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Supriya Munshaw and
Thomas B. Kepler. An Information-Theoretic Method for the Treatment of Plural Ancestry in Phylogenetics. In MBE, Vol. 25(6):1199-1208, 2008. Keywords: explicit network, from sequences, heuristic, phylogenetic network, reconstruction, simulated annealing, software. Note: http://dx.doi.org/10.1093/molbev/msn066.
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"In the presence of recombination and gene conversion, a given genomic segment may inherit information from 2 distinct immediate ancestors. The importance of this type of molecular inheritance has become increasingly clear over the years, and the potential for erroneous inference when it is not accounted for in the statistical model is well documented. Yet, the inclusion of plural ancestry (PA) in phylogenetic analysis is still not routine. This omission is due to the greater difficulty of phylogenetic inference on general acyclic graphs compared that on with trees and the accompanying computational burden. We have developed a technique for phylogenetic inference in the presence of PA based on the principle of minimum description length, which assigns a cost - the description length - to each network topology given the observed sequence data. The description length combines the cost of poor data fit and model complexity in terms of information. This device allows us to search through network topologies to minimize the total description length. By comparing the best models obtained with and without PA, one can determine whether or not recombination has played an active role in the evolution of the genes under investigation, identify those genes that appear to be mosaic, and infer the phylogenetic network that best represents the history of the alignment. We show that the method performs well on simulated data and demonstrate its application on HIV env gene sequence data from 8 human subjects. The software implementation of the method is available upon request. © The Author 2008. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. All rights reserved."
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Changiz Eslahchi,
Reza Hassanzadeh,
Ehsan Mottaghi,
Mahnaz Habibi,
Hamid Pezeshk and
Mehdi Sadeghi. Constructing circular phylogenetic networks from weighted quartets using simulated annealing. In MBIO, Vol. 235(2):123-127, 2012. Keywords: abstract network, from quartets, heuristic, phylogenetic network, phylogeny, Program SAQ-Net, Program SplitsTree, reconstruction, simulated annealing, software, split network. Note: http://dx.doi.org/10.1016/j.mbs.2011.11.003.
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"In this paper, we present a heuristic algorithm based on the simulated annealing, SAQ-Net, as a method for constructing phylogenetic networks from weighted quartets. Similar to QNet algorithm, SAQ-Net constructs a collection of circular weighted splits of the taxa set. This collection is represented by a split network. In order to show that SAQ-Net performs better than QNet, we apply these algorithm to both the simulated and actual data sets containing salmonella, Bees, Primates and Rubber data sets. Then we draw phylogenetic networks corresponding to outputs of these algorithms using SplitsTree4 and compare the results. We find that SAQ-Net produces a better circular ordering and phylogenetic networks than QNet in most cases. SAQ-Net has been implemented in Matlab and is available for download at http://bioinf.cs.ipm.ac.ir/softwares/saq.net. © 2011 Elsevier Inc."
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Reza Hassanzadeh,
Changiz Eslahchi and
Wing-Kin Sung. Constructing phylogenetic supernetworks based on simulated annealing. In MPE, Vol. 63(3):738-744, 2012. Keywords: abstract network, from unrooted trees, heuristic, phylogenetic network, phylogeny, Program SNSA, reconstruction, simulated annealing, software, split network. Note: http://dx.doi.org/10.1016/j.ympev.2012.02.009.
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Different partial phylogenetic trees can be derived from different sources of evidence and different methods. One important problem is to summarize these partial phylogenetic trees using a supernetwork. We propose a novel simulated annealing based method called SNSA which uses an optimization function to produce a simple network that still retains a great deal of phylogenetic information. We report the performance of this new method on real and simulated datasets. © 2012 Elsevier Inc.
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