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26 results on '"Pierre Larmande"'

1. Managing High-Density Genotyping Data with Gigwa

Author

Guilhem, Sempéré, Pierre, Larmande, and Mathieu, Rouard

Subjects
Genotype, Genotyping Techniques, High-Throughput Nucleotide Sequencing, Genomics, Polymorphism, Single Nucleotide, Software
Abstract

Next generation sequencing technologies enabled high-density genotyping for large numbers of samples. Nowadays SNP calling pipelines produce up to millions of such markers, but which need to be filtered in various ways according to the type of analyses. One of the main challenges still lies in the management of an increasing volume of genotyping files that are difficult to handle for many applications. Here, we provide a practical guide for efficiently managing large genomic variation data using Gigwa, a user-friendly, scalable and versatile application that may be deployed either remotely on web servers or on a local machine.

Published
2022

2. AgroLD: A Knowledge Graph Database for Plant Functional Genomics

Author

Pierre, Larmande, Gildas, Tagny Ngompe, Aravind, Venkatesan, and Manuel, Ruiz

Subjects
Proteomics, Databases, Factual, Genomics, Plants, Pattern Recognition, Automated
Abstract

Recent advances in high-throughput technologies have resulted in tremendous increase in the amount of data in the agronomic domain. There is an urgent need to effectively integrate complementary information to understand the biological system in its entirety. We have developed AgroLD, a knowledge graph that exploits the Semantic Web technology and some of the relevant standard domain ontologies, to integrate information on plant species and in this way facilitating the formulation of new scientific hypotheses. This chapter outlines some integration results of the project, which initially focused on genomics, proteomics and phenomics.

Published
2022

3. Managing High-Density Genotyping Data with Gigwa

Author

Guilhem Sempéré, Pierre Larmande, Mathieu Rouard, Interactions hôtes-vecteurs-parasites-environnement dans les maladies tropicales négligées dues aux trypanosomatides (UMR INTERTRYP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut de Recherche pour le Développement (IRD)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Université de Bordeaux (UB), Département Systèmes Biologiques (Cirad-BIOS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), South Green Bioinformatics Platform [Montpellier], Diversité, adaptation, développement des plantes (UMR DIADE), 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 [France-Sud])-Université de Montpellier (UM), Alliance of Bioversity International and the International Center for Tropical Agriculture (CIAT) [Rome] (Alliance), and Consultative Group on International Agricultural Research [CGIAR] (CGIAR)

Subjects
INDELs, VCF, NoSQL database, interoperability, web tool, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], SNP markers
Abstract

International audience; Next generation sequencing technologies enabled high-density genotyping for large numbers of samples. Nowadays SNP calling pipelines produce up to millions of such markers, but which need to be filtered in various ways according to the type of analyses. One of the main challenges still lies in the management of an increasing volume of genotyping files that are difficult to handle for many applications. Here, we provide a practical guide for efficiently managing large genomic variation data using Gigwa, a user-friendly, scalable and versatile application that may be deployed either remotely on web servers or on a local machine.

Published
2022

5. Enabling Fast Annotation Process With Table2Annotation Tool

Author

Kazim Muhammed Jibril and Pierre Larmande

Subjects
Annotation, Information retrieval, Computer science, Ontology, Natural language
Abstract

Semantic annotation is the process in which semantic concepts are linked to natural language. It helps in boosting the search and access of resources and can be used in information retrieval systems to increase the queries from the user. In this paper, we are interested in identifying ontological concepts in scientific text contained in spreadsheet. We developed a tool which is able to handle various types of spreadsheet. Furthermore, we used the benefits of NCBO Annotator API provided by BioPortal to enhance the semantic annotation functionalities covering spreadsheet data. Table2Annotation developed strengths in certain criteria like speed, error handling and complex concept matching.AvailabilityGitHub : https://github.com/pierrelarmande/ontology-project

Published
2020

6. PyRice: a Python package for querying Oryza sativa databases

Author

Pierre Larmande, Quan Do, Ho Bich Hai, University of Science and Technology of Hanoi (USTH), 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]), and South Green Bioinformatics Platform [Montpellier]

Subjects
Statistics and Probability, Databases, Factual, Computer science, Information Storage and Retrieval, computer.software_genre, Biochemistry, 03 medical and health sciences, Documentation, Molecular Biology, Gene, 030304 developmental biology, computer.programming_language, 0303 health sciences, Oryza sativa, Database, business.industry, 030302 biochemistry & molecular biology, Oryza, Modular design, Python (programming language), Computer Science Applications, Computational Mathematics, Computational Theory and Mathematics, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], business, computer, Software
Abstract

SummaryCurrently, gene information available for Oryza sativa species is located in various online heterogeneous data sources. Moreover, methods of access are also diverse, mostly web-based and sometimes query APIs, which might not always be straightforward for domain experts. The challenge is to collect information quickly from these applications and combine it logically, to facilitate scientific research. We developed a Python package named PyRice, a unified programming API to access all supported databases at the same time with consistent output. PyRice design is modular and implements a smart query system which fits the computing resources to optimize the query speed. As a result, PyRice is easy to use and produces intuitive results.Availability and implementationhttps://github.com/SouthGreenPlatform/PyRiceDocumentationhttps://pyrice.readthedocs.ioContactpierre.larmande@ird.frLicence informationMITSupplementary informationSupplementary data are available online.

Published
2020

7. Corrigendum to: Rice Galaxy: an open resource for plant science

Author

Joshua Dizon, Nickolai Alexandrov, Tobias Kretzschmar, Pierre Larmande, Venice Margarette Juanillas, Gabriel Zhou, Ramil Mauleon, John Robert Mendoza, Nicolas Beaume, Michael J. Thomson, Kunalan Ratharanjan, Jon Peter Perdon, Alexis Dereeper, Manuel Ruiz, Lindsay R. Triplett, Gaëtan Droc, Jillian M. Lang, Jason H. Haga, Beth Plale, Jan E. Leach, and Locedie Mansueto

Subjects
Resource (biology), business.industry, Environmental resource management, Oryza, Health Informatics, Genomics, Galaxy, Computer Science Applications, Plant Breeding, Geography, Plant science, Seed Bank, Databases, Genetic, Corrigendum, business, Software
Abstract

Rice molecular genetics, breeding, genetic diversity, and allied research (such as rice-pathogen interaction) have adopted sequencing technologies and high-density genotyping platforms for genome variation analysis and gene discovery. Germplasm collections representing rice diversity, improved varieties, and elite breeding materials are accessible through rice gene banks for use in research and breeding, with many having genome sequences and high-density genotype data available. Combining phenotypic and genotypic information on these accessions enables genome-wide association analysis, which is driving quantitative trait loci discovery and molecular marker development. Comparative sequence analyses across quantitative trait loci regions facilitate the discovery of novel alleles. Analyses involving DNA sequences and large genotyping matrices for thousands of samples, however, pose a challenge to non-computer savvy rice researchers.The Rice Galaxy resource has shared datasets that include high-density genotypes from the 3,000 Rice Genomes project and sequences with corresponding annotations from 9 published rice genomes. The Rice Galaxy web server and deployment installer includes tools for designing single-nucleotide polymorphism assays, analyzing genome-wide association studies, population diversity, rice-bacterial pathogen diagnostics, and a suite of published genomic prediction methods. A prototype Rice Galaxy compliant to Open Access, Open Data, and Findable, Accessible, Interoperable, and Reproducible principles is also presented.Rice Galaxy is a freely available resource that empowers the plant research community to perform state-of-the-art analyses and utilize publicly available big datasets for both fundamental and applied science.

Published
2019

8. AgroPortal: A vocabulary and ontology repository for agronomy

Author

Vincent Emonet, Pierre Larmande, Sophie Aubin, Esther Dzale Yeumo, Clement Jonquet, Valeria Pesce, Marie-Angélique Laporte, John Graybeal, Elizabeth Arnaud, Anne Toulet, Mark A. Musen, Système Multi-agent, Interaction, Langage, Evolution (SMILE), 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), Stanford Center for BioMedical Informatics Research (BMIR), Stanford University, Bioversity International [Montpellier], Bioversity International [Rome], Consultative Group on International Agricultural Research [CGIAR] (CGIAR)-Consultative Group on International Agricultural Research [CGIAR] (CGIAR), DIST Délégation Information Scientifique et Technique (DV-IST), Institut National de la Recherche Agronomique (INRA), Food and Agriculture Organization of the United Nations [Rome, Italie] (FAO), 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]), French National Research Agency [ANR-12-JS02-01001], NUMEV Labex [ANR-10-LABX-20], Computational Biology Institute of Montpellier [ANR-11-BINF-0002], University of Montpellier, CNRS, ANR-12-JS02-0010,SIFR,Indexation sémantique de ressources biomédicales francophones(2012), 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), European Project: 701771,H2020,H2020-MSCA-IF-2015,SIFRm(2016), 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), Stanford University [Stanford], Bioversity International, Consultative Group on International Agricultural Research [CGIAR], Food and Agricultural Organization of the United Nations (FAO), United Nations Organization, 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
0301 basic medicine, Computer science, 02 engineering and technology, Horticulture, Knowledge organization system, Ontology (information science), Domain (software engineering), Ontology repository, Open Biomedical Ontologies, 03 medical and health sciences, A50 - Recherche agronomique, Controlled vocabulary, Ontologies, 0202 electrical engineering, electronic engineering, information engineering, Semantic Web, ontologie, vocabulaire, Metadata, Semantic annotation, U10 - Informatique, mathématiques et statistiques, [INFO.INFO-WB]Computer Science [cs]/Web, Forestry, Biodiversity, computer.file_format, Recommendation, Knowledge organization systems or artifacts, Agronomy, Computer Science Applications, 030104 developmental biology, C30 - Documentation et information, Mapping, Food, Simple Knowledge Organization System, 020201 artificial intelligence & image processing, Controlled vocabularies, Banque de données, Plant sciences, Agronomy and Crop Science, computer, Modèle mathématique
Abstract

International audience; Many vocabularies and ontologies are produced to represent and annotate agronomic data. However, those ontologies are spread out, in different formats, of different size, with different structures and from overlapping domains. Therefore, there is need for a common platform to receive and host them, align them, and enabling their use in agro-informatics applications. By reusing the National Center for Biomedical Ontologies (NCBO) BioPortal technology, we have designed AgroPortal, an ontology repository for the agronomy domain. The AgroPortal project re-uses the biomedical domain's semantic tools and insights to serve agronomy, but also food, plant, and biodiversity sciences. We offer a portal that features ontology hosting, search, versioning, visualization, comment, and recommendation; enables semantic annotation; stores and exploits ontology alignments; and enables interoperation with the semantic web. The AgroPortal specifically satisfies requirements of the agronomy community in terms of ontology formats (e.g., SKOS vocabularies and trait dictionaries) and supported features (offering detailed metadata and advanced annotation capabilities). In this paper, we present our platform's content and features, including the additions to the original technology, as well as preliminary outputs of five driving agronomic use cases that participated in the design and orientation of the project to anchor it in the community. By building on the experience and existing technology acquired from the biomedical domain, we can present in AgroPortal a robust and feature-rich repository of great value for the agronomic domain.

Published
2018

9. Scientific workflows for computational reproducibility in the life sciences: Status, challenges and opportunities

Author

Olivier Collin, Sarah Cohen-Boulakia, Konrad Hinsen, Alban Gaignard, Khalid Belhajjame, Pierre Larmande, Fabien Mareuil, Christine Froidevaux, Frédéric Lemoine, Christophe Blanchet, Jérôme Chopard, Christophe Pradal, Hervé Ménager, Yvan Le Bras, 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), Laboratoire de Recherche en Informatique (LRI), Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-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), Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision (LAMSADE), Université Paris Dauphine-PSL, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS), Plateforme bioinformatique GenOuest [Rennes], Université de Rennes (UR)-Plateforme Génomique Santé Biogenouest®-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 (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 Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-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), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), 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), Centre hospitalier universitaire de Nantes (CHU Nantes), Synchrotron SOLEIL (SSOLEIL), Centre National de la Recherche Scientifique (CNRS), Centre de biophysique moléculaire (CBM), Université d'Orléans (UO)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Chimie du CNRS (INC)-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]), Bioinformatique évolutive - Evolutionary Bioinformatics, Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS), Centre d'Informatique pour la Biologie, Institut Pasteur [Paris] (IP), Centre de Bioinformatique, Biostatistique et Biologie Intégrative (C3BI), 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), 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 Français de Bioinformatique - UMS CNRS 3601 (IFB-CORE), Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), The authors acknowledge the support of GDR CNRS MaDICS, pro-gramme CPER Region Bretagne 'CeSGO', and programme Region Paysde la Loire 'Connect Talent' (SyMeTRIC). We acknowledge funding by thecall Infrastructures in Biology and Health' in the framework of the FrenchInvestments for the Future' (ANR-11-INBS-0012 and ANR-11-INBS-0013).This work was conducted in part at the IBC (Institute of Computational Bi-ology) in Montpellier, France., ANR-11-INBS-0013,IFB (ex Renabi-IFB),Institut français de bioinformatique(2011), ANR-11-INBS-0011,NeurATRIS,Infrastructure de Recherche Translationnelle pour les Biothérapies en Neurosciences(2011), 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), CentraleSupélec-Université Paris-Sud - Paris 11 (UP11)-Centre National de la Recherche Scientifique (CNRS), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Plateforme Génomique Santé Biogenouest®-Inria Rennes – Bretagne Atlantique, 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 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 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), Université d'Orléans (UO)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Chimie du CNRS (INC), Institut Pasteur [Paris]-Centre National de la Recherche Scientifique (CNRS), Institut Pasteur [Paris], 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'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), 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)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), 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 National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Université Paris Dauphine-PSL-Centre National de la Recherche Scientifique (CNRS), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), 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), Université d'Orléans (UO)-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 de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de la Recherche Agronomique (INRA), ANR-11-INBS-0013/11-INBS-0013,ReNaBi-IFB,Institut français de bioinformatique(2011), ANR-11-INBS-0011/11-INBS-0011,NeurATRIS,Infrastructure de Recherche Translationnelle pour les Biothérapies en Neurosciences(2011), Cohen-Boulakia, Sarah, Infrastructures - Institut français de bioinformatique - - IFB (ex Renabi-IFB)2011 - ANR-11-INBS-0013 - INBS - VALID, and Infrastructures - Infrastructure de Recherche Translationnelle pour les Biothérapies en Neurosciences - - NeurATRIS2011 - ANR-11-INBS-0011 - INBS - VALID

Subjects
0301 basic medicine, Scientific Workflows, Computer Networks and Communications, Computer science, [INFO.INFO-DS]Computer Science [cs]/Data Structures and Algorithms [cs.DS], [INFO.INFO-DS] Computer Science [cs]/Data Structures and Algorithms [cs.DS], Context (language use), 02 engineering and technology, Domain (software engineering), 03 medical and health sciences, 0202 electrical engineering, electronic engineering, information engineering, [INFO.INFO-DB] Computer Science [cs]/Databases [cs.DB], Packaging environments, Use case, Set (psychology), environments, [INFO.INFO-BI] Computer Science [cs]/Bioinformatics [q-bio.QM], Reproducibility, [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], Data science, 030104 developmental biology, Workflow, C30 - Documentation et information, Packaging, 13. Climate action, Hardware and Architecture, Scientific workflows, Provenance, [INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR], 020201 artificial intelligence & image processing, [INFO.INFO-IR] Computer Science [cs]/Information Retrieval [cs.IR], U30 - Méthodes de recherche, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], Software
Abstract

International audience; With the development of new experimental technologies, biologists are faced with an avalanche of data to be computationally analyzed for scientific advancements and discoveries to emerge. Faced with the complexity of analysis pipelines, the large number of computational tools, and the enormous amount of data to manage, there is compelling evidence that many if not most scientific discoveries will not stand the test of time: increasing the reproducibility of computed results is of paramount importance. The objective we set out in this paper is to place scientific workflows in the context of reproducibility. To do so, we define several kinds of repro-ducibility that can be reached when scientific workflows are used to perform experiments. We characterize and define the criteria that need to be catered for by reproducibility-friendly scientific workflow systems, and use such criteria to place several representative and widely used workflow systems and companion tools within such a framework. We also discuss the remaining challenges posed by reproducible scientific workflows in the life sciences. Our study was guided by three use cases from the life science domain involving in silico experiments.

Published
2017

10. BrAPI-an application programming interface for plant breeding applications

Author

Guilhem Sempere, Patrick König, Bruno Costa, Jan Erik Backlund, Jack Elendil B. Lagare, Viana C. Calaminos, Jeremy D. Edwards, Gordon Stephen, Omar Eduardo Benites-Alfaro, Pierre Carceller, Martin Basterrechea Salido, David LeBauer, Maikel P. H. Verouden, Guillaume Cornut, Nicolas Morales, Philip Glaser, David Marshall, Evangelia Papoutsoglou, Cyril Pommier, Guillaume Bauchet, Valentin Guignon, Kelly R. Robbins, Iain Milne, David A. Lyon, Pascal Neveu, Brian Pearce, Chaitanya Sarma, Qi Sun, Nahuel Soldevilla, Sebastian Raubach, Peter Selby, Clarysabel Tovar, Grzegorz Uszynski, Rafael Abbeloos, Reinhard Simon, Paul Shaw, Marie-Angélique Laporte, Mathieu Rouard, Star Yanxin Gao, Naymesh Mistry, Puthick Hok, Uwe Scholz, Trevor W. Rife, Ricardo H. Ramirez-Gonzalez, Dave Matthews, Mehmood Ghaffar, Pierre Larmande, Matthias Lange, Andrzej Kilian, Clayton Birkett, Ivan Perez-Masias, Angel Manica Raquel, Abhishek Rathore, Lukas A. Mueller, Richard Finkers, Wren, Jonathan, Flanders Institute for Biotechnology, Leibniz Institute of Plant Genetics and Crop Plant Research [Gatersleben] (IPK-Gatersleben), Boyce Thompson Institute [Ithaca], International Potato Center, USDA-ARS : Agricultural Research Service, International Rice Research Institute [Philippines] (IRRI), Consultative Group on International Agricultural Research [CGIAR] (CGIAR), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Unité de Recherche Génomique Info (URGI), Institut National de la Recherche Agronomique (INRA), Instituto de Biologia Experimental e Tecnológica (IBET), Wageningen University and Research Centre (WUR), Cornell University [New York], CGIAR, Diversity Arrays Technology Pty Ltd (DArT P/L), Institut de Recherche pour le Développement (IRD), University of Arizona, The James Hutton Institute, LeafNode Technology, Partenaires INRAE, 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), John Innes Centre [Norwich], Biotechnology and Biological Sciences Research Council (BBSRC), International Crops Research Institute for the Semi-Arid Tropics [Inde] (ICRISAT), Kansas State University, Leafnode Technolog, Biometris, Gates Foundation, Bioversity International, Federal Ministry of Education & Research (BMBF) : FKZ 031A536A, 031B0190A, Boyce Thompson Institute for Plant Research CGIAR Research Program on Roots, Tubers and Bananas, Cornell University Excellence in Breeding Platform, 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 John Innes Centre

Subjects
0106 biological sciences, Germplasm, programme informatique, Computer science, [SDV]Life Sciences [q-bio], Data management, computer.software_genre, 01 natural sciences, Biochemistry, F30 - Génétique et amélioration des plantes, User-Computer Interface, Software, Laboratorium voor Plantenveredeling, [MATH]Mathematics [math], 0303 health sciences, Application programming interface, U10 - Informatique, mathématiques et statistiques, Genomics, Original Papers, Computer Science Applications, Computational Mathematics, Computational Theory and Mathematics, Web service, sélection végétale, Statistics and Probability, Technology and Engineering, Databases and Ontologies, génomique, 03 medical and health sciences, Bioinformatique, application informatique, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, Life Science, [INFO]Computer Science [cs], Plant breeding, Genotyping, Molecular Biology, 030304 developmental biology, business.industry, Biology and Life Sciences, Amélioration des plantes, Data science, Plant Breeding, business, computer, 010606 plant biology & botany
Abstract

Motivation Modern genomic breeding methods rely heavily on very large amounts of phenotyping and genotyping data, presenting new challenges in effective data management and integration. Recently, the size and complexity of datasets have increased significantly, with the result that data are often stored on multiple systems. As analyses of interest increasingly require aggregation of datasets from diverse sources, data exchange between disparate systems becomes a challenge. Results To facilitate interoperability among breeding applications, we present the public plant Breeding Application Programming Interface (BrAPI). BrAPI is a standardized web service API specification. The development of BrAPI is a collaborative, community-based initiative involving a growing global community of over a hundred participants representing several dozen institutions and companies. Development of such a standard is recognized as critical to a number of important large breeding system initiatives as a foundational technology. The focus of the first version of the API is on providing services for connecting systems and retrieving basic breeding data including germplasm, study, observation, and marker data. A number of BrAPI-enabled applications, termed BrAPPs, have been written, that take advantage of the emerging support of BrAPI by many databases. Availability and implementation More information on BrAPI, including links to the specification, test suites, BrAPPs, and sample implementations is available at https://brapi.org/. The BrAPI specification and the developer tools are provided as free and open source.

Published
2018

11. Evaluating Named-Entity Recognition approaches in plant molecular biology

Author

Pierre Larmande, Khoat Than, and Huy Do

Subjects
Conditional random field, business.industry, computer.software_genre, Molecular biology, Long short term memory, ComputingMethodologies_PATTERNRECOGNITION, Text mining, Named-entity recognition, Face (geometry), Benchmark (computing), State (computer science), business, CRFS, computer
Abstract

Text mining research is becoming an important topic in biology with the aim to extract biological entities from scientific papers in order to extend the biological knowledge. 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 able to detect entities such as genes, proteins and phenotypic traits. Since there is rare benchmarks 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 (LSTM) model, and combined with Conditional Random Fields (CRFs) to extract information of rice genes and proteins. We analyzed the performance of LSTM-CRF when applying to the Oryzabase dataset and improved the results up to 86% in F1. We found that on average, the result from LSTM-CRF is more exploitable with the new benchmark.

Published
2018

12. Evaluating Named-Entity Recognition Approaches in Plant Molecular Biology

Author

Khoat Than, Huy Do, and Pierre Larmande

Subjects
0301 basic medicine, Conditional random field, Sequence, Exploit, business.industry, Computer science, 02 engineering and technology, computer.software_genre, Molecular biology, 03 medical and health sciences, Long short term memory, ComputingMethodologies_PATTERNRECOGNITION, 030104 developmental biology, Text mining, Named-entity recognition, 0202 electrical engineering, electronic engineering, information engineering, Benchmark (computing), 020201 artificial intelligence & image processing, CRFS, business, computer
Abstract

Text mining research is becoming an important topic in biology with the aim to extract biological entities from scientific papers in order to extend the biological knowledge. However, few thorough studies are developed for plant molecular biology data, especially rice, thus resulting a lack of datasets available to exploit advanced machine learning methods able to detect entities such as genes and proteins. In this article, we first developed a dataset from the Ozyzabase - a database of rice gene, and used it as the benchmark. Then, we evaluated the performance of two Name Entities Recognition (NER) methods for sequence tagging: a Long Short Term Memory (LSTM) model, combined with Conditional Random Fields (CRFs), and a hybrid method based on the dictionary lookup combining with some machine learning systems to improve result. We analyzed the performance of these methods when apply to the Oryzabase dataset and improved the results. On average, the result from LSTM-CRF reaching 86% in \(F_{1}\) is more exploitable.

Published
2018

13. AgBioData consortium recommendations for sustainable genomics and genetics databases for agriculture

Author

Christopher J. Mungall, Ethalinda K. S. Cannon, Rex T. Nelson, Pankaj Jaiswal, Daureen Nesdill, Marie-Angélique Laporte, Steven B. Cannon, Margaret R. Woodhouse, James P. Carson, David Grant, Margaret Staton, Jacqueline D. Campbell, Marcela K. Tello-Ruiz, Ramona Walls, Carson M. Andorf, Monica Munoz-Torres, Laurel Cooper, Jill L. Wegrzyn, Victor P. Unda, Sabarinath Subramaniam, Stephen P. Ficklin, Sook Jung, Pierre Larmande, James M. Reecy, Christine G. Elsik, Tanya Z. Berardini, Nathan Dunn, Fiona M. McCarthy, Monica F. Poelchau, Liya Wang, Elizabeth Arnaud, Clement Jonquet, Jodi L. Humann, Nic Herndon, Doreen Ware, Deepak Unni, Emily S. Grau, Leonore Reiser, Clayton Birkett, Doreen Main, Taner Z. Sen, Zhi-Liang Hu, Jing Yu, Gerard R. Lazo, Jason Williams, Carissa A. Park, Bradford Condon, Lacey-Anne Sanderson, Lisa C. Harper, Naama Menda, Andrew Farmer, Sushma Naithani, Dept Hort & Landscape Architecture, Washington State University (WSU), Georgetown University [Washington] (GU), Department of Botany and Plant Pathology, Oregon State University (OSU), University of Missouri [Columbia] (Mizzou), University of Missouri System, Energy and Sustainability Research Division, University of Nottingham, UK (UON), Department of Animal Science and Center for Integrated Animal Genomics, Iowa State University (ISU), Plastes et différenciation cellulaire (PDC), Université Joseph Fourier - Grenoble 1 (UJF)-Centre National de la Recherche Scientifique (CNRS), WEB-CUBE, 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), Stanford School of Medicine [Stanford], Stanford Medicine, Stanford University-Stanford University, German Centre for Integrative Biodiversity Research, Diversité, adaptation, développement des plantes (UMR DIADE), 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), Lawrence Berkeley National Laboratory [Berkeley] (LBNL), Department of Animal Science, Department of Entomology and Plant Pathology, The University of Tennessee [Knoxville], Department of Anatomy, Cold Spring Harbor Laboratory, Novartis Institutes for BioMedical Research (NIBR), WEB Architecture x Semantic WEB x WEB of Data (WEB3), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), and 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])

Subjects
[SDV.SA]Life Sciences [q-bio]/Agricultural sciences, 0301 basic medicine, Computer science, Best practice, Data management, Interoperability, Biological database, Breeding, computer.software_genre, General Biochemistry, Genetics and Molecular Biology, 03 medical and health sciences, Databases, 0302 clinical medicine, Genetic, Library and Information Studies, Surveys and Questionnaires, Databases, Genetic, 2. Zero hunger, Biological data, Metadata, [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], Data curation, Database, business.industry, [INFO.INFO-WB]Computer Science [cs]/Web, Agriculture, Genomics, Data Format, [INFO.INFO-TT]Computer Science [cs]/Document and Text Processing, 030104 developmental biology, Data access, Gene Ontology, [INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR], Zero Hunger, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], General Agricultural and Biological Sciences, business, computer, 030217 neurology & neurosurgery, Information Systems
Abstract

© The Author(s) 2018. Published by Oxford University Press. The future of agricultural research depends on data. The sheer volume of agricultural biological data being produced today makes excellent data management essential. Governmental agencies, publishers and science funders require data management plans for publicly funded research. Furthermore, the value of data increases exponentially when they are properly stored, described, integrated and shared, so that they can be easily utilized in future analyses. AgBioData (https://www.agbiodata.org) is a consortium of people working at agricultural biological databases, data archives and knowledgbases who strive to identify common issues in database development, curation and management, with the goal of creating database products that are more Findable, Accessible, Interoperable and Reusable. We strive to promote authentic, detailed, accurate and explicit communication between all parties involved in scientific data. As a step toward this goal, we present the current state of biocuration, ontologies, metadata and persistence, database platforms, programmatic (machine) access to data, communication and sustainability with regard to data curation. Each section describes challenges and opportunities for these topics, along with recommendations and best practices.

Published
2018

14. Erratum to: Gigwa-Genotype investigator for genome-wide analyses

Author

Guilhem Sempere, Florian Philippe, Pierre Larmande, Alexis Dereeper, Gautier Sarah, and Manuel Ruiz

Subjects
0301 basic medicine, Integrative bioinformatics, Computer science, SNP, Health Informatics, NoSQL, Computational biology, INDEL, Proteomics, Genome, Human genetics, Computer Science Applications, Web interface, Genomic variations, 03 medical and health sciences, HapMap, 030104 developmental biology, MongoDB, VCF, Genotype, Technical Note
Abstract

Background Exploring the structure of genomes and analyzing their evolution is essential to understanding the ecological adaptation of organisms. However, with the large amounts of data being produced by next-generation sequencing, computational challenges arise in terms of storage, search, sharing, analysis and visualization. This is particularly true with regards to studies of genomic variation, which are currently lacking scalable and user-friendly data exploration solutions. Description Here we present Gigwa, a web-based tool that provides an easy and intuitive way to explore large amounts of genotyping data by filtering it not only on the basis of variant features, including functional annotations, but also on genotype patterns. The data storage relies on MongoDB, which offers good scalability properties. Gigwa can handle multiple databases and may be deployed in either single- or multi-user mode. In addition, it provides a wide range of popular export formats. Conclusions The Gigwa application is suitable for managing large amounts of genomic variation data. Its user-friendly web interface makes such processing widely accessible. It can either be simply deployed on a workstation or be used to provide a shared data portal for a given community of researchers. Electronic supplementary material The online version of this article (doi:10.1186/s13742-016-0131-8) contains supplementary material, which is available to authorized users.

Published
2016

15. In-depth molecular and phenotypic characterization in a rice insertion line library facilitates gene identification through reverse and forward genetics approaches

Author

Mathias Lorieux, Mélisande Blein, Jaime Lozano, Mathieu Bouniol, Gaétan Droc, Anne Diévart, Christophe Périn, Delphine Mieulet, Nadège Lanau, Martine Bès, Claire Rouvière, Céline Gay, Pietro Piffanelli, Pierre Larmande, Corinne Michel, Isabelle Barnola, Corinne Biderre-Petit, Christophe Sallaud, Pascual Perez, Fabienne Bourgis, Alain Ghesquière, Pascal Gantet, Joe Tohme, Jean Benoit Morel, and Emmanuel Guiderdoni

Subjects
0106 biological sciences, 2. Zero hunger, Genetics, 0303 health sciences, Oryza sativa, Sequence analysis, Mutant, food and beverages, Plant Science, Plant disease resistance, Biology, Oryza, biology.organism_classification, 01 natural sciences, Forward genetics, Reverse genetics, 03 medical and health sciences, Agronomy and Crop Science, Gene, 030304 developmental biology, 010606 plant biology & botany, Biotechnology
Abstract

We report here the molecular and phenotypic features of a library of 31,562 insertion lines generated in the model japonica cultivar Nipponbare of rice (Oryza sativa L.), called Oryza Tag Line (OTL). Sixteen thousand eight hundred and fourteen T-DNA and 12,410 Tos17 discrete insertion sites have been characterized in these lines. We estimate that 8686 predicted gene intervals--i.e. one-fourth to one-fifth of the estimated rice nontransposable element gene complement--are interrupted by sequence-indexed T-DNA (6563 genes) and/or Tos17 (2755 genes) inserts. Six hundred and forty-three genes are interrupted by both T-DNA and Tos17 inserts. High quality of the sequence indexation of the T2 seed samples was ascertained by several approaches. Field evaluation under agronomic conditions of 27,832 OTL has revealed that 18.2% exhibit at least one morphophysiological alteration in the T1 progeny plants. Screening 10,000 lines for altered response to inoculation by the fungal pathogen Magnaporthe oryzae allowed to observe 71 lines (0.7%) developing spontaneous lesions simulating disease mutants and 43 lines (0.4%) exhibiting an enhanced disease resistance or susceptibility. We show here that at least 3.5% (four of 114) of these alterations are tagged by the mutagens. The presence of allelic series of sequence-indexed mutations in a gene among OTL that exhibit a convergent phenotype clearly increases the chance of establishing a linkage between alterations and inserts. This convergence approach is illustrated by the identification of the rice ortholog of AtPHO2, the disruption of which causes a lesion-mimic phenotype owing to an over-accumulation of phosphate, in nine lines bearing allelic insertions.

Published
2012

17. Reversible Nuclear Oculomotor Nerve Paralysis

Author

Alain Autret, Pierre Larmande, B. De Toffol, and Bertr Gaymard

Subjects
medicine.diagnostic_test, business.industry, Right hemiparesis, Computed tomography, Anatomy, medicine.disease, Oculomotor nucleus, Resorption, Midbrain, Hematoma, Weber's syndrome, nervous system, Neurology, Medicine, Neurology (clinical), business, Oculomotor Nerve Paralysis
Abstract

A patient presented with a right hemiparesis and a left nuclear third nerve syndrome. The CT scan showed a hematoma of the left mesencephalon, near the oculomotor nucleus. After resorption of the hema

Published
1990

18. A new cacao linkage map based on codominant markers: development and integration of 201 new microsatellite markers

Author

Claire Lanaud, M. Abouladze, Brigitte Courtois, Jeanne A.K. N'Goran, Didier Clément, Ange-Marie Risterucci, Tatiana Pugh, C. Deletrez, Philippe Brottier, Olivier Fouet, and Pierre Larmande

Subjects
Population, Distribution des populations, Biology, Genome, F30 - Génétique et amélioration des plantes, Variation génétique, Gene mapping, Genetic linkage, Genetics, Theobroma cacao, Marqueur génétique, Genomic library, education, Crosses, Genetic, DNA Primers, Linkage (software), education.field_of_study, Cacao, Genomic Library, Génome, Polymorphism, Genetic, Microsatellite, Chromosome Mapping, General Medicine, Carte génétique, RFLP, Restriction fragment length polymorphism, Agronomy and Crop Science, Polymorphism, Restriction Fragment Length, Biotechnology, Microsatellite Repeats
Abstract

A linkage map of cacao based on codominant markers has been constructed by integrating 201 new simple sequence repeats (SSR) developed in this study with a number of isoenzymes, restriction fragment length polymorphisms (RFLP), microsatellite markers and resis- tance and defence gene analogs (Rgenes-RFLP) previ- ously mapped in cacao. A genomic library enriched for (GA)n and (CA)n was constructed, and 201 new mi- crosatellite loci were mapped on 135 individuals from the same mapping population used to establish the first reference maps. This progeny resulted from a cross between two heterozygous cacao clones: an Upper-Ama- zon Forastero (UPA 402) and a Trinitario (UF 676). The new map contains 465 markers (268 SSRs, 176 RFLPs, five isoenzymes and 16 Rgenes-RFLP) arranged in ten linkage groups corresponding to the haploid chromosome number of cacao. Its length is 782.8 cM, with an average interval distance between markers of 1.7 cM. The new microsatellite markers were distributed throughout all linkage groups of the map, but their distribution was not random. The length of the map established with only SSRs was 769.6 cM, representing 94.8% of the total map. The current level of genome coverage is approximately one microsatellite every 3 cM. This new reference map provides a set of useful markers that is transferable across different mapping populations and will allow the identi- fication and comparison of the most important regions involved in the variation of the traits of interest and the development of marker-assisted selection strategies.

Published
2003

19. Trouble de la lecture du braille par lesion droite chez une patiente devenue aveugle

Author

Catherine Belin, Danièle Perrier, and Pierre Larmande

Subjects
Reading disability, medicine.medical_specialty, Cognitive Neuroscience, Experimental and Cognitive Psychology, Context (language use), Audiology, Braille, Developmental psychology, Behavioral Neuroscience, Cerebral hemisphere, Laterality, Braille reading, medicine, Right hemisphere, Psychology
Abstract

A right-handed woman who became blind at 25 was a good braille reader. After a right parieto-occipital brain damage, she developed a braille reading disorder which contrasted with a completely normal language. This reading disability cannot be analysed in terms of left hemineglect syndrome; rather, it is explained in the broader context of the patient's haptic disorders in many others fields. This hypothesis is connected to others works which suggest a right hemispheric specialization for tactile information processing.

Published
1988

20. Théorie sur l’inversion du nystagmus opto-cinétique chez les sujets porteurs d’un nystagmus congénital

Author

Pierre Larmande, Bertr Pautrizel, and Serge Prier

Subjects
Ophthalmology, medicine.medical_specialty, genetic structures, business.industry, Medicine, In patient, General Medicine, Optokinetic reflex, Audiology, business, eye diseases, Sensory Systems, Congenital nystagmus
Abstract

In patients with congenital nystagmus, the optokinetic nystagmus is often reversed: the quick phase of the optokinetic nystagmus is directed toward the side of rotation of the drum. Based on a clinical and mathematical study, the authors suggest that the pendular appearance of numerous congenital nystagmus can explain this abnormality.

Published
1983

21. Influence de la deviation pathologique du regard par lesion du tronc cerebral, sur l'equilibre inter-hemispherique etudie par le test d'ecoute dichotique non verbal

Author

Danièle Perrier, Catherine Belin, Pierre Larmande, and Jean Cambier

Subjects
medicine.medical_specialty, Palsy, genetic structures, Dichotic listening, Cognitive Neuroscience, Eye disease, Experimental and Cognitive Psychology, Audiology, medicine.disease, Brainstem lesion, Gaze, Lesion, Behavioral Neuroscience, otorhinolaryngologic diseases, medicine, Gaze deviation, medicine.symptom, Psychology, Neuroscience, Pathological
Abstract

Three cases of patients with lateral gaze palsy due to brainstem lesion were studied. They underwent a non-verbal dichotic listening test which evidenced a significant decrease of performance in the ear located on the same side as the lesion. This result can be interpreted as an inter-hemispheric imbalance linked to the pathological gaze deviation.

Published
1988

22. Periodic Alternating Gaze

Author

Dominique Henin, Michel Jan, Pierre Larmande, and André Gouazé

Subjects
nervous system, business.industry, Abnormal ocular motility, Medicine, Surgery, Neurology (clinical), business, Gaze, Neuroscience, Periodic alternating
Abstract

A clinicopathological study with electro-oculographic recording of a patient with periodic alternating gaze (PAG) is presented. There have been five previously reported anatomical observations of patients with this disorder. Bilateral cerebral disease with a mesencephalic lesion is observed. A study of oculovestibular reactions suggests that the generator of PAG is independent of the vestibular system

Published
1982

23. Down-beat nystagmus: case report with magnetic resonance imaging and surgical treatment

Author

Pierre Larmande, Yvon Guegan, Marie Thérèse Iba-Zizen, and Jean François Pinel

Subjects
Adult, medicine.medical_specialty, Eye disease, Nystagmus, Nystagmus, Pathologic, Oscillopsia, Medicine, Humans, Cyst, Medulla, Medulla Oblongata, medicine.diagnostic_test, business.industry, Magnetic resonance imaging, medicine.disease, Magnetic Resonance Imaging, Syringomyelia, Surgery, Vertical nystagmus, Female, Neurology (clinical), Radiology, medicine.symptom, business
Abstract

A woman with down-beat nystagmus (DBN) underwent magnetic resonance imaging (MRI), which showed a syringo-myelic cyst in the medulla. Surgical treatment of the cyst ended the abnormal eye movement. This case demonstrates the use of MRI for the diagnosis of DBN. It also shows the value of surgical treatment when a cyst is responsible for this symptom, as in Arnold-Chiari malformation. (Neurosurgery 21:736-739, 1987)

Published
1987

24. Periodic alternating gaze

Author

Pierre Larmande, L Dongmo, J Limodin, and Belin C

Subjects
business.industry, Medicine, Computer vision, Surgery, Artificial intelligence, Neurology (clinical), business, Gaze, Periodic alternating
Published
1987

25. Baclofen as a treatment for nystagmus

Author

Pierre Larmande

Subjects
chemistry.chemical_compound, Baclofen, Neurology, chemistry, business.industry, Anesthesia, Medicine, Neurology (clinical), Nystagmus, medicine.symptom, business
Published
1982

26. Periodic alternating gaze

Author

Louis Dongmo, Marie Ruchoux, Jocelyne Limodin, and Pierre Larmande

Subjects
Periodicity, Eye Movements, business.industry, Ischemia, Anatomy, Cerebral Infarction, Middle Aged, medicine.disease, Crus cerebri, Gaze, Periodic alternating, Lesion, nervous system, Mesencephalon, Medicine, Humans, Female, Surgery, Neurology (clinical), medicine.symptom, business, Brain Stem
Abstract

A clinicopathological study of a patient with periodic alternating gaze (PAG) is presented. PAG is usually associated with bilateral cerebral hemispheric ischemia. In our case, the anatomical lesion is very limited, restricted to the crus cerebri. The anatomical pathways involved and the mechanism of PAG are discussed.

Published
1987

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