An Exact Algorithm to Compute the DCJ Distance for Genomes with Duplicate Genes

Shao, Mingfu; Lin, Yu; Moret, Bernard M. E.

doi:10.1007/978-3-319-05269-4_22

Cited by 27 publications

(28 citation statements)

References 21 publications

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“…2. for the subgraph in the component which contains paths, since no type of the paths has count more than one (which is the count of a cycle), following the similar proof strategy in [25], we can get the same conclusion.…”

Section: Optimization Methodsmentioning

confidence: 54%

“…1. for the subgraph in the component which only contains cycles, this is a case that is exactly the same as mentioned in [25], proof.…”

Section: Optimization Methodsmentioning

confidence: 80%

“…Optimal Assignments Although branch-and-bound algorithms are based on enumerating the number of cycles/path in BPG, it is not necessary to enumerate every component in the graph, as both [11,25] indicated that there are some specific patterns in BPG which can be fixed before the distance computation.…”

Section: Optimization Methodsmentioning

confidence: 99%

“…DCJ-Exemplar(Matching) distance There are in general two approaches to cope with duplicated genes. One is by removing all but keeping one copy in a gene family to generate an exemplar pair [23] and the other is by relabeling duplicated genes to ensure that every duplicated gene has unique number [24,25]. Both of these two distances can be computed with BPG using branch-andbound methods.…”

Section: Dcj-indel Distancementioning

confidence: 99%

“…To the best of our knowlege, there is no software package that can handle both duplications and Indels. We compare our DCJ-Indel-Exemplar (Matching) distances with GREDO [25], a software package based on linear programming that can handle duplications.…”

Section: Distance Computationmentioning

confidence: 99%

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Exemplar or matching: modeling DCJ problems with unequal content genome data

et al. 2015

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The edit distance under the DCJ model can be computed in linear time for genomes with equal content or with Indels. But it becomes NP-Hard in the presence of duplications, a problem largely unsolved especially when Indels (i.e., insertions and deletions) are considered. In this paper, we compare two mainstream methods to deal with duplications and associate them with Indels: one by deletion, namely DCJ-IndelExemplar distance; versus the other by gene matching, namely DCJ-Indel-Matching distance. We design branch-and-bound algorithms with set of optimization methods to compute exact distances for both. Furthermore, median problems are discussed in alignment with both of these distance methods, which are to find a median genome that minimizes distances between itself and three given genomes. Lin-Kernighan heuristic is leveraged and powered up by sub-graph decomposition and search space reduction technologies to handle median computation. A wide range of experiments are conducted on synthetic data sets and real data sets to exhibit pros and cons of B Jijun Tang

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Section: Optimization Methodsmentioning

confidence: 54%

“…1. for the subgraph in the component which only contains cycles, this is a case that is exactly the same as mentioned in [25], proof.…”

Section: Optimization Methodsmentioning

confidence: 80%

Section: Optimization Methodsmentioning

confidence: 99%

Section: Dcj-indel Distancementioning

confidence: 99%

Section: Distance Computationmentioning

confidence: 99%

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Exemplar or matching: modeling DCJ problems with unequal content genome data

et al. 2015

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Genome Rearrangement Analysis: Cut and Join Genome Rearrangements and Gene Cluster Preserving Approaches

Hartmann

Middendorf

Bernt

2017

Methods in Molecular Biology

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Genome rearrangements are mutations that change the gene content of a genome or the arrangement of the genes on a genome. Several years of research on genome rearrangements have established different algorithmic approaches for solving some fundamental problems in comparative genomics based on gene order information. This review summarizes the literature on genome rearrangement analysis along two lines of research. The first line considers rearrangement models that are particularly well suited for a theoretical analysis. These models use rearrangement operations that cut chromosomes into fragments and then join the fragments into new chromosomes. The second line works with rearrangement models that reflect several biologically motivated constraints, e.g., the constraint that gene clusters have to be preserved. In this chapter, the border between algorithmically "easy" and "hard" rearrangement problems is sketched and a brief review is given on the available software tools for genome rearrangement analysis.

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Estimation of the True Evolutionary Distance Under the INFER Model

Zabelkin

Alexeev

2018

Comparative Genomics

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An Exact Algorithm to Compute the DCJ Distance for Genomes with Duplicate Genes

Cited by 27 publications

References 21 publications

Exemplar or matching: modeling DCJ problems with unequal content genome data

Exemplar or matching: modeling DCJ problems with unequal content genome data

Genome Rearrangement Analysis: Cut and Join Genome Rearrangements and Gene Cluster Preserving Approaches

Estimation of the True Evolutionary Distance Under the INFER Model

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