Who is Who in Phylogenetic Networks

Publications related to 'Program SHRUB' : SHRUB constructs a minimum recombination phylogenetic network (ARG) that derives a set of binary sequences under the infinite sites assumption. SHRUB, SHRUB-GC   Order by:   Type | Year

Only And Or     related to:

Associated keywords

abstract-network ARG distance-between-networks evaluation from-sequences integer-linear-programming median-network MedianJoining minimum-number minimum-spanning-network NeighborNet parsimony phylogenetic-network phylogeny Program-Arlequin Program-CombineTrees Program-HapBound Program-Network Program-SHRUB Program-SplitsTree Program-TCS recombination reconstruction split-decomposition

2008

1 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

2 Yun S. Song, Zhihong Ding, Dan Gusfield, Charles Langley and Yufeng Wu. Algorithms to Distinguish the Role of Gene-Conversion from Single-Crossover Recombination in the Derivation of SNP Sequences in Populations. In JCB, Vol. 14(10):1273-1286, 2007. Keywords: ARG, from sequences, phylogenetic network, phylogeny, Program SHRUB, reconstruction. Note: http://dx.doi.org/10.1089/cmb.2007.0096.         Toggle abstract "Meiotic recombination is a fundamental biological event and one of the principal evolutionary forces responsible for shaping genetic variation within species. In addition to its fundamental role, recombination is central to several critical applied problems. The most important example is "association mapping" in populations, which is widely hoped to help find genes that influence genetic diseases (Carlson et al., 2004; Clark, 2003). Hence, a great deal of recent attention has focused on problems of inferring the historical derivation of sequences in populations when both mutations and recombinations have occurred. In the algorithms literature, most of that recent work has been directed to single-crossover recombination. However, gene-conversion is an important, and more common, form of (two-crossover) recombination which has been much less investigated in the algorithms literature. In this paper, we explicitly incorporate gene-conversion into discrete methods to study historical recombination. We are concerned with algorithms for identifying and locating the extent of historical crossing-over and gene-conversion (along with single-nucleotide mutation), and problems of constructing full putative histories of those events. The novel technical issues concern the incorporation of gene-conversion into recently developed discrete methods (Myers and Griffiths, 2003; Song et al., 2005) that compute lower and upper-bound information on the amount of needed recombination without gene-conversion. We first examine the most natural extension of the lower bound methods from Myers and Griffiths (2003), showing that the extension can be computed efficiently, but that this extension can only yield weak lower bounds. We then develop additional ideas that lead to higher lower bounds, and show how to solve, via integer-linear programming, a more biologically realistic version of the lower bound problem. We also show how to compute effective upper bounds on the number of needed single-crossovers and gene-conversions, along with explicit networks showing a putative history of mutations, single-crossovers and gene-conversions. Both lower and upper bound methods can handle data with missing entries, and the upper bound method can be used to infer missing entries with high accuracy. We validate the significance of these methods by showing that they can be effectively used to distinguish simulation-derived sequences generated without gene-conversion from sequences that were generated with gene-conversion. We apply the methods to recently studied sequences of Arabidopsis thaliana, identifying many more regions in the sequences than were previously identified (Plagnol et al., 2006), where gene-conversion may have played a significant role. Demonstration software is available at www.csif.cs.ucdavis.edu/∼gusfield. © 2007 Mary Ann Liebert, Inc."

2005

3 Yun S. Song, Yufeng Wu and Dan Gusfield. Efficient computation of close lower and upper bounds on the minimum number of recombinations in biological sequence evolution. In ISMB05, Vol. 21:i413-i422 of BIO, 2005. Keywords: integer linear programming, minimum number, Program HapBound, Program SHRUB, recombination. Note: http://dx.doi.org/10.1093/bioinformatics/bti1033.         Toggle abstract "Motivation: We are interested in studying the evolution of DNA single nucleotide polymorphism sequences which have undergone (meiotic) recombination. For a given set of sequences, computing the minimum number of recombinations needed to explain the sequences (with one mutation per site) is a standard question of interest, but it has been shown to be NP-hard, and previous algorithms that compute it exactly work either only on very small datasets or on problems with special structure. Results: In this paper, we present efficient, practical methods for computing both upper and lower bounds on the minimum number of needed recombinations, and for constructing evolutionary histories that explain the input sequences. We study in detail the efficiency and accuracy of these algorithms on both simulated and real data sets. The algorithms produce very close upper and lower bounds, which match exactly in a surprisingly wide range of data. Thus, with the use of new, very effective lower bounding methods and an efficient algorithm for computing upper bounds, this approach allows the efficient, exact computation of the minimum number of needed recombinations, with high frequency in a large range of data. When upper and lower bounds match, evolutionary histories found by our algorithm correspond to the most parsimonious histories. © The Author 2005. Published by Oxford University Press. All rights reserved."