Thomas Schiex

FrameDP: sensitive peptide detection on noisy matured sequences

Summary: Transcriptome sequencing represents a fundamental source of information for genome-wide studies and transcriptome analysis and will become increasingly important for expression analysis as new sequencing technologies takes over array technology. The identification of the protein-coding region in transcript sequences is a prerequisite for systematic amino acid-level analysis and more specifically for domain identification. In this article, we present FrameDP, a self-training integrative pipeline for predicting CDS in transcripts which can adapt itself to different levels of sequence qualities. Availability: FrameDP for Linux (web-server and underlying pipeline) is available at {{http://iant.toulouse.inra.fr/FrameDP}} for direct use or a standalone installation. Contact: thomas.schiex@toulouse.inra.fr

Hybridization and polyploidy enable genomic plasticity without sex in the most devastating plant-parasitic nematodes

Root-knot nematodes (genus Meloidogyne) exhibit a diversity of reproductive modes ranging from obligatory sexual to fully asexual reproduction. Intriguingly, the most widespread and devastating species to global agriculture are those that reproduce asexually, without meiosis. To disentangle this surprising parasitic success despite the absence of sex and genetic exchanges, we have sequenced and assembled the genomes of three obligatory ameiotic and asexual Meloidogyne. We have compared them to those of relatives able to perform meiosis and sexual reproduction. We show that the genomes of ameiotic asexual Meloidogyne are large, polyploid and made of duplicated regions with a high within-species average nucleotide divergence of ~8%. Phylogenomic analysis of the genes present in these duplicated regions suggests that they originated from multiple hybridization events and are thus homoeologs. We found that up to 22% of homoeologous gene pairs were under positive selection and these genes covered a wide spectrum of predicted functional categories. To biologically assess functional divergence, we compared expression patterns of homoeologous gene pairs across developmental life stages using an RNAseq approach in the most economically important asexually-reproducing nematode. We showed that >60% of homoeologous gene pairs display diverged expression patterns. These results suggest a substantial functional impact of the genome structure. Contrasting with high within-species nuclear genome divergence, mitochondrial genome divergence between the three ameiotic asexuals was very low, signifying that these putative hybrids share a recent common maternal ancestor. Transposable elements (TE) cover a ~1.7 times higher proportion of the genomes of the ameiotic asexual Meloidogyne compared to the sexual relative and might also participate in their plasticity. The intriguing parasitic success of asexually-reproducing Meloidogyne species could be partly explained by their TE-rich composite genomes, resulting from allopolyploidization events, and promoting plasticity and functional divergence between gene copies in the absence of sex and meiosis.

EuGene-PP: a next-generation automated annotation pipeline for prokaryotic genomes.

UNLABELLED It is now easy and increasingly usual to produce oriented RNA-Seq data as a prokaryotic genome is being sequenced. However, this information is usually just used for expression quantification. EuGene-PP is a fully automated pipeline for structural annotation of prokaryotic genomes integrating protein similarities, statistical information and any oriented expression information (RNA-Seq or tiling arrays) through a variety of file formats to produce a qualitatively enriched annotation including coding regions but also (possibly antisense) non-coding genes and transcription start sites. AVAILABILITY AND IMPLEMENTATION EuGene-PP is an open-source software based on EuGene-P integrating a Galaxy configuration. EuGene-PP can be downloaded at eugene.toulouse.inra.fr.

Triangle-based consistencies for cost function networks

Cost Function Networks (aka Weighted CSP) allow to model a variety of problems, such as optimization of deterministic and stochastic graphical models including Markov random Fields and Bayesian Networks. Solving cost function networks is thus an important problem for deterministic and probabilistic reasoning. This paper focuses on local consistencies which define essential tools to simplify Cost Function Networks, and provide lower bounds on their optimal solution cost. To strengthen arc consistency bounds, we follow the idea of triangle-based domain consistencies for hard constraint networks (path inverse consistency, restricted or max-restricted path consistencies), describe their systematic extension to cost function networks, study their relative strengths, define enforcing algorithms, and experiment with them on a large set of benchmark problems. On some of these problems, our improved lower bounds seem necessary to solve them.

Fitness landscape analysis around the optimum in computational protein design

The geometry and properties of the fitness landscapes of Computational Protein Design (CPD) are not well understood, due to the difficulty for sampling methods to access the NP-hard optima and explore their neighborhoods. In this paper, we enumerate all solutions within a 2 kcal/mol energy interval of the optimum of two CPD problems. We compute the number of local minima, the size of the attraction basins, and the local optima network. We provide various features in order to characterize the fitness landscapes, in particular the multimodality, and the ruggedness of the fitness landscape. Results show some key differences in the fitness landscapes and help to understand the successes and failures of metaheuristics on CPD problems. Our analysis gives some previously inaccessible and valuable information on the problem structure related to the optima of the CPD instances (multi-funnel structure), and could lead to the development of more efficient metaheuristic methods.

Guaranteed Weighted Counting for Affinity Computation: Beyond Determinism and Structure

Computing the constant Z that normalizes an arbitrary distribution into a probability distribution is a difficult problem that has applications in statistics, biophysics and probabilistic reasoning. In biophysics, it is a prerequisite for the computation of the binding affinity between two molecules, a central question for protein design. In the case of a discrete stochastic Graphical Model, the problem of computing Z is equivalent to weighted model counting in SAT or CSP, known to be #P-complete [38]. SAT solvers have been used to accelerate guaranteed normalizing constant computation, leading to exact tools such as cachet [33], ace [8] or minic2d [28]. They exploit determinism in the stochastic model to prune during counting and the dependency structure of the model (partially captured by tree-width) to cache intermediary counts, trading time for space. When determinism or structure are not sufficient, we consider the idea of discarding sufficiently negligible contributions to Z to speedup counting. We test and compare this approach with other solvers providing deterministic guarantees on various benchmarks, including protein binding affinity computations, and show that it can provide important speedups.