M.K. Kuhner and J. Felsenstein. A simulation comparison of phylogeny algorithms under equal and unequal evolutionary rates. Mol. Biol. Evol., 11:459-- 468, 1994.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....regulatory elements in genetic sequences [1] The problem of phylogenetic inference is considered hard from both a biological and computational perspective. However many biologists believe that maximum likelihood (ML) based methods are the best vehicle for conducting phylogenetic analysis [9], 14] ML methods are, however, NP hard optimization problems requiring expensive parameter estimation and topology searching procedures, and is computationally impractical on large data sets [3] To date many approximation methods for ML based inference have been proposed such as quartet ....

Kuhner, M., and Felsenstein, J. A simulation comparison of phylogeny algorithms under equal and unequal evolutionary rates. Molecular Biology and Evolution 11 (1994), 459-468.

....regulatory elements in genetic sequences [1] The problem of phylogenetic inference is considered hard from both a biological and computational perspective. However many biologists believe that maximum likelihood (ML) based methods are the best vehicle for conducting phylogenetic analysis [9], 14] ML methods are, however, NP hard optimization problems requiring expensive parameter estimation and topology searching procedures, and is computationally impractical on large data sets [3] To date many approximation methods for ML based inference have been proposed such as quartet ....

Kuhner, M., and Felsenstein, J. A simulation comparison of phylogeny algorithms under equal and unequal evolutionary rates. Molecular Biology and Evolution 11 (1994), 459-468.

....polynomial p such that, for all (T;fl(e)g) 2 M f ;g , on a set S of n sequences of length at least p(n) generated on T , we have Pr[F(S) T ] 1 e. 3 Experimental Design Simulation Study: Simulation studies are the standard technique used in phylogenetic performance studies (see, for example, [14, 15, 21]) In a simulation study, a DNA sequence at the root of a model tree (i.e. tree topology with branch lengths) is evolved down the tree under some assumed stochastic model of evolution, such as the K2P or JC models. This process generates a set of sequences at the leaves of the tree. The sequences ....

Kuhner, K., and Felsenstein, J., A simulation comparison of phylogeny algorithms under equal and unequal evolutionary rates. Mol. Biol. Evol., 11:459--468, 1994.

....exhaustive and heuristic. Since the number of candidate trees grows exponentially with the number of taxa, exhaustive approaches, even branch and bound based methods, are infeasible for more than 20 taxa [79] Therefore, many heuristics were proposed with an eye toward run time efficiency [39] [48]. Most heuristics are greedy in nature and employ moves that are locally best according to the chosen criterion. Greedy methods are run time efficient because they explore only a small portion of the solution space [81] The performance of a tree construction heuristic can be checked for small ....

....and Nei [72] and later modified by Studier and Kepler [78] Neighbor Joining seeks to build a tree which minimizes the sum of all edge lengths, i.e. it adopts the minimum evolution (ME) criterion. A number of studies have corroborated NJ s performance in reconstructing correct evolutionary trees [48] [40] 71] For small numbers of taxa, NJ solutions are likely to be identical to the optimal ME tree [71] Neighbor Joining begins with a star tree, then iteratively finds the closest neighboring pair (i.e. the pair that induces a tree of minimum sum of edge lengths) among all possible pairs of ....

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M. K. Kuhner and J. Felsenstein, A Simulation Comparison of Phylogeny Algorithms Under Equal and Unequal Evolutionary Rates, Mol. Biol. Evol., 11 (1994), pp. 459--468.

....T ) Several metrics have been proposed to measure dissimilarity between two evolutionary histories. The most popular include the nearest neighbor interchange (NNI) distance (Robinson, 1971) subtree transfer distance (Hein, 1993) triples distance (Critchlow et al. 1996) and branch score (Bs) (Kuhner and Felsenstein, 1994). Subtree transfer is most appropriate when considering events such as recombination or gene conversions and the triples distance is not uniquely de ned for unrooted trees. The Bs is straight forward to calculate and is less sensitive to small di erences in topology than NNI (DasGupta et al. ....

....triples distance is not uniquely de ned for unrooted trees. The Bs is straight forward to calculate and is less sensitive to small di erences in topology than NNI (DasGupta et al. 1999) This property is ideal as we expect small di erences to occur and do not wish to overweight them. Following Kuhner and Felsenstein (1994), Bs examines the di erence in the partitions of taxa induced by the branches in x 1 and x 2 . Let P = P 1 ; P 2 ; Pn ) where n = 2 N 1 1, be the set of all possible unordered partitions P i = R i1 ; R i2 ) of N taxa into two disjoint sets R i1 and R i2 and let the partition value ....

[Article contains additional citation context not shown here]

Kuhner, M. and Felsenstein, J. (1994). A simulation comparison of phylogeny algorithms under equal and unequal evolutionary rates. Molecular Biology and Evolution, 11:459-468.

....these cases, parsimony and likelihood) Then they will return the trees with the best score. The problems of finding Maximum Parsimony and Maximum Likelihood evolutionary trees are NP Hard [13, 23] Each method (especially Maximum Likelihood) has shown good performance under certain circumstances [40]. 1.3 Quartet Methods In practice, heuristic methods for Maximum Parsimony and Maximum Likelihood are often used. These heuristic methods sometimes either are too slow or give sub optimal solutions with low accuracy. Although finding the optimal trees with highest score for these models is ....

....be estimated from aligned sequences by statistical models. The distances estimated this way will inevitably lose some information of the sequence set. Therefore, on average, the methods that take these estimated distances do not perform as well as the methods that take aligned sequences directly ([40]) However, sequence alignment has its limitations. First of all, it is not computationally feasible to align very long sequences (say whole genomes) Second of all, sequence alignment does not take into consideration inversions. Recently, it has been attempted to estimate evolutionary distances ....

Kuhner, M. K. and J. Felsenstein. 1994. A simulation comparison of phylogeny algorithms under equal and unequal evolutionary rates. Molecular Biology Evolution 11:459-468.

.... x and y and constructed a tree (Figure 1) using the neighbor joining (Saitou and Nei, 1987) program in the MOLPHY package (Adachi and Hasegawa, 1996) The tree is identical to the maximum likelihood tree of Cao, et al. Because neighbor joining is sometimes distrusted 3 (Hillis et al. 1994; Kuhner and Felsenstein, 1994), to further corroborate this grouping we applied our own hypercleaning program (Bryant et al. 2000) to the same distance matrix and obtained the same tree. The hypercleaning program constructs an evolutionary tree using the edges best supported by all possible four taxa subtrees (commonly called ....

Kuhner, M. K. and Felsenstein, J. (1994). A simulation comparison of phylogeny algorithms under equal and unequal evolutionary rates. Mol. Biol.

....have been proposed to measure dissimilarity between two evolutionary histories. The most popular include the nearest neighbor interchange (NNI) distance (Robinson, 1971; Moore et al. 1973) subtree transfer distance (Hein, 1993) triples distance (Critchlow et al. 1996) and branch score (Bs) (Kuhner and Felsenstein, 1994). Subtree transfer is most appropriate when considering events such as recombination or gene conversions and the triples distance is not uniquely de ned for unrooted trees. The Bs is straight forward to calculate and is less sensitive to small di erences in topology than NNI (DasGupta et al. ....

....distance is not uniquely de ned for unrooted trees. The Bs is straight forward to calculate and is less sensitive to small di erences in topology than NNI (DasGupta et al. 1997; 1999) This property is ideal as we expect small di erences to occur and do not wish to overweight them. Following Kuhner and Felsenstein (1994), Bs examines the di erence in the partitions (splits) of taxa induced by the branches in x 1 and x 2 . Let P = P 1 ; P 2 ; Pn ) where n = 2 N 1 1, be the set of all possible unordered partitions P i = R i1 ; R i2 ) of N taxa into two disjoint sets R i1 and R i2 . For example, the ....

[Article contains additional citation context not shown here]

Kuhner, M. and Felsenstein, J. (1994), \A simulation comparison of phylogeny algorithms under equal and unequal evolutionary rates," Molecular Biology and Evolution, 11, 459-468.

....have been proposed to measure dissimilarity between two evolutionary histories. The most popular include the nearest neighbor interchange (NNI) distance (Robinson, 1971; Moore et al. 1973) subtree transfer distance (Hein, 1993) triples distance (Critchlow et al. 1996) and branch score (Bs) (Kuhner and Felsenstein, 1994). Subtree transfer is most appropriate when considering events such as recombination or gene conversions and the triples distance is not uniquely de ned for unrooted trees. The Bs is straight forward to calculate and is less sensitive to small di erences in topology than NNI (DasGupta et al. ....

....uniquely de ned for unrooted trees. The Bs is straight forward to calculate and is less sensitive to small di erences in topology than NNI (DasGupta et al. 1997; DasGupta et al. 1999) This property is ideal as we expect small di erences to occur and do not wish to overweight them. Following Kuhner and Felsenstein (1994), Bs examines the di erence in the partitions of taxa induced by the branches in x 1 and x 2 . Let P = P 1 ; P 2 ; Pn ) where n = 2 N 1 1, be the set of all possible unordered partitions P i = R i1 ; R i2 ) of N taxa into two disjoint sets R i1 and R i2 . For example, the topologies ....

[Article contains additional citation context not shown here]

Kuhner, M. and Felsenstein, J. (1994), \A simulation comparison of phylogeny algorithms under equal and unequal evolutionary rates," Molecular Biology and Evolution, 11, 459-468.

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M.K. Kuhner and J. Felsenstein. A simulation comparison of phylogeny algorithms under equal and unequal evolutionary rates. Mol. Biol. Evol., 11:459-- 468, 1994.

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Kuhner, M.K., Felsenstein, J., 1994. A simulation comparison of phylogeny algorithms under equal and unequal evolutionary rates. Mol. Biol. Evol. 11, 459--468.

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Kuhner, M., Felsenstein, J., 1994. A simulation comparison of phylogeny algorithms under equal and unequal evolutionary rates. Mol. Biol. Evol. 11, 459--468.

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M. K. Kuhner and J. Felsenstein. A simulation comparison of phylogeny algorithms under equal and unequal evolutionary rates. Molecular Biology and Evolution, 11:459--468, 1994.

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KUHNER,M.K.,AND J. FELSENSTEIN. 1994. A simulation comparison of phylogeny algorithms under equal and unequal evolutionary rates. Mol. Biol. Evol. 11:459--468.

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KUHNER, M.K., and J. FELSTEIN. 1994. A simulation comparison of phylogeny algorithms under equal and unequal evolutionary rates. Mol. Biol. Evol. 11:459-468.

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M.K. Kuhner, J. Felsenstein, A simulation comparison of phylogeny algorithms under equal and unequal evolutionary rates, Mol. Biol. Evol. 11 (1994) 459 -- 468.

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M. Kuhner and J. Felsenstein, "A Simulation Comparison of Phylogeny Algorithms under Equal and Unequal Evolutionary Rates," Molecular Biology and Evolution, vol. 11, pp. 459-468, 1994.

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M.K. Kuhner and J. Felsenstein. A simulation comparison of phylogeny algorithms under equal and unequal evolutionary rates [published erratum appears in Molecular Biology and Evolution 1995 May;12(3):525]. Molecular Biology and Evolution, 11(3):459--468, 1994.

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Kuhner, M.K., and Felsenstein, J. 1994. A simulation comparison of phylogeny algorithms under equal and unequal rates. Mol. Biol. Evol. 11, 459--468.

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M.K. Kuhner and J. Felsenstein. A simulation comparison of phylogeny algorithms under equal and unequal evolutionary rates. Molecular Biology and Evolution, 11:459--468, 1994.

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Kuhner, M., and Felsenstein, J. A simulation comparison of phylogeny algorithms under equal and unequal evolutionary rates. Molecular Biology and Evolution 11 (1994), 459--468.

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Kuhner, M.K., Felsenstein, J.: A simulation comparison of phylogeny algorithms under equal and unequal rates. Mol. Biol. Evol. 11 #1994# 459#468

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K. Kuhner and J. Felsenstein. A simulation comparison of phylogeny algorithms under equal and unequal evolutionary rates. Mol. Biol. Evol., 11:459--468, 1994.

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K. Kuhner and J. Felsenstein. A simulation comparison of phylogeny algorithms under equal and unequal evolutionary rates. Mol. Biol. Evol., 11:459--468, 1994.

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Kuhner, M.K. and Felsenstein, J. (1994) A simulation comparison of phylogeny algorithms under equal and unequal evolutionary rates, Mol. Biol. Evol. 11,459-468

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