Annie Chateau

Reconstructing Ancestral Gene Orders Using Conserved Intervals

Conserved intervals were recently introduced as a measure of similarity between genomes whose genes have been shuffled during evolution by genomic rearrangements. Phylogenetic reconstruction based on such similarity measures raises many biological, formal and algorithmic questions, in particular the labelling of internal nodes with putative ancestral gene orders, and the selection of a good tree topology. In this paper, we investigate the properties of sets of permutations associated to conserved intervals as a representation of putative ancestral gene orders for a given tree topology. We define set-theoretic operations on sets of conserved intervals, together with the associated algorithms, and we apply these techniques, in a manner similar to the Fitch-Hartigan algorithm for parsimony, to a subset of chloroplast genes of 13 species.

Ancestral gene synteny reconstruction improves extant species scaffolding

We exploit the methodological similarity between ancestral genome reconstruction and extant genome scaffolding. We present a method, called ARt-DeCo that constructs neighborhood relationships between genes or contigs, in both ancestral and extant genomes, in a phylogenetic context. It is able to handle dozens of complete genomes, including genes with complex histories, by using gene phylogenies reconciled with a species tree, that is, annotated with speciation, duplication and loss events. Reconstructed ancestral or extant synteny comes with a support computed from an exhaustive exploration of the solution space. We compare our method with a previously published one that follows the same goal on a small number of genomes with universal unicopy genes. Then we test it on the whole Ensembl database, by proposing partial ancestral genome structures, as well as a more complete scaffolding for many partially assembled genomes on 69 eukaryote species. We carefully analyze a couple of extant adjacencies proposed by our method, and show that they are indeed real links in the extant genomes, that were missing in the current assembly. On a reduced data set of 39 eutherian mammals, we estimate the precision and sensitivity of ARt-DeCo by simulating a fragmentation in some well assembled genomes, and measure how many adjacencies are recovered. We find a very high precision, while the sensitivity depends on the quality of the data and on the proximity of closely related genomes.

Inferring positional homologs with common intervals of sequences

Inferring orthologous and paralogous genes is an important problem in whole genomes comparisons, both for functional or evolutionary studies. In this paper, we introduce a new approach for inferring candidate pairs of orthologous genes between genomes, also called positional homologs, based on the conservation of the genomic context. We consider genomes represented by their gene order – i.e. sequences of signed integers – and common intervals of these sequences as the anchors of the final gene matching. We show that the natural combinatorial problem of computing a maximal cover of the two genomes using the minimum number of common intervals is NP-complete and we give a simple heuristic for this problem. We illustrate the effectiveness of this first approach using common intervals of sequences on two datasets, respectively 8 γ-proteobacterial genomes and the human and mouse whole genomes.

A complexity and approximation framework for the maximization scaffolding problem

We explore in this paper some complexity issues inspired by the contig scaffolding problem in bioinformatics. We focus on the following problem: given an undirected graph with no loop, and a perfect matching on this graph, find a set of cycles and paths covering every vertex of the graph, with edges alternatively in the matching and outside the matching, and satisfying a given constraint on the numbers of cycles and paths. We show that this problem is NP -complete, even in planar bipartite graphs. Moreover, we show that there is no subexponential-time algorithm for several related problems unless the Exponential-Time Hypothesis fails. Lastly, we also design two polynomial-time approximation algorithms for complete graphs.

A CSP Approach for Metamodel Instantiation

This work is a contribution of Artificial Intelligence to Software Engineering. We present a comprehensive approach to metamodel instantiation using CSP. The generation of models which conform to a given metamodel is a crucial issue in Software Engineering, especially when it comes to produce a variate and large dataset of relevant models to test model transformations or to properly design new metamodels. We define an original constraint modeling of the problem of generating a model conform to a metamodel, also taking into account its additional OCL constraints. The generation process we describe appears to be quicker, more efficient and flexible than any other state-of-the-art approach.

Computation of perfect DCJ rearrangement scenarios with linear and circular chromosomes.

We study the problem of transforming a multichromosomal genome into another using Double Cut-and-Join (DCJ) operations, which simulates several types of rearrangements, as reversals, translocations, and block-interchanges. We introduce the notion of a DCJ scenario that does not break families of common intervals (groups of genes co-localized in both genomes). Such scenarios are called perfect, and their properties are well known when the only considered rearrangements are reversals. We show that computing the minimum perfect DCJ rearrangement scenario is NP-hard, and describe an exact algorithm which exponential running time is bounded in terms of a specific pattern used in the NP-completeness proof. The study of perfect DCJ rearrangement leads to some surprising properties. The DCJ model has often yielded algorithmic problems which complexities are comparable to the reversal-only model. In the perfect rearrangement framework, however, while perfect sorting by reversals is NP-hard if the family of common intervals to be preserved is nested, we show that finding a shortest perfect DCJ scenario can be answered in polynomial time in this case. Conversely, while perfect sorting by reversals is tractable when the family of common intervals is weakly separable, we show that the corresponding problem is still NP-hard in the DCJ case. This shows that despite the similarity of the two operations, easy patterns for reversals are hard ones for DCJ, and vice versa.

Complexity and Polynomial-Time Approximation Algorithms around the Scaffolding Problem

We explore in this paper some complexity issues inspired by the contig scaffolding problem in bioinformatics. We focus on the following problem: given an undirected graph with no loop, and a perfect matching on this graph, find a set of cycles and paths covering every vertex of the graph, with edges alternatively in the matching and outside the matching, and satisfying a given constraint on the numbers of cycles and paths. We show that this problem is \(\mathcal{NP}\)-complete, even in bipartite graphs. We also exhibit non-approximability and polynomial-time approximation results, in the optimization versions of the problem.

Perfect DCJ Rearrangement

We study the problem of transforming a multichromosomal genome into another using Double-Cut-and-Join (DCJ) operations. We introduce the notion of DCJ scenario that does not break families of common intervals (groups of genes co-localized in both genomes). Such scenarios are called perfect, and generalize the notion of perfect reversal scenarios. While perfect sorting by reversals is NP-hard if the family of common intervals is nested, we show that finding a shortest perfect DCJ scenario can be answered in polynomial time in this case. Moreover, while perfect sorting by reversals is easy when the family of common intervals is weakly separable, we show that the corresponding problem is NP-hard in the DCJ case. These contrast with previous comparisons between the reversal and DCJ models, that showed that most problems have similar complexity in both models.

Aligning the unalignable: bacteriophage whole genome alignments

BackgroundIn recent years, many studies focused on the description and comparison of large sets of related bacteriophage genomes. Due to the peculiar mosaic structure of these genomes, few informative approaches for comparing whole genomes exist: dot plots diagrams give a mostly qualitative assessment of the similarity/dissimilarity between two or more genomes, and clustering techniques are used to classify genomes. Multiple alignments are conspicuously absent from this scene. Indeed, whole genome aligners interpret lack of similarity between sequences as an indication of rearrangements, insertions, or losses. This behavior makes them ill-prepared to align bacteriophage genomes, where even closely related strains can accomplish the same biological function with highly dissimilar sequences.ResultsIn this paper, we propose a multiple alignment strategy that exploits functional collinearity shared by related strains of bacteriophages, and uses partial orders to capture mosaicism of sets of genomes. As classical alignments do, the computed alignments can be used to predict that genes have the same biological function, even in the absence of detectable similarity. The Alpha aligner implements these ideas in visual interactive displays, and is used to compute several examples of alignments of Staphylococcus aureus and Mycobacterium bacteriophages, involving up to 29 genomes. Using these datasets, we prove that Alpha alignments are at least as good as those computed by standard aligners. Comparison with the progressiveMauve aligner – which implements a partial order strategy, but whose alignments are linearized – shows a greatly improved interactive graphic display, while avoiding misalignments.ConclusionsMultiple alignments of whole bacteriophage genomes work, and will become an important conceptual and visual tool in comparative genomics of sets of related strains.A python implementation of Alpha, along with installation instructions for Ubuntu and OSX, is available on bitbucket (https://bitbucket.org/thekswenson/alpha).

Instantiation of meta-models constrained with OCL: A CSP approach

The automated generation of models that conform to a given meta-model is an important challenge in Model Driven Engineering, as well for model transformation testing, as for designing and exploring new meta-models. Amongst the main issues, we are mainly concerned by scalability, flexibility and a reasonable computing time. This paper presents an approach for model generation, which relies on Constraint Programming. After the translation of a meta-model into a CSP, our software generates models that conform to this meta-model, using a Constraint Solver. Our model also includes the most frequent types of OCL constraints. Since we are concerned by the relevance of the produced models, we describe a first attempt to improve them. We outperform the existing approaches from the mentioned point of view, and propose a configurable, easy-to-use and free-access tool, together with an on-line demonstrator.