Stéphane Vialette

Combinatorics of Genome Rearrangements

From one cell to another, from one individual to another, and from one species to another, the content of DNA molecules is often similar. The organization of these molecules, however, differs dramatically, and the mutations that affect this organization are known as genome rearrangements. Combinatorial methods are used to reconstruct putative rearrangement scenarios in order to explain the evolutionary history of a set of species, often formalizing the evolutionary events that can explain the multiple combinations of observed genomes as combinatorial optimization problems. This book offers the first comprehensive survey of this rapidly expanding application of combinatorial optimization. It can be used as a reference for experienced researchers or as an introductory text for a broader audience. Genome rearrangement problems have proved so interesting from a combinatorial point of view that the field now belongs as much to mathematics as to biology. This book takes a mathematically oriented approach, but provides biological background when necessary. It presents a series of models, beginning with the simplest (which is progressively extended by dropping restrictions), each constructing a genome rearrangement problem. The book also discusses an important generalization of the basic problem known as the median problem, surveys attempts to reconstruct the relationships between genomes with phylogenetic trees, and offers a collection of summaries and appendixes with useful additional information. Computational Molecular Biology series

On the parameterized complexity of multiple-interval graph problems

Multiple-interval graphs are a natural generalization of interval graphs where each vertex may have more than one interval associated with it. Many applications of interval graphs also generalize to multiple-interval graphs, often allowing for more robustness in the modeling of the specific application. With this motivation in mind, a recent systematic study of optimization problems in multiple-interval graphs was initiated. In this sequel, we study multiple-interval graph problems from the perspective of parameterized complexity. The problems under consideration are k-Independent Set, k-Dominating Set, and k-Clique, which are all known to be W[1]-hard for general graphs, and NP-complete for multiple-interval graphs. We prove that k-Clique is in FPT, while k-Independent Set and k-Dominating Set are both W[1]-hard. We also prove that k-Independent Dominating Set, a hybrid of the two above problems, is also W[1]-hard. Our hardness results hold even when each vertex is associated with at most two intervals, and all intervals have unit length. Furthermore, as an interesting byproduct of our hardness results, we develop a useful technique for showing W[1]-hardness via a reduction from the k-Multicolored Clique problem, a variant of k-Clique. We believe this technique has interest in its own right, as it should help in simplifying W[1]-hardness results which are notoriously hard to construct and technically tedious.

Sharp tractability borderlines for finding connected motifs in vertex-colored graphs

We study the problem of finding occurrences of motifs in vertex-colored graphs, where a motif is a multiset of colors, and an occurrence of a motif is a subset of connected vertices whose multiset of colors equals the motif. This problem has applications in metabolic network analysis, an important area in bioinformatics. We give two positive results and three negative results that together draw sharp borderlines between tractable and intractable instances of the problem

Upper and lower bounds for finding connected motifs in vertex-colored graphs

We study the problem of finding occurrences of motifs in vertex-colored graphs, where a motif is a multiset of colors, and an occurrence of a motif is a subset of connected vertices whose multiset of colors equals the motif. This problem is a natural graph-theoretic pattern matching variant where we are not interested in the actual structure of the occurrence of the pattern, we only require it to preserve the very basic topological requirement of connectedness. We give two positive results and three negative results that together give an extensive picture of tractable and intractable instances of the problem.

Approximating the 2-interval pattern problem

We address the issue of approximating the 2-Interval Pattern problem over its various models and restrictions. This problem, motivated by RNA secondary structure prediction, asks to find a maximum cardinality subset of a 2-interval set with respect to some prespecified geometric constraints. We present several constant factor approximation algorithms whose performance guarantee depends on the different possible restrictions imposed on the input 2-interval set. In addition, we show that our results extend to the weighted variant of the problem.

Comparing Genomes with Duplications: A Computational Complexity Point of View

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 G1 and G2: First, one establishes a one-to-one correspondence between the genes of G2; and the genes of G2; 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.

Exemplar Longest Common Subsequence

In this paper, we investigate the computational and approximation complexity of the Exemplar Longest Common Subsequence (ELCS) of a set of sequences (ELCS problem), a generalization of the Longest Common Subsequence problem, where the input sequences are over the union of two disjoint sets of symbols, a set of mandatory symbols and a set of optional symbols. We show that different versions of the problem are APX-hard even for instances with two sequences. Moreover, we show that the related problem of determining the existence of a feasible solution of the ELCS of two sequences is NP-hard. On the positive side, we first present an efficient algorithm for the ELCS problem over instances of two sequences where each mandatory symbol can appear in total at most three times in the sequences. Furthermore, we present two fixed-parameter algorithms for the ELCS problem over instances of two sequences where the parameter is the number of mandatory symbols.

Efficient tools for computing the number of breakpoints and the number of adjacencies between two genomes with duplicate genes.

Comparing genomes of different species is a fundamental problem in comparative genomics. Recent research has resulted in the introduction of different measures between pairs of genomes: for example, reversal distance, number of breakpoints, and number of common or conserved intervals. However, classical methods used for computing such measures are seriously compromised when genomes have several copies of the same gene scattered across them. Most approaches to overcome this difficulty are based either on the exemplar model, which keeps exactly one copy in each genome of each duplicated gene, or on the maximum matching model, which keeps as many copies as possible of each duplicated gene. The goal is to find an exemplar matching, respectively a maximum matching, that optimizes the studied measure. Unfortunately, it turns out that, in presence of duplications, this problem for each above-mentioned measure is NP-hard. In this paper, we propose to compute the minimum number of breakpoints and the maximum number of adjacencies between two genomes in presence of duplications using two different approaches. The first one is an exact, generic 0-1 linear programming approach, while the second is a collection of three heuristics. Each of these approaches is applied on each problem and for each of the following models: exemplar, maximum matching and intermediate model, that we introduce here. All these programs are run on a well-known public benchmark dataset of gamma-Proteobacteria, and their performances are discussed.

Finding approximate and constrained motifs in graphs

One of the most relevant topics in the analysis of biological networks is the identification of functional motifs inside a network. A recent approach introduced in literature, called Graph Motif, represents the network as a vertex-colored graph, and the motif M as a multiset of colors. An occurrence of a motif M in a vertex-colored graph G is a connected induced subgraph of G whose vertex set is colored exactly as M. In this paper we investigate three different variants of the Graph Motif problem. The first two variants, Minimum Adding Motif (Min-Add Graph Motif) and Minimum Substitution Motif (Min-Sub Graph Motif), deal with approximate occurrences of a motif in the graph, while the third variant, Constrained Graph Motif (CGM), constrains the motif to contain a given set of vertices. We investigate the computational and parameterized complexity of the three problems. We show that Min-Add Graph Motifand Min-Sub Graph Motifare both NP-hard, even when M is a set, and the graph is a tree with maximum degree 4 in which each color appears at most twice. Then, we show that Min-Sub Graph Motifis fixed-parameter tractable when parameterized by the size of M. Finally, we consider the parameterized complexity of the CGMproblem; we give a fixed-parameter algorithm for graphs of bounded treewidth, and show that the problem is W[2]-hard when parameterized by |M|, even if the input graph has diameter 2.

The ExemplarBreakpointDistance for Non-trivial Genomes Cannot Be Approximated

A promising and active field of comparative genomics consists in comparing two genomes by establishing a one-to-one correspondence (i.e., a matching) between their genes. This correspondence is usually chosen in order to optimize a predefined measure. One such problem is the Exemplar Breakpoint Distance problem (or EBD , for short), which asks, given two genomes modeled by signed sequences of characters, to keep and match exactly one occurrence of each character in the two genomes (a process called exemplarization ), so as to minimize the number of breakpoints of the resulting genomes. Bryant [6] showed that EBD is NP -complete. In this paper, we close the study of the approximation of EBD by showing that no approximation factor can be derived for EBD considering non-trivial genomes --- i.e. genomes that contain duplicated genes.