LEV1ATHAN Program-Lev1Generator Program-TriLoNet reconstruction software

2016

1 James Oldman, Taoyang Wu, Leo van Iersel and Vincent Moulton. TriLoNet: Piecing together small networks to reconstruct reticulate evolutionary histories. In MBE, Vol. 33(8):2151-2162, 2016. Keywords: explicit network, from subnetworks, from trinets, galled tree, phylogenetic network, phylogeny, Program LEV1ATHAN, Program TriLoNet, reconstruction.

2011

2 Katharina Huber, Leo van Iersel, Steven Kelk and Radoslaw Suchecki. A Practical Algorithm for Reconstructing Level-1 Phylogenetic Networks. In TCBB, Vol. 8(3):607-620, 2011. Keywords: explicit network, from triplets, galled tree, generation, heuristic, phylogenetic network, phylogeny, Program LEV1ATHAN, Program Lev1Generator, reconstruction, software. Note: http://arxiv.org/abs/0910.4067.         Toggle 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. © 2011 IEEE."