Author: "Université de Montpellier (UM)-Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)" - Searchworks@Jio Institute Digital Library Search Results

Your search keyword '"Université de Montpellier (UM)-Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)"' showing total 214 results

Start Over Author "Université de Montpellier (UM)-Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)"

214 results on '"Université de Montpellier (UM)-Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)"'

1. La protéine NF-Y contrôle la fidelité de l'initiation de la transcription aux promoteurs de gènes à travers la maintenance de région déplétée en nucléosome

Author: Raja Jothi, Telmo Henriques, Christopher A. Lavender, Dhirendra Kumar, Senthilkumar Cinghu, Karen Adelman, Andrew J. Oldfield, Brian D. Bennett, Adam B. Burkholder, Pengyi Yang, Damien Paulet, Benjamin S. Scruggs, Eric Rivals, Epigenetics and Stem Cell Biology Laboratory [Durham] (ESCBL-NIEHS-NIH), National Institute of Environmental Health Sciences [Durham] (NIEHS-NIH), National Institutes of Health [Bethesda] (NIH)-National Institutes of Health [Bethesda] (NIH), Harvard Medical School [Boston] (HMS), Maharishi Valmiki Hospital, Méthodes et Algorithmes pour la Bioinformatique (MAB), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Institut de Biologie Computationnelle (IBC), Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Marquette University, National Institutes of Health [Bethesda] (NIH), ATGC bioinformatics platform Montpellier. the Institut de Biologie Computationnelle (ANR-11-BINF-0002) Institut Français de Bioinformatique (ANR-11-INBS-0013)GEM Flagship project funded from Labex NUMEV (ANR-10-LABX-0020)France Génomique., ANR-10-LABX-0020/10-LABX-0020,NUMEV,Digital and Hardware Solutions and Modeling for the Environement and Life Sciences(2010), ANR-11-INBS-0013/11-INBS-0013,ReNaBi-IFB,Institut français de bioinformatique(2011), ANR-11-BINF-0002,IBC,Institut de Biologie Computationnelle de Montpellier(2011), ANR-10-INBS-09-01/10-INBS-0009,France-Génomique,Organisation et montée en puissance d'une Infrastructure Nationale de Génomique(2010), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Université de Montpellier (UM)-Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS), Marquette University [Milwaukee], ANR-10-LABX-0020,NUMEV,Digital and Hardware Solutions and Modeling for the Environement and Life Sciences(2010), ANR-11-INBS-0013,IFB (ex Renabi-IFB),Institut français de bioinformatique(2011), ANR-11-BINF-0002,IBC,Institut de biologie Computationnelle(2011), and ANR-10-INBS-0009,France-Génomique,Organisation et montée en puissance d'une Infrastructure Nationale de Génomique(2010)
Subjects: 0301 basic medicine, General Physics and Astronomy, [SDV.BC.BC]Life Sciences [q-bio]/Cellular Biology/Subcellular Processes [q-bio.SC], 02 engineering and technology, Mice, 0302 clinical medicine, Transcription (biology), Gene expression, Gene Knockdown Techniques, RNA, Small Interfering, Promoter Regions, Genetic, lcsh:Science, Transcription Initiation, Genetic, 0303 health sciences, Multidisciplinary, 021001 nanoscience & nanotechnology, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], Chromatin, Nucleosomes, 3. Good health, Cell biology, Transcription Initiation Site, 0210 nano-technology, Transcription, Science, Protein domain, Site Transcription Initiation, Genomics, Biology, Small Interfering, Article, General Biochemistry, Genetics and Molecular Biology, Cell Line, Promoter Regions, 03 medical and health sciences, [SDV.BBM.GTP]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Genomics [q-bio.GN], Animals, Nucleosome, Transcription factor, Embryonic Stem Cells, 030304 developmental biology, Promoter, General Chemistry, Metabolism, 030104 developmental biology, CCAAT-Binding Factor, RNA, lcsh:Q, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], 030217 neurology & neurosurgery
Abstract: Faithful transcription initiation is critical for accurate gene expression, yet the mechanisms underlying specific transcription start site (TSS) selection in mammals remain unclear. Here, we show that the histone-fold domain protein NF-Y, a ubiquitously expressed transcription factor, controls the fidelity of transcription initiation at gene promoters in mouse embryonic stem cells. We report that NF-Y maintains the region upstream of TSSs in a nucleosome-depleted state while simultaneously protecting this accessible region against aberrant and/or ectopic transcription initiation. We find that loss of NF-Y binding in mammalian cells disrupts the promoter chromatin landscape, leading to nucleosomal encroachment over the canonical TSS. Importantly, this chromatin rearrangement is accompanied by upstream relocation of the transcription pre-initiation complex and ectopic transcription initiation. Further, this phenomenon generates aberrant extended transcripts that undergo translation, disrupting gene expression profiles. These results suggest NF-Y is a central player in TSS selection in metazoans and highlight the deleterious consequences of inaccurate transcription initiation., The mechanisms underlying specific TSS selection in mammals remain unclear. Here the authors show that the ubiquitously expressed transcription factor NF-Y regulate fidelity of transcription initiation at gene promoters, maintaining the region upstream of TSSs in a nucleosome-depleted state, while protecting this region from ectopic transcription initiation.
Published: 2019

2. Structure of multilocus genetic diversity in predominantly selfing populations

Author: Margaux Jullien, Miguel Navascués, Joëlle Ronfort, Karine Loridon, Laurène Gay, Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Centre de Biologie pour la Gestion des Populations (UMR CBGP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Université de Montpellier (UM)-Institut de Recherche pour le Développement (IRD [France-Sud])-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut de Biologie Computationnelle (IBC), Université de Montpellier (UM)-Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS), NRA (French National Institute of Agronomical Research) Department of Genetics and Plant Breeding, INRA Metaprogram ACCAF, Agence Nationale de la Recherche : ANR SEAD-ANR-13-ADAP-0011, ANR-13-ADAP-0011,SEAD,Comment l'autofécondation affecte-t-elle l'adaptation : Conséquences génétiques et démographiques(2013), and Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)
Subjects: Male, 0106 biological sciences, 0301 basic medicine, Genotype, [SDV]Life Sciences [q-bio], Population, Outcrossing, Biology, 010603 evolutionary biology, 01 natural sciences, Article, 03 medical and health sciences, Diversity index, Effective population size, Genetics, Animals, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, education, Genetics (clinical), Selection (genetic algorithm), Population Density, Genetic diversity, education.field_of_study, Models, Genetic, Population size, Genetic Variation, food and beverages, Selfing, Biological Evolution, Genetics, Population, 030104 developmental biology, Evolutionary biology, [SDE]Environmental Sciences, Female
Abstract: AGAP : équipe GE2pop; International audience; Predominantly selfing populations are expected to have reduced effective population sizes due to nonrandom sampling of gametes, demographic stochasticity (bottlenecks or extinction–recolonization), and large scale hitchhiking (reduced effective recombination). Thus, they are expected to display low genetic diversity, which was confirmed by empirical studies. The structure of genetic diversity in predominantly selfing species is dramatically different from outcrossing ones, with populations often dominated by one or a few multilocus genotypes (MLGs) coexisting with several rare genotypes. Therefore, multilocus diversity indices are relevant to describe diversity in selfing populations. Here, we use simulations to provide analytical expectations for multilocus indices and examine whether selfing alone can be responsible for the highfrequency MLGs persistent through time in the absence of selection. We then examine how combining single and multilocus indices of diversity may be insightful to distinguish the effects of selfing, population size, and more complex demographic events (bottlenecks, migration, admixture, or extinction–recolonization). Finally, we examine how temporal changes in MLG frequencies can be insightful to understand the evolutionary trajectory of a given population. We show that combinations of selfing and small demographic sizes can result in high-frequency MLGs, as observed in natural populations. We also show how different demographic scenarios can be distinguished by the parallel analysis of single and multilocus indices of diversity, and we emphasize the importance of temporal data for the study of predominantly selfing populations. Finally, the comparison of our simulations with empirical data on populations of Medicago truncatula confirms the pertinence of our simulation framework.
Published: 2019

3. Data reduction in scientific workflows using provenance monitoring and user steering

Author: Renan Souza, Marta Mattoso, Patrick Valduriez, Alvaro L. G. A. Coutinho, Vítor Silva, Instituto Alberto Luiz Coimbra de Pós-Graduação e Pesquisa de Engenharia (COPPE-UFRJ), Universidade Federal do Rio de Janeiro (UFRJ), Scientific Data Management (ZENITH), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Institut de Biologie Computationnelle (IBC), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), SciDISC Inria associated team with Brazil, European Project: 689772,H2020 Pilier Industrial Leadership,H2020-EUB-2015,HPC4E(2015), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Inria Sophia Antipolis - Méditerranée (CRISAM), COPPE - Instituto Alberto Luiz Coimbra de Pós-Graduação e Pesquisa de Engenharia (COPPE-UFRJ), Universidade Federal do Rio de Janeiro [Rio de Janeiro] (UFRJ), Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), and Université de Montpellier (UM)-Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)
Subjects: Human in the Loop, Dynamic Workflows, [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], Scientific Workflows, Computer Networks and Communications, Computer science, 020206 networking & telecommunications, 02 engineering and technology, computer.software_genre, Domain (software engineering), Reduction (complexity), Workflow, Data model, Hardware and Architecture, Provenance Data, Online Data Reduction, 0202 electrical engineering, electronic engineering, information engineering, Key (cryptography), 020201 artificial intelligence & image processing, Data mining, [INFO.INFO-DC]Computer Science [cs]/Distributed, Parallel, and Cluster Computing [cs.DC], Computational steering, computer, Software, Data reduction
Abstract: International audience; Scientific workflows need to be iteratively, and often interactively, executed for large input datasets. Reducing data from input datasets is a powerful way to reduce overall execution time in such workflows. When this is accomplished online (i.e., without requiring the user to stop execution to reduce the data, and then resume), it can save much time. However, determining which subsets of the input data should be removed becomes a major problem. A related problem is to guarantee that the workflow system will maintain execution and data consistent with the reduction. Keeping track of how users interact with the workflow is essential for data provenance purposes. In this paper, we adopt the " human-in-the-loop " approach, which enables users to steer the running workflow and reduce subsets from datasets online. We propose an adaptive workflow monitoring approach that combines provenance data monitoring and computational steering to support users in analyzing the evolution of key parameters and determining the subset of data to remove. We extend a provenance data model to keep track of users' interactions when they reduce data at runtime. In our experimental validation, we develop a test case from the oil and gas domain, using a 936-cores cluster. The results on this test case show that the approach yields reductions of 32% of execution time and 14% of the data processed.
Published: 2020

4. Finding a most parsimonious or likely tree in a network with respect to an alignment

Author: Fabio Pardi, Celine Scornavacca, Steven Kelk, Leo van Iersel, Maastricht University [Maastricht], Méthodes et Algorithmes pour la Bioinformatique (MAB), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Institut de Biologie Computationnelle (IBC), Université de Montpellier (UM)-Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS), Institut des Sciences de l'Evolution de Montpellier (UMR ISEM), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-École pratique des hautes études (EPHE)-Université de Montpellier (UM)-Institut de recherche pour le développement [IRD] : UR226-Centre National de la Recherche Scientifique (CNRS), Delft University of Technology (TU Delft), Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Montpellier (UM)-Institut de recherche pour le développement [IRD] : UR226-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-École pratique des hautes études (EPHE), DKE Scientific staff, and RS: FSE DACS BMI
Subjects: Character evolution, Genetic Speciation, RECOMBINATION, 92D20, [SDV.BID.SPT]Life Sciences [q-bio]/Biodiversity/Systematics, Phylogenetics and taxonomy, 01 natural sciences, Article, 010305 fluids & plasmas, Evolution, Molecular, APX-hardness, Set (abstract data type), Combinatorics, 03 medical and health sciences, [MATH.MATH-CO]Mathematics [math]/Combinatorics [math.CO], 0103 physical sciences, NP-hardness, Animals, Humans, MAXIMUM-LIKELIHOOD, Phylogeny, 030304 developmental biology, Mathematics, Phylogenetic network, 0303 health sciences, Multiple sequence alignment, Models, Genetic, Phylogenetic tree, Applied Mathematics, 68Q25, Computational Biology, Mathematical Concepts, Directed acyclic graph, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], Agricultural and Biological Sciences (miscellaneous), Tree (graph theory), EVOLUTION, 92D15, Maximum parsimony, MODEL, Modeling and Simulation, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], BAYESIAN-INFERENCE, Algorithms, Maximum likelihood
Abstract: International audience; Phylogenetic networks are often constructed by merging multiple conflicting phyloge-netic signals into a directed acyclic graph. It is interesting to explore whether a network constructed in this way induces biologically-relevant phylogenetic signals that were not present in the input. Here we show that, given a multiple alignment A for a set of taxa X and a rooted phylogenetic network N whose leaves are labelled by X , it is NP-hard to locate a most parsimonious phylogenetic tree displayed by N (with respect to A) even when the level of N-the maximum number of reticulation nodes within a biconnected component-is 1 and A contains only 2 distinct states. (If, additionally, gaps are allowed the problem becomes APX-hard.) We also show that under the same conditions, and assuming a simple binary symmetric model of character evolution, finding a most likely tree displayed by the network is NP-hard. These negative results contrast with earlier work on parsimony in which it is shown that if A consists of a single column the problem is fixed parameter tractable in the level. We conclude with a discussion of why, despite the NP-hardness, both the parsimony and likelihood problem can likely be well-solved in practice.
Published: 2018

5. Finding local genome rearrangements

Author: Swenson, Krister, Simonaitis, Pijus, École normale supérieure - Lyon (ENS Lyon), Méthodes et Algorithmes pour la Bioinformatique (MAB), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Institut de Biologie Computationnelle (IBC), Université de Montpellier (UM)-Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS), ANR-11-BINF-0002,IBC,Institut de biologie Computationnelle(2011), École normale supérieure de Lyon (ENS de Lyon), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), and Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)
Subjects: 000 Computer science, knowledge, general works, Double cut and join, lcsh:QH426-470, Genome rearrangement, Quantitative Biology::Genomics, Maximum edge-disjoint cycle packing, lcsh:Genetics, lcsh:Biology (General), Hi-C, Computer Science, Chromatin conformation, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], lcsh:QH301-705.5, NP-complete
Abstract: Background The double cut and join (DCJ) model of genome rearrangement is well studied due to its mathematical simplicity and power to account for the many events that transform gene order. These studies have mostly been devoted to the understanding of minimum length scenarios transforming one genome into another. In this paper we search instead for rearrangement scenarios that minimize the number of rearrangements whose breakpoints are unlikely due to some biological criteria. One such criterion has recently become accessible due to the advent of the Hi-C experiment, facilitating the study of 3D spacial distance between breakpoint regions. Results We establish a link between the minimum number of unlikely rearrangements required by a scenario and the problem of finding a maximum edge-disjoint cycle packing on a certain transformed version of the adjacency graph. This link leads to a 3/2-approximation as well as an exact integer linear programming formulation for our problem, which we prove to be NP-complete. We also present experimental results on fruit flies, showing that Hi-C data is informative when used as a criterion for rearrangements. Conclusions A new variant of the weighted DCJ distance problem is addressed that ignores scenario length in its objective function. A solution to this problem provides a lower bound on the number of unlikely moves necessary when transforming one gene order into another. This lower bound aids in the study of rearrangement scenarios with respect to chromatin structure, and could eventually be used in the design of a fixed parameter algorithm with a more general objective function.
Published: 2018

6. AQUAPONY: visualization and interpretation of phylogeographic information on phylogenetic trees

Author: Bastien Cazaux, Eric Rivals, Guillaume Castel, Méthodes et Algorithmes pour la Bioinformatique (MAB), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Institut de Biologie Computationnelle (IBC), Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Centre de Biologie pour la Gestion des Populations (UMR CBGP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Université de Montpellier (UM)-Institut de Recherche pour le Développement (IRD [France-Sud])-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), ANR-11-BINF-0002,IBC,Institut de Biologie Computationnelle de Montpellier(2011), ANR-11-BINF-0002,IBC,Institut de biologie Computationnelle(2011), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Université de Montpellier (UM)-Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), and French National Research Agency (ANR) : ANR-11-BINF-0002
Subjects: Interactive visualisation, Statistics and Probability, Computer science, MESH: Biological Evolution G05.045.513, [SDV]Life Sciences [q-bio], analyse phylogénétique, computer.software_genre, Biochemistry, 03 medical and health sciences, Inference, Phylogenetics, Biologie animale, Molecular Biology, Phylogeny, 030304 developmental biology, Animal biology, 0303 health sciences, Phylogenetic tree, [SDV.BA]Life Sciences [q-bio]/Animal biology, 030302 biochemistry & molecular biology, Uncertainty, Probabilistic logic, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], Web, Computer Science Applications, Visualization, Phylogeography, Computational Mathematics, Tree (data structure), Phenotype, Computational Theory and Mathematics, arbre phylogénétique, Data mining, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], computer, Tree, Software
Abstract: Motivation The visualization and interpretation of evolutionary spatiotemporal scenarios is broadly and increasingly used in infectious disease research, ecology or agronomy. Using probabilistic frameworks, well-known tools can infer from molecular data ancestral traits for internal nodes in a phylogeny, and numerous phylogenetic rendering tools can display such evolutionary trees. However, visualizing such ancestral information and its uncertainty on the tree remains tedious. For instance, ancestral nodes can be associated to several geographical annotations with close probabilities and thus, several migration or transmission scenarios exist. Results We expose a web-based tool, named AQUAPONY, that facilitates such operations. Given an evolutionary tree with ancestral (e.g. geographical) annotations, the user can easily control the display of ancestral information on the entire tree or a subtree, and can view alternative phylogeographic scenarios along a branch according to a chosen uncertainty threshold. AQUAPONY interactively visualizes the tree and eases the objective interpretation of evolutionary scenarios. AQUAPONY’s implementation makes it highly responsive to user interaction, and instantaneously updates the tree visualizations even for large trees (which can be exported as image files). Availability and implementation AQUAPONY is coded in JavaScript/HTML, available under Cecill license, and can be freely used at http://www.atgc-montpellier.fr/aquapony/.
Published: 2019

7. Advances in Databases and Information Systems

Author: Patrick Valduriez, Ladjel Bellatreche, Tadeusz Morzy, Laboratoire d'Informatique et d'Automatique pour les Systèmes (LIAS), Université de Poitiers-ENSMA, Institut de Biologie Computationnelle (IBC), Université de Montpellier (UM)-Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS), Scientific Data Management (ZENITH), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Poznan University of Technology (PUT), Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Inria Sophia Antipolis - Méditerranée (CRISAM), and Poznan University of Technology
Subjects: Computer Networks and Communications, Computer science, 02 engineering and technology, computer.software_genre, Query optimization, Database design, Theoretical Computer Science, Data modeling, 020204 information systems, 0502 economics and business, 0202 electrical engineering, electronic engineering, information engineering, Information system, [INFO]Computer Science [cs], ComputingMilieux_MISCELLANEOUS, Database model, Data administration, Information retrieval, Database, 05 social sciences, Materialized view, Business process modeling, Data structure, Data science, Hardware and Architecture, 020201 artificial intelligence & image processing, 050211 marketing, Database theory, Semi-structured data, computer, Software, Information Systems, Data integration
Abstract: This volume is the second one of the 16th East-European Conference on Advances in Databases and Information Systems (ADBIS 2012), held on September 18-21, 2012, in Pozna, Poland. The first one has been published in the LNCS series. This volume includes 27 research contributions, selected out of 90. The contributions cover a wide spectrum of topics in the database and information systems field, including: database foundation and theory, data modeling and database design, business process modeling, query optimization in relational and object databases, materialized view selection algorithms, index data structures, distributed systems, system and data integration, semi-structured data and databases, semantic data management, information retrieval, data mining techniques, data stream processing, trust and reputation in the Internet, and social networks. Thus, the content of this volume covers the research areas from fundamentals of databases, through still hot topic research problems (e.g., data mining, XML data processing), to novel research areas (e.g., social networks, trust and reputation, and data stream processing). The editors of this volume believe that its content will inspire the researchers with new ideas for future development. It may also serve as an overview of the ongoing work in the field of databases and information systems.
Published: 2017

8. Accurate template-based modeling in CASP12 using the IntFOLD4-TS, ModFOLD6, and ReFOLD methods

Author: Bajuna R. Salehe, Ali H. A. Maghrabi, Daniel B. Roche, John O. Nealon, Ahmad N. Shuid, Robert Kempster, Liam J. McGuffin, Jennifer D. Atkins, University of Reading (UOR), Méthodes et Algorithmes pour la Bioinformatique (MAB), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Institut de Biologie Computationnelle (IBC), Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), and Université de Montpellier (UM)-Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)
Subjects: Models, Molecular, 0301 basic medicine, Protein Folding, Protein Conformation, Computer science, computer.software_genre, Machine learning, Biochemistry, 03 medical and health sciences, Sequence Analysis, Protein, Structural Biology, Humans, Databases, Protein, Molecular Biology, Selection (genetic algorithm), Protocol (science), business.industry, Model selection, Rank (computer programming), Computational Biology, Proteins, Protein structure prediction, 3D modeling, 030104 developmental biology, Ranking, Data mining, Artificial intelligence, Template based, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], business, Sequence Alignment, computer, Software
Abstract: International audience; Our aim in CASP12 was to improve our Template‐Based Modeling (TBM) methods through better model selection, accuracy self‐estimate (ASE) scores and refinement. To meet this aim, we developed two new automated methods, which we used to score, rank, and improve upon the provided server models. Firstly, the ModFOLD6_rank method, for improved global Quality Assessment (QA), model ranking and the detection of local errors. Secondly, the ReFOLD method for fixing errors through iterative QA guided refinement. For our automated predictions we developed the IntFOLD4‐TS protocol, which integrates the ModFOLD6_rank method for scoring the multiple‐template models that were generated using a number of alternative sequence‐structure alignments. Overall, our selection of top models and ASE scores using ModFOLD6_rank was an improvement on our previous approaches. In addition, it was worthwhile attempting to repair the detected errors in the top selected models using ReFOLD, which gave us an overall gain in performance. According to the assessors' formula, the IntFOLD4 server ranked 3rd/5th (average Z‐score > 0.0/–2.0) on the server only targets, and our manual predictions (McGuffin group) ranked 1st/2nd (average Z‐score > −2.0/0.0) compared to all other groups.
Published: 2017

9. InfraPhenoGrid: A scientific workflow infrastructure for plant phenomics on the Grid

Author: Simon Artzet, Christian Fournier, Vincent Negre, Michael Mielewczik, Christophe Pradal, Patrick Valduriez, Jérôme Chopard, Sarah Cohen-Boulakia, Pascal Neveu, Dimitri Dupuis, Didier Parigot, Modeling plant morphogenesis at different scales, from genes to phenotype (VIRTUAL PLANTS), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de la Recherche Agronomique (INRA)-Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut de Biologie Computationnelle (IBC), Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Écophysiologie des Plantes sous Stress environnementaux (LEPSE), Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Mathématiques, Informatique et STatistique pour l'Environnement et l'Agronomie (MISTEA), Institut National de la Recherche Agronomique (INRA)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Scientific Data Management (ZENITH), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), École nationale supérieure d'architecture de Paris-La Villette (ENSAPLV), HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM), Laboratoire de Recherche en Informatique (LRI), Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), The authors acknowledge the support of France Grilles for providing computing resources on the French National Grid Infrastructure, ANR-11-INBS-0012,PHENOME,Centre français de phénomique végétale(2011), European Project: 267196,EC:FP7:PEOPLE,FP7-PEOPLE-2010-COFUND,AGREENSKILLS(2012), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Inria Sophia Antipolis - Méditerranée (CRISAM), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), Université de Montpellier (UM)-Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), ANR-11-INBS-0012/11-INBS-0012,PHENOME,Centre français de phénomique végétale(2011), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Inria Sophia Antipolis - Méditerranée (CRISAM), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), and Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)
Subjects: 0106 biological sciences, 0301 basic medicine, Computer Networks and Communications, Computer science, F60 - Physiologie et biochimie végétale, Distributed computing, acquisition de données, Context (language use), Phenome, 01 natural sciences, F30 - Génétique et amélioration des plantes, 03 medical and health sciences, Phenomics, phénotypage, caractère physiologique, condition environnementale, base de données, 2. Zero hunger, Vegetal Biology, Food security, SIMPLE (military communications protocol), Point (typography), U10 - Informatique, mathématiques et statistiques, Scientic Work ows, Grid, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, Data science, plateforme de phénotypage, Scientific workflows, Provenance, Grid computing, 030104 developmental biology, Workflow, C30 - Documentation et information, OpenAlea, 13. Climate action, Hardware and Architecture, dispositif de recherche, Biologie végétale, Software, 010606 plant biology & botany
Abstract: International audience; Plant phenotyping consists in the observation of physical and biochemical traits of plant genotypes in response to environmental conditions. Challenges , in particular in context of climate change and food security, are numerous. High-throughput platforms have been introduced to observe the dynamic growth of a large number of plants in different environmental conditions. Instead of considering a few genotypes at a time (as it is the case when phenomic traits are measured manually), such platforms make it possible to use completely new kinds of approaches. However, the data sets produced by such widely instrumented platforms are huge, constantly augmenting and produced by increasingly complex experiments, reaching a point where distributed computation is mandatory to extract knowledge from data. In this paper, we introduce InfraPhenoGrid, the infrastructure we designed and deploy to efficiently manage data sets produced by the PhenoArch plant phenomics platform in the context of the French Phenome Project. Our solution consists in deploying scientific workflows on a Grid using a middle-ware to pilot workflow executions. Our approach is user-friendly in the sense that despite the intrinsic complexity of the infrastructure, running scientific workflows and understanding results obtained (using provenance information) is kept as simple as possible for end-users.
Published: 2017

10. Short Communication: High Viral Load and Multidrug Resistance Due to Late Switch to Second-Line Regimens Could Be a Major Obstacle to Reach the 90-90-90 UNAIDS Objectives in Sub-Saharan Africa

Author: Laura Ciaffi, Christian-Julian Villabona-Arenas, Eric Delaporte, Emilande Guichet, Coumba Toure-Kane, Guillaume Bado, Sabrina Eymard-Duvernay, Avelin F. Aghokeng, Martine Peeters, Laetitia Serrano, Recherches Translationnelles sur le VIH et les maladies infectieuses (TransVIHMI), Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Recherche pour le Développement (IRD)-Université Montpellier 1 (UM1)-Université Cheikh Anta Diop [Dakar, Sénégal] (UCAD)-Universtié Yaoundé 1 [Cameroun]-Université de Montpellier (UM), Centre Hospitalier Universitaire Souro Sanou [Bobo-Dioulasso] (CHUSS), Institut de Biologie Computationnelle (IBC), Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Méthodes et Algorithmes pour la Bioinformatique (MAB), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Recherches Translationnelles sur le VIH et les maladies infectieuses endémiques er émergentes (TransVIHMI), Université Cheikh Anta Diop [Dakar, Sénégal] (UCAD)-Institut de Recherche pour le Développement (IRD)-Université de Yaoundé I-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Montpellier (UM)-Université Montpellier 1 (UM1), Université de Montpellier (UM)-Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Recherches Translationnelles sur le VIH et les maladies infectieuses endémiques et émergentes (TransVIHMI), and Institut de Recherche pour le Développement (IRD)-Université de Yaoundé I-Université Cheikh Anta Diop [Dakar, Sénégal] (UCAD)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Montpellier (UM)
Subjects: Adult, Male, 0301 basic medicine, Drug, medicine.medical_specialty, Anti-HIV Agents, media_common.quotation_subject, antiretroviral therapy, 030106 microbiology, Immunology, HIV Infections, Context (language use), Drug resistance, Asymptomatic, law.invention, 03 medical and health sciences, 0302 clinical medicine, Drug Resistance, Multiple, Viral, Acquired immunodeficiency syndrome (AIDS), [SDV.MHEP.MI]Life Sciences [q-bio]/Human health and pathology/Infectious diseases, law, Antiretroviral Therapy, Highly Active, Virology, Internal medicine, Burkina Faso, Prevalence, Humans, Medicine, Cameroon, 030212 general & internal medicine, media_common, drug resistance, business.industry, HIV, medicine.disease, Senegal, viral load, 3. Good health, Multiple drug resistance, Infectious Diseases, Transmission (mechanics), Africa, [SDV.MP.VIR]Life Sciences [q-bio]/Microbiology and Parasitology/Virology, [SDV.IMM]Life Sciences [q-bio]/Immunology, Female, medicine.symptom, business, Viral load
Abstract: International audience; In the context of lifelong antiretroviral treatment (ART) as early as possible and to end the HIV/AIDS epidemic as a public health treat by 2030, it is important to evaluate the potential risk of transmission of HIV-1 drug resistance (HIVDR) in resource-limited countries (RLCs). Since HIV transmission is driven by HIV-1 RNA viral load (VL), we studied the association between plasma VL and HIVDR profiles in 451 adults failing first-line ART from the 2LADY-ANRS12169/EDCTP trial in Burkina Faso, Cameroon, and Senegal. Median duration on first-line ART was 49 months (IQR: 33-69) and 91% patients were asymptomatic. Genotypic drug resistance testing was successful for 446 patients and 98.7% of them were resistant to at least one of the first-line drugs; 40.6% and 55.8% were resistant to two or three drugs of their ongoing first-line ART, respectively. The median VL was higher in patients with HIVDR to all ongoing first-line drugs than in those still susceptible to at least one drug; 4.7 log10 copies/ml (IQR: 4.3-5.2) versus 4.2 log10 copies/ml (IQR: 3.7-4.7), respectively (p 5.0 log10 copies/ml. High rates of cross-resistance to other nucleoside reverse-transcriptase inhibitors were observed and were also highest in patients with high VL. Without improvement of patient monitoring to avoid late switch to second-line regimens, a potential new epidemic caused by HIVDR strains could emerge in sub-Saharan Africa and compromise all efforts to reach 90-90-90 UNAIDS objective by 2020.
Published: 2016

11. NF-Y controls fidelity of transcription initiation at gene promoters through maintenance of the nucleosome-depleted region

Author: Oldfield, Andrew, Henriques, Telmo, Kumar, Dhirendra, Burkholder, Adam, Cinghu, Senthilkumar, Paulet, Damien, Bennett, Brian, Yang, Pengyi, Scruggs, Benjamin, Lavender, Christopher, Rivals, Eric, Adelman, Karen, Jothi, Raja, Epigenetics and Stem Cell Biology Laboratory [Durham] (ESCBL-NIEHS-NIH), National Institute of Environmental Health Sciences [Durham] (NIEHS-NIH), National Institutes of Health [Bethesda] (NIH)-National Institutes of Health [Bethesda] (NIH), Harvard Medical School [Boston] (HMS), Maharishi Valmiki Hospital, Méthodes et Algorithmes pour la Bioinformatique (MAB), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Institut de Biologie Computationnelle (IBC), Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Marquette University, National Institutes of Health [Bethesda] (NIH), ATGC bioinformatics platform Montpellier. the Institut de Biologie Computationnelle (ANR-11-BINF-0002) Institut Français de Bioinformatique (ANR-11-INBS-0013)GEM Flagship project funded from Labex NUMEV (ANR-10-LABX-0020)France Génomique., ANR-10-LABX-0020/10-LABX-0020,NUMEV,Digital and Hardware Solutions and Modeling for the Environement and Life Sciences(2010), ANR-11-INBS-0013/11-INBS-0013,ReNaBi-IFB,Institut français de bioinformatique(2011), ANR-11-BINF-0002,IBC,Institut de Biologie Computationnelle de Montpellier(2011), ANR-10-INBS-09-01/10-INBS-0009,France-Génomique,Organisation et montée en puissance d'une Infrastructure Nationale de Génomique(2010), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Université de Montpellier (UM)-Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS), Marquette University [Milwaukee], ANR-10-LABX-0020,NUMEV,Digital and Hardware Solutions and Modeling for the Environement and Life Sciences(2010), ANR-11-INBS-0013,IFB (ex Renabi-IFB),Institut français de bioinformatique(2011), ANR-11-BINF-0002,IBC,Institut de biologie Computationnelle(2011), and ANR-10-INBS-0009,France-Génomique,Organisation et montée en puissance d'une Infrastructure Nationale de Génomique(2010)
Subjects: Site Transcription Initiation, [SDV.BC.BC]Life Sciences [q-bio]/Cellular Biology/Subcellular Processes [q-bio.SC], Small Interfering, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], Chromatin, Nucleosomes, Promoter Regions, Mice, Metabolism, CCAAT-Binding Factor, Gene Knockdown Techniques, [SDV.BBM.GTP]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Genomics [q-bio.GN], RNA, Transcription Initiation Site, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], Embryonic Stem Cells
Abstract: International audience; Faithful transcription initiation is critical for accurate gene expression, yet the mechanisms underlying specific transcription start site (TSS) selection in mammals remain unclear. Here, we show that the histone-fold domain protein NF-Y, a ubiquitously expressed transcription factor, controls the fidelity of transcription initiation at gene promoters in mouse embryonic stem cells. We report that NF-Y maintains the region upstream of TSSs in a nucleosome-depleted state while simultaneously protecting this accessible region against aberrant and/or ectopic transcription initiation. We find that loss of NF-Y binding in mammalian cells disrupts the promoter chromatin landscape, leading to nucleosomal encroachment over the canonical TSS. Importantly, this chromatin rearrangement is accompanied by upstream relocation of the transcription pre-initiation complex and ectopic transcription initiation. Further, this phenomenon generates aberrant extended transcripts that undergo translation, disrupting gene expression profiles. These results suggest NF-Y is a central player in TSS selection in metazoans and highlight the deleterious consequences of inaccurate transcription initiation.
Published: 2019

12. AgroLD: un graphe de connaissances pour la caractérisation des mécanismes moléculaires complexes impactant le phénome des plantes

Author: Larmande, Pierre, Tagny Ngompé, Gildas, Manuel Ruiz, Diversité, adaptation, développement des plantes (UMR DIADE), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud]), Institut de Biologie Computationnelle (IBC), Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), AFIA, Nathalie Hernandez, Hernandez, Nathalie, Institut de Recherche pour le Développement (IRD [France-Sud])-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), and Université de Montpellier (UM)-Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)
Subjects: [INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], Phénome, Agronomie, Base de connaissances, Web sémantique, Génomique fonctionnelle, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Abstract: National audience; La compréhension des interactions génotype-phénotype est un des axes les plus importants de la recherche en agronomie dont l'un des objectifs est d'accélérer la reproduction des caractères importants pour la production agricole. Or ces interactions sont complexes à identifier car elles s'expriment à différentes échelles moléculaires dans la plante et subissent de fortes influences de la part des facteurs environnemen-taux. Les technologies d'analyse haut-débit ne permettent de capturer que partiellement cette dynamique. Même si ces technologies sont de plus en plus performantes dans l'acquisition de données, notre connais-sance du système reste encore parcellaire pour pouvoir comprendre les relations complexes existant entre les différents éléments moléculaires responsables de l'expression du phénome-ensemble des phénotypes observés pour un individu-. Cet objectif ne peut être atteint qu'en intégrant des informations de différents niveaux dans un modèle intégrateur utilisant une approche systémique afin de comprendre le fonctionnement réel d'un système biologique. Aujourd'hui, le Web sémantique propose des technologies pour l'intégration de données hétérogènes et leur transformation en connaissances explicites grâce aux ontologies. Nous avons développé AgroLD (Venkatesan et al., 2018) (Agronomic Linked Data-www.agrold.org), une base de connaissances reposant sur les technologies du Web sémantique et exploitant des ontologies du domaine biologique, afin d'intégrer des données issues de plusieurs espèces de plantes présentant un intérêt important pour la communauté scientifique, comme par exemple le riz, le blé et arabidopsis. Nous présentons les résultats du projet, qui portait initialement sur la génomique, la protéomique et la phénomique. AgroLD est aujourd'hui une base de plus de 100 millions de triplets créée à partir de plus de 50 jeux de données provenant d'une dizaine de sources de données, telles que Gramene (Tello-Ruiz et al., 2018) et TropGeneDB (Hamelin et al., 2012). Par ailleurs, nous avons utilisé une dizaine d'ontologies du domaine biologique, telles que Gene Ontology (The Gene Ontology Consortium, 2014) et Plant Ontology (Plant & Consortium, 2002) pour annoter et intégrer ces ressources. Pour cette phase, chaque jeu de données a été transformé à partir de sources sélectionnées et annotées sémantiquement en réutilisant les champs textuels correspondant avec des termes d'ontologies lorsqu'ils ont été fournis par la source d'origine. De plus, nous avons utilisé les services Web d'AgroPortal (Jonquet et al., 2018) pour annoter sémantiquement des éléments supplémentaires tels que par exemple, les URIs correspondant à la taxonomie des espèces ou des éléments d'anatomie. Dans ces cas, nous avons généré des propriétés supplémentaires à partir des ontologies correspondantes, ajoutant ainsi 22% de triplets supplémentaires qui ont été validés manuellement. L'objectif d'AgroLD est d'offrir une plate-forme de connaissances spécifiques du domaine agronomique afin de répondre à des questions biologiques complexes. De telles questions peuvent concerner le rôle de gènes spécifiques dans les mécanismes de résistance aux maladies des plantes ou de caractères de production identifiés à partir des analyses GWAS. Afin de rendre AgroLD accessible par un plus grand nombre d'uti-lisateurs, nous avons également développé une application Web proposant plusieurs interfaces de requêtes. Tout d'abord une interface simple qui permet aux utilisateurs de rechercher par mots-clés sur l'ensemble des valeurs de la base et ainsi de parcourir le contenu d'AgroLD. Puis une interface de recherche avancée qui permet de combiner du texte libre et des filtres à facettes ainsi que des services Web externes proposant ainsi une interface d'agrégation de données distribuées. AgroLD possède également une interface de visualisation des graphes qu'il est possible de configurer pour mettre en valeur certains types de relations. Finalement, un éditeur SPARQL propose un environnement interactif pour formuler des requêtes et manipuler des ré-sultats. Actuellement, de nouveaux jeux de données sont en cours d'intégration. Ils portent sur les réseaux d'interaction protéine-protéine, les facteurs de transcription et réseaux de co-expression afin d'étendre les connaissances sur les mécanismes moléculaires. De nombreux développements sont également réalisés au niveau des interfaces de requêtes, notamment au niveau de la visualisation des graphes afin de fournir des outils plus dynamiques, interactifs et contextualisés. Enfin, une attention particulière est portée sur la qualité des données intégrées. Des méthodes de liage et de machine learning sont développées pour rechercher des liens et des ressources similaires dans la base de connaissances ou dans des ressources externes.
Published: 2019

13. Detecting Sections and Entities in Court Decisions Using HMM and CRF Graphical Models

Author: Stéphane Mussard, Jacky Montmain, Sébastien Harispe, Guillaume Zambrano, Gildas Tagny Ngompé, Laboratoire de Génie Informatique et Ingénierie de Production (LGI2P), IMT - MINES ALES (IMT - MINES ALES), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Détection, évaluation, gestion des risques CHROniques et éMErgents (CHROME) / Université de Nîmes (CHROME), Université de Nîmes (UNIMES), Institut de Biologie Computationnelle (IBC), Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Institut Mines-Télécom [Paris] (IMT), Université Grenoble alpes (UGA), and Université de Montpellier (UM)-Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)
Subjects: Data analysis and processing methods, Computer science, [SHS.INFO]Humanities and Social Sciences/Library and information sciences, 02 engineering and technology, Conditional random fields, computer.software_genre, Analyse de données et data mining, Sequence labeling, Task (project management), [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Hidden Markov Model - HMM, [SHS.DROIT]Humanities and Social Sciences/Law, 0202 electrical engineering, electronic engineering, information engineering, Feature (machine learning), Selection (linguistics), [INFO]Computer Science [cs], Graphical model, Chaine de Markov cachée, [SHS.LANGUE]Humanities and Social Sciences/Linguistics, Set (psychology), Hidden Markov model, ComputingMilieux_MISCELLANEOUS, [SHS.STAT]Humanities and Social Sciences/Methods and statistics, business.industry, 020206 networking & telecommunications, 16. Peace & justice, Champ aléatoire conditionnel, Analyse de données lexicales, 020201 artificial intelligence & image processing, Décision de justice, Artificial intelligence, business, computer, Natural language processing, Decision analysis
Abstract: Court decisions are legal documents that undergo careful analysis by lawyers in order to understand how judges make decisions. Such analyses can indeed provide invaluable insight into application of the law for the purpose of conducting many types of studies. As an example, a decision analysis may facilitate the handling of future cases and detect variations in judicial decision-making with respect to specific variables, like court location. This paper presents a set of results and lessons learned during a project intended to address a number of challenges related to searching and analyzing a large body of French court decisions. In particular, this paper focuses on a concrete and detailed application of the HMM and CRF sequence labeling models for the tasks of: (i) sectioning decisions, and (ii) detecting entities of interest in their content (e.g. locations, dates, participants, rules of law). The effect of several key design and fine-tuning features is studied for both task categories. Moreover, the present study covers steps that often receive little discussion yet remain critical to the practical application of sequence labeling models, i.e.: candidate feature definition, selection of good feature subsets, segment representations, and impact of the training dataset size on model performance.
Published: 2019

14. GECKO is a genetic algorithm to classify and explore high throughput sequencing data

Author: Grégory Beurier, Julie Brooke, Christelle Reynes, William Ritchie, Sylvain Barriere, Robert Sabatier, Alban Mancheron, Jean-Philippe Villemin, Lucile Broseus, Aubin Thomas, Claudio Lorenzi, Institut de génétique humaine (IGH), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut de Génomique Fonctionnelle (IGF), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Institut de Biologie Computationnelle (IBC), Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Méthodes et Algorithmes pour la Bioinformatique (MAB), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), French National Research Agency (ANR), Labex EpiGenMed, MUSE initiative, Institut National de la Recherche Agronomique (INRA)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Université de Montpellier (UM)-Université Montpellier 1 (UM1)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Montpellier 2 - Sciences et Techniques (UM2)-Centre National de la Recherche Scientifique (CNRS), Université de Montpellier (UM)-Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), and Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)
Subjects: Computer science, Medicine (miscellaneous), Genome, F30 - Génétique et amélioration des plantes, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], 0302 clinical medicine, Software, Séquence d'ADN, apprentissage machine, lcsh:QH301-705.5, 0303 health sciences, U10 - Informatique, mathématiques et statistiques, High-Throughput Nucleotide Sequencing, 030220 oncology & carcinogenesis, Bio-informatique, General Agricultural and Biological Sciences, Algorithms, Data Structures and Algorithms, Bioinformatics, Sequencing data, Algorithme et structure de données, [INFO.INFO-DS]Computer Science [cs]/Data Structures and Algorithms [cs.DS], Predictive medicine, Breast Neoplasms, Feature selection, Computational biology, Data loss, Article, General Biochemistry, Genetics and Molecular Biology, DNA sequencing, 03 medical and health sciences, Bioinformatique, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], Machine learning, Genetic algorithm, Humans, RNA, Messenger, 030304 developmental biology, Blood Cells, business.industry, Computational Biology, DNA Methylation, MicroRNAs, lcsh:Biology (General), Sciences médicales, Mutation, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], business
Abstract: Comparative analysis of high throughput sequencing data between multiple conditions often involves mapping of sequencing reads to a reference and downstream bioinformatics analyses. Both of these steps may introduce heavy bias and potential data loss. This is especially true in studies where patient transcriptomes or genomes may vary from their references, such as in cancer. Here we describe a novel approach and associated software that makes use of advances in genetic algorithms and feature selection to comprehensively explore massive volumes of sequencing data to classify and discover new sequences of interest without a mapping step and without intensive use of specialized bioinformatics pipelines. We demonstrate that our approach called GECKO for GEnetic Classification using k-mer Optimization is effective at classifying and extracting meaningful sequences from multiple types of sequencing approaches including mRNA, microRNA, and DNA methylome data., Aubin Thomas, Sylvain Barriere et al. present a computational method for classifying and extracting meaningful sequences from high-throughput sequencing data. The method, called GECKO, uses k-mer counts that are able to classify the input data with high accuracy.
Published: 2019

15. Linking BWT and XBW via Aho-Corasick Automaton: Applications to Run-Length Encoding

Author: Cazaux, Bastien, Rivals, Eric, Méthodes et Algorithmes pour la Bioinformatique (MAB), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Institut de Biologie Computationnelle (IBC), Université de Montpellier (UM)-Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS), Support from the Institut de Biologie Computationnelle (ANR-11-BINF-0002).Eric Rivals: thanks the GEM Flagship project funded from Labex NUMEV (ANR-10-LABX-0020)., University of Pisa, Nadia Pisanti, Solon P. Pissis, ANR-11-BINF-0002,IBC,Institut de biologie Computationnelle(2011), ANR-10-LABX-0020,NUMEV,Digital and Hardware Solutions and Modeling for the Environement and Life Sciences(2010), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), ANR-11-BINF-0002,IBC,Institut de Biologie Computationnelle de Montpellier(2011), and ANR-10-LABX-0020/10-LABX-0020,NUMEV,Digital and Hardware Solutions and Modeling for the Environement and Life Sciences(2010)
Subjects: Run Length Encoding (RLE), Aho-Corasick automaton, Aho-Corasick Tree, [INFO.INFO-DS]Computer Science [cs]/Data Structures and Algorithms [cs.DS], Burrows Wheeler Transform, Compression, XBWT, [INFO.INFO-DM]Computer Science [cs]/Discrete Mathematics [cs.DM], Data Structures, Algorithm, 2012 ACM Subject Classification Mathematics of computing → Discrete mathematics, Theory of computation → Randomness, geometry and discrete structures, Theory of computation → Data structures and algorithms for data management, Indexing, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], Stringology
Abstract: URN: urn:nbn:de:0030-drops-104955URL: http://drops.dagstuhl.de/opus/volltexte/2019/10495/ISBN ={978-3-95977-103-0},ISSN ={1868-8969},; International audience; The boom of genomic sequencing makes compression of sets of sequences inescapable. This underlies the need for multi-string indexing data structures that helps compressing the data. The most prominent example of such data structures is the Burrows-Wheeler Transform (BWT), a reversible permutation of a text that improves its compressibility. A similar data structure, the eXtended Burrows-Wheeler Transform (XBW), is able to index a tree labelled with alphabet symbols. A link between a multi-string BWT and the Aho-Corasick automaton has already been found and led to a way to build a XBW from a multi-string BWT. We exhibit a stronger link between a multi-string BWT and a XBW by using the order of the concatenation in the multi-string. This bijective link has several applications: first, it allows one to build one data structure from the other; second, it enables one to compute an ordering of the input strings that optimises a Run-Length measure (i.e., the compressibility) of the BWT or of the XBW.
Published: 2019

16. Correction: Reconstructible Phylogenetic Networks: Do Not Distinguish the Indistinguishable

Author: Pardi, Fabio, Scornavacca, Celine, Méthodes et Algorithmes pour la Bioinformatique (MAB), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Institut de Biologie Computationnelle (IBC), Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Institut des Sciences de l'Evolution de Montpellier (UMR ISEM), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-École pratique des hautes études (EPHE)-Université de Montpellier (UM)-Institut de recherche pour le développement [IRD] : UR226-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Université de Montpellier (UM)-Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS), École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Montpellier (UM)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre National de la Recherche Scientifique (CNRS)-Institut de recherche pour le développement [IRD] : UR226, Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-École Pratique des Hautes Études (EPHE), and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Montpellier (UM)-Institut de recherche pour le développement [IRD] : UR226-Centre National de la Recherche Scientifique (CNRS)
Subjects: [MATH.MATH-GM]Mathematics [math]/General Mathematics [math.GM], [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM]
Abstract: This article was republished on September 18, 2015, to correct errors that were introduced during the typesetting process. The publisher apologizes for the errors. Please download this article again to view the correct version.; International audience; Phylogenetic networks represent the evolution of organisms that have undergone reticulate events, such as recombination, hybrid speciation or lateral gene transfer. An important way to interpret a phylogenetic network is in terms of the trees it displays, which represent all the possible histories of the characters carried by the organisms in the network. Interestingly, however, different networks may display exactly the same set of trees, an observation that poses a problem for network reconstruction: from the perspective of many inference methods such networks are indistinguishable. This is true for all methods that evaluate a phylogenetic network solely on the basis of how well the displayed trees fit the available data, including all methods based on input data consisting of clades, triples, quartets, or trees with any number of taxa, and also sequence-based approaches such as popular formalisations of maximum parsimony and maximum likelihood for networks. This identifiability problem is partially solved by accounting for branch lengths, although this merely reduces the frequency of the problem. Here we propose that network inference methods should only attempt to reconstruct what they can uniquely identify. To this end, we introduce a novel definition of what constitutes a uniquely reconstructible network. For any given set of indistinguishable networks, we define a canonical network that, under mild assumptions, is unique and thus representative of the entire set. Given data that underwent reticulate evolution, only the canonical form of the underlying phylogenetic network can be uniquely reconstructed. While on the methodological side this will imply a drastic reduction of the solution space in network inference, for the study of reticulate evolution this is a fundamental limitation that will require an important change of perspective when interpreting phylogenetic networks.
Published: 2019

17. High performance text indexing and applications in life sciences

Author: Rivals, Eric, Institut de Biologie Computationnelle (IBC), Université de Montpellier (UM)-Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS), Méthodes et Algorithmes pour la Bioinformatique (MAB), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Flagship project GEM (Gene Expression Modelling) funded by Labex NUMEV, ANR-10-LABX-0020,NUMEV,Digital and Hardware Solutions and Modeling for the Environement and Life Sciences(2010), Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), and ANR-10-LABX-0020/10-LABX-0020,NUMEV,Digital and Hardware Solutions and Modeling for the Environement and Life Sciences(2010)
Subjects: Bioinformatics, Data structure, NGS, Query, Services, Sequencing, Indexing, Efficiency, Genomics, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM]
Abstract: International audience; Large corpura of texts or of sequences serve as references and are interrogated through web site or programming interfaces. In life sciences, new sequencing technologies have revolutionised the acquisition of genomic sequences and triggered an exponential accumulation of reference sequences in international, public databases. Several kinds of text queries form the basic operation of programs that analyse genomic sequences. For instance, the webserver of EMBL-EBI receives 27 million queries a day. A typical sequencing experiment yields a hundred million sequencing reads-about 150 nucleotides long-each of which needs to be compared to a reference genome. To analyse such data or to mine public sequence repositories demands very efficient programs and algorithms. Only, the use of complex and specific, indexing data structures allows us to match the needs of Life sciences communities. I will present some indexing data structures that enables high performance computational analyses in genomics, and mention their pracical applications. Beyond text data, such data structures can be adapted to index other types of discrete data like trees or graphs. This will be key for the development of computational pan-genomics.
Published: 2019

18. Using positional information for predicting transcription factor binding sites

Author: Romero, Raphaël, Menichelli, Christophe, Marin, Jean-Michel, Lèbre, Sophie, Lecellier, Charles-Henri, Brehelin, Laurent, Institut Montpelliérain Alexander Grothendieck (IMAG), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Institut de Biologie Computationnelle (IBC), Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Université Paul-Valéry - Montpellier 3 (UM3), Institut de Génétique Moléculaire de Montpellier (IGMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Méthodes et Algorithmes pour la Bioinformatique (MAB), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Université de Montpellier (UM)-Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS), Université Paul-Valéry - Montpellier 3 (UPVM), and Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)
Subjects: [STAT.AP]Statistics [stat]/Applications [stat.AP], [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], [SDV.GEN.GH]Life Sciences [q-bio]/Genetics/Human genetics, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], ComputingMilieux_MISCELLANEOUS
Abstract: International audience
Published: 2019

19. Origin, tempo, and mode of the spread of DENV-4 Genotype IIB across the state of São Paulo, Brazil during the 2012-2013 outbreak

Author: Christian Julian Villabona-Arenas, Ayda Susana Ortiz-Baez, Tatiana Elias Colombo, Danila Vedovello, Maurício Lacerda Nogueira, Marielton dos Passos Cunha, Paolo Marinho de Andrade Zanotto, Universidade de São Paulo (USP), Faculdade de Medicina de São José do Rio Preto, Institut de Biologie Computationnelle (IBC), Université de Montpellier (UM)-Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS), Institut de Recherche pour le Développement (IRD), Méthodes et Algorithmes pour la Bioinformatique (MAB), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Universidade de São Paulo = University of São Paulo (USP), Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), and Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)
Subjects: Microbiology (medical), lcsh:Arctic medicine. Tropical medicine, Genotype, lcsh:RC955-962, dengue virus type 4, viruses, 030231 tropical medicine, lcsh:QR1-502, Dengue virus, medicine.disease_cause, lcsh:Microbiology, Disease Outbreaks, law.invention, Dengue fever, Dengue, 03 medical and health sciences, 0302 clinical medicine, law, parasitic diseases, medicine, Humans, Outbreak, virus diseases, Bayes Theorem, Dengue Virus, medicine.disease, phylodynamics, 3. Good health, São Paulo, Phylogeography, [MATH.MATH-LO]Mathematics [math]/Logic [math.LO], Transmission (mechanics), Viral phylodynamics, Geography, Biological dispersal, Original Article, genotype IIB, Brazil, Demography
Abstract: BACKGROUND Dengue virus type 4 (DENV-4) was first reported in Brazil in 1982 and since then no more cases were detected again in Brazil until 2010, when the virus was reintroduced. Over the following years, the virus spread to several Brazilian states and resulted in about 1,400,000 dengue cases, in 2013. The largest number of cases were documented in the Southeast macro-region. OBJECTIVES To determine the phylogeography of DENV-4 Genotype IIB strains isolated during the epidemics in 2012-2013 in São Paulo, Brazil, we aimed to contextualise the contribution of viruses sampled in different localities across the overall movement of DENV-4 in Brazil. METHODS Based on the envelope gene sequences retrieved from GenBank, we employed a Bayesian phylogeographic approach to assess the spatiotemporal dynamics of DENV-4 Genotype IIB in São Paulo, Brazil. FINDINGS The dispersal dynamics of DENV-4 Genotype IIB in Brazil indicated Rio de Janeiro and Mato Grosso states as the most likely routes toward São Paulo before the 2012-2013 outbreak. Likewise, Guarujá and São José do Rio Preto facilitated viral spread and transmission to other localities in the South and Southeast macro-regions in Brazil. CONCLUSIONS The spread pattern of DENV-4 Genotype IIB strains across the country supports two independent introductions of the virus in São Paulo in a short period of time. Furthermore, São Paulo appears to have played a pivotal role in the dissemination of DENV-4 to other locations in Brazil.
Published: 2019

20. Pleistocene origins of chorusing diversity in Mediterranean bush-cricket populations (Ephippiger diurnus)

Author: Réjane Streiff, Sabine Nidelet, Miguel Navascués, Virginie Party, Michael D. Greenfield, Yareli Esquer-Garrigos, Biologie et Génétique des Interactions Plante-Parasite (UMR BGPI), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), Diversité, Génomes & Interactions Microorganismes - Insectes [Montpellier] (DGIMI), Institut National de la Recherche Agronomique (INRA)-Université Montpellier 2 - Sciences et Techniques (UM2)-Université de Montpellier (UM), Institut de Biologie Computationnelle (IBC), Université de Montpellier (UM)-Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS), Centre de Biologie pour la Gestion des Populations (UMR CBGP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Université de Montpellier (UM)-Institut de Recherche pour le Développement (IRD [France-Sud])-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Université de Tours, Department of Ecology and Evolutionary Biology, Cornell University [New York], Agence Nationale de la Recherche de France : ANR-11-BSV7-025-01, Centre National de la Recherche Scientifique (CNRS), Universite de Tours, Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA), Université de Montpellier (UM)-Université Montpellier 2 - Sciences et Techniques (UM2)-Institut National de la Recherche Agronomique (INRA), Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Cornell University, Université de Tours (UT), and Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)
Subjects: 0106 biological sciences, Species complex, Biology, 010603 evolutionary biology, 01 natural sciences, Gene flow, Hybrid zone, secondary contact, Ecology, Evolution, Behavior and Systematics, Isolation by distance, Ephippiger diurnus, post-glacial expansion, cryptic species, Phylogenetic tree, acoustic insects, criquet, [SDV.BA]Life Sciences [q-bio]/Animal biology, endemic species, Pleistocene refuge, Cline (biology), 15. Life on land, Phylogeography, locust, Evolutionary biology, Molecular phylogenetics, hybrid zone
Abstract: We studied the Pleistocene diversification of a relatively endemic Mediterranean insect (Ephippiger diurnus; Orthoptera: Tettigoniidae) to understand how species with restricted range may nonetheless exhibit the complex phylogeography normally associated with broad distribution. A time-calibrated molecular phylogeny based on two mitochondrial genes showed that E. diurnus diverged into two major clades, distinguished largely by male song, before or early during the Pleistocene. Several subclades also diverged before the most recent glacial period. Data from 20 microsatellite loci indicated higher genetic diversity in populations along the Mediterranean coast in France, consistent with the hypothesis that glacial refuges were located there. Isolation by distance' accounts for much genetic differentiation between populations, but some adjacent populations are highly differentiated. A Bayesian approach defined genetically distinct clusters and assigned individuals to their most probable cluster. Clusters corresponded to clades in the phylogenetic tree, and we used cluster assignments to estimate interclade gene flow in areas of potential secondary contact. Gene flow is negligible in potential contact areas in the Pyrenees, but a narrow hybrid zone featuring a steep cline exists on the coast. This hybrid zone suggests that the major clades represent distinct species that diverged within a restricted area during the Pleistocene.
Published: 2019

21. Lien entre Transformée de Burrows-Wheeler (BWT) et BWT étendue (XBW) via l'automate d'Aho-Corasick : applications en compression par encodage des longueurs

Author: Cazaux, Bastien, Rivals, Eric, Pisanti, Nadia, Pissis, Solon P., Department of Computer Science, Méthodes et Algorithmes pour la Bioinformatique (MAB), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Institut de Biologie Computationnelle (IBC), Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Support from the Institut de Biologie Computationnelle (ANR-11-BINF-0002).Eric Rivals: thanks the GEM Flagship project funded from Labex NUMEV (ANR-10-LABX-0020)., University of Pisa, Nadia Pisanti, Solon P. Pissis, ANR-11-BINF-0002,IBC,Institut de Biologie Computationnelle de Montpellier(2011), ANR-10-LABX-0020/10-LABX-0020,NUMEV,Digital and Hardware Solutions and Modeling for the Environement and Life Sciences(2010), ANR-11-BINF-0002,IBC,Institut de biologie Computationnelle(2011), ANR-10-LABX-0020,NUMEV,Digital and Hardware Solutions and Modeling for the Environement and Life Sciences(2010), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), and Université de Montpellier (UM)-Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)
Subjects: Run Length Encoding (RLE), 000 Computer science, knowledge, general works, Aho-Corasick automaton, Aho-Corasick Tree, [INFO.INFO-DS]Computer Science [cs]/Data Structures and Algorithms [cs.DS], education, Burrows Wheeler Transform, Compression, Data_CODINGANDINFORMATIONTHEORY, 0102 computer and information sciences, 02 engineering and technology, XBWT, [INFO.INFO-DM]Computer Science [cs]/Discrete Mathematics [cs.DM], Data Structures, 113 Computer and information sciences, 01 natural sciences, Algorithm, 2012 ACM Subject Classification Mathematics of computing → Discrete mathematics, Theory of computation → Randomness, geometry and discrete structures, Theory of computation → Data structures and algorithms for data management, 010201 computation theory & mathematics, Computer Science, 0202 electrical engineering, electronic engineering, information engineering, Indexing, 020201 artificial intelligence & image processing, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], Stringology
Abstract: URN: urn:nbn:de:0030-drops-104955URL: http://drops.dagstuhl.de/opus/volltexte/2019/10495/ISBN ={978-3-95977-103-0},ISSN ={1868-8969},; International audience; The boom of genomic sequencing makes compression of sets of sequences inescapable. This underlies the need for multi-string indexing data structures that helps compressing the data. The most prominent example of such data structures is the Burrows-Wheeler Transform (BWT), a reversible permutation of a text that improves its compressibility. A similar data structure, the eXtended Burrows-Wheeler Transform (XBW), is able to index a tree labelled with alphabet symbols. A link between a multi-string BWT and the Aho-Corasick automaton has already been found and led to a way to build a XBW from a multi-string BWT. We exhibit a stronger link between a multi-string BWT and a XBW by using the order of the concatenation in the multi-string. This bijective link has several applications: first, it allows one to build one data structure from the other; second, it enables one to compute an ordering of the input strings that optimises a Run-Length measure (i.e., the compressibility) of the BWT or of the XBW.
Published: 2019

22. Comprehensive classification of the plant non-specific lipid transfer protein superfamily towards its Sequence -Structure -Function analysis

Author: Cecile Fleury, Jerome Gracy, Marie-Francoise Gautier, Jean-Luc Pons, Jean-Francois Dufayard, Gilles Labesse, Manuel Ruiz, Frederic de Lamotte, Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Institut National de la Recherche Agronomique (INRA)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Centre de Biochimie Structurale [Montpellier] (CBS), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Institut National de la Santé et de la Recherche Médicale (INSERM), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Institut de Biologie Computationnelle (IBC), Université de Montpellier (UM)-Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), and DE LAMOTTE, Frédéric
Subjects: 2. Zero hunger, [SDV.BV.AP]Life Sciences [q-bio]/Vegetal Biology/Plant breeding, Quantitative Biology - Biomolecules, FOS: Biological sciences, Biomolecules (q-bio.BM), [SDV.BV.AP] Life Sciences [q-bio]/Vegetal Biology/Plant breeding, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], [SDV.BBM.BC]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Biochemistry [q-bio.BM], Quantitative Biology - Quantitative Methods, [SDV.BBM.BC] Life Sciences [q-bio]/Biochemistry, Molecular Biology/Biochemistry [q-bio.BM], Quantitative Methods (q-bio.QM), [INFO.INFO-BI] Computer Science [cs]/Bioinformatics [q-bio.QM], [SDV.BBM.BC]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Biomolecules [q-bio.BM]
Abstract: Background. Non-specific Lipid Transfer Proteins (nsLTPs) are widely distributed in the plant kingdom and constitute a superfamily of related proteins. More than 800 different sequences have been characterized so far, but their biological functions remain unclear. It has been clear for years that they present a certain interest for agronomic and nutritional issues. Deciphering their functions means collecting and analyzing a variety of data from gene sequence to protein structure, from cellular localization to the physiological role. As a huge and growing number of new protein sequences are available nowadays, extracting meaningful knowledge from sequence-structure-function relationships calls for the development of new tools and approaches. As nsLTPs show high evolutionary divergence, but a conserved common right-handed superhelix structural fold, and as they are involved in a large number of key roles in plant development and defense, they are a stimulating case study for validating such an approach. Methods. In this study, we comprehensively investigated 797 nsLTP protein sequences, including a phylogenetic analysis on canonical protein sequences, three-dimensional (3D) structure modeling and functional annotation using several well-established bioinformatics programs. Additionally, two integrative methodologies using original tools were developed. The first was a new method for the detection of i) conserved amino acid residues involved in structure stabilization and ii) residues potentially involved in ligand interaction. The second was a structure-function classification based on the Evolutionary Trace Display method using a new tree visualization interface. We also present a new tool for visualizing phylogenetic trees. Results. Following this new protocol, an updated classification of the nsLTP superfamily was established and a new functional hypothesis for key residues is suggested. Lastly, this work allows a better representation of the diversity of plant nsLTPs in terms of sequence, structure, and function.
Published: 2019

23. Machine learning meets genome assembly

Author: Ronnie Alves, Kleber Padovani de Souza, Annie Chateau, João C. Setubal, Guilherme Oliveira, André C. P. L. F. de Carvalho, Federal University of Para - Universidade Federal do Para [Belem - Brésil], University of São Paulo (USP), Méthodes et Algorithmes pour la Bioinformatique (MAB), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Institut de Biologie Computationnelle (IBC), Université de Montpellier (UM)-Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS), Federal University of Para - Universidade Federal do Pará - UFPA [Belém, Brazil] (UFPA), Universidade de São Paulo = University of São Paulo (USP), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), and Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)
Subjects: 0301 basic medicine, Artificial intelligence, Computer science, Sequence assembly, Machine learning, computer.software_genre, DNA sequencing, Field (computer science), Task (project management), 03 medical and health sciences, Fragment (logic), De novo assembly, [INFO]Computer Science [cs], Molecular Biology, Genome, Genome assembly, 030102 biochemistry & molecular biology, business.industry, Scale (chemistry), High-Throughput Nucleotide Sequencing, Sequence Analysis, DNA, NP, 030104 developmental biology, Metagenomics, SEQUENCIAMENTO GENÉTICO, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], business, computer, Algorithms, Information Systems
Abstract: Motivation: With the recent advances in DNA sequencing technologies, the study of the genetic composition of living organisms has become more accessible for researchers. Several advances have been achieved because of it, especially in the health sciences. However, many challenges which emerge from the complexity of sequencing projects remain unsolved. Among them is the task of assembling DNA fragments from previously unsequenced organisms, which is classified as an NP-hard (nondeterministic polynomial time hard) problem, for which no efficient computational solution with reasonable execution time exists. However, several tools that produce approximate solutions have been used with results that have facilitated scientific discoveries, although there is ample room for improvement. As with other NP-hard problems, machine learning algorithms have been one of the approaches used in recent years in an attempt to find better solutions to the DNA fragment assembly problem, although still at a low scale. Results: This paper presents a broad review of pioneering literature comprising artificial intelligence-based DNA assemblers—particularly the ones that use machine learning—to provide an overview of state-of-the-art approaches and to serve as a starting point for further study in this field.
Published: 2019

24. Fast and accurate genome-scale identification of DNA-binding sites

Author: Vincent Maillol, Eric Rivals, David Martin, Méthodes et Algorithmes pour la Bioinformatique (MAB), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Institut de Biologie Computationnelle (IBC), Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), ATGC bioinformatics platform., ANR-11-BINF-0002,IBC,Institut de Biologie Computationnelle de Montpellier(2011), ANR-06-MDCA-0014,PlasmoExplore,Fouille des données génomiques de Plasmodium falciparum pour prédire la fonction des gènes orphelins et identifier de nouvelles cibles thérapeutiques(2006), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Université de Montpellier (UM)-Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS), and ANR-11-BINF-0002,IBC,Institut de biologie Computationnelle(2011)
Subjects: 0301 basic medicine, Computer science, stringology, binding sites, Computational biology, computer.software_genre, Genome, web, ACM: H.: Information Systems/H.3: INFORMATION STORAGE AND RETRIEVAL/H.3.3: Information Search and Retrieval, 03 medical and health sciences, interactive, Pattern matching, Binding site, Transcription factor, genome, transcription factor, Whole genome sequencing, search, software, motif, Search engine indexing, tool, ACM: F.: Theory of Computation/F.2: ANALYSIS OF ALGORITHMS AND PROBLEM COMPLEXITY/F.2.2: Nonnumerical Algorithms and Problems/F.2.2.3: Pattern matching, bioinformatics, DNA binding site, 030104 developmental biology, ComputingMethodologies_PATTERNRECOGNITION, pattern matching, efficiency, interface, Web service, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], computer, transcriptome
Abstract: This is the author version of the article published in the conference proceedings. It includes supplementary information. A software called MOTIF is available on the ATGC bioinformatics platform.; International audience; Motivation: Discovering DNA binding sites in genome sequences is crucial for understanding genomic regulation. Currently available computational tools for finding binding sites with Position Weight Matrices of known motifs are often used in restricted genomic regions because of their long run times. The ever-increasing number of complete genome sequences points to the need for new generations of algorithms capable of processing large amounts of data. Results: Here we present MOTIF, a new algorithm for seeking transcription factor binding sites in whole genome sequences in a few seconds. We propose a web service that enables the users to search for their own matrix or for multiple JASPAR matrices. Beyond its efficacy , the service properly handles undetermined positions within the genome sequence and provides an adequate output listing for each position the matching word and its score. Availability: MOTIF is freely available for use through an interface at http://www. atgc-montpellier.fr/motif. The source code of the stand-alone search method of MOTIF is freely available at https://gite.lirmm.fr/rivals/motif.git. It is written in C++ and tested on Linux platforms.
Published: 2018

25. Tally: a scoring tool for boundary determination between repetitive and non-repetitive protein sequences

Author: Francois Richard, Ronnie Alves, Andrey V. Kajava, Centre de recherche en Biologie Cellulaire (CRBM), Université Montpellier 2 - Sciences et Techniques (UM2)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Université Montpellier 1 (UM1), Institut de Biologie Computationnelle (IBC), Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Programa de Pós-Graduação em Ciências Contábeis [Belém, Brazil], Universidade Federal do Pará - UFPA [Belém, Brazil]-Instituto Tecnológico Vale [Belém, Brazil], National Research University of Information Technologies, Mechanics and Optics [St. Petersburg] (ITMO), Université Montpellier 1 (UM1)-Université Montpellier 2 - Sciences et Techniques (UM2)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Université de Montpellier (UM)-Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS), Centre de recherche en Biologie cellulaire de Montpellier (CRBM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), and Federal University of Para - Universidade Federal do Pará - UFPA [Belém, Brazil] (UFPA)-Instituto Tecnológico Vale [Belém, Brazil] (ITV)
Subjects: 0301 basic medicine, Statistics and Probability, Source code, Proteome, Sequence analysis, media_common.quotation_subject, [SDV.BC]Life Sciences [q-bio]/Cellular Biology, Biology, Biochemistry, Machine Learning, Set (abstract data type), 03 medical and health sciences, Tandem repeat, Sequence Analysis, Protein, Protein methods, [SDV.BBM]Life Sciences [q-bio]/Biochemistry, Molecular Biology, Amino Acid Sequence, Sensitivity (control systems), Molecular Biology, ComputingMilieux_MISCELLANEOUS, media_common, business.industry, Computational Biology, Proteins, Pattern recognition, Computer Science Applications, Computational Mathematics, 030104 developmental biology, Computational Theory and Mathematics, Tandem Repeat Sequences, Aperiodic graph, Benchmark (computing), Artificial intelligence, business, Algorithm, Algorithms
Abstract: Motivation: Tandem Repeats (TRs) are abundant in proteins, having a variety of fundamental functions. In many cases, evolution has blurred their repetitive patterns. This leads to the problem of distinguishing between sequences that contain highly imperfect TRs, and the sequences without TRs. The 3D structure of proteins can be used as a benchmarking criterion for TR detection in sequences, because the vast majority of proteins having TRs in sequences are built of repetitive 3D structural blocks. According to our benchmark, none of the existing scoring methods are able to clearly distinguish, based on the sequence analysis, between structures with and without 3D TRs. Results: We developed a scoring tool called Tally, which is based on a machine learning approach. Tally is able to achieve a better separation between sequences with structural TRs and sequences of aperiodic structures, than existing scoring procedures. It performs at a level of 81% sensitivity, while achieving a high specificity of 74% and an Area Under the Receiver Operating Characteristic Curve of 86%. Tally can be used to select a set of structurally and functionally meaningful TRs from all TRs detected in proteomes. The generated dataset is available for benchmarking purposes. Availability and implementation: Source code is available upon request. Tool and dataset can be accessed through our website: http://bioinfo.montp.cnrs.fr/?r=Tally. Contact: andrey.kajava@crbm.cnrs.fr Supplementary information: Supplementary data are available at Bioinformatics online.
Published: 2016

26. Effective and efficient similarity search in scientific workflow repositories

Author: Susan B. Davidson, Sarah Cohen-Boulakia, Sanjeev Khanna, Ulf Leser, Johannes Starlinger, Department of Computer Science [Berlin], Humboldt Universität zu Berlin, Laboratoire de Recherche en Informatique (LRI), Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), Scientific Data Management (ZENITH), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Modeling plant morphogenesis at different scales, from genes to phenotype (VIRTUAL PLANTS), Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Inria Sophia Antipolis - Méditerranée (CRISAM), Institut de Biologie Computationnelle (IBC), Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), University of Pennsylvania [Philadelphia], Humboldt-Universität zu Berlin, Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Inria Sophia Antipolis - Méditerranée (CRISAM), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de la Recherche Agronomique (INRA)-Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Université de Montpellier (UM)-Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS), Humboldt University Of Berlin, Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), University of Pennsylvania, Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), Institut National de la Recherche Agronomique (INRA)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), and Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)
Subjects: [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], Information retrieval, Computer Networks and Communications, Computer science, Dataflow, Nearest neighbor search, Search engine indexing, 02 engineering and technology, Reuse, computer.software_genre, Workflow engine, Workflow technology, Ranking (information retrieval), Workflow, Similarity (network science), Hardware and Architecture, 020204 information systems, 0202 electrical engineering, electronic engineering, information engineering, [INFO]Computer Science [cs], 020201 artificial intelligence & image processing, Data mining, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], computer, Software
Abstract: Scientific workflows have become a valuable tool for large-scale data processing and analysis. This has led to the creation of specialized online repositories to facilitate workflow sharing and reuse. Over time, these repositories have grown to sizes that call for advanced methods to support workflow discovery, in particular for similarity search. Effective similarity search requires both high quality algorithms for the comparison of scientific workflows and efficient strategies for indexing, searching, and ranking of search results. Yet, the graph structure of scientific workflows poses severe challenges to each of these steps. Here, we present a complete system for effective and efficient similarity search in scientific workflow repositories, based on the Layer Decomposition approach to scientific workflow comparison. Layer Decomposition specifically accounts for the directed dataflow underlying scientific workflows and, compared to other state-of-the-art methods, delivers best results for similarity search at comparably low runtimes. Stacking Layer Decomposition with even faster, structure-agnostic approaches allows us to use proven, off-the-shelf tools for workflow indexing to further reduce runtimes and scale similarity search to sizes of current repositories. We present a complete system for efficient scientific workflow similarity search.Workflow indexing integrates with repositories' existing search-no graphs needed.Layer Decomposition reranking of candidate workflows ensures high result quality.We evaluate on a large corpus of workflows with similarity ratings by human experts.Our system greatly improves previous results in speed and retains superior quality.
Published: 2016

27. On the hardness of approximating Linearization of Scaffolds sharing Repeated Contigs

Author: Annie Chateau, Tom Davot, Rodolphe Giroudeau, Mathias Weller, Méthodes Algorithmes pour l'Ordonnancement et les Réseaux (MAORE), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Méthodes et Algorithmes pour la Bioinformatique (MAB), Institut de Biologie Computationnelle (IBC), Université de Montpellier (UM)-Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'Informatique Gaspard-Monge (LIGM), Centre National de la Recherche Scientifique (CNRS)-Fédération de Recherche Bézout-ESIEE Paris-École des Ponts ParisTech (ENPC)-Université Paris-Est Marne-la-Vallée (UPEM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'Informatique Gaspard-Monge (ligm), Université Paris-Est Marne-la-Vallée (UPEM)-École des Ponts ParisTech (ENPC)-ESIEE Paris-Fédération de Recherche Bézout-Centre National de la Recherche Scientifique (CNRS), and Methods, Algorithms for Operations REsearch (MAORE)
Subjects: 0301 basic medicine, Contig, Computer science, 0102 computer and information sciences, 01 natural sciences, Combinatorics, 03 medical and health sciences, 030104 developmental biology, Planar, 010201 computation theory & mathematics, Linearization, Bipartite graph, Graph (abstract data type), [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], Genome scaffolding, MathematicsofComputing_DISCRETEMATHEMATICS
Abstract: International audience; Solutions to genome scaffolding problems can be represented as paths and cycles in a "solution graph". However, when working with repetitions, such solution graph may contain branchings and they may not be uniquely convertible into sequences. Having introduced, in a previous work, various ways of extracting the unique parts of such solutions, we extend previously known NP-hardness results to the case that the solution graph is planar, bipartite, and subcubic, and show the APX-completeness in this case. We also provide some practical tests.
Published: 2018

28. New Results About the Linearization of Scaffolds Sharing Repeated Contigs

Author: Rodolphe Giroudeau, Mathias Weller, Annie Chateau, Tom Davot, Dorine Tabary, Méthodes Algorithmes pour l'Ordonnancement et les Réseaux (MAORE), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Institut de Biologie Computationnelle (IBC), Université de Montpellier (UM)-Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'Informatique Gaspard-Monge (LIGM), Centre National de la Recherche Scientifique (CNRS)-Fédération de Recherche Bézout-ESIEE Paris-École des Ponts ParisTech (ENPC)-Université Paris-Est Marne-la-Vallée (UPEM), Méthodes et Algorithmes pour la Bioinformatique (MAB), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'Informatique Gaspard-Monge (ligm), Université Paris-Est Marne-la-Vallée (UPEM)-École des Ponts ParisTech (ENPC)-ESIEE Paris-Fédération de Recherche Bézout-Centre National de la Recherche Scientifique (CNRS), and Methods, Algorithms for Operations REsearch (MAORE)
Subjects: 0301 basic medicine, Contig, Computer science, 0102 computer and information sciences, 01 natural sciences, Graph, Combinatorics, 03 medical and health sciences, 030104 developmental biology, Planar, 010201 computation theory & mathematics, Linearization, Bipartite graph, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], Genome scaffolding
Abstract: International audience; Solutions to genome scaffolding problems can be represented as paths and cycles in a "solution graph". However, when working with repetitions, such solution graphs may contain branchings and, thus, they may not be uniquely convertible into sequences. Having introduced various ways of extracting the unique parts of such solutions, we extend previously known NP-hardness results to the case that the solution graph is planar, bipartite, and subcubic, and show that there is no PTAS in this case.
Published: 2018

29. Location-based species recommendation using co-occurrences and environment-GeoLifeCLEF 2018 challenge

Author: Deneu, Benjamin, Servajean, Maximilien, Botella, Christophe, Joly, Alexis, Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Institut de Biologie Computationnelle (IBC), Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Biostatistique et Processus Spatiaux (BIOSP), Institut National de la Recherche Agronomique (INRA), Scientific Data Management (ZENITH), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), ADVanced Analytics for data SciencE (ADVANSE), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Biostatistique et Processus Spatiaux (BioSP), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Université de Montpellier (UM)-Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS), and Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Inria Sophia Antipolis - Méditerranée (CRISAM)
Subjects: Species, [SDE]Environmental Sciences, [INFO]Computer Science [cs], Plant
Abstract: International audience; This paper presents several approaches for plant predictions given their location in the context of the GeoLifeCLEF 2018 challenge. We have developed three kinds of prediction models, one convolutional neural network on environmental data (CNN), one neural network on co-occurrences data and two other models only based on the spatial occurrences of species (a closest-location classifier and a random forest fitted on the spatial coordinates). We also evaluated the combination of these models through two different late fusion methods (one based on predictive probabilities and the other one based on predictive ranks). Results show the effectiveness of the CNN which obtained the best prediction score of the whole GeoLifeCLEF challenge. The fusion of this model with the spatial ones only provides slight improvements suggesting that the CNN already captured most of the spatial information in addition to the environmental preferences of the plants.
Published: 2018

30. A geography-aware reconciliation method to investigate diversification patterns in host/parasite interactions

Author: Jean-Philippe Doyon, Emmanuelle Jousselin, Vincent Berry, François Chevenet, Institut de Biologie Computationnelle (IBC), Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Méthodes et Algorithmes pour la Bioinformatique (MAB), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Maladies infectieuses et vecteurs : écologie, génétique, évolution et contrôle (MIVEGEC), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud]), Centre de Biologie pour la Gestion des Populations (UMR CBGP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Université de Montpellier (UM)-Institut de Recherche pour le Développement (IRD [France-Sud])-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Université de Montpellier (UM)-Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Institut des Sciences de l'Evolution de Montpellier (UMR ISEM), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Montpellier (UM)-Institut de recherche pour le développement [IRD] : UR226-Centre National de la Recherche Scientifique (CNRS), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), ANR-09-PEXT-0002,PhyloSpace,Intégrer des cophylogénies, changements d'aires et cartes paléoclimatiques pour inférer l'histoire d'associations sous changements climatiques.(2009), ANR-11-BINF-0002,IBC,Institut de biologie Computationnelle(2011), and Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-École Pratique des Hautes Études (EPHE)
Subjects: 0106 biological sciences, 0301 basic medicine, Association (object-oriented programming), Biogeography, Diversification (marketing strategy), cophylogeny, [SDV.BID.SPT]Life Sciences [q-bio]/Biodiversity/Systematics, Phylogenetics and taxonomy, 010603 evolutionary biology, 01 natural sciences, host parasite, 03 medical and health sciences, Phylogenetics, Genetics, tree visualization, Ecology, Evolution, Behavior and Systematics, biogeography, biology, Phylogenetic tree, software, [SDV.BID.EVO]Life Sciences [q-bio]/Biodiversity/Populations and Evolution [q-bio.PE], biology.organism_classification, host/parasite, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], 030104 developmental biology, Geography, Taxon, reconciliation, Evolutionary biology, ancestral trait, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], Host (network), Fig wasp, Biotechnology, [SDV.EE.IEO]Life Sciences [q-bio]/Ecology, environment/Symbiosis
Abstract: Mowgli is available at http://www.atgc-montpellier.fr/Mowgli with manual, example files and GeoRecHelper script, it runs on Linux and Mac (OSX) computers; SylvX is available at www.sylvx.org with manual and example files and can be installed on any platforms.; International audience; Cospeciation studies aim at investigating whether hosts and symbionts speciate simultaneously or whether the associations diversify through host shifts. This problem is often tackled through reconciliation analyses that map the symbiont phylogeny onto the host phylogeny by mixing different types of diversification events. These reconciliations can be difficult to interpret and are not always biologically realistic. Researchers have underlined that the biogeographic histories of both hosts and symbionts influence the probability of cospeciation and host switches, but up to now no reconciliation software integrates geographic data. We present a new functionality in the Mowgli software that bridges this gap. The user can provide geographic information on both the host and symbiont extant and ancestral taxa. Constraints in the reconciliation algorithm have been implemented to generate biologically realistic codiversification scenarios. We apply our method to the fig/fig wasp association and infer diversification scenarios that differ from reconciliations ignoring geographic information. In addition, we updated the reconciliation viewer SylvX to visualize ancestral character states on the phylogenetic trees and highlight parts of reconciliations that are geographically inconsistent when not accounting for geographic constraints. We suggest that the comparison of reconciliations obtained with and without such constraints can help solving ambiguities in the biogeographic histories of the partners. With the development of robust methods in historical biogeography, and the advent of next‐generation sequencing that leads to better‐resolved trees, a geography‐aware reconciliation method represents a substantial advance that is likely to be useful to researchers studying the evolution of biotic interactions and biogeography.
Published: 2018

31. Relationship between superstring and compression measures: New insights on the greedy conjecture

Author: Cazaux, Bastien, Rivals, Eric, Méthodes et Algorithmes pour la Bioinformatique (MAB), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Institut de Biologie Computationnelle (IBC), Université de Montpellier (UM)-Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS), CNRS defi mastosons, ANR-11-BINF-0002,IBC,Institut de biologie Computationnelle(2011), ANR-12-BS02-0008,Colib'read,Méthodes d'extraction d'information biologique dans les données HTS non assemblées(2012), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), and ANR-11-BINF-0002,IBC,Institut de Biologie Computationnelle de Montpellier(2011)
Subjects: Shortest Common Superstring Problem, ACM: G.: Mathematics of Computing/G.2: DISCRETE MATHEMATICS, Data compression, Assembly, ACM: F.: Theory of Computation/F.2: ANALYSIS OF ALGORITHMS AND PROBLEM COMPLEXITY, [INFO.INFO-DS]Computer Science [cs]/Data Structures and Algorithms [cs.DS], Greedy conjecture, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], [INFO.INFO-DM]Computer Science [cs]/Discrete Mathematics [cs.DM], Approximation algorithm, Stringology, ACM: F.: Theory of Computation
Abstract: Earlier version as a report: lirmm-01070596v1; International audience; A superstring of a set of words is a string that contains each input word as a substring. Given such a set, the Shortest Superstring Problem (SSP) asks for a superstring of minimum length. SSP is an important theoretical problem related to the Asymmetric Travelling Salesman Problem, and also has practical applications in data compression and in bioinformatics. Indeed, it models the question of assembling a genome from a set of sequencing reads. Unfortunately, SSP is known to be NP-hard even on a binary alphabet and also hard to approximate with respect to the superstring length or to the compression achieved by the superstring. Even the variant in which all words share the same length r, called r-SSP, is NP-hard whenever r > 2. Numerous involved approximation algorithms achieve approximation ratio above 2 for the superstring, but remain difficult to implement in practice. In contrast the greedy conjecture asked in 1988 whether a simple greedy algorithm achieves ratio of 2 for SSP. Here, we present a novel approach to bound the superstring approximation ratio with the compression ratio, which, when applied to the greedy algorithm, shows a 2 approximation ratio for 3-SSP, and also that greedy achieves ratios smaller than 2. This leads to a new version of the greedy conjecture.
Published: 2018

32. A Distributed Collaborative Filtering Algorithm Using Multiple Data Sources

Author: Bouadjenek, Mohamed Reda, Pacitti, Esther, Servajean, Maximilien, Masseglia, Florent, Abbadi, Amr El, University of Toronto, Scientific Data Management (ZENITH), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Institut de Biologie Computationnelle (IBC), Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Department of Computer Science [Santa Barbara], University of California [Santa Barbara] (UCSB), University of California-University of California, University of California [Santa Barbara] (UC Santa Barbara), University of California (UC)-University of California (UC), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Inria Sophia Antipolis - Méditerranée (CRISAM), and Université de Montpellier (UM)-Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)
Subjects: FOS: Computer and information sciences, Artificial Intelligence (cs.AI), Collaborative Filtering, Computer Science - Artificial Intelligence, [INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR], Recommender Systems, Matrix Factorization, [INFO]Computer Science [cs], [INFO.INFO-DC]Computer Science [cs]/Distributed, Parallel, and Cluster Computing [cs.DC], Social Recommendation, Information Retrieval (cs.IR), Computer Science - Information Retrieval
Abstract: Collaborative Filtering (CF) is one of the most commonly used recommendation methods. CF consists in predicting whether, or how much, a user will like (or dislike) an item by leveraging the knowledge of the user's preferences as well as that of other users. In practice, users interact and express their opinion on only a small subset of items, which makes the corresponding user-item rating matrix very sparse. Such data sparsity yields two main problems for recommender systems: (1) the lack of data to effectively model users' preferences, and (2) the lack of data to effectively model item characteristics. However, there are often many other data sources that are available to a recommender system provider, which can describe user interests and item characteristics (e.g., users' social network, tags associated to items, etc.). These valuable data sources may supply useful information to enhance a recommendation system in modeling users' preferences and item characteristics more accurately and thus, hopefully, to make recommenders more precise. For various reasons, these data sources may be managed by clusters of different data centers, thus requiring the development of distributed solutions. In this paper, we propose a new distributed collaborative filtering algorithm, which exploits and combines multiple and diverse data sources to improve recommendation quality. Our experimental evaluation using real datasets shows the effectiveness of our algorithm compared to state-of-the-art recommendation algorithms., The Tenth International Conference on Advances in Databases, Knowledge, and Data Applications, DBKDA 2018 May 20, 2018 to May 24, 2018 - Nice, France
Published: 2018

33. Point pattern search in big data

Author: Eduardo Ogasawara, Alberto Krone-Martins, Fabio Porto, Dennis Shasha, Patrick Valduriez, João Guilherme Nobre Rittmeyer, Laboratorio Nacional de Computação Cientifica [Rio de Janeiro] (LNCC / MCT), Centro Federal de Educação Tecnológica Celso Suckow da Fonseca (Rio de Janeiro) ( CEFET/RJ), University of Lisboa, Scientific Data Management (ZENITH), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Institut de Biologie Computationnelle (IBC), Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Courant Institute of Mathematical Sciences [New York] (CIMS), New York University [New York] (NYU), NYU System (NYU)-NYU System (NYU), Inria associated team SciDISC, Biology Computational Institute (IBC), European Project: 689772,H2020 Pilier Industrial Leadership,H2020-EUB-2015,HPC4E(2015), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Inria Sophia Antipolis - Méditerranée (CRISAM), and Université de Montpellier (UM)-Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)
Subjects: Big Data, Query processing, [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], Matching (graph theory), Computer science, Isotropy, Point set registration, Scale (descriptive set theory), 02 engineering and technology, Scale factor, ACM: H.: Information Systems/H.3: INFORMATION STORAGE AND RETRIEVAL/H.3.3: Information Search and Retrieval, Combinatorics, Set (abstract data type), Stretch factor, 020204 information systems, 0202 electrical engineering, electronic engineering, information engineering, Information systems, Information retrieval, 020201 artificial intelligence & image processing, Point (geometry), Pattern search
Abstract: International audience; Consider a set of points P in space with at least some of the pairwise distances specified. Given this set P, consider the following three kinds of queries against a database D of points : (i) pure constellation query: find all sets S in D of size |P| that exactly match the pairwise distances within P up to an additive error ϵ; (ii) isotropic constellation queries: find all sets S in D of size |P| such that there exists some scale factor f for which the distances between pairs in S exactly match f times the distances between corresponding pairs of P up to an additive ϵ; (iii) non-isotropic constellation queries: find all sets S in D of size |P| such that there exists some scale factor f and for at least some pairs of points, a maximum stretch factor m i, j > 1 such that (f × m i, j ×dist(pi, pj))+ϵ > dist(si,sj) >(f × dist(pi, pj))-ϵ. Finding matches to such queries has applications to spatial data in astronomical, seismic, and any domain in which (approximate, scale-independent) geometrical matching is required. Answering the isotropic and non-isotropic queries is challenging because scale factors and stretch factors may take any of an infinite number of values. This paper proposes practically efficient sequential and distributed algorithms for pure, isotropic, and non-isotropic constellation queries. As far as we know, this is the first work to address isotropic and non-isotropic queries.
Published: 2018

34. Superstrings with multiplicities

Author: Rivals, Eric, Cazaux, Bastien, Institut de Biologie Computationnelle (IBC), Université de Montpellier (UM)-Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS), Méthodes et Algorithmes pour la Bioinformatique (MAB), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Défi MASTODONS C3G http://www.lirmm.fr/~rivals/C3G/ from CNRS, Qingdao University, Gonzalo Navarro, David Sankoff, Binhai Zhu, ANR-11-BINF-0002,IBC,Institut de biologie Computationnelle(2011), Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), ANR-11-BINF-0002,IBC,Institut de Biologie Computationnelle de Montpellier(2011), Navarro, Gonzalo, Sankoff, David, Zhu, Binhai, and Department of Computer Science
Subjects: [INFO.INFO-CC]Computer Science [cs]/Computational Complexity [cs.CC], APX, subset system, ACM: F.: Theory of Computation/F.2: ANALYSIS OF ALGORITHMS AND PROBLEM COMPLEXITY, [INFO.INFO-DS]Computer Science [cs]/Data Structures and Algorithms [cs.DS], Cyclic covers, 02 engineering and technology, ACM: G.: Mathematics of Computing/G.2: DISCRETE MATHEMATICS, 0202 electrical engineering, electronic engineering, information engineering, overlap, algorithm analysis, Approximation, 060201 languages & linguistics, 000 Computer science, knowledge, general works, Pattern matching, Approximation, 06 humanities and the arts, Cyclic cover, Subset system, String theory, 113 Computer and information sciences, ACM: F.: Theory of Computation, Approximation algorithms, Greedy algorithms, greedy algorithm, 0602 languages and literature, Computer Science, 020201 artificial intelligence & image processing, ACM: G.: Mathematics of Computing/G.1: NUMERICAL ANALYSIS/G.1.2: Approximation, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM]
Abstract: A superstring of a set of words P = s1, · · · , sp is a string that contains each word of P as substring. Given P, the well known Shortest Linear Superstring problem (SLS), asks for a shortest superstring of P. In a variant of SLS, called Multi-SLS, each word si comes with an integer m(i), its multiplicity, that sets a constraint on its number of occurrences, and the goal is to find a shortest superstring that contains at least m(i) occurrences of si. Multi-SLS generalizes SLS and is obviously as hard to solve, but it has been studied only in special cases (with words of length 2 or with a fixed number of words). The approximability of Multi-SLS in the general case remains open. Here, we study the approximability of Multi-SLS and that of the companion problem Multi-SCCS, which asks for a shortest cyclic cover instead of shortest superstring. First, we investigate the approximation of a greedy algorithm for maximizing the compression offered by a superstring or by a cyclic cover: the approximation ratio is 1/2 for Multi-SLS and 1 for Multi-SCCS. Then, we exhibit a linear time approximation algorithm, Concat-Greedy, and show it achieves a ratio of 4 regarding the superstring length. This demonstrates that for both measures Multi-SLS belongs to the class of APX problems. © 2018 Yoshifumi Sakai; licensed under Creative Commons License CC-BY.
Published: 2018

35. Practical lower and upper bounds for the Shortest Linear Superstring

Author: Cazaux, B., Juhel, Samuel, Rivals, Eric, Méthodes et Algorithmes pour la Bioinformatique (MAB), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Institut de Biologie Computationnelle (IBC), Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Défi MASTODONS C3G from CNRS., Gianlorenzo D'Angelo, ANR-11-BINF-0002,IBC,Institut de Biologie Computationnelle de Montpellier(2011), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Université de Montpellier (UM)-Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS), ANR-11-BINF-0002,IBC,Institut de biologie Computationnelle(2011), D'Angelo, Gianlorenzo, and Department of Computer Science
Subjects: Overlap, 000 Computer science, knowledge, general works, education, [INFO.INFO-DS]Computer Science [cs]/Data Structures and Algorithms [cs.DS], Linear time, 0102 computer and information sciences, 02 engineering and technology, Concat-Cycles, [INFO.INFO-DM]Computer Science [cs]/Discrete Mathematics [cs.DM], 113 Computer and information sciences, Cyclic cover, 01 natural sciences, 010201 computation theory & mathematics, Text compression, Computer Science, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], Greedy, Approximation
Abstract: ISBN ={978-3-95977-070-5}, ISSN ={1868-8969},; International audience; Given a set P of words, the Shortest Linear Superstring (SLS) problem is an optimization problem that asks for a superstring of $P$ of minimal length. SLS has applications in data compression, where a superstring is a compact representation of $P$, and in bioinformatics where it models the first step of genome assembly. Unfortunately SLS is hard to solve (NP-hard) and to closely approximate (MAX-SNP-hard). If numerous polynomial time approximation algorithms have been devised, few articles report on their practical performance. We lack knowledge about how closely an approximate superstring can be from an optimal one in practice. Here, we exhibit a linear time algorithm that reports an upper and a lower bound on the length of an optimal superstring. The upper bound is the length of a superstring. This algorithm can be used to evaluate beforehand whether one can get an approximate superstring whose length is close to the optimum for a given instance. Experimental results suggest that its approximation performance is orders of magnitude better than previously reported practical values. Moreover, the proposed algorithm remain efficient even on large instances and can serve to explore in practice the approximability of SLS.
Published: 2018

36. Rapid alignment-free phylogenetic identification of metagenomic sequences

Author: Krister M. Swenson, Benjamin Linard, Fabio Pardi, Méthodes et Algorithmes pour la Bioinformatique (MAB), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), Institut de Biologie Computationnelle (IBC), Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Institut des Sciences de l'Evolution de Montpellier (UMR ISEM), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Montpellier (UM)-Institut de recherche pour le développement [IRD] : UR226-Centre National de la Recherche Scientifique (CNRS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Labex CeMEB : ANR-10-LABX-0004, Labex NUMEV : ANR-10-LABX-20, Labex: Labex Agro : ANR10-LABX-0001-01, European Project: 634650,H2020,H2020-PHC-2014-two-stage,VIROGENESIS(2015), Université de Montpellier (UM)-Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Institut National de la Recherche Agronomique (INRA)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Montpellier (UM)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre National de la Recherche Scientifique (CNRS)-Institut de recherche pour le développement [IRD] : UR226, Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), and Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)
Subjects: 0106 biological sciences, Statistics and Probability, Computer science, Sequence analysis, Sequence alignment, Computational biology, computer.software_genre, [SDV.BID.SPT]Life Sciences [q-bio]/Biodiversity/Systematics, Phylogenetics and taxonomy, 010603 evolutionary biology, 01 natural sciences, Biochemistry, DNA sequencing, 03 medical and health sciences, chemistry.chemical_compound, Phylogenetics, [SDV.BBM.GTP]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Genomics [q-bio.GN], Environmental DNA, Molecular Biology, Phylogeny, 030304 developmental biology, Sequence, 0303 health sciences, Likelihood Functions, Phylogenetic tree, Biological classification, Sequence Analysis, DNA, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], Computer Science Applications, Computational Mathematics, Tree (data structure), Computational Theory and Mathematics, chemistry, Metagenomics, Precomputation, Metagenome, Data mining, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], computer, Sequence Alignment, DNA, Algorithms, Software
Abstract: Motivation Taxonomic classification is at the core of environmental DNA analysis. When a phylogenetic tree can be built as a prior hypothesis to such classification, phylogenetic placement (PP) provides the most informative type of classification because each query sequence is assigned to its putative origin in the tree. This is useful whenever precision is sought (e.g. in diagnostics). However, likelihood-based PP algorithms struggle to scale with the ever-increasing throughput of DNA sequencing. Results We have developed RAPPAS (Rapid Alignment-free Phylogenetic Placement via Ancestral Sequences) which uses an alignment-free approach, removing the hurdle of query sequence alignment as a preliminary step to PP. Our approach relies on the precomputation of a database of k-mers that may be present with non-negligible probability in relatives of the reference sequences. The placement is performed by inspecting the stored phylogenetic origins of the k-mers in the query, and their probabilities. The database can be reused for the analysis of several different metagenomes. Experiments show that the first implementation of RAPPAS is already faster than competing likelihood-based PP algorithms, while keeping similar accuracy for short reads. RAPPAS scales PP for the era of routine metagenomic diagnostics. Availability and implementation Program and sources freely available for download at https://github.com/blinard-BIOINFO/RAPPAS. Supplementary information Supplementary data are available at Bioinformatics online.
Published: 2018

37. Rapid alignment-free phylogenec placement via ancestral k-mers

Author: Linard, Benjamin, Swenson, Krister, Pardi, Fabio, Méthodes et Algorithmes pour la Bioinformatique (MAB), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Institut de Biologie Computationnelle (IBC), Université de Montpellier (UM)-Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), and Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)
Subjects: [SDV.BBM.GTP]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Genomics [q-bio.GN], [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], [SDV.BID.SPT]Life Sciences [q-bio]/Biodiversity/Systematics, Phylogenetics and taxonomy, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], ComputingMilieux_MISCELLANEOUS
Abstract: International audience
Published: 2018

38. Données génomiques massives : applications, enjeux computationnels et perspectives

Author: Rivals, Eric, Institut de Biologie Computationnelle (IBC), Université de Montpellier (UM)-Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS), Méthodes et Algorithmes pour la Bioinformatique (MAB), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Maison Interuniversitaire des Sciences de l'Homme –Alsace (MISHA), ANR-11-BINF-0002,IBC,Institut de biologie Computationnelle(2011), Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), and ANR-11-BINF-0002,IBC,Institut de Biologie Computationnelle de Montpellier(2011)
Subjects: Algorithm, Bioinformatics, NGS, [INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR], Sequence, [INFO.INFO-DS]Computer Science [cs]/Data Structures and Algorithms [cs.DS], read, RNA, DNA, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], Analysis
Abstract: International audience; De nombreux aspects du fonctionnement biologique peuvent être étudiés grâce aux nouvelles technologies de séquençage à haut débit (SHD) : de l'échelle moléculaire (ex. épigénomique ou épitranscriptomique) jusqu'à celle d'un système écologique (structure et dynamique des interactions entre espèces d'un environnement). Le SHD est devenu indispensable aux avancées des recherches fondamentales aussi bien qu'aux développements industriels-par ex. en agronomie. Bien évidement, la médecine de précision (aussi nommée médecine personnalisée) s'appuie sur ces technologies qui commencent à s'implanter dans les hôpitaux. Les caractéristiques principales des données de séquençage sont d'une part le volume de données pro-duites par séquençage (volume qui augmente exponentiellement), d'autre part la capacité d'exploration exhaustive (du génome, du transcriptome, des interactions dans leur ensemble). L'enjeu computation-nel majeur devient alors d'analyser avec précision, efficacité et robustesse ces données génomiques. Après quelques exemples d'applications, j'aborderai le rôle clef de l'indexation de données dans les algorithmes d'analyse de SHD. Les données génomiques codent une part cruciale de ce qui déter-mine un individu et contiennent des informations sur sa vie et son histoire. Je conclurai sur les enjeux globaux pour la recherche et la société que posent la disponibilité de ces informations.
Published: 2018

39. Artificial barriers prevent genetic recovery of small isolated populations of a low-mobility freshwater fish

Author: Bertrand Gauffre, Jarod Lyon, Luciano B. Beheregaray, Alexandra Pavlova, Rhys A. Coleman, Raphaël Leblois, Carla M. Sgrò, Paul Sunnucks, Minami Sasaki, Joanne Kearns, Melbourne Water Corporation, Partenaires INRAE, Monash University [Clayton], Centre d'Études Biologiques de Chizé - UMR 7372 (CEBC), Institut National de la Recherche Agronomique (INRA)-La Rochelle Université (ULR)-Centre National de la Recherche Scientifique (CNRS), Flinders University [Adelaide, Australia], Arthur Rylah Institute for Environmental Research (ARI), Centre de Biologie pour la Gestion des Populations (UMR CBGP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Université de Montpellier (UM)-Institut de Recherche pour le Développement (IRD [France-Sud])-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut de Biologie Computationnelle (IBC), Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), This work was supported by an Australian Research Council Linkage Grant (LP110200017) to Monash University, Flinders University of South Australia, the University of Canberra, and University of Montana. Funding and other support was also contributed by industry partner organizations namely, Melbourne Water Corporation, ACTEW Corporation, Victorian Department of Sustainability and Environment (now Department of Environment, Land, Water and Planning) and Fisheries Victoria (now Department of Economic Development, Jobs, Transport and Resources). L.B.B. was supported by an ARC FT130101068 grant. Justin O'Mahony, Renae Ayres, Scott Raymond and Mike Nicol from the Arthur Rylah Institute assisted with the collection of tissue samples. This publication has been written with the support of the AgreenSkills+fellowship programme, which has received funding from the EU's Seventh Framework Programme under grant agreement No FP7-609398 (AgreenSkills+ contract). RL was supported by the Agence Nationale de la Recherche (GENOSPACE project, ANR-16-CE02-0008)., ANR-16-CE02-0008,GenoSpace,Nouveaux outils statistiques pour l'analyse spatiale des données génétiques(2016), European Project: 609398,EC:FP7:PEOPLE,FP7-PEOPLE-2013-COFUND,AGREENSKILLSPLUS(2014), Université de La Rochelle (ULR)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Université de Montpellier (UM)-Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-Institut National de la Recherche Agronomique (INRA)-Université de La Rochelle (ULR), School of Biological Sciences [Norwich], University of East Anglia [Norwich] (UEA), School of Biological Sciences, Flinders University, Arthur Rylah Institute, Australian Research Council : LP110200017, University of Canberra, Flinders University of South Australia, University of Montana, ACTEW Corporation, Department of Environment, Land, Water and Planning, Department of Economic Development, Jobs, Transport and Resources, ARC : FT130101068, Agence Nationale de la Recherche : GENOSPACE ANR-16-CE02-0008, Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Centre d'Etudes Biologiques de Chizé, and Institut National de la Recherche Agronomique (INRA)-Centre National de la Recherche Scientifique (CNRS)
Subjects: 0106 biological sciences, 0301 basic medicine, [SDV.SA]Life Sciences [q-bio]/Agricultural sciences, Reproductive Isolation, Population, Population Dynamics, Fresh Water, Biology, 010603 evolutionary biology, 01 natural sciences, Article, 03 medical and health sciences, Effective population size, Genetic variation, Genetics, Inbreeding depression, Animals, Inbreeding, 14. Life underwater, education, ComputingMilieux_MISCELLANEOUS, Genetics (clinical), Ecosystem, Population Density, education.field_of_study, Genetic diversity, [SDV.GEN.GPO]Life Sciences [q-bio]/Genetics/Populations and Evolution [q-bio.PE], Geography, Models, Genetic, Ecology, Fishes, Genetic Variation, Small population size, Agricultural sciences, 030104 developmental biology, Genetics, Population, Genetic structure, [SDE]Environmental Sciences, Genetic Background, Sciences agricoles
Abstract: This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons. org/licenses/by/4.0/., Habitat loss and fragmentation often result in small, isolated populations vulnerable to environmental disturbance and loss of genetic diversity. Low genetic diversity can increase extinction risk of small populations by elevating inbreeding and inbreeding depression, and reducing adaptive potential. Due to their linear nature and extensive use by humans, freshwater ecosystems are especially vulnerable to habitat loss and fragmentation. Although the effects of fragmentation on genetic structure have been extensively studied in migratory fishes, they are less understood in low-mobility species. We estimated impacts of instream barriers on genetic structure and diversity of the low-mobility river blackfish (Gadopsis marmoratus) within five streams separated by weirs or dams constructed 45–120 years ago. We found evidence of small-scale (, This work was supported by an Australian Research Council Linkage Grant (LP110200017) to Monash University, Flinders University of South Australia, the University of Canberra, and University of Montana. Funding and other support was also contributed by industry partner organizations namely, Melbourne Water Corporation, ACTEW Corporation, Victorian Department of Sustainability and Environment (now Department of Environment, Land, Water and Planning) and Fisheries Victoria (now Department of Economic Development, Jobs, Transport and Resources). L.B.B. was supported by an ARC FT130101068 grant. This publication has been written with the support of the AgreenSkills+fellowship programme, which has received funding from the EU’s Seventh Framework Programme under grant agreement N° FP7-609398 (AgreenSkills+contract). Part of this work was carried out by using the resources of the INRA MIGALE (http://migale.jouy.inra.fr) and GENOTOUL (Toulouse Midi-Pyrénées) bioinformatics platforms and the Montpellier Bioinformatics Biodiversity platform services. RL was supported by the Agence Nationale de la Recherche (project GENOSPACE ANR-16-CE02-0008).
Published: 2018

40. Agronomic Linked Data (AgroLD): a Knowledge-based System to Enable Integrative Biology in Agronomy

Author: Bertrand Pitollat, Imène Chentli, Pierre Larmande, Clement Jonquet, Valentin Guignon, Nordine El Hassouni, Ndomassi Tando, Gildas Tagny, Aravind Venkatesan, Gaëtan Droc, Mathieu Rouard, Manuel Ruiz, Institut de Biologie Computationnelle (IBC), Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Bioversity International [Montpellier], Bioversity International [Rome], Consultative Group on International Agricultural Research [CGIAR] (CGIAR)-Consultative Group on International Agricultural Research [CGIAR] (CGIAR), WEB Architecture x Semantic WEB x WEB of Data (WEB3), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Diversité, adaptation, développement des plantes (UMR DIADE), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud]), Computational Biology Institute of Montpellier : ANR-11-INBS-0013, IA-11-BINF-0002, Institut Francais de Bioinformatique : ANR-11-INBS-0013, IA-11-INBS-0013, Labex Agro : ANR-10LABX-001-01, IA-10-LABX-0001, ANR-11-BINF-0002,IBC,Institut de biologie Computationnelle(2011), Université de Montpellier (UM)-Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), WEB-CUBE, Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Institut de Recherche pour le Développement (IRD [France-Sud])-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Institut National de la Recherche Agronomique (INRA)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), and Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)
Subjects: Proteomics, Agronomie, Computer science, Knowledge Bases, [SDV.SA.AGRO]Life Sciences [q-bio]/Agricultural sciences/Agronomy, Social Sciences, ontologie de domaine, Ontology (information science), Logiciel, Infographics, Computer Architecture, F30 - Génétique et amélioration des plantes, Database and Informatics Methods, 0302 clinical medicine, Phenomics, céréale, Psychology, Ensembl, Génétique, Data Management, Language, 2. Zero hunger, 0303 health sciences, Gene Ontologies, Agriculture, Genomics, computer.file_format, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], [INFO.INFO-TT]Computer Science [cs]/Document and Text Processing, Knowledge base, C30 - Documentation et information, Information Retrieval, Medicine, Recherche de l'information, Graphs, Functional genomics, Biologie, Genome, Plant, Research Article, Computer and Information Sciences, Science, Crops, Phenome, Research and Analysis Methods, 03 medical and health sciences, Ontologies, Genetics, RDF, Semantic Web, 030304 developmental biology, [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], business.industry, Data Visualization, [INFO.INFO-WB]Computer Science [cs]/Web, Cognitive Psychology, Biology and Life Sciences, Computational Biology, 15. Life on land, Genome Analysis, Data science, Agronomy, [INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR], Cognitive Science, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], business, computer, 030217 neurology & neurosurgery, User Interfaces, Neuroscience, Crop Science, Cereal Crops
Abstract: Exploring the links between genetic and phenotypic traits is an important area of research in agronomy. One of the main objectives of this is to accelerate the development of important traits that can positively impact the agricultural economy. However, due to the existence of complex molecular interactions, to gain complete understanding will warrant data analyses performed at different molecular and environmental levels for a given (plant) subject. For instance, to understand how rice genes involved in metabolism or signaling of growth regulators control the rice panicle architecture. While high-throughput technologies have played a key role in accelerating and generating the much-needed data, these can only partially capture the dynamics in genotypephenotype relations. Consequently, our knowledge of the complex relationships between the different molecular actors responsible for the expression of the phenome in various plant systems remains fragmented. Hence, there is an urgent need to effectively integrate and assimilate complementary information to understand the biological system in its entirety. We have developed AgroLD [1] (www.agrold.org), a knowledge graph system that exploits the Semantic Web technology and FAIR principles [2], to integrate information to integrate data about plant species of high interest for the plant science community e.g., rice, wheat, Arabidopsis and in this way facilitating the formulation of new scientific hypotheses. We present some integration results of the project, which currently focused on genomics, proteomics and phenomics. AgroLD is now an RDF knowledge base of 900M triples created by annotating and integrating more than 100 datasets coming from 15 data sources –such as Ensembl plants [3], Gramene.org [4] and TropGeneDB [5]– with 15 ontologies –such as the Gene Ontology [6] and Plant Ontology [7]. Our objective is to offer a domain specific knowledge platform to solve complex biological and agronomical questions related to the implication of genes in, for instances, plant disease resistance or high yield traits. We expect the resolution of these questions to facilitate the formulation of new scientific hypotheses to be validated with a knowledge-oriented approach.
Published: 2018
Full Text: View/download PDF

41. Using crop-pathogen modeling to identify plant traits to control Zymoseptoria tritici epidemics on wheat

Author: Gigot, Christophe, Claessen, David, Garin, Guillaume, Fournier, Christian, Pradal, Christophe, Robert, Corinne, Ecologie fonctionnelle et écotoxicologie des agroécosystèmes (ECOSYS), Institut National de la Recherche Agronomique (INRA)-AgroParisTech, Université Paris-Saclay, Institut de biologie de l'ENS Paris (IBENS), Département de Biologie - ENS Paris, École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), ITK, Écophysiologie des Plantes sous Stress environnementaux (LEPSE), Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut de Biologie Computationnelle (IBC), Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), ProdInra, Archive Ouverte, Institut de biologie de l'ENS Paris (UMR 8197/1024) (IBENS), École normale supérieure - Paris (ENS Paris)-École normale supérieure - Paris (ENS Paris)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), Université de Montpellier (UM)-Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Département de Biologie - ENS Paris, École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), Institut National de la Recherche Agronomique (INRA)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), and Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)
Subjects: consumer-resource dynamics, tolerance, [SDV]Life Sciences [q-bio], fungi, food and beverages, virulence, [SDV] Life Sciences [q-bio], fertilization, trait-based modeling, wheat, disease-escaping traits, Septoria tritici blotch, Zymoseptoria tritici, epidemiology
Abstract: Diversification in pathogen control methods to reduce the severity of economically important foliar diseases such as Zymoseptoria tritici on wheat is needed. One way is to identify plant physiological and architectural traits that influence disease development and that can be selected in the process of crop breeding. Such traits may be used for improving tolerance or disease escape. Traits favoring disease escape, the focus of our work, may significantly decrease crop epidemics (Robert et al., 2018). However, understanding the role of such traits in crop-pathogen interactions is a daunting task because the interactions are multiple and dynamic in time. To characterize and quantify crop-pathogen interactions, an innovative trait-based and resource-based modeling framework was developed (Precigout et al., 2017). In this framework, the pathosystem is assumed to respond dynamically to both architecture and physiological status of the host canopy. A canopy consists of plenty of small patches, i.e. small functional and infectable units of leaf tissue. Production of new patches, for canopy growth and renewal of photosynthetically active plant tissues, is a function of the available resources produced by the other patches. Pathogen spores can contaminate nearby healthy patches. The definition of patch proximity depends on dispersal abilities of the pathogen and canopy architecture. We used and adapted this modeling framework to quantify the effects of several plant traits on Zymoseptoria tritici epidemics for varied climate scenarios. The complex infection cycle of Z. tritici characterized by a long symptomless incubation period was implemented in the model. We studied plant architectural traits such as leaf size or stem height, and plant physiological traits such as leaf lifespan or leaf metabolite contents. In our simulations, these traits impacted the epidemics dynamics though their effects on pathogen dispersal and on the amount of resources available for the pathogen. Sensitivity analyses showed how disease severity depended on plant traits and pathogen virulence. The importance of several plant and pathogen traits could be linked to the pathogen’s ability to manage the race for the colonization of the canopy in the face of canopy growth. Playing on host traits also made it possible to simulate different wheat varieties - with contrasted heights, pathogen resistance or precocity - to characterize the behavior of the pathosystem of interest for different host ideotypes. We argue that this kind of trait-based modeling approach is a valuable tool to identify plant traits promoting more resilient agroecosystems in particular for crop breeding in a context of innovative and sustainable crop protection.
Published: 2018

42. Comparative study of Named-Entity Recognition methods in the agronomical domain

Author: Do, Huy, Tran, Hanh, Khoat Than, Quang, Larmande, Pierre, University of sciences and technologies of hanoi (USTH), Hanoi University of Science and Technology (HUST), Institut de Biologie Computationnelle (IBC), Université de Montpellier (UM)-Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS), Institut de Recherche pour le Développement (IRD), University of Science and Technology of Hanoi (USTH), and Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)
Subjects: [INFO.INFO-TT]Computer Science [cs]/Document and Text Processing, ComputingMethodologies_PATTERNRECOGNITION, Text mining, Bioinformatics, NER, Plant Genomics, LSTM-CRF, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE], [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Abstract: International audience; Text mining is becoming an important part of research topic in biology with the original purpose to extract biological entities such as genes, proteins and traits to extend the knowledge from scientific papers. However, few thorough studies on text mining and applications are developed for plant molecular biology data, especially rice, thus resulting a lack of datasets available to train models. Since there is no/rare benchmark for rice, we have to face various difficulties in exploiting advanced machine learning methods for accurate analysis of rice bibliography. In this article, we developed a new training datasets (Oryzabase) as the benchmark. Then, we evaluated the performance of several current approaches to find a methodology with the best results and assigned it as the state of the art method for our own technique in the future. We applied Name Entities Recognition (NER) tagger, which is built from a Long Short Term Memory model, and combined with Conditional Random Fields (LSTM-CRF) to extract information of plant genes and proteins. We analyzed the performance of LSTM-CRF when applying to the Oryzabase dataset and improved the results up to 89% in F1. We found that on average, the result from LSTM-CRF is more exploitable with the new benchmark.
Published: 2018

43. Measuring Genetic Differentiation from Pool-seq Data

Author: Raphaël Leblois, Mathieu Gautier, Valentin Hivert, Eric J. Petit, Renaud Vitalis, Centre de Biologie pour la Gestion des Populations (UMR CBGP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Université de Montpellier (UM)-Institut de Recherche pour le Développement (IRD [France-Sud])-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut de Biologie Computationnelle (IBC), Université de Montpellier (UM)-Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS), Écologie et santé des écosystèmes (ESE), Institut National de la Recherche Agronomique (INRA)-AGROCAMPUS OUEST, Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Institut National de la Recherche Agronomique's Plant Health and Environment (SPE) Division, Biodi-vERsA project EXOTIC : ANR-13-EBID-0001, project SWING of the French National Research Agency : ANR-16-CE02-0015, CORBAM project of the French region Hauts-de-France, ANR-16-CE02-0015,SWING,Invasion mondiale de la drosophile à aile tachetée: Génétique, plasticité et potentiel évolutif(2016), and Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)
Subjects: 0106 biological sciences, 0301 basic medicine, population genomics, Genotype, F-ST, genetic differentiation, pool sequencing, Computational biology, Biology, Investigations, 010603 evolutionary biology, 01 natural sciences, Polymorphism, Single Nucleotide, DNA sequencing, Population genomics, 03 medical and health sciences, [SDV.EE.ECO]Life Sciences [q-bio]/Ecology, environment/Ecosystems, Gene Frequency, [SDV.BA.ZV]Life Sciences [q-bio]/Animal biology/Vertebrate Zoology, Databases, Genetic, Genetics, Computer Simulation, Genotyping, ComputingMilieux_MISCELLANEOUS, Alleles, 030304 developmental biology, 0303 health sciences, [SDV.GEN.GPO]Life Sciences [q-bio]/Genetics/Populations and Evolution [q-bio.PE], [SDV.BID.EVO]Life Sciences [q-bio]/Biodiversity/Populations and Evolution [q-bio.PE], Estimator, Robustness (evolution), Genetic Variation, High-Throughput Nucleotide Sequencing, DNA, Genomics, Sequence Analysis, DNA, biology.organism_classification, Genetic differentiation, [SDV.BA.ZI]Life Sciences [q-bio]/Animal biology/Invertebrate Zoology, 030104 developmental biology, Genetic structure, Cottus asper, [SDV.EE.IEO]Life Sciences [q-bio]/Ecology, environment/Symbiosis
Abstract: The recent advent of high throughput sequencing and genotyping technologies enables the comparison of patterns of polymorphisms at a very large number of markers. While the characterization of genetic structure from individual sequencing data remains expensive for many non-model species, it has been shown that sequencing pools of individual DNAs (Pool-seq) represents an attractive and cost-effective alternative. However, analyzing sequence read counts from a DNA pool instead of individual genotypes raises statistical challenges in deriving correct estimates of genetic differentiation. In this article, we provide a method-of-moments estimator ofFSTfor Pool-seq data, based on an analysis-of-variance framework. We show, by means of simulations, that this new estimator is unbiased, and outperforms previously proposed estimators. We evaluate the robustness of our estimator to model misspecification, such as sequencing errors and uneven contributions of individual DNAs to the pools. Last, by reanalyzing published Pool-seq data of different ecotypes of the prickly sculpinCottus asper, we show how the use of an unbiasedFSTestimator may question the interpretation of population structure inferred from previous analyses.
Published: 2018

44. Probing instructions for expression regulation in gene nucleotide compositions

Author: Chloé, Bessière, May, Taha, Florent, Petitprez, Jimmy, Vandel, Jean-Michel, Marin, Laurent, Bréhélin, Sophie, Lèbre, Charles-Henri, Lecellier, Institut de Biologie Computationnelle (IBC), Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Institut de Génétique Moléculaire de Montpellier (IGMM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Méthodes et Algorithmes pour la Bioinformatique (MAB), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Institut Montpelliérain Alexander Grothendieck (IMAG), Université Paul-Valéry - Montpellier 3 (UPVM), ANR-11-BINF-0002,IBC,Institut de biologie Computationnelle(2011), Université de Montpellier (UM)-Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Université Paul-Valéry - Montpellier 3 (UM3), ANR-11-BINF-0002,IBC,Institut de Biologie Computationnelle de Montpellier(2011), Institut de Biologie Computationnelle ( IBC ), Centre de Coopération Internationale en Recherche Agronomique pour le Développement ( CIRAD ) -Institut National de la Recherche Agronomique ( INRA ) -Institut National de Recherche en Informatique et en Automatique ( Inria ) -Université de Montpellier ( UM ) -Centre National de la Recherche Scientifique ( CNRS ), Institut de Génétique Moléculaire de Montpellier ( IGMM ), Université de Montpellier ( UM ) -Centre National de la Recherche Scientifique ( CNRS ), Méthodes et Algorithmes pour la Bioinformatique ( MAB ), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier ( LIRMM ), Université de Montpellier ( UM ) -Centre National de la Recherche Scientifique ( CNRS ) -Université de Montpellier ( UM ) -Centre National de la Recherche Scientifique ( CNRS ), Institut Montpelliérain Alexander Grothendieck ( IMAG ), and ANR-11-BINF-0002,IBC,Institut de Biologie Computationnelle de Montpellier ( 2011 )
Subjects: Decision Analysis, Gene Expression, Regulatory Sequences, Nucleic Acid, Biochemistry, Database and Informatics Methods, Neoplasms, Biology (General), Promoter Regions, Genetic, Base Composition, DNA methylation, Genomics, Chromatin, Nucleic acids, Enhancer Elements, Genetic, Engineering and Technology, Epigenetics, DNA modification, Sequence Analysis, Management Engineering, Chromatin modification, Research Article, Chromosome biology, Cell biology, DNA Copy Number Variations, Bioinformatics, QH301-705.5, Quantitative Trait Loci, Nucleotide Sequencing, Research and Analysis Methods, Genome Complexity, Polymorphism, Single Nucleotide, Sequence Motif Analysis, [ INFO.INFO-BI ] Computer Science [cs]/Bioinformatics [q-bio.QM], Genetics, Humans, Gene Regulation, RNA, Messenger, Gene Prediction, Molecular Biology Techniques, Sequencing Techniques, Molecular Biology, Models, Genetic, Genome, Human, Decision Trees, Computational Biology, Biology and Life Sciences, DNA, Genome Analysis, Introns, Gene Expression Regulation, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], Transcription Factors
Abstract: Gene expression is orchestrated by distinct regulatory regions to ensure a wide variety of cell types and functions. A challenge is to identify which regulatory regions are active, what are their associated features and how they work together in each cell type. Several approaches have tackled this problem by modeling gene expression based on epigenetic marks, with the ultimate goal of identifying driving regions and associated genomic variations that are clinically relevant in particular in precision medicine. However, these models rely on experimental data, which are limited to specific samples (even often to cell lines) and cannot be generated for all regulators and all patients. In addition, we show here that, although these approaches are accurate in predicting gene expression, inference of TF combinations from this type of models is not straightforward. Furthermore these methods are not designed to capture regulation instructions present at the sequence level, before the binding of regulators or the opening of the chromatin. Here, we probe sequence-level instructions for gene expression and develop a method to explain mRNA levels based solely on nucleotide features. Our method positions nucleotide composition as a critical component of gene expression. Moreover, our approach, able to rank regulatory regions according to their contribution, unveils a strong influence of the gene body sequence, in particular introns. We further provide evidence that the contribution of nucleotide content can be linked to co-regulations associated with genome 3D architecture and to associations of genes within topologically associated domains., Author summary Identifying a maximum of DNA determinants implicated in gene regulation will accelerate genetic analyses and precision medicine approaches by identifying key gene features. In that context decoding the sequence-level instructions for gene regulation is of prime importance. Among global efforts to achieve this objective, we propose a novel approach able to explain gene expression in each patient sample using only DNA features. Our approach, which is as accurate as methods based on epigenetics data, reveals a strong influence of the nucleotide content of gene body sequences, in particular introns. In contrast to canonical regulations mediated by specific DNA motifs, our model unveils a contribution of global nucleotide content notably in co-regulations associated with genome 3D architecture and to associations of genes within topologically associated domains. Overall our study confirms and takes advantage of the existence of sequence-level instructions for gene expression, which lie in genomic regions largely underestimated in regulatory genomics but which appear to be linked to chromatin architecture.
Published: 2018

45. Inferring sex-specific demographic history from SNP data

Author: Florian Clemente, Renaud Vitalis, Mathieu Gautier, Université de Montpellier (UM), Centre de Biologie pour la Gestion des Populations (UMR CBGP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Université de Montpellier (UM)-Institut de Recherche pour le Développement (IRD [France-Sud])-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), EMBO. DEU. European Molecular Biology Laboratory (EMBL), DEU., Institut de Biologie Computationnelle (IBC), Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Computational Biology Institute (IBC), Montpellier, France, and Université de Montpellier (UM)-Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)
Subjects: analyse de données, 0301 basic medicine, Male, Cancer Research, Heredity, Genetic Linkage, Breeding, genetique des populations, Effective population size, Gene Frequency, Medicine and Health Sciences, 10. No inequality, [SDV.BDD]Life Sciences [q-bio]/Development Biology, Genetics (clinical), ComputingMilieux_MISCELLANEOUS, Data Management, Mammals, Sex Chromosomes, Chromosome Biology, Population size, [SDV.BID.EVO]Life Sciences [q-bio]/Biodiversity/Populations and Evolution [q-bio.PE], [SDV.BA]Life Sciences [q-bio]/Animal biology, Autosomes, Biologie du développement, Eukaryota, Phylogenetic Analysis, X Chromosomes, Ruminants, Development Biology, Phylogenetics, dynamique des populations, X-Linked Traits, Sex Linkage, Vertebrates, Female, Count data, Research Article, Computer and Information Sciences, lcsh:QH426-470, Demographic history, Biology, Polymorphism, Single Nucleotide, Chromosomes, Molecular Genetics, 03 medical and health sciences, Sex Factors, Genetic drift, Population Metrics, Bovines, calcul bayésien, Genetics, Animals, Humans, Evolutionary Systematics, Sex Ratio, Allele, Domestication, Allele frequency, Molecular Biology, Ecology, Evolution, Behavior and Systematics, Alleles, Taxonomy, Demography, Clinical Genetics, Population Density, Evolutionary Biology, [SDV.GEN.GPO]Life Sciences [q-bio]/Genetics/Populations and Evolution [q-bio.PE], Population Biology, Models, Genetic, démographie de population, Organisms, Australia, Biology and Life Sciences, Bayes Theorem, Cell Biology, Sex Determination Processes, lcsh:Genetics, 030104 developmental biology, Genetics, Population, Evolutionary biology, Genetic Loci, Amniotes, Cattle
Abstract: The relative female and male contributions to demography are of great importance to better understand the history and dynamics of populations. While earlier studies relied on uniparental markers to investigate sex-specific questions, the increasing amount of sequence data now enables us to take advantage of tens to hundreds of thousands of independent loci from autosomes and the X chromosome. Here, we develop a novel method to estimate effective sex ratios or ESR (defined as the female proportion of the effective population) from allele count data for each branch of a rooted tree topology that summarizes the history of the populations of interest. Our method relies on Kimura’s time-dependent diffusion approximation for genetic drift, and is based on a hierarchical Bayesian model to integrate over the allele frequencies along the branches. We show via simulations that parameters are inferred robustly, even under scenarios that violate some of the model assumptions. Analyzing bovine SNP data, we infer a strongly female-biased ESR in both dairy and beef cattle, as expected from the underlying breeding scheme. Conversely, we observe a strongly male-biased ESR in early domestication times, consistent with an easier taming and management of cows, and/or introgression from wild auroch males, that would both cause a relative increase in male effective population size. In humans, analyzing a subsample of non-African populations, we find a male-biased ESR in Oceanians that may reflect complex marriage patterns in Aboriginal Australians. Because our approach relies on allele count data, it may be applied on a wide range of species., Author summary The history of populations and their social organization is often intricate due to breeding structures, migration patterns or population bottlenecks. Estimation of the female proportion of the effective population (sex ratio) is therefore important to better understand this underlying social structure and dynamics. This question has been mainly investigated so far by comparing genetic variation of mitochondrial DNA and the Y chromosome, two uniparentally inherited markers that reflect the demographic history of females and males, respectively. To overcome the intrinsic limitations of these genetic markers, and to take advantage of the increasing amount of sequence data, we propose a new approach that uses large numbers of independent polymorphisms from autosomes and the X chromosome to estimate sex ratios, throughout the history of populations. This method allows us to confirm a strongly female-biased sex ratio in modern dairy and beef cattle breeds. Yet, we find a strongly male-biased sex ratio during domestication times, consistent with an easier taming and management of cows, and/or introgression from wild auroch males. Analyzing human data from a sample of non-African populations, we find a male bias in Oceanians, possibly indicating complex marriage patterns among Aboriginal Australian groups.
Published: 2018

46. Plant architecture and foliar senescence impact the race between wheat growth and Zymoseptoria tritici epidemics

Author: Guillaume Garin, Corinne Robert, Christian Fournier, Christophe Pradal, Vianney Houlès, Mariem Abichou, Ecologie fonctionnelle et écotoxicologie des agroécosystèmes (ECOSYS), Institut National de la Recherche Agronomique (INRA)-AgroParisTech, ITK [Clapiers], Modeling plant morphogenesis at different scales, from genes to phenotype (VIRTUAL PLANTS), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de la Recherche Agronomique (INRA)-Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut de Biologie Computationnelle (IBC), Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Écophysiologie des Plantes sous Stress environnementaux (LEPSE), Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), Université de Montpellier (UM)-Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS), Institut National de la Recherche Agronomique (INRA)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), and Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)
Subjects: 0106 biological sciences, 0301 basic medicine, Canopy, architecture, leaf senescence, [SDV]Life Sciences [q-bio], F62 - Physiologie végétale - Croissance et développement, Plant Science, F50 - Anatomie et morphologie des plantes, 01 natural sciences, epidemics, Crop, 03 medical and health sciences, Race (biology), Septoria, Ascomycota, wheat, virtual plant model, canopy traits, Colonization, Triticum, H20 - Maladies des plantes, Plant Diseases, 2. Zero hunger, FSPM, biology, Host (biology), fungi, food and beverages, Original Articles, Spores, Fungal, 15. Life on land, biology.organism_classification, Plant disease, Plant Leaves, 030104 developmental biology, Agronomy, 13. Climate action, Host-Pathogen Interactions, Zymoseptoria tritici, Septoria tritici blotch, Epidemic model, 010606 plant biology & botany
Abstract: Background and Aims: In order to optimize crop management in innovative agricultural production systems, it is crucial to better understand how plant disease epidemics develop and what factors influence them. This study explores how canopy growth, its spatial organization and leaf senescence impact Zymoseptoria tritici epidemics. Methods: We used the Septo3D model, an epidemic model of Septoria tritici blotch (STB) coupled with a 3-D virtual wheat structural plant model (SPM). The model was calibrated and evaluated against field experimental data. Sensitivity analyses were performed on the model to explore how wheat plant traits impact the interaction between wheat growth and Z. tritici epidemics. Key Results: The model reproduces consistently the effects of crop architecture and weather on STB progress on the upper leaves. Model sensitivity analyses show that the effects of plant traits on epidemics depended on weather conditions. The simulations confirm the known effect of increased stem height and stem elongation rate on limiting STB progress on upper leaves. Strikingly, the timing of leaf senescence is one of the most influential traits on simulated STB epidemics. When the green life span duration of leaves is reduced by early senescence, epidemics are strongly reduced. Conclusions: We introduce the notion of a 'race' for the colonization of emerging healthy host tissue between the growing canopy and the developing epidemics. This race is 2-fold: (1) an upward race at the canopy scale where STB must catch the newly emerging leaves before they grow away from the spore sources; and (2) a local race at the leaf scale where STB must use the resources of its host before it is caught by leaf apical senescence. The results shed new light on the importance of dynamic interactions between host and pathogen. (Resume d'auteur)
Published: 2018

47. Discontinuities in quinoa biodiversity in the dry Andes : an 18 - century perspective based on allelic genotyping

Author: Thierry Winkel, María Gabriela Aguirre, Carla Marcela Arizio, Carlos Alberto Aschero, María del Pilar Babot, Laure Benoit, Concetta Burgarella, Sabrina Costa-Tártara, Marie-Pierre Dubois, Laurène Gay, Salomón Hocsman, Margaux Jullien, Sara María Luisa López-Campeny, María Marcela Manifesto, Miguel Navascués, Nurit Oliszewski, Elizabeth Pintar, Saliha Zenboudji, Héctor Daniel Bertero, Richard Joffre, Centre d’Ecologie Fonctionnelle et Evolutive (CEFE), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Université Paul-Valéry - Montpellier 3 (UPVM)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut de Recherche pour le Développement (IRD [France-Sud]), Universidad Nacional de Tucumán (UNT), Instituto Nacional de Recursos Biológicos (INRB/L-IPIMAR), Consejo Nacional de Investigaciones Científicas y Técnicas, Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Institut National de la Recherche Agronomique (INRA)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Universidad Nacional de Luján, Partenaires INRAE, Centre de Biologie pour la Gestion des Populations (UMR CBGP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Université de Montpellier (UM)-Institut de Recherche pour le Développement (IRD [France-Sud])-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Institut de Biologie Computationnelle (IBC), Université de Montpellier (UM)-Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS), Austin Community College, Universidad de Buenos Aires (UBA), ECOS-Sud : A11B03, CNRS-PEPS-INEE : BFC 85508, PIP-CONICET : 464, Université Paul-Valéry - Montpellier 3 (UM3)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Centre de Recherches sur les Macromolécules Végétales [2016-2019] (CERMAV [2016-2019]), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut de Recherche pour le Développement (IRD [France-Sud])-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Université de Montpellier (UM)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Facultad de Agronomia, Catedra de Produccion Vegetal, University of Buenos Aires, Université Paul-Valéry - Montpellier 3 (UPVM)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-École Pratique des Hautes Études (EPHE), Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Université Paul-Valéry - Montpellier 3 (UM3)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-École pratique des hautes études (EPHE)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), Centre de Recherches sur les Macromolécules Végétales (CERMAV), Université Joseph Fourier - Grenoble 1 (UJF)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA), School of Biological Sciences (BIO), University of East Anglia [Norwich] (UEA), Université Paul-Valéry - Montpellier 3 (UPVM)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-École pratique des hautes études (EPHE), Centre de Recherches sur les Macromolécules Végétales (CERMAV ), Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Centre National de la Recherche Scientifique (CNRS), and Winkel, Thierry
Subjects: 0106 biological sciences, Genotyping Techniques, ADN, Biodiversity, Plant Science, 01 natural sciences, Quinua, AGRICULTURAL LAND, NON HUMAN, 0601 history and archaeology, Genética de Poblaciones, ComputingMilieux_MISCELLANEOUS, Flowering Plants, POPULATION, PLANT GENE, 0303 health sciences, education.field_of_study, Vegetal Biology, 060102 archaeology, Ecology, Paleogenética, Eukaryota, 06 humanities and the arts, Plants, dry Andes, AGRICULTURAL WORKER, Geography, Seeds, Medicine, Biodiversité, BOTTLE NECK, Génotype, Genotype, P40 - Météorologie et climatologie, Science, Argentina, Reservas Genéticas, Chenopodium quinoa, Biodiversidad, Ciencias Biológicas, 03 medical and health sciences, allelic genotyping, Genetics, education, Alleles, Changement climatique, Genetic Drift, Organisms, Biology and Life Sciences, Genetic Variation, 15. Life on land, 030104 developmental biology, Gene Pools, BIODIVERSITY, Biologie végétale, Population Genetics, 0301 basic medicine, GENETIC VARIATION, GENETIC VARIABILITY, CHENOPODIUM QUINOA, Plant Genetics, History, 18th Century, F30 - Génétique et amélioration des plantes, purl.org/becyt/ford/1 [https], Genética y Herencia, Populations Genetics, quinoa biodiversity, DROUGHT, 2. Zero hunger, Multidisciplinary, Agriculture, Gene Pool, GENOTYPE, Environnement, ALLELE, Gene pool, CIENCIAS NATURALES Y EXACTAS, Research Article, Evolutionary Processes, Population, Crops, 010603 evolutionary biology, Variation génétique, Genetic drift, Quinoa, Paleogenetics, crop genetics, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, DNA, Ancient, purl.org/becyt/ford/1.6 [https], 030304 developmental biology, Crop Genetics, Genetic diversity, Evolutionary Biology, Population Biology, GENETIC DRIFT, Paleontology, Population bottleneck, Ancient DNA, 13. Climate action, Quinoa, Earth Sciences, [SDE.BE]Environmental Sciences/Biodiversity and Ecology, Paleogenetics, Crop Science
Abstract: History and environment shape crop biodiversity, particularly in areas with vulnerable human communities and ecosystems. Tracing crop biodiversity over time helps understand how rural societies cope with anthropogenic or climatic changes. Exceptionally well preserved ancient DNA of quinoa (Chenopodium quinoa Willd.) from the cold and arid Andes of Argentina has allowed us to track changes and continuities in quinoa diversity over 18 centuries, by coupling genotyping of 157 ancient and modern seeds by 24 SSR markers with cluster and coalescence analyses. Cluster analyses revealed clear population patterns separating modern and ancient quinoas. Coalescence-based analyses revealed that genetic drift within a single population cannot explain genetic differentiation among ancient and modern quinoas. The hypothesis of a genetic bottleneck related to the Spanish Conquest also does not seem to apply at a local scale. Instead, the most likely scenario is the replacement of preexisting quinoa gene pools with new ones of lower genetic diversity. This process occurred at least twice in the last 18 centuries: first, between the 6th and 12th centuries?a time of agricultural intensification well before the Inka and Spanish conquests?and then between the 13th century and today?a period marked by farming marginalization in the late 19th century likely due to a severe multidecadal drought. While these processes of local gene pool replacement do not imply losses of genetic diversity at the metapopulation scale, they support the view that gene pool replacement linked to social and environmental changes can result from opposite agricultural trajectories. Fil: Winkel, Thierry. Université de Montpellier; Fil: Aguirre, María Gabriela. Universidad Nacional de Tucumán; Argentina. Universidad Nacional de Tucumán. Facultad de Ciencias Naturales e Instituto Miguel Lillo. Laboratorio de Geoarqueología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán; Argentina Fil: Arizio, Carla Marcela. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Buenos Aires; Argentina. Instituto Nacional de Tecnología Agropecuaria. Centro de Investigación de Recursos Naturales. Instituto de Recursos Biológicos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Aschero, Carlos Alberto. Universidad Nacional de Tucumán; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán. Instituto Superior de Estudios Sociales. Universidad Nacional de Tucumán. Instituto Superior de Estudios Sociales; Argentina Fil: Babot, Maria del Pilar. Universidad Nacional de Tucumán; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán. Instituto Superior de Estudios Sociales. Universidad Nacional de Tucumán. Instituto Superior de Estudios Sociales; Argentina Fil: Benoit, Laure. Université de Montpellier; Fil: Burgarella, Concetta. Amélioration Génétique Et Adaptation Des Plantes Méditerranéennes Et Tropicales (agap); Fil: Costa Tártara, Sabrina María. Universidad Nacional de Luján; Argentina. Instituto Nacional de Tecnología Agropecuaria. Centro de Investigación de Recursos Naturales. Instituto de Recursos Biológicos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Dubois, Marie-Pierre. Université de Montpellier; Fil: Gay, Laurène. Amélioration Génétique Et Adaptation Des Plantes Méditerranéennes Et Tropicales (agap); Fil: Hocsman, Salomón. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán. Instituto Superior de Estudios Sociales. Universidad Nacional de Tucumán. Instituto Superior de Estudios Sociales; Argentina Fil: Jullien, Margaux. Amélioration Génétique Et Adaptation Des Plantes Méditerranéennes Et Tropicales (agap); Fil: Lopez Campeny, Sara Maria Luisa. Universidad Nacional de Tucumán; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán. Instituto Superior de Estudios Sociales. Universidad Nacional de Tucumán. Instituto Superior de Estudios Sociales; Argentina Fil: Manifesto, Maria Marcela. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Buenos Aires; Argentina Fil: Navascués, Miguel. Faculte de Medecine Universite Montpellier 1; . Montpellier Supagro; Fil: Oliszewski, Nurit. Universidad Nacional de Tucumán; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán. Instituto Superior de Estudios Sociales. Universidad Nacional de Tucumán. Instituto Superior de Estudios Sociales; Argentina Fil: Pintar, Elizabeth Lucia. Austin Community College; Estados Unidos Fil: Zenboudji, Saliha. Université de Montpellier; Fil: Bertero, Hector Daniel. Universidad de Buenos Aires; Argentina Fil: Joffre, Richard. Université de Montpellier
Published: 2018

48. Package ‘ape’

Author: Paradis, Emmanuel, Blomberg, Simon, Bolker, Ben, Brown, Joseph, Claude, Julien, Cuong, Hoa Sien, Desper, Richard, Didier, Gilles, Institut des Sciences de l'Evolution de Montpellier (UMR ISEM), École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Montpellier (UM)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre National de la Recherche Scientifique (CNRS)-Institut de recherche pour le développement [IRD] : UR226, Centre National de la Recherche Scientifique (CNRS), Institut de Recherche pour le Développement (IRD [France-Ouest]), Institut de Biologie Computationnelle (IBC), Université de Montpellier (UM)-Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS), Institut de Mathématiques de Marseille (I2M), and Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS)
Subjects: [SDV.BID.EVO]Life Sciences [q-bio]/Biodiversity/Populations and Evolution [q-bio.PE], ComputingMilieux_MISCELLANEOUS
Abstract: International audience
Published: 2018

49. Computational pan-genomics : Status, promises and challenges

Author: Lodewyk F. A. Wessels, Jan O. Korbel, Jasmijn A. Baaijens, Francesca Chiaromonte, Gunnar W. Klau, Kai Ye, Alexander Schönhuth, Francesca D. Ciccarelli, Tobias Marschall, Robin Cijvat, Matthias Schlesner, Adam M. Novak, Eleazar Eskin, Ashley D. Sanders, Sven Rahmann, Carl Shneider, Wigard P. Kloosterman, Knut Reinert, Eric-Wubbo Lameijer, Sandra Smit, Benedict Paten, Mohammed El-Kebir, Valentina Boeva, Evan E. Eichler, Cornelia M. van Duijn, Can Alkan, Jeroen de Ridder, Benjamin Langmead, Dick de Ridder, Jiayin Wang, David Porubsky, Fabio Vandin, Erwin Datema, Ben Raphael, Paul Kersey, Nadia Pisanti, Corinna Ernst, Klaasjan G. Ouwens, Y. Zhang, Thomas Abeel, Erik Garrison, Veli Mäkinen, Paul I.W. de Bakker, Victor Guryev, Siavash Sheikhizadeh, Manja Marz, Marcel Martin, Ole Schulz-Trieglaff, Pieter B. Neerincx, Rayan Chikhi, Eric Rivals, John C. Mu, Raoul J. P. Bonnal, Bas E. Dutilh, Paul Medvedev, Louis Dijkstra, Pierre Peterlongo, Ali Ghaffaari, Daniel Valenzuela, Epidemiology, Gastroenterology & Hepatology, Erasmus MC other, Alkan, Can, Saarland University [Saarbrücken], Max Planck Institute for Informatics [Saarbrücken], Karlsruhe Institute of Technology (KIT), Broad Institute of MIT and Harvard (BROAD INSTITUTE), Harvard Medical School [Boston] (HMS)-Massachusetts Institute of Technology (MIT)-Massachusetts General Hospital [Boston], Delft Bioinformatics Lab [Delft], Delft University of Technology (TU Delft), Computational Science Lab [Amsterdam], University of Amsterdam [Amsterdam] (UvA), Theoretical Biology & Bioinformatics [Utrecht], University Medical Center [Utrecht], Universidade Federal do Rio de Janeiro (UFRJ), European Bioinformatics Institute [Hinxton] (EMBL-EBI), EMBL Heidelberg, Section genetics [Utrecht], Center for Molecular medicine [Utrecht], University Medical Center [Utrecht]-University Medical Center [Utrecht], Helsinki Institute for Information Technology, Department of Computer Science [Helsinki], Falculty of Science [Helsinki], University of Helsinki-University of Helsinki, Howard Hughes Medical Institute [Santa Cruz] (HHMI), Center for Biomolecular Science & Engineering, European Research Institute for the Biology of Ageing [Groningen] (ERIBA), University Medical Center Groningen [Groningen] (UMCG), Institut de Biologie Computationnelle (IBC), Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Méthodes et Algorithmes pour la Bioinformatique (MAB), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Department of Computer Engineering (Bilkent-CS), Bilkent, Life Sciences [Amsterdam] (MAC4), Centrum Wiskunde & Informatica (CWI), Institut Cochin (IC UM3 (UMR 8104 / U1016)), Université Paris Descartes - Paris 5 (UPD5)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), PSL Research University (PSL), Centre de Bioinformatique (CBIO), MINES ParisTech - École nationale supérieure des mines de Paris-PSL Research University (PSL), Cancer et génôme: Bioinformatique, biostatistiques et épidémiologie d'un système complexe, MINES ParisTech - École nationale supérieure des mines de Paris-Institut Curie-Institut National de la Santé et de la Recherche Médicale (INSERM), Integrative Biology Program [Milano], Istituto Nazionale Genetica Molecolare [Milano] (INGM), Department of Statistics [Pennsylvania], Pennsylvania State University (Penn State), Penn State System-Penn State System, Bioinformatics and Sequence Analysis (BONSAI), Université de Lille, Sciences et Technologies-Inria Lille - Nord Europe, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189 (CRIStAL), Centre National de la Recherche Scientifique (CNRS)-Université de Lille-Ecole Centrale de Lille-Centre National de la Recherche Scientifique (CNRS)-Université de Lille-Ecole Centrale de Lille-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS), Monet DB Solutions [Amsterdam], KeyGene [Wageningen], Department of Epidemiology [Rotterdam], Erasmus University Medical Center [Rotterdam] (Erasmus MC), Howard Hughes Medical Institute [Berkeley], University of California [Berkeley], University of California-University of California, Genome Informatics [Duisburg], Universität Duisburg-Essen [Essen], Departement of human genetics [Los Angeles], Computer Science Department [Los Angeles] (UCLA), University of California [Los Angeles] (UCLA), University of California-University of California-University of California [Los Angeles] (UCLA), The Wellcome Trust Sanger Institute [Cambridge], Equipe de recherche européenne en algorithmique et biologie formelle et expérimentale (ERABLE), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Universiteit Leiden [Leiden], Department of Computer Science [Baltimore], Johns Hopkins University (JHU), Centre de Physique des Particules de Marseille (CPPM), Centre National de la Recherche Scientifique (CNRS)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Aix Marseille Université (AMU), University of Pennsylvania [Philadelphia], China Agricultural University (CAU), University of Groningen [Groningen], Department of Biological Psychology [Amsterdam], Vrije Universiteit Amsterdam [Amsterdam] (VU), Scalable, Optimized and Parallel Algorithms for Genomics (GenScale), Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-GESTION DES DONNÉES ET DE LA CONNAISSANCE (IRISA-D7), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes 1 (UR1), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Dipartimento di Informatica [Pisa] (DI), University of Pisa - Università di Pisa, Faculty of Computer Science [Dortmund], Technische Universität Dortmund [Dortmund] (TU), Service de Pneumologie A [Paris], AP-HP - Hôpital Bichat - Claude Bernard [Paris], Department of Mathematics and Computer Science (Freie Universität Berlin), Freie Universität Berlin, Wageningen University and Research Centre [Wageningen] (WUR), Catholic University of Leuven - Katholieke Universiteit Leuven (KU Leuven), Division of Theoretical Bioinformatics [Heidelberg], German Cancer Research Center - Deutsches Krebsforschungszentrum [Heidelberg] (DKFZ), Illumina Cambridge, Illumina, Terry Fox Laboratory, BC Cancer Agency (BCCRC)-British Columbia Cancer Agency Research Centre, Bioinformatics Group [Wageningen], Leiden Observatory [Leiden], Regeneron Pharmaceuticals [Tarrytown], Shaanxi University of Science and Technology, Netherlands Cancer Institute (NKI), Antoni van Leeuwenhoek Hospital, Universita degli Studi di Padova, The Netherlands Organization for Scientific Research (NWO) Vidi (639.072.309 to A.S., 864.14.004 to B.E.D.), CAPES/BRASIL (to B.E.D.), the Academy of Finland (284598 [CoECGR] to V.M. and D.V.), the Russian Scientific Foundation (14–11–00826 to L.D.), Institut de Biologie Computationnelle (ANR-11-BINF- 0002 to E.R.), and the French Colib’read project (ANR–12– BS02–0008 to E.R.). NSFC 31671372 (to K. Y.), the Dutch Graduate School for Experimental Plant Sciences (054EPS15 to S.S.), the EMGO Institute for Health and Care Research (EMGO+) to K.O., the National Human Genome Research Institute (1U54HG007990 [BD2K] to B.P. and A.M.N., 5U41HG007234 [GENCODE] to B.P.), the W. M. Keck Foundation (DT06172015 to B.P. and A.M.N.), the Simons Foundation (SFLIFE# 351901 to B.P. and A.M.N.), the ARCS Foundation (2014–15 ARCS fellowship to A.M.N.), Edward Schulak (Edward Schulak Fellowship in Genomics to A.M.N.), ANR-11-BINF-0002,IBC,Institut de Biologie Computationnelle de Montpellier(2011), ANR-12-BS02-0008,Colib'read,Méthodes d'extraction d'information biologique dans les données HTS non assemblées(2012), Karlsruhe Institute of Technology ( KIT ), Broad Institute of MIT and Harvard ( BROAD INSTITUTE ), Harvard Medical School [Boston] ( HMS ) -Massachusetts General Hospital [Boston] ( MGH ) -Massachusetts Institute of Technology ( MIT ), Delft University of Technology ( TU Delft ), University of Amsterdam [Amsterdam] ( UvA ), Universidade Federal do Rio de Janeiro ( UFRJ ), European Bioinformatics Institute [Hinxton] ( EMBL-EBI ), European Molecular Biology Laboratory [Hinxton], Department of Computer Science, Howard Hughes Medical Institute [Santa Cruz] ( HHMI ), European Research Institute for the Biology of Ageing [Groningen] ( ERIBA ), University Medical Center Groningen [Groningen] ( UMCG ), Institut de Biologie Computationnelle ( IBC ), Centre de Coopération Internationale en Recherche Agronomique pour le Développement ( CIRAD ) -Institut National de la Recherche Agronomique ( INRA ) -Institut National de Recherche en Informatique et en Automatique ( Inria ) -Université de Montpellier ( UM ) -Centre National de la Recherche Scientifique ( CNRS ), Méthodes et Algorithmes pour la Bioinformatique ( MAB ), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier ( LIRMM ), Université de Montpellier ( UM ) -Centre National de la Recherche Scientifique ( CNRS ) -Université de Montpellier ( UM ) -Centre National de la Recherche Scientifique ( CNRS ), Department of Computer Engineering ( Bilkent-CS ), Life Sciences [Amsterdam] ( MAC4 ), Centrum Wiskunde & Informatica ( CWI ), Institut Cochin ( UM3 (UMR 8104 / U1016) ), Université Paris Descartes - Paris 5 ( UPD5 ) -Institut National de la Santé et de la Recherche Médicale ( INSERM ) -Centre National de la Recherche Scientifique ( CNRS ), PSL Research University ( PSL ), Centre de Bioinformatique ( CBIO ), MINES ParisTech - École nationale supérieure des mines de Paris-PSL Research University ( PSL ), MINES ParisTech - École nationale supérieure des mines de Paris-Institut National de la Santé et de la Recherche Médicale ( INSERM ) -INSTITUT CURIE, Istituto Nazionale Genetica Molecolare [Milano] ( INGM ), PennState University [Pennsylvania] ( PSU ), Bioinformatics and Sequence Analysis ( BONSAI ), Institut National de Recherche en Informatique et en Automatique ( Inria ) -Institut National de Recherche en Informatique et en Automatique ( Inria ) -Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189 ( CRIStAL ), Ecole Centrale de Lille-Institut National de Recherche en Informatique et en Automatique ( Inria ) -Institut Mines-Télécom [Paris]-Université de Lille-Centre National de la Recherche Scientifique ( CNRS ) -Ecole Centrale de Lille-Institut Mines-Télécom [Paris]-Université de Lille-Centre National de la Recherche Scientifique ( CNRS ) -Centre National de la Recherche Scientifique ( CNRS ), Centre National de la Recherche Scientifique ( CNRS ), Erasmus University Medical Center [Rotterdam], Computer Science Department [Los Angeles] ( UCLA ), University of California at Los Angeles [Los Angeles] ( UCLA ) -University of California at Los Angeles [Los Angeles] ( UCLA ), Equipe de recherche européenne en algorithmique et biologie formelle et expérimentale ( ERABLE ), Institut National de Recherche en Informatique et en Automatique ( Inria ) -Institut National de Recherche en Informatique et en Automatique ( Inria ), Johns Hopkins University ( JHU ), Centre de Physique des Particules de Marseille ( CPPM ), Centre National de la Recherche Scientifique ( CNRS ) -Institut National de Physique Nucléaire et de Physique des Particules du CNRS ( IN2P3 ) -Aix Marseille Université ( AMU ), China Agricultural University ( CAU ), Vrije Universiteit Amsterdam [Amsterdam] ( VU ), Scalable, Optimized and Parallel Algorithms for Genomics ( GenScale ), Institut National de Recherche en Informatique et en Automatique ( Inria ) -Institut National de Recherche en Informatique et en Automatique ( Inria ) -GESTION DES DONNÉES ET DE LA CONNAISSANCE ( IRISA_D7 ), Institut de Recherche en Informatique et Systèmes Aléatoires ( IRISA ), Université de Rennes 1 ( UR1 ), Université de Rennes ( UNIV-RENNES ) -Université de Rennes ( UNIV-RENNES ) -Institut National des Sciences Appliquées - Rennes ( INSA Rennes ) -Université de Bretagne Sud ( UBS ) -École normale supérieure - Rennes ( ENS Rennes ) -Institut National de Recherche en Informatique et en Automatique ( Inria ) -CentraleSupélec-Centre National de la Recherche Scientifique ( CNRS ) -IMT Atlantique Bretagne-Pays de la Loire ( IMT Atlantique ) -Université de Rennes 1 ( UR1 ), Université de Rennes ( UNIV-RENNES ) -Université de Rennes ( UNIV-RENNES ) -Institut National des Sciences Appliquées - Rennes ( INSA Rennes ) -Université de Bretagne Sud ( UBS ) -École normale supérieure - Rennes ( ENS Rennes ) -Institut National de Recherche en Informatique et en Automatique ( Inria ) -CentraleSupélec-Centre National de la Recherche Scientifique ( CNRS ) -IMT Atlantique Bretagne-Pays de la Loire ( IMT Atlantique ) -Institut de Recherche en Informatique et Systèmes Aléatoires ( IRISA ), Université de Rennes ( UNIV-RENNES ) -Université de Rennes ( UNIV-RENNES ) -Institut National des Sciences Appliquées - Rennes ( INSA Rennes ) -Université de Bretagne Sud ( UBS ) -École normale supérieure - Rennes ( ENS Rennes ) -CentraleSupélec-Centre National de la Recherche Scientifique ( CNRS ) -IMT Atlantique Bretagne-Pays de la Loire ( IMT Atlantique ), Dipartimento di Informatica [Pisa] ( DI ), University of Pisa [Pisa], Technische Universität Dortmund [Dortmund] ( TU ), Department of Mathematics and Computer Science ( Freie Universität Berlin ), Freie Universität Berlin [Berlin], Wageningen University and Research Centre [Wageningen] ( WUR ), K.U.Leuven, German Cancer Research Center [Heidelberg] ( DKFZ ), Helmholtz-Gemeinschaft-Helmholtz-Gemeinschaft, BC Cancer Agency ( BCCRC ) -British Columbia Cancer Agency Research Centre, Netherlands Cancer Institute ( NKI ), ANR-11-BINF-0002,IBC,Institut de Biologie Computationnelle de Montpellier ( 2011 ), ANR-12-BS02-0008,Colib'read,Méthodes d'extraction d'information biologique dans les données HTS non assemblées ( 2012 ), Helsinki Institute for Information Technology (HIIT), Helsingin yliopisto = Helsingfors universitet = University of Helsinki-Aalto University, Helsingin yliopisto = Helsingfors universitet = University of Helsinki-Helsingin yliopisto = Helsingfors universitet = University of Helsinki, University of California [Santa Cruz] (UC Santa Cruz), University of California (UC), Bilkent University [Ankara], Université Paris sciences et lettres (PSL), Mines Paris - PSL (École nationale supérieure des mines de Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL), Cancer et génome: Bioinformatique, biostatistiques et épidémiologie d'un système complexe, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut Curie [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM), Department of Statistics [PennState], Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 (CRIStAL), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), University of Washington [Seattle], Howard Hughes Medical Institute (HHMI), Universität Duisburg-Essen = University of Duisburg-Essen [Essen], University of California (UC)-University of California (UC)-University of California [Los Angeles] (UCLA), University of California (UC)-University of California (UC), Universiteit Leiden, Aix Marseille Université (AMU)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS), University of Pennsylvania, Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Department of Mathematics and Computer Science, Wageningen University and Research [Wageningen] (WUR), Università degli Studi di Padova = University of Padua (Unipd), ANR-11-BINF-0002,IBC,Institut de biologie Computationnelle(2011), Aalto University-University of Helsinki, University of California [Santa Cruz] (UCSC), University of California, Université de Montpellier (UM)-Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Centre National de la Recherche Scientifique (CNRS)-Université Paris Descartes - Paris 5 (UPD5)-Institut National de la Santé et de la Recherche Médicale (INSERM), MINES ParisTech - École nationale supérieure des mines de Paris, Centre National de la Recherche Scientifique (CNRS)-Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 (CRIStAL), Université de Lille-Centrale Lille-Centre National de la Recherche Scientifique (CNRS)-Université de Lille-Centrale Lille-Centre National de la Recherche Scientifique (CNRS)-Université de Lille, Sciences et Technologies-Inria Lille - Nord Europe, Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-CentraleSupélec-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), and Groningen Research Institute for Asthma and COPD (GRIAC)
Subjects: 0301 basic medicine, Data structures, Computer science, Medizin, 02 engineering and technology, computer.software_genre, Genome, Computational and Statistical Genetics, data structures, haplotypes, pan-genome, read mapping, sequence graph, Tumours of the digestive tract Radboud Institute for Molecular Life Sciences [Radboudumc 14], 0303 health sciences, education.field_of_study, Point (typography), Genomics, Papers, Data mining, Sequence graph, Computational problem, Construct (philosophy), Algorithms, Human, Information Systems, Bioinformatics, 0206 medical engineering, Population, Pan-genome, 03 medical and health sciences, [ INFO.INFO-BI ] Computer Science [cs]/Bioinformatics [q-bio.QM], Bioinformatica, Humans, Haplotypes, Read mapping, Computational Biology, Genome, Human, Software, Molecular Biology, education, Representation (mathematics), 030304 developmental biology, Computational genomics, Data structure, Data science, Human genetics, 030104 developmental biology, Paradigm shift, ddc:004, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], EPS, computer, 020602 bioinformatics
Abstract: Many disciplines, from human genetics and oncology to plant breeding, microbiology and virology, commonly face the challenge of analyzing rapidly increasing numbers of genomes. In case ofHomo sapiens, the number of sequenced genomes will approach hundreds of thousands in the next few years. Simply scaling up established bioinformatics pipelines will not be sufficient for leveraging the full potential of such rich genomic datasets. Instead, novel, qualitatively different computational methods and paradigms are needed. We will witness the rapid extension ofcomputational pan-genomics, a new sub-area of research in computational biology. In this paper, we generalize existing definitions and understand apan-genomeas any collection of genomic sequences to be analyzed jointly or to be used as a reference. We examine already available approaches to construct and use pan-genomes, discuss the potential benefits of future technologies and methodologies, and review open challenges from the vantage point of the above-mentioned biological disciplines. As a prominent example for a computational paradigm shift, we particularly highlight the transition from the representation of reference genomes as strings to representations as graphs. We outline how this and other challenges from different application domains translate into common computational problems, point out relevant bioinformatics techniques and identify open problems in computer science. With this review, we aim to increase awareness that a joint approach to computational pan-genomics can help address many of the problems currently faced in various domains.
Published: 2018

50. On the Linearization of Scaffolds Sharing Repeated Contigs

Author: Annie Chateau, Mathias Weller, Rodolphe Giroudeau, Institut de Biologie Computationnelle (IBC), Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Méthodes et Algorithmes pour la Bioinformatique (MAB), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Méthodes Algorithmes pour l'Ordonnancement et les Réseaux (MAORE), Methods, Algorithms for Operations REsearch (MAORE), Université de Montpellier (UM)-Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS), and Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)
Subjects: 0301 basic medicine, [INFO.INFO-CC]Computer Science [cs]/Computational Complexity [cs.CC], Computational model, Mathematical optimization, Degree (graph theory), Contig, 0102 computer and information sciences, Translation (geometry), Quantitative Biology::Genomics, 01 natural sciences, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], Planarity testing, 03 medical and health sciences, 030104 developmental biology, 010201 computation theory & mathematics, Linearization, Bounded function, Computational problem, Algorithm, Mathematics
Abstract: International audience; Scaffolding is the final step in assembling Next Generation Sequencing data, in which pre-assembled contiguous regions (“contigs”) are oriented and ordered using information that links them (for example, mapping of paired-end reads). As the genome of some species is highly repetitive, we allow placing some contigs multiple times, thereby generalizing established computational models for this problem. We study the subsequent problems induced by the translation of solutions of the model back to actual sequences, proposing models and analyzing the complexity of the resulting computational problems. We find both polynomial-time and $NP$ -hard special cases like planarity or bounded degree.
Published: 2017

Catalog

Books, media, physical & digital resources

See catalog results

Searchworks

Select search scope, currently: Articles Catalog books, media & more in Jio Institute collections Articles journal articles & other e-resources

Search

Search Constraints

Refine your results

Search Limiters

Topic

Publication Year Range

Language

Journal

Database

Publisher

214 results on '"Université de Montpellier (UM)-Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)"'

Search Results

Catalog

Select search scope, currently: Articles

Catalog

books, media & more in Jio Institute collections

Articles

journal articles & other e-resources