On the Approximability of Comparing Genomes with Duplicates

International audienceA central problem in comparative genomics consists in computing a (dis-)similarity measure between two genomes, e.g. in order to construct a phylogenetic tree. A large number of such measures has been proposed in the recent past: number of reversals, number of breakpoints, number of common or conserved intervals etc. In their initial definitions, all these measures suppose that genomes contain no duplicates. However, we now know that genes can be duplicated within the same genome. One possible approach to overcome this difficulty is to establish a one-to-one correspondence (i.e. a matching) between genes of both genomes, where the correspondence is chosen in order to optimize the studied measure. Then, after a gene relabeling according to this matching and a deletion of the unmatched signed genes, two genomes without duplicates are obtained and the measure can be computed. In this paper, we are interested in three measures (number of breakpoints, number of common intervals and number of conserved intervals) and three models of matching (exemplar, intermediate and maximum matching models). We prove that, for each model and each measureM, computing a matching between two genomes that optimizes M is APX–hard. We show that this result remains true even for two genomes G1 and G2 such that G1 contains no duplicates and no gene of G2 appears more than twice. Therefore, our results extend those of [7, 10, 13]. Besides, in order to evaluate the possible existence of approximation algorithms concerning the number of breakpoints, we also study the complexity of the following decision problem: is there an exemplarization (resp. an intermediate matching, a maximum matching) that induces no breakpoint ? In particular, we extend a result of [13] by proving the problem to be NP–complete in the exemplar model for a new class of instances, we note that the problems are equivalent in the intermediate and the exemplar models and we show that the problem is in P in the maximum matching model. Finally, we focus on a fourth measure, closely related to the number of breakpoints: the number of adjacencies, for which we give several constant ratio approximation algorithms in the maximum matching model, in the case where genomes contain the same number of duplications of each gene

On the distribution of cycles and paths in multichromosomal breakpoint graphs and the expected value of rearrangement distance

Feijão P, Martinez F, Thévenin A. On the distribution of cycles and paths in multichromosomal breakpoint graphs and the expected value of rearrangement distance. BMC Bioinformatics. 2015;16(Suppl 19): S1.Finding the smallest sequence of operations to transform one genome into another is an important problem in comparative genomics. The breakpoint graph is a discrete structure that has proven to be effective in solving distance problems, and the number of cycles in a cycle decomposition of this graph is one of the remarkable parameters to help in the solution of related problems. For a fixed k, the number of linear unichromosomal genomes (signed or unsigned) with n elements such that the induced breakpoint graphs have k disjoint cycles, known as the Hultman number, has been already determined. In this work we extend these results to multichromosomal genomes, providing formulas to compute the number of multichromosal genomes having a fixed number of cycles and/or paths. We obtain an explicit formula for circular multichromosomal genomes and recurrences for general multichromosomal genomes, and discuss how these series can be used to calculate the distribution and expected value of the rearrangement distance between random genomes

Aspects algorithmiques des réarrangements génomiques : duplications et ordres partiels

Comparative genomics is an important tool to better understand the different between species. Several methods exist to compare two genomes such that the computation of (dis)similarities' measures. In this work, we study three measures : numbers of adjacencies, of breakpoints and of common intervals. In presence of duplicated genes or when the gene order is only partially known, calculate these measures is a NP-hard problem. In first, we want to compute the numbers of adjacencies and breakpoints for three models (exemplar, intermediate, maximum) between two genomes with duplications. To get an exact result, we express these problems by pseudo-boolean programs. Thus, we can use the solver CPLEX, efficient for this study. Thanks a experimentation with 12 genomes of γ-proteobacteria, we get enough results to: compare the both measures and the 3 models and evaluate heuristics. In particular, we propose heuristics (based on a search for longest common subsequence) giving very good results. In parallel, we have established for different computational problems measures between two genomes with duplication, whether it exists a polynomial approximation. Secondly, we calculate the number of adjacencies and common intervals between two partial orders (with the possibility that one order is total). We use a programming approach pseudo-Boolean too and an other solver, efficient for this study: minisat+. Using nearly 800 simulated genomes, we study the influence of parameters associated with partial orders and we compare the two measures studied.La génomique comparative est une discipline importante pour la compréhension de l'évolution du vivant. Différentes méthodes de comparaison existent, nous nous intéressons ici en particulier aux mesures de (dis)similarités entre les génomes. Dans cette étude, nous étudions 3 mesures : les nombres d'adjacences, de points de cassures et d'intervalles communs. En présence de gènes dupliqués ou lorsque l'ordre des gènes n'est que partiellement connu, calculer ces mesures est un problème connu pour être NP-difficile. D'une part, nous désirons calculer les nombres d'adjacences et de points de cassures pour trois modèles (exemplaire, intermédiaire, maximum) entre deux génomes possédant des duplications. Afin d'obtenir un algorithme exact, nous modélisons ces problèmes en programmes pseudo-booléens. Après expérimentation sur 12 génomes de γ-protéobactéries, nous obtenons suffisamment de résultats pour : comparer les deux mesures et les 3 modèles et évaluer des heuristiques. À ce titre, nous proposons une famille d'heuristiques basée sur une recherche de plus longue sous-séquence commune qui donne de très bons résultats sur ces données. Parallèlement à cela, nous avons étudié, pour différents problèmes de calcul de mesures entre deux génomes avec duplication, l'approximation polynomial. D'autre part, nous calculons les nombres d'adjacences et d'intervalles communs entre deux ordres partiels (avec la possibilité qu'un des ordres soit total). Nous utilisons de nouveau une approche de programmation pseudo-booléenne. À l'aide de près de 800 génomes simulés, nous étudions l'influence de paramètres inhérents aux ordres partiels et nous comparons les deux mesures étudiées

Gene family assignment-free comparative genomics

Abstract Background The comparison of relative gene orders between two genomes offers deep insights into functional correlations of genes and the evolutionary relationships between the corresponding organisms. Methods for gene order analyses often require prior knowledge of homologies between all genes of the genomic dataset. Since such information is hard to obtain, it is common to predict homologous groups based on sequence similarity. These hypothetical groups of homologous genes are called gene families. Results This manuscript promotes a new branch of gene order studies in which prior assignment of gene families is not required. As a case study, we present a new similarity measure between pairs of genomes that is related to the breakpoint distance. We propose an exact and a heuristic algorithm for its computation. We evaluate our methods on a dataset comprising 12 γ-proteobacteria from the literature. Conclusions In evaluating our algorithms, we show that the exact algorithm is suitable for computations on small genomes. Moreover, the results of our heuristic are close to those of the exact algorithm. In general, we demonstrate that gene order studies can be improved by direct, gene family assignment-free comparisons.</p

On the Approximability of Comparing Genomes with Duplicates

International audienceA central problem in comparative genomics consists in computing a (dis-)similarity measure between two genomes, e.g. in order to construct a phylogenetic tree. A large number of such measures has been proposed in the recent past: number of reversals, number of breakpoints, number of common or conserved intervals etc. In their initial definitions, all these measures suppose that genomes contain no duplicates. However, we now know that genes can be duplicated within the same genome. One possible approach to overcome this difficulty is to establish a one-to-one correspondence (i.e. a matching) between genes of both genomes, where the correspondence is chosen in order to optimize the studied measure. Then, after a gene relabeling according to this matching and a deletion of the unmatched signed genes, two genomes without duplicates are obtained and the measure can be computed. In this paper, we are interested in three measures (number of breakpoints, number of common intervals and number of conserved intervals) and three models of matching (exemplar, intermediate and maximum matching models). We prove that, for each model and each measureM, computing a matching between two genomes that optimizes M is APX–hard. We show that this result remains true even for two genomes G1 and G2 such that G1 contains no duplicates and no gene of G2 appears more than twice. Therefore, our results extend those of [7, 10, 13]. Besides, in order to evaluate the possible existence of approximation algorithms concerning the number of breakpoints, we also study the complexity of the following decision problem: is there an exemplarization (resp. an intermediate matching, a maximum matching) that induces no breakpoint ? In particular, we extend a result of [13] by proving the problem to be NP–complete in the exemplar model for a new class of instances, we note that the problems are equivalent in the intermediate and the exemplar models and we show that the problem is in P in the maximum matching model. Finally, we focus on a fourth measure, closely related to the number of breakpoints: the number of adjacencies, for which we give several constant ratio approximation algorithms in the maximum matching model, in the case where genomes contain the same number of duplications of each gene