A New Implementation and Detailed Study of Breakpoint Analysis

Moret, Bernard M. E.; Wyman, Stacia K.; Bader, David A.; Warnow, Tandy; Yan, Mi

doi:10.1142/9789814447362_0056

Cited by 106 publications

(102 citation statements)

References 12 publications

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“…Most of parsimony methods (such as GRAPPA [3], MGR [4,5]) typically solve the SPP exactly by searching a set of ancestral gene orders to minimize the sum of the rearrangement distance over the entire edges of the phylogeny. Ma proposed another method for the SPP in the probabilistic framework (InferCARsPro [6]) by approximating the conditional probabilities for all possible gene adjacencies in an ancestral genome.…”

Section: Overviewmentioning

confidence: 99%

Reconstructing Ancestral Genomic Orders Using Binary Encoding and Probabilistic Models

Hu¹,

Zhou²,

Tang³

2013

Bioinformatics Research and Applications

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Abstract. Changes of gene ordering under rearrangements have been extensively used as a signal to reconstruct phylogenies and ancestral genomes. Inferring the gene order of an extinct species has the potential in revealing a more detailed evolutionary history of species descended from it. Current tools used in ancestral reconstruction may fall into parsimonious and probabilistic methods according to the criteria they follow. In this study, we propose a new probabilistic method called PMAG to infer the ancestral genomic orders by calculating the conditional probabilities of gene adjacencies using Bayes' theorem. The method incorporates a transition model designed particularly for genomic rearrangement scenarios, a reroot procedure to relocate the root to the target ancestor that is inferred as well as a greedy algorithm to connect adjacencies with high conditional probabilities into valid gene orders.We conducted a series of simulation experiments to assess the performance of PMAG and compared it against previously existing probabilistic methods (InferCARsPro) and parsimonious methods (GRAPPA). As we learned from the results, PMAG can reconstruct more correct ancestral adjacencies and yet run several orders of magnitude faster than InferCARsPro and GRAPPA.

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Section: Overviewmentioning

confidence: 99%

Reconstructing Ancestral Genomic Orders Using Binary Encoding and Probabilistic Models

Hu¹,

Zhou²,

Tang³

2013

Bioinformatics Research and Applications

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“…However, the decision problem associated with searching for the best tree from a given set of taxa is NP-complete [1]. Many authors have proposed greedy heuristic solutions in an attempt to reduce the effective search space [2]- [7]. These algorithms have made the process of producing large phylogenetic trees possible using only a single processor.…”

Section: Introductionmentioning

confidence: 99%

Building Large Phylogenetic Trees on Coarse-Grained Parallel Machines

et al. 2006

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Abstract. Phylogenetic analysis is an area of computational biology concerned with the reconstruction of evolutionary relationships between organisms, genes, and gene families. Maximum likelihood evaluation has proven to be one of the most reliable methods for constructing phylogenetic trees. The huge computational requirements associated with maximum likelihood analysis means that it is not feasible to produce large phylogenetic trees using a single processor. We have completed a fully cross platform coarse-grained distributed application, DPRml, which overcomes many of the limitations imposed by the current set of parallel phylogenetic programs. We have completed a set of efficiency tests that show how to maximise efficiency while using the program to build large phylogenetic trees. The software is publicly available under the terms of the GNU general public licence from the system webpage at http://www.cs.nuim.ie/distributed.

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“…Much recent work on rearrangement-based phylogeny [5,6,14,15,18] stems from an algorithm by Sankoff and Blanchette [17] that iterates over a prospective tree, repeatedly finds medians of the three permutations adjacent to each internal vertex, and uses them to improve the tree until convergence occurs. This method finds locally optimal trees and simultaneously allows an estimation of the configurations of ancestral genomes.…”

Section: Introductionmentioning

confidence: 99%

Finding an Optimal Inversion Median: Experimental Results

Siepel

Moret

2001

Lecture Notes in Computer Science

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Abstract. We derive a branch-and-bound algorithm to find an optimal inversion median of three signed permutations. The algorithm prunes to manageable size an extremely large search tree using simple geometric properties of the problem and a newly available linear-time routine for inversion distance. Our experiments on simulated data sets indicate that the algorithm finds optimal medians in reasonable time for genomes of medium size when distances are not too large, as commonly occurs in phylogeny reconstruction. In addition, we have compared inversion and breakpoint medians, and found that inversion medians generally score significantly better and tend to be far more unique, which should make them valuable in median-based tree-building algorithms.

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A New Implementation and Detailed Study of Breakpoint Analysis

Cited by 106 publications

References 12 publications

Reconstructing Ancestral Genomic Orders Using Binary Encoding and Probabilistic Models

Reconstructing Ancestral Genomic Orders Using Binary Encoding and Probabilistic Models

Building Large Phylogenetic Trees on Coarse-Grained Parallel Machines

Finding an Optimal Inversion Median: Experimental Results

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