Quartet Cleaning: Improved Algorithms and Simulations

Berry, Vincent; Jiang, Tao; Kearny, Paul E.; Li, Ming; Wareham, Todd

doi:10.1007/3-540-48481-7_28

Cited by 55 publications

(63 citation statements)

References 14 publications

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“…Then, a set of simple quartet topologies is selected at random according to these probabilities to form the maximum-likelihood evolutionary tree. Berry et al (1999) reported an interesting result. They presented two "quartet cleaning" algorithms for correcting bounded numbers of quartet errors (i.e.…”

Section: The Quartet Methods Of Hierarchical Clusteringmentioning

confidence: 91%

The development and application of metaheuristics for problems in graph theory: a computational study

Consoli¹

2011

4OR-Q J Oper Res

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Section: The Quartet Methods Of Hierarchical Clusteringmentioning

confidence: 91%

The development and application of metaheuristics for problems in graph theory: a computational study

Consoli¹

2011

4OR-Q J Oper Res

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“…The Q * method seeks the maximally resolved tree T ′ that obeys Q(T ′ ) ⊆ Q, a very conservative method that generally produces many polytomies [4]. Quartet-cleaning methods establish a bound on the number of quartet errors around each reconstructed tree edge [1,3,12]. None of these methods produces satisfactory results on sequence data [24].…”

Section: Phylogenetic Reconstruction From Quartetsmentioning

confidence: 99%

“…If every quartet tree is computed correctly from noiseless data, then there is a single tree compatible with all n 4 quartet trees and that tree is the true tree [4]; in practice, of course, many of the quartet trees are in error and no single tree is compatible with all quartet trees. With sequence data, biologists have long used the heuristic method known as quartet-puzzling [25], while computer scientists have developed several theoretical methods, such as quartet cleaning [1,3,12]-see [24] for a review and experimental comparison of these methods. In the case of gene orders, however, finding the best quartet tree is NP-hard-it includes finding the median of three genomes, a known NP-hard problem [5], as a special case.…”

Section: Introductionmentioning

confidence: 99%

Quartet-Based Phylogeny Reconstruction from Gene Orders

Liu

Tang

Moret

2005

Lecture Notes in Computer Science

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Abstract. Phylogenetic reconstruction based on gene rearrangements is attracting increasing attention from biologists and computer scientists. Methods used in reconstruction include distance-based methods, parsimony methods using sequence encodings, and direct optimization. The latter, pioneered by Sankoff and extended by us with the software suite GRAPPA, is the most accurate approach; however, its exhaustive nature means that it can be applied only to small datasets (of fewer than 15 taxa). While we have successfully scaled it up to 1,000 taxa by integrating it with a disk-covering method, yielding DCM-GRAPPA, the recursive decomposition in the DCM may require many levels of recursion to handle datasets with 1,000 or more taxa. In order to handle larger datasets and reduce the need for recursive decomposition, we investigate quartet-based approaches, which directly decompose the datasets into subsets of four taxa each. Such approaches have been well studied for sequence data, but not for gene-order data. We give an optimization algorithm for the NP-hard problem of computing optimal trees for each quartet, present a variation of the dyadic method (using heuristics to choose suitable short quartets), and use both in simulation studies. We find that our quartet-based method can handle more taxa than the base version of GRAPPA, thus enabling us to reduce the number of levels of recursion in DCM-GRAPPA, but is more sensitive to the rate of evolution, with error rates rapidly increasing when saturation is approached.

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“…Analogously for the problem of reconstructing unrooted trees, there have been several approaches for identifying quartets that are most strongly supported. The methods of quartet cleaning [21], [7] concern ways to use internal evidence to identify the quartets that are most strongly supported. In particular, they find an upper bound on the number of quartets that can be corrected which involves a factor 1/2, analogous to the maximal possible robustness radius in this paper.…”

Section: Introductionmentioning

confidence: 99%

Robustness of Topological Supertree Methods for Reconciling Dense Incompatible Data

Willson

2009

IEEE/ACM Trans. Comput. Biol. and Bioinf.

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Abstract-Given a collection of rooted phylogenetic trees with overlapping sets of leaves, a compatible supertree S is a single tree whose set of leaves is the union of the input sets of leaves and such that S agrees with each input tree when restricted to the leaves of the input tree. Typically with trees from real data, no compatible supertree exists, and various methods may be utilized to reconcile the incompatibilities in the input trees. This paper focuses on a measure of robustness of a supertree method called its "radius" R. The larger the value of R is, the further the data set can be from a natural correct tree T and yet the method will still output T .It is shown that the maximal possible radius for a method is R = 1/2. Many familiar methods, both for supertrees and consensus trees, are shown to have R = 0, indicating that they need not output a tree T that would seem to be the natural correct answer. A polynomial-time method Normalized Triplet Supertree (NTS) with the maximal possible R = 1/2 is defined. A geometric interpretion is given, and NTS is shown to solve an optimization problem. Additional properties of NTS are described.

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Quartet Cleaning: Improved Algorithms and Simulations

Cited by 55 publications

References 14 publications

The development and application of metaheuristics for problems in graph theory: a computational study

The development and application of metaheuristics for problems in graph theory: a computational study

Quartet-Based Phylogeny Reconstruction from Gene Orders

Robustness of Topological Supertree Methods for Reconciling Dense Incompatible Data

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