Who is Who in Phylogenetic Networks

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Associated keywords

agreement-forest coalescent counting distance-between-networks enumeration evaluation explicit-network exponential-algorithm from-network from-rooted-trees from-sequences from-triplets galled-tree generation heuristic hybridization lateral-gene-transfer NNI-moves parsimony phylogenetic-network phylogeny population-genetics Program-HGT_simul Program-LEV1ATHAN Program-Lev1Generator Program-NetGen Program-PhyloNetwork Program-Recodon Program-Serial-NetEvolve Program-SPNet Program-TCGenerators Program-Treevolve recombination reconstruction simulation software split statistical-model survey time-consistent-network tree-sibling-network tree-child-network unicyclic-network

Article (Journal)

1 Monique M. Morin and Bernard M. E. Moret. NetGen: generating phylogenetic networks with diploid hybrids. In BIO, Vol. 22(15):1921-1923, 2006. Keywords: generation, hybridization, Program NetGen, software. Note: http://dx.doi.org/10.1093/bioinformatics/btl191.         Toggle abstract "Summary: NetGen is an event-driven simulator that creates phylogenetic networks by extending the birth-death model to include diploid hybridizations. DNA sequences are evolved in conjunction with the topology, enabling hybridization decisions to be based on contemporary evolutionary distances. NetGen supports variable rate lineages, root sequence specification, outgroup generation and many other options. This note describes the NetGen application and proposes an extension of the Newick format to accommodate phylogenetic networks. © 2006 Oxford University Press." 2 Patricia Buendia and Giri Narasimhan. Serial NetEvolve: A flexible utility for generating serially-sampled sequences along a tree or recombinant network. In BIO, Vol. 18(22):2313-2314, 2006. Keywords: generation, phylogenetic network, phylogeny, Program Serial NetEvolve, Program Treevolve, recombination, software. Note: http://dx.doi.org/10.1093/bioinformatics/btl387.         Toggle abstract "Summary: Serial NetEvolve is a flexible simulation program that generates DNA sequences evolved along a tree or recombinant network. It offers a user-friendly Windows graphical interface and a Windows or Linux simulator with a diverse selection of parameters to control the evolutionary model. Serial NetEvolve is a modification of the Treevolve program with the following additional features: simulation of serially-sampled data, the choice of either a clock-like or a variable rate model of sequence evolution, sampling from the internal nodes and the output of the randomly generated tree or network in our newly proposed NeTwick format. © 2006 Oxford University Press." 3 Miguel Arenas and David Posada. Recodon: Coalescent simulation of coding DNA sequences with recombination, migration and demography. In BMCB, Vol. 8(458), 2008. Keywords: coalescent, generation, Program Recodon, software. Note: http://dx.doi.org/10.1186/1471-2105-8-458.         Toggle abstract "Background: Coalescent simulations have proven very useful in many population genetics studies. In order to arrive to meaningful conclusions, it is important that these simulations resemble the process of molecular evolution as much as possible. To date, no single coalescent program is able to simulate codon sequences sampled from populations with recombination, migration and growth. Results: We introduce a new coalescent program, called Recodon, which is able to simulate samples of coding DNA sequences under complex scenarios in which several evolutionary forces can interact simultaneously (namely, recombination, migration and demography). The basic codon model implemented is an extension to the general time-reversible model of nucleotide substitution with a proportion of invariable sites and among-site rate variation. In addition, the program implements non-reversible processes and mixtures of different codon models. Conclusion: Recodon is a flexible tool for the simulation of coding DNA sequences under realistic evolutionary models. These simulations can be used to build parameter distributions for testing evolutionary hypotheses using experimental data. Recodon is written in C, can run in parallel, and is freely available from http://darwin.uvigo.es/. © 2007 Arenas and Posada; licensee BioMed Central Ltd." 4 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." 5 Nicolas Galtier. A model of horizontal gene transfer and the bacterial phylogeny problem. In Systematic Biology, Vol. 56(4):633-642, 2007. Keywords: explicit network, generation, lateral gene transfer, phylogenetic network, phylogeny, Program HGT_simul, software, statistical model. Note: http://dx.doi.org/10.1080/10635150701546231.         Toggle abstract "How much horizontal gene transfer (HGT) between species influences bacterial phylogenomics is a controversial issue. This debate, however, lacks any quantitative assessment of the impact of HGT on phylogenies and of the ability of tree-building methods to cope with such events. I introduce a Markov model of genome evolution with HGT, accounting for the constraints on time-an HGT event can only occur between concomitantly living species. This model is used to simulate multigene sequence data sets with or without HGT. The consequences of HGT on phylogenomic inference are analyzed and compared to other well-known phylogenetic artefacts. It is found that supertree methods are quite robust to HGT, keeping high levels of performance even when gene trees are largely incongruent with each other. Gene tree incongruence per se is not indicative of HGT. HGT, however, removes the (otherwise observed) positive relationship between sequence length and gene tree congruence to the estimated species tree. Surprisingly, when applied to a bacterial and a eukaryotic multigene data set, this criterion rejects the HGT hypothesis for the former, but not the latter data set. Copyright © Society of Systematic Biologists." 6 Louxin Zhang. Generating normal networks via leaf insertion and nearest neighbor interchange. In BMC Bioinformatics, Vol. 20(642):1-9, 2019. Keywords: enumeration, explicit network, generation, NNI moves, phylogenetic network, phylogeny. Note: https://link.springer.com/content/pdf/10.1186/s12859-019-3209-3.pdf.         7 Gabriel Cardona, Joan Carles Pons and Celine Scornavacca. Generation of Binary Tree-Child phylogenetic networks. In PLoS Computational Biology, Vol. 15(10):e1007440.1-29, 2019. Keywords: enumeration, explicit network, generation, phylogenetic network, phylogeny, Program PhyloNetwork, Program TCGenerators, software, tree-child network. Note: https://doi.org/10.1371/journal.pcbi.1007440.

InProceedings

8 Ernst Althaus and Rouven Naujoks. Reconstructing Phylogenetic Networks with One Recombination. In Proceedings of the seventh International Workshop on Experimental Algorithms (WEA'08), Vol. 5038:275-288 of LNCS, springer, 2008. Keywords: enumeration, explicit network, exponential algorithm, from sequences, generation, parsimony, phylogenetic network, phylogeny, reconstruction, unicyclic network. Note: http://dx.doi.org/10.1007/978-3-540-68552-4_21.         Toggle abstract "In this paper we propose a new method for reconstructing phylogenetic networks under the assumption that recombination events have occurred rarely. For a fixed number of recombinations, we give a generalization of the maximum parsimony criterion. Furthermore, we describe an exact algorithm for one recombination event and show that in this case our method is not only able to identify the recombined sequence but also to reliably reconstruct the complete evolutionary history. © 2008 Springer-Verlag Berlin Heidelberg."

InBook

9 Gabriel Valiente. Combinatorial Pattern Matching Algorithms in Computational Biology Using Perl and R. Pages 184-208, Taylor & Francis/CRC Press, 2009. Keywords: counting, distance between networks, galled tree, generation, phylogenetic network, phylogeny, survey, time consistent network, tree sibling network, tree-child network. Note: http://books.google.fr/books?id=F4YIIUWb7yMC.

PhdThesis

10 Luay Nakhleh. Phylogenetic Networks. PhD thesis, University of Texas at Austin, U.S.A., 2004. Keywords: distance between networks, evaluation, generation, phylogenetic network, phylogeny, Program SPNet, reconstruction, split, statistical model, tree sibling network. Note: http://www.library.utexas.edu/etd/d/2004/nakhlehl042/nakhlehl042.pdf.         11 Sha Zhu. Stochastic tree models and probabilistic modelling of gene trees of given species networks. PhD thesis, University of Canterbury, New Zealand, 2013. Keywords: from network, generation, phylogenetic network, phylogeny, simulation, statistical model. Note: http://hdl.handle.net/10092/7944.         12 Josh Voorkamp né Collins. Untangling Evolution. PhD thesis, University of Otago, New Zealand, 2014. Keywords: agreement forest, explicit network, from rooted trees, generation, phylogenetic network, phylogeny, reconstruction. Note: http://otago.ourarchive.ac.nz/handle/10523/4802.

Misc

13 Nicholas C. Grassly and Andrew Rambaut. Treevolve, a program to simulate the evolution of DNA sequences under different population dynamic scenarios. 1999. Keywords: generation, phylogenetic network, phylogeny, population genetics, Program Treevolve, software. Note: http://evolve.zoo.ox.ac.uk/software/treevolve/manual.php.         14 Sha Zhu, James H. Degnan and Mike Steel. Probabilistic modeling of gene trees given species networks. 2011. Keywords: from network, generation, phylogenetic network, phylogeny, statistical model. Note: Poster, http://www.newton.ac.uk/programmes/PLG/Zhu.pdf.