Who is Who in Phylogenetic Networks

Publications related to 'split decomposition' : Split decomposition is a method to build a system of weighted weakly compatible splits from a distance matrix.   Order by:   Type | Year

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abstract-network circular-split-system consensus distance-between-networks evaluation explicit-network from-distances from-gene-order from-quartets hybridization lateral-gene-transfer likelihood median-network MedianJoining minimum-spanning-network NeighborNet netting parsimony phylogenetic-network phylogeny population-genetics Program-Arlequin Program-CombineTrees Program-Network Program-Pyramids Program-Reticlad Program-SHRUB Program-Spectronet Program-SplitsTree Program-T-REX Program-TCS Program-WeakHierarchies pyramid recombination reconstruction reticulogram software split split-decomposition split-network statistical-parsimony survey visualization weak-hierarchy weakly-compatible

2013

1 Alexey A. Morozov, Yuri P. Galachyants and Yelena V. Likhoshway. Inferring Phylogenetic Networks from Gene Order Data. In BMRI, Vol. 2013(503193):1-7, 2013. Keywords: abstract network, from distances, from gene order, NeighborNet, phylogenetic network, phylogeny, Program SplitsTree, reconstruction, split decomposition, split network.         Toggle abstract "Existing algorithms allow us to infer phylogenetic networks from sequences (DNA, protein or binary), sets of trees, and distance matrices, but there are no methods to build them using the gene order data as an input. Here we describe several methods to build split networks from the gene order data, perform simulation studies, and use our methods for analyzing and interpreting different real gene order datasets. All proposed methods are based on intermediate data, which can be generated from genome structures under study and used as an input for network construction algorithms. Three intermediates are used: set of jackknife trees, distance matrix, and binary encoding. According to simulations and case studies, the best intermediates are jackknife trees and distance matrix (when used with Neighbor-Net algorithm). Binary encoding can also be useful, but only when the methods mentioned above cannot be used. © 2013 Alexey Anatolievich Morozov et al."

2010

2 David A. Morrison. Using data-display networks for exploratory data analysis in phylogenetic studies. In MBE, Vol. 27(5):1044-1057, 2010. Keywords: abstract network, hybridization, NeighborNet, Program SplitsTree, recombination, split decomposition. Note: http://dx.doi.org/10.1093/molbev/msp309.         Toggle abstract "Exploratory data analysis (EDA) is a frequently undervalued part of data analysis in biology. It involves evaluating the characteristics of the data "before" proceeding to the definitive analysis in relation to the scientific question at hand. For phylogenetic analyses, a useful tool for EDA is a data-display network. This type of network is designed to display any character (or tree) conflict in a data set, without prior assumptions about the causes of those conflicts. The conflicts might be caused by 1) methodological issues in data collection or analysis, 2) homoplasy, or 3) horizontal gene flow of some sort. Here, I explore 13 published data sets using splits networks, as examples of using data-display networks for EDA. In each case, I performed an original EDA on the data provided, to highlight the aspects of the resulting network that will be important for an interpretation of the phylogeny. In each case, there is at least one important point (possibly missed by the original authors) that might affect the phylogenetic analysis. I conclude that EDA should play a greater role in phylogenetic analyses than it has done. © 2010 The Author. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. All rights reserved."

2008

3 Steven M. Woolley, David Posada and Keith A. Crandall. A Comparison of Phylogenetic Network Methods Using Computer Simulation. In PLoS ONE, Vol. 3(4):e1913, 2008. Keywords: abstract network, distance between networks, evaluation, median network, MedianJoining, minimum spanning network, NeighborNet, parsimony, phylogenetic network, phylogeny, Program Arlequin, Program CombineTrees, Program Network, Program SHRUB, Program SplitsTree, Program TCS, split decomposition. Note: http://dx.doi.org/10.1371/journal.pone.0001913.         Toggle abstract "Background: We present a series of simulation studies that explore the relative performance of several phylogenetic network approaches (statistical parsimony, split decomposition, union of maximum parsimony trees, neighbor-net, simulated history recombination upper bound, median-joining, reduced median joining and minimum spanning network) compared to standard tree approaches (neighbor-joining and maximum parsimony) in the presence and absence of recombination. Principal Findings: In the absence of recombination, all methods recovered the correct topology and branch lengths nearly all of the time when the subtitution rate was low, except for minimum spanning networks, which did considerably worse. At a higher substitution rate, maximum parsimony and union of maximum parsimony trees were the most accurate. With recombination, the ability to infer the correct topology was halved for all methods and no method could accurately estimate branch lengths. Conclusions: Our results highlight the need for more accurate phylogenetic network methods and the importance of detecting and accounting for recombination in phylogenetic studies. Furthermore, we provide useful information for choosing a network algorithm and a framework in which to evaluate improvements to existing methods and novel algorithms developed in the future. © 2008 Woolley et al."

2007

4 Olivier Gauthier and François-Joseph Lapointe. Hybrids and Phylogenetics Revisited: A Statistical Test of Hybridization Using Quartets. In Systematic Botany, Vol. 32(1):8-15, 2007. Keywords: explicit network, from quartets, hybridization, phylogenetic network, phylogeny, reconstruction, reticulogram, split decomposition. Note: http://dx.doi.org/10.1600/036364407780360238.         Toggle abstract "The occurrence of reticulations in the evolutionary history of species poses serious challenges for all modern practitioners of phylogenetic analysis. Such events, including hybridization, introgression, and lateral gene transfer, lead to evolutionary histories that cannot be adequately represented in the form of phylogenetic trees. Although numerous methods that allow for the reconstruction of phylogenetic networks have been proposed in recent years, the detection of reticulations still remains problematic. In this paper we present a Hybrid Detection Criterion (HDC) along with a statistical procedure that allows for the identification of hybrid taxa. The test assesses whether a putative hybrid is consistently intermediate between its postulated parents, with respect to the other taxa. The performance of the statistical method is evaluated using known hybrids of the genus Aphelandra (Acanthaceae) using two network methods: reticulograms and split decomposition graphs. Our results indicate that the HDC test is reliable when used jointly with split decomposition. On the other hand, the test lacks power and provides misleading results when using reticulograms. We then show how the procedure can be used as a tool to identify putative hybrids. © Copyright 2007 by the American Society of Plant Taxonomists." 5 Bastienne Vriesendorp. Phylogenenetworks, exploring reticulate evolution and its consequences for phylogenetic reconstruction. PhD thesis, Wageningen University, The Netherlands, 2007. Keywords: consensus, distance between networks, evaluation, hybridization, median network, NeighborNet, parsimony, phylogenetic network, phylogeny, Program SplitsTree, split decomposition, survey. Note: http://library.wur.nl/wda/dissertations/dis4239.pdf.

2006

6 Vladimir Makarenkov, Dmytro Kevorkov and Pierre Legendre. Phylogenetic Network Construction Approaches. In Applied Mycology and Biotechnology, Vol. 6:61-97, 2006. Keywords: from distances, hybridization, lateral gene transfer, median network, NeighborNet, netting, Program Arlequin, Program Network, Program Pyramids, Program Reticlad, Program SplitsTree, Program T REX, Program TCS, Program WeakHierarchies, pyramid, reticulogram, split, split decomposition, split network, survey, weak hierarchy. Note: http://www.labunix.uqam.ca/~makarenv/makarenv/MKL_article.pdf.

2005

7 David A. Morrison. Networks in phylogenetic analysis: new tools for population biology. In IJP, Vol. 35:567-582, 2005. Keywords: median network, NeighborNet, phylogenetic network, phylogeny, population genetics, Program Network, Program Spectronet, Program SplitsTree, Program T REX, Program TCS, reconstruction, reticulogram, split decomposition, survey. Note: http://hem.fyristorg.com/acacia/papers/networks.pdf.

2004

8 Mohd Abdul Hai Zahid, Ankush Mittal and Ramesh C. Joshi. Use of Phylogenetic network and its reconstruction Algorithms. In Bioinformatics India, Vol. 2:47-58, 2004. Keywords: evaluation, from distances, NeighborNet, Program SplitsTree, Program T REX, split decomposition. Note: http://www.isical.ac.in/~zahid_t/publications/papers/1.pdf.

2003

9 Daniel H. Huson. What If I Don't Have a Tree? Split Decomposition and Related Models. In Current Protocols in Bioinformatics, Vol. Unit 6.7, 2003. Keywords: abstract network, phylogenetic network, Program SplitsTree, software, split decomposition, split network. Note: http://dx.doi.org/10.1002/0471250953.bi0607s01.

2001

10 David Posada and Keith A. Crandall. Intraspecific gene genealogies: trees grafting into networks. In TEE, Vol. 16(1):37-45, 2001. Keywords: likelihood, median network, netting, parsimony, phylogenetic network, phylogeny, Program Arlequin, Program SplitsTree, Program T REX, Program TCS, pyramid, reticulogram, split decomposition, statistical parsimony, survey. Note: http://darwin.uvigo.es/download/papers/09.networks01.pdf.

1993

11 Joaquin Dopazo, Andreas W. M. Dress and Arndt von Haeseler. Split decomposition: a new technique to analyse viral evolution. In PNAS, Vol. 90:10320-10324, 1993. Keywords: abstract network, phylogenetic network, phylogeny, split, split decomposition, split network, visualization. Note: http://dx.doi.org/10.1073/pnas.90.21.10320.

1992

12 Hans-Jürgen Bandelt and Andreas W. M. Dress. A canonical decomposition theory for metrics on a finite set. In Advances in Mathematics, Vol. 92(1):47-105, 1992. Keywords: abstract network, circular split system, from distances, split, split decomposition, split network, weak hierarchy, weakly compatible.         Toggle abstract "We consider specific additive decompositions d = d1 + ... + dn of metrics, defined on a finite set X (where a metric may give distance zero to pairs of distinct points). The simplest building stones are the slit metrics, associated to splits (i.e., bipartitions) of the given set X. While an additive decomposition of a Hamming metric into split metrics is in no way unique, we achieve uniqueness by restricting ourselves to coherent decompositions, that is, decompositions d = d1 + ... + dn such that for every map f:X → R with f(x) + f(y) ≥ d(x, y) for all x, y ε{lunate} X there exist maps f1, ..., fn: X → R with f = f1 + ... + fn and fi(x) + fi(y) ≥ di(x, y) for all i = 1,..., n and all x, y ε{lunate} X. These coherent decompositions are closely related to a geometric decomposition of the injective hull of the given metric. A metric with a coherent decomposition into a (weighted) sum of split metrics will be called totally split-decomposable. Tree metrics (and more generally, the sum of two tree metrics) are particular instances of totally split-decomposable metrics. Our main result confirms that every metric admits a coherent decomposition into a totally split-decomposable metric and a split-prime residue, where all the split summands and hence the decomposition can be determined in polynomial time, and that a family of splits can occur this way if and only if it does not induce on any four-point subset all three splits with block size two. © 1992." 13 Hans-Jürgen Bandelt and Andreas W. M. Dress. Split Decomposition: A new and useful approach to phylogenetic analysis of distance data. In MPE, Vol. 1(3):242-252, 1992. Keywords: abstract network, from distances, phylogenetic network, phylogeny, reconstruction, split, split decomposition, split network. Note: http://dx.doi.org/10.1016/1055-7903(92)90021-8.         Toggle abstract "In order to analyze the structure inherent to a matrix of dissimilarities (such as evolutionary distances) we propose to use a new technique called split decomposition. This method accurately dissects the given dissimilarity measure as a sum of elementary "split" metrics plus a (small) residue. The split summands identify related groups which are susceptible to further interpretation when casted against the available biological information. Reanalysis of previously published ribosomal RNA data sets using split decomposition illustrate the potential of this approach. © 1992."