Average-case analysis of perfect sorting by reversals (Journal Version)

Perfect sorting by reversals, a problem originating in computational genomics, is the process of sorting a signed permutation to either the identity or to the reversed identity permutation, by a sequence of reversals that do not break any common interval. B\'erard et al. (2007) make use of strong interval trees to describe an algorithm for sorting signed permutations by reversals. Combinatorial properties of this family of trees are essential to the algorithm analysis. Here, we use the expected value of certain tree parameters to prove that the average run-time of the algorithm is at worst, polynomial, and additionally, for sufficiently long permutations, the sorting algorithm runs in polynomial time with probability one. Furthermore, our analysis of the subclass of commuting scenarios yields precise results on the average length of a reversal, and the average number of reversals.Comment: A preliminary version of this work appeared in the proceedings of Combinatorial Pattern Matching (CPM) 2009. See arXiv:0901.2847; Discrete Mathematics, Algorithms and Applications, vol. 3(3), 201

Perfect sorting by reversals

In computational biology, gene order data is often modelled as signed permutations. A classical problem in genome comparison is to detect conserved segments in a permutation, that is, genes that are co-localised in several species, indicating that they remained grouped during evolution. A second largely studied problem related to gene order data is to compute a minimum scenario of reversals that transforms a signed permutation into another. Several studies began to mix the two problems, and it was observed that their results are not always compatible : often parsimonious scenarios of reversals break conserved segments. In a recent study, Bérard, Bergeron and Chauve stated as an open question whether it was possible to design a polynomial time algorithm to decide if there exists a minimum scenario of reversals that transforms a genome into another while keeping the clusters of co-localised genes together. In this paper, we give this polynomial algorithm, and thus generalise the theoretical result of the aforementioned paper

Perfect Sorting by Reversals

In computational biology, gene order data is often modelled as signed permutations. A classical problem in genome comparison is to detect conserved segments in a permutation, that is, genes that are co-localised in several species, indicating that they remained grouped during evolution. A second largely studied problem related to gene order data is to compute a minimum scenario of reversals that transforms a signed permutation into another. Several studies began to mix the two problems, and it was observed that their results are not always compatible : often parsimonious scenarios of reversals break conserved segments. In a recent study, Bérard, Bergeron and Chauve stated as an open question whether it was possible to design a polynomial time algorithm to decide if there exists a minimum scenario of reversals that transforms a genome into another while keeping the clusters of co-localised genes together. In this paper, we give this polynomial algorithm, and thus generalise the theoretical result of the aforementioned paper

The distribution of cycles in breakpoint graphs of signed permutations

Breakpoint graphs are ubiquitous structures in the field of genome rearrangements. Their cycle decomposition has proved useful in computing and bounding many measures of (dis)similarity between genomes, and studying the distribution of those cycles is therefore critical to gaining insight on the distributions of the genomic distances that rely on it. We extend here the work initiated by Doignon and Labarre, who enumerated unsigned permutations whose breakpoint graph contains $k$ cycles, to signed permutations, and prove explicit formulas for computing the expected value and the variance of the corresponding distributions, both in the unsigned case and in the signed case. We also compare these distributions to those of several well-studied distances, emphasising the cases where approximations obtained in this way stand out. Finally, we show how our results can be used to derive simpler proofs of other previously known results

Parking functions, labeled trees and DCJ sorting scenarios

In genome rearrangement theory, one of the elusive questions raised in recent years is the enumeration of rearrangement scenarios between two genomes. This problem is related to the uniform generation of rearrangement scenarios, and the derivation of tests of statistical significance of the properties of these scenarios. Here we give an exact formula for the number of double-cut-and-join (DCJ) rearrangement scenarios of co-tailed genomes. We also construct effective bijections between the set of scenarios that sort a cycle and well studied combinatorial objects such as parking functions and labeled trees.Comment: 12 pages, 3 figure

A survey on algorithmic aspects of modular decomposition

The modular decomposition is a technique that applies but is not restricted to graphs. The notion of module naturally appears in the proofs of many graph theoretical theorems. Computing the modular decomposition tree is an important preprocessing step to solve a large number of combinatorial optimization problems. Since the first polynomial time algorithm in the early 70's, the algorithmic of the modular decomposition has known an important development. This paper survey the ideas and techniques that arose from this line of research

An asymmetric approach to preserve common intervals while sorting by reversals

Dias Vieira Braga M, Gautier C, Sagot M-F. An asymmetric approach to preserve common intervals while sorting by reversals. Algorithms for Molecular Biology. 2009;4(1):16.Background: The reversal distance and optimal sequences of reversals to transform a genome into another are useful tools to analyse evolutionary scenarios. However, the number of sequences is huge and some additional criteria should be used to obtain a more accurate analysis. One strategy is searching for sequences that respect constraints, such as the common intervals (clusters of co-localised genes). Another approach is to explore the whole space of sorting sequences, eventually grouping them into classes of equivalence. Recently both strategies started to be put together, to restrain the space to the sequences that respect constraints. In particular an algorithm has been proposed to list classes whose sorting sequences do not break the common intervals detected between the two inital genomes A and B. This approach may reduce the space of sequences and is symmetric (the result of the analysis sorting A into B can be obtained from the analysis sorting B into A). Results: We propose an alternative approach to restrain the space of sorting sequences, using progressive instead of initial detection of common intervals (the list of common intervals is updated after applying each reversal). This may reduce the space of sequences even more, but is shown to be asymmetric. Conclusions: We suggest that our method may be more realistic when the relation ancestor-descendant between the analysed genomes is clear and we apply it to do a better characterisation of the evolutionary scenario of the bacterium Rickettsia felis with respect to one of its ancestors