Approximating the true evolutionary distance between two genomes

Swenson, Krister M.; Marron, Mark; Earnest-DeYoung, Joel V.; Moret, Bernard M. E.

doi:10.1145/1227161.1402297

Cited by 42 publications

(43 citation statements)

References 23 publications

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“…El-Mabrouk [19] developed an approach to reconstructing the ancestor of a modern genome by minimizing the number of duplication transpositions and reversals. The work in [20][21] proposed methods that attempt to find one-to-one gene correspondence between gene families based on conserved segments. Very recently, Swenson et al [22] presented some algorithmic results on the cycle splitting problem in a combinatorial framework similar to the one introduced in [8][9][10].…”

Section: Existing Ortholog Assignment Methods and Related Workmentioning

confidence: 99%

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Some Algorithmic Challenges in Genome-Wide Ortholog Assignment

Jiang

2010

J. Comput. Sci. Technol.

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Genome-scale assignment of orthologous genes is a fundamental and challenging problem in computational biology and has a wide range of applications in comparative genomics, functional genomics, and systems biology. Many methods based on sequence similarity, phylogenetic analysis, chromosomal syntenic information, and genome rearrangement have been proposed in recent years for ortholog assignment. Although these methods produce results that largely agree with each other, their results may still contain significant differences. In this article, we consider the recently proposed parsimony approach for assigning orthologs between closely related genomes based on genome rearrangement, which essentially attempts to transform one genome into another by the smallest number of genome rearrangement events including reversal, translocation, fusion, and fission, as well as gene duplication events. We will highlight some of the challenging algorithmic problems that arise in the approach including (i) minimum common substring partition, (ii) signed reversal distance with duplicates, and (iii) signed transposition distance with duplicates. The most recent progress towards the solution of these problems will be reviewed and some open questions will be posed. We will also discuss some possible extensions of the approach to the simultaneous comparison of multiple genomes.

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Section: Existing Ortholog Assignment Methods and Related Workmentioning

confidence: 99%

“…The problem of MCSP was also independently introduced recently in [21], under the name sequence cover.…”

Section: Comparison Of Multiple Genomesmentioning

confidence: 99%

Some Algorithmic Challenges in Genome-Wide Ortholog Assignment

Jiang

2010

J. Comput. Sci. Technol.

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“…Recently, there were many attempts to generalize the Hannenhalli-Pevzner theory for genomes with duplicated and deleted genes [6], [8], [13], [22], [23], [24]. However, the only known option for solving the reversal distance problem for duplicated genomes exactly is to consider all possible labelings, to compute the reversal distance problem for each labeling, and to choose the labeling with the minimal reversal distance.…”

Section: Reversal Distance Between Duplicated Genomesmentioning

confidence: 99%

Colored de Bruijn Graphs and the Genome Halving Problem

Alekseyev

Pevzner

2007

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

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Abstract-Breakpoint graph analysis is a key algorithmic technique in studies of genome rearrangements. However, breakpoint graphs are defined only for genomes without duplicated genes, thus limiting their applications in rearrangement analysis. We discuss a connection between the breakpoint graphs and de Bruijn graphs that leads to a generalization of the notion of breakpoint graph for genomes with duplicated genes. We further use the generalized breakpoint graphs to study the Genome Halving Problem (first introduced and solved by Nadia El-Mabrouk and David Sankoff). The El-Mabrouk-Sankoff algorithm is rather complex, and, in this paper, we present an alternative approach that is based on generalized breakpoint graphs. The generalized breakpoint graphs make the El-Mabrouk-Sankoff result more transparent and promise to be useful in future studies of genome rearrangements.

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“…The matching model is more general as it allows us to conserve more than one copy of a gene family and seeks a maximal one-to-one correspondence between these copies [2]. Several distances have been considered under the exemplar and matching models which are either based on minimizing the number of evolutionary events that allow us to transform a genome into the other, for events like reversals 2 [1], [9], [10], [11], [12], [13], reversals and insertions and deletions [14], [15], reversals and translocations [16], or on maximizing a similarity measure based on conserved structure in permutations like the number of adjacencies (which is equivalent to minimizing the number of breakpoints) [1], [9], [12], [13], [17] or the number of conserved intervals [18], [19], [20], [21]. As far as we know, none of the above problems has been shown to be solvable in polynomial time and, in fact, most of them have been shown to be NP-complete as soon as duplicates are present in the genomes (see Tables 1 and 2, in Section 6).…”

Section: Introductionmentioning

confidence: 99%

Comparing Genomes with Duplications: A Computational Complexity Point of View

Blin

Chauve

Fertin

et al. 2007

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

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Abstract-In this paper, we are interested in the computational complexity of computing (dis)similarity measures between two genomes when they contain duplicated genes or genomic markers, a problem that happens frequently when comparing whole nuclear genomes. Recently, several methods [1], [2] have been proposed that are based on two steps to compute a given (dis)similarity measure M between two genomes G 1 and G 2 : First, one establishes a one-to-one correspondence between the genes of G 1 and the genes of G 2 ; second, once this correspondence is established, it explicitly defines a permutation and it is then possible to quantify their similarity using classical measures defined for permutations like the number of breakpoints. Hence, these methods rely on two elements: a way to establish a one-to-one correspondence between genes of a pair of genomes and a (dis)similarity measure for permutations. The problem is then, given a (dis)similarity measure for permutations, compute a correspondence that defines an optimal permutation for this measure. We are interested here in two models to compute a one-to-one correspondence: the exemplar model, where all but one copy is deleted in both genomes for each gene family, and the matching model, which computes a maximal correspondence for each gene family. We show that, for these two models and for three (dis)similarity measures on permutations, namely, the number of common intervals, the maximum adjacency disruption (MAD) number, and the summed adjacency disruption (SAD) number, the problem of computing an optimal correspondence is NP-complete and even APX-hard for the MAD number and the SAD number.Index Terms-Comparative genomics, computational complexity, common intervals, maximum adjacency disruption number, summed adjacency disruption number.

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Approximating the true evolutionary distance between two genomes

Cited by 42 publications

References 23 publications

Some Algorithmic Challenges in Genome-Wide Ortholog Assignment

Some Algorithmic Challenges in Genome-Wide Ortholog Assignment

Colored de Bruijn Graphs and the Genome Halving Problem

Comparing Genomes with Duplications: A Computational Complexity Point of View

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