Cedric Chauve

A Methodological Framework for the Reconstruction of Contiguous Regions of Ancestral Genomes and Its Application to Mammalian Genomes

The reconstruction of ancestral genome architectures and gene orders from homologies between extant species is a long-standing problem, considered by both cytogeneticists and bioinformaticians. A comparison of the two approaches was recently investigated and discussed in a series of papers, sometimes with diverging points of view regarding the performance of these two approaches. We describe a general methodological framework for reconstructing ancestral genome segments from conserved syntenies in extant genomes. We show that this problem, from a computational point of view, is naturally related to physical mapping of chromosomes and benefits from using combinatorial tools developed in this scope. We develop this framework into a new reconstruction method considering conserved gene clusters with similar gene content, mimicking principles used in most cytogenetic studies, although on a different kind of data. We implement and apply it to datasets of mammalian genomes. We perform intensive theoretical and experimental comparisons with other bioinformatics methods for ancestral genome segments reconstruction. We show that the method that we propose is stable and reliable: it gives convergent results using several kinds of data at different levels of resolution, and all predicted ancestral regions are well supported. The results come eventually very close to cytogenetics studies. It suggests that the comparison of methods for ancestral genome reconstruction should include the algorithmic aspects of the methods as well as the disciplinary differences in data aquisition.

FragAnchor: A Large-Scale Predictor of Glycosylphosphatidylinositol Anchors in Eukaryote Protein Sequences by Qualitative Scoring

A glycosylphosphatidylinositol (GPI) anchor is a common but complex C-terminal post-translational modification of extracellular proteins in eukaryotes. Here we investigate the problem of correctly annotating GPI-anchored proteins for the growing number of sequences in public databases. We developed a computational system, called FragAnchor, based on the tandem use of a neural network (NN) and a hidden Markov model (HMM). Firstly, NN selects potential GPI-anchored proteins in a dataset, then HMM parses these potential GPI signals and refines the prediction by qualitative scoring. FragAnchor correctly predicted 91% of all the GPI-anchored proteins annotated in the Swiss-Prot database. In a large-scale analysis of 29 eukaryote proteomes, FragAnchor predicted that the percentage of highly probable GPI-anchored proteins is between 0.21% and 2.01%. The distinctive feature of FragAnchor, compared with other systems, is that it targets only the C-terminus of a protein, making it less sensitive to the background noise found in databases and possible incomplete protein sequences. Moreover, FragAnchor can be used to predict GPI-anchored proteins in all eukaryotes. Finally, by using qualitative scoring, the predictions combine both sensitivity and information content. The predictor is publicly available at http://navet.ics.hawaii.edu/~fraganchor/NNHMM/NNHMM.html.

Identification, Expression, and Evolutionary Analyses of Plant Lipocalins

Lipocalins are a group of proteins that have been characterized in bacteria, invertebrate, and vertebrate animals. However, very little is known about plant lipocalins. We have previously reported the cloning of the first true plant lipocalins. Here we report the identification and characterization of plant lipocalins and lipocalin-like proteins using an integrated approach of data mining, expression studies, cellular localization, and phylogenetic analyses. Plant lipocalins can be classified into two groups, temperature-induced lipocalins (TILs) and chloroplastic lipocalins (CHLs). In addition, violaxanthin de-epoxidases (VDEs) and zeaxanthin epoxidases (ZEPs) can be classified as lipocalin-like proteins. CHLs, VDEs, and ZEPs possess transit peptides that target them to the chloroplast. On the other hand, TILs do not show any targeting peptide, but localization studies revealed that the proteins are found at the plasma membrane. Expression analyses by quantitative real-time PCR showed that expression of the wheat (Triticum aestivum) lipocalins and lipocalin-like proteins is associated with abiotic stress response and is correlated with the plants capacity to develop freezing tolerance. In support of this correlation, data mining revealed that lipocalins are present in the desiccation-tolerant red algae Porphyra yezoensis and the cryotolerant marine yeast Debaryomyces hansenii, suggesting a possible association with stress-tolerant organisms. Considering the plant lipocalin properties, tissue specificity, response to temperature stress, and their association with chloroplasts and plasma membranes of green leaves, we hypothesize a protective function of the photosynthetic system against temperature stress. Phylogenetic analyses suggest that TIL lipocalin members in higher plants were probably inherited from a bacterial gene present in a primitive unicellular eukaryote. On the other hand, CHLs, VDEs, and ZEPs may have evolved from a cyanobacterial ancestral gene after the formation of the cyanobacterial endosymbiont from which the chloroplast originated.

Computing Common Intervals of

We introduce a new approach to compute the common intervals of

K

K

Permutations, with Applications to Modular Decomposition of Graphs

permutations based on a very simple and general notion of generators of common intervals. This formalism leads to simple and efficient algorithms to compute the set of all common intervals of

Perfect Sorting by Reversals Is Not Always Difficult

K

New Perspectives on Gene Family Evolution: Losses in Reconciliation and a Link with Supertrees

permutations that can contain a quadratic number of intervals, as well as a linear space basis of this set of common intervals. Finally, we show how our results on permutations can be used for computing the modular decomposition of graphs.

Gene family evolution by duplication, speciation, and loss.

We propose new algorithms for computing pairwise rearrangement scenarios that conserve the combinatorial structure of genomes. More precisely, we investigate the problem of sorting signed permutations by reversals without breaking common intervals. We describe a combinatorial framework for this problem that allows us to characterize classes of signed permutations for which one can compute, in polynomial time, a shortest reversal scenario that conserves all common intervals. In particular, we define a class of permutations for which this computation can be done in linear time with a very simple algorithm that does not rely on the classical Hannenhalli-Pevzner theory for sorting by reversals. We apply these methods to the computation of rearrangement scenarios between permutations obtained from 16 synteny blocks of the X chromosomes of the human, mouse, and rat

Computing common intervals of K permutations, with applications to modular decomposition of graphs

Reconciliation between a set of gene trees and a species tree is the most commonly used approach to infer the duplication and loss events in the evolution of gene families, given a species tree. When a species tree is not known, a natural algorithmic problem is to infer a species tree such that the corresponding reconciliation minimizes the number of duplications and/or losses. In this paper, we clarify several theoretical questions and study various algorithmic issues related to these two problems. (1) For a given gene tree T and species tree S , we show that there is a single history explaining T and consistent with S that minimizes gene losses, and that this history also minimizes the number of duplications. We describe a simple linear-time and space algorithm to compute this parsimonious history, that is not based on the Lowest Common Ancestor (LCA) mapping approach; (2) We show that the problem of computing a species tree that minimizes the number of gene duplications, given a set of gene trees, is in fact a slight variant of a supertree problem; (3) We show that deciding if a set of gene trees can be explained using only apparent duplications can be done efficiently, as well as computing a parsimonious species tree for such gene trees. We also characterize gene trees that can be explained using only apparent duplications in terms of compatible triplets of leaves.