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1. AI-based mobile application to fight antibiotic resistance

Author

Marco Pascucci, Guilhem Royer, Jakub Adamek, Mai Al Asmar, David Aristizabal, Laetitia Blanche, Amine Bezzarga, Guillaume Boniface-Chang, Alex Brunner, Christian Curel, Gabriel Dulac-Arnold, Rasheed M. Fakhri, Nada Malou, Clara Nordon, Vincent Runge, Franck Samson, Ellen Sebastian, Dena Soukieh, Jean-Philippe Vert, Christophe Ambroise, and Mohammed-Amin Madoui

Subjects
Science
Abstract

Antimicrobial resistance is a major global health threat and its development is promoted by antibiotic misuse. Here, the authors present an offline smartphone application for automated and standardized antibiotic susceptibility testing, to be deployed in resource-limited settings.

Published
2021
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2. Learning the optimal scale for GWAS through hierarchical SNP aggregation

Author

Florent Guinot, Marie Szafranski, Christophe Ambroise, and Franck Samson

Subjects
Genome-wide association study, Statistical genetics, Variable selection, Hierarchical clustering, Computer applications to medicine. Medical informatics, R858-859.7, Biology (General), QH301-705.5
Abstract

Abstract Background Genome-Wide Association Studies (GWAS) seek to identify causal genomic variants associated with rare human diseases. The classical statistical approach for detecting these variants is based on univariate hypothesis testing, with healthy individuals being tested against affected individuals at each locus. Given that an individual’s genotype is characterized by up to one million SNPs, this approach lacks precision, since it may yield a large number of false positives that can lead to erroneous conclusions about genetic associations with the disease. One way to improve the detection of true genetic associations is to reduce the number of hypotheses to be tested by grouping SNPs. Results We propose a dimension-reduction approach which can be applied in the context of GWAS by making use of the haplotype structure of the human genome. We compare our method with standard univariate and group-based approaches on both synthetic and real GWAS data. Conclusion We show that reducing the dimension of the predictor matrix by aggregating SNPs gives a greater precision in the detection of associations between the phenotype and genomic regions.

Published
2018
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3. Genome-Wide Study of Structural Variants in Bovine Holstein, Montbéliarde and Normande Dairy Breeds.

Author

Mekki Boussaha, Diane Esquerré, Johanna Barbieri, Anis Djari, Alain Pinton, Rabia Letaief, Gérald Salin, Frédéric Escudié, Alain Roulet, Sébastien Fritz, Franck Samson, Cécile Grohs, Maria Bernard, Christophe Klopp, Didier Boichard, and Dominique Rocha

Subjects
Medicine, Science
Abstract

High-throughput sequencing technologies have offered in recent years new opportunities to study genome variations. These studies have mostly focused on single nucleotide polymorphisms, small insertions or deletions and on copy number variants. Other structural variants, such as large insertions or deletions, tandem duplications, translocations, and inversions are less well-studied, despite that some have an important impact on phenotypes. In the present study, we performed a large-scale survey of structural variants in cattle. We report the identification of 6,426 putative structural variants in cattle extracted from whole-genome sequence data of 62 bulls representing the three major French dairy breeds. These genomic variants affect DNA segments greater than 50 base pairs and correspond to deletions, inversions and tandem duplications. Out of these, we identified a total of 547 deletions and 410 tandem duplications which could potentially code for CNVs. Experimental validation was carried out on 331 structural variants using a novel high-throughput genotyping method. Out of these, 255 structural variants (77%) generated good quality genotypes and 191 (75%) of them were validated. Gene content analyses in structural variant regions revealed 941 large deletions removing completely one or several genes, including 10 single-copy genes. In addition, some of the structural variants are located within quantitative trait loci for dairy traits. This study is a pan-genome assessment of genomic variations in cattle and may provide a new glimpse into the bovine genome architecture. Our results may also help to study the effects of structural variants on gene expression and consequently their effect on certain phenotypes of interest.

Published
2015
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4. Plant-PLMview: a database for identifyingcis-regulatory sequences with preferential positions in gene-proximal regions of plants

Author

Julien Rozière, Franck Samson, Cécile Guichard, Margot Correa, Sylvie Coursol, Marie-Laure Martin, and Véronique Brunaud

Abstract

BackgroundEstablishing relationships between transcription factors and target genes is essential for understanding the mechanisms regulating gene expression, which play a fundamental role in plant adaptation to the local environment. Despite the importance of this research area and the tremendous progress in sequencing methods such as ChIP-seq and DAP-seq, we are still far from a complete reconstruction of thecis-regulatory landscape. Only a small number of transcription factors can be assessed from experimental data to identify theircis-regulatory binding sites. This highlights the role thatin silicoapproaches can play to complement experimental data.ResultsWe have developed Plant-PLMview, a web-accessible database for detecting preferentiallycis-regulatory sequences in the gene-proximal regions of 20 plant species. Users of Plant-PLMview can (i) access the proximal regions of controlled genes from 20 plant species, (ii) query their own DNA motifs or access 840cis-regulatory sequences from various plant resources, and (iii) use a tool called PLMdetect to search for preferentially located motifs in the gene-proximal regions of a list of genes. Results are displayed via a web interface with a list of DNA motifs preferentially located in a region near the start or end of genes, the distribution of these motifs and associated annotations. In addition, a graphical map of the preferential locations of the motifs in the 5’ and 3’-proximal regions of genes provides an overview of all motifs and genes examined.ConclusionPlant-PLMview provides the opportunity to study the proximal landscape of gene regulation in 20 plant genomes. The originality of the database lies in its ease of use thanks to the curated data (cis-regulatory sequences and proximal regions of genes) and the possibility to search through PLMdetect in the 5’-gene-proximal region, but also in the 3’-gene-proximal region, which is rarely explored. The web interface provides numerous graphical views that allow the users to get an overview and interpret the results more easily.

Published
2022
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10. The Transposable Element Environment of Human Genes Differs According to Their Duplication Status and Essentiality

Author

Bérengère Bouillon, Emmanuelle Lerat, Franck Samson, Kévin Normand, Margot Correa, Carène Rizzon, Etienne Birmelé, Laboratoire de Mathématiques et Modélisation d'Evry (LaMME), Université d'Évry-Val-d'Essonne (UEVE)-ENSIIE-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Laboratoire de Biométrie et Biologie Evolutive - UMR 5558 (LBBE), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS), Mathématiques Appliquées Paris 5 (MAP5 - UMR 8145), Institut National des Sciences Mathématiques et de leurs Interactions (INSMI)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), Institut de Recherche Mathématique Avancée (IRMA), Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS), Bioinformatique, phylogénie et génomique évolutive (BPGE), Département PEGASE [LBBE] (PEGASE), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire de Biométrie et Biologie Evolutive - UMR 5558 (LBBE), Eléments transposables, évolution, populations, Département génétique, interactions et évolution des génomes [LBBE] (GINSENG), Institut National des Sciences Mathématiques et de leurs Interactions (INSMI)-Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP), and Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)

Subjects
Transposable element, AcademicSubjects/SCI01140, LINE, Retrotransposon, Biology, Genome, 03 medical and health sciences, chemistry.chemical_compound, 0302 clinical medicine, Gene Duplication, Gene duplication, Genetics, essential genes, Animals, Humans, Gene, Ecology, Evolution, Behavior and Systematics, 030304 developmental biology, Short Interspersed Nucleotide Elements, Regulation of gene expression, Mammals, Recombination, Genetic, 0303 health sciences, Base Composition, Genes, Essential, Genome, Human, [SDV.BID.EVO]Life Sciences [q-bio]/Biodiversity/Populations and Evolution [q-bio.PE], AcademicSubjects/SCI01130, food and beverages, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], SINE, chemistry, Evolutionary biology, gene evolution, DNA Transposable Elements, Human genome, transposable elements, Erratum, 030217 neurology & neurosurgery, DNA, Research Article
Abstract

Erratum: https://doi.org/10.1093/gbe/evab175; International audience; Transposable elements (TEs) are major components of eukaryotic genomes and represent approximately 45% of the human genome. TEs can be important sources of novelty in genomes and there is increasing evidence that TEs contribute to the evolution of gene regulation in mammals. Gene duplication is an evolutionary mechanism that also provides new genetic material and opportunities to acquire new functions. To investigate how duplicated genes are maintained in genomes, here, we explored the TE environment of duplicated and singleton genes. We found that singleton genes have more short-interspersed nuclear elements and DNA transposons in their vicinity than duplicated genes, whereas long-interspersed nuclear elements and long-terminal repeat retrotransposons have accumulated more near duplicated genes. We also discovered that this result is highly associated with the degree of essentiality of the genes with an unexpected accumulation of short-interspersed nuclear elements and DNA transposons around the more-essential genes. Our results underline the importance of taking into account the TE environment of genes to better understand how duplicated genes are maintained in genomes.

Published
2021
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11. The first AI-based mobile application for antibiotic resistance testing

Author

Alex Brunner, Clara Nordon, Ellen Marie Sebastian, Christophe Ambroise, Nada Malou, Jean-Philippe Vert, Jakub Adamek, Gabriel Dulac-Arnold, Marco Pascucci, Dena Soukieh, Mohammed-Amin Madoui, Guillaume Boniface-Chang, Franck Samson, David Aristizabal, Laetitia Blanche, Amine Bezzarga, Vincent Runge, Christian Curel, and Guilhem Royer

Subjects
Susceptibility testing, medicine.diagnostic_test, business.industry, Computer science, medicine.drug_class, media_common.quotation_subject, Antibiotics, Gold standard (test), Antibiotic misuse, Machine learning, computer.software_genre, Antibiotic resistance, Antibiogram, Reading (process), medicine, Artificial intelligence, business, computer, media_common
Abstract

Antimicrobial resistance is a major global health threat and its development is promoted by antibiotic misuse. While disk diffusion antibiotic susceptibility testing (AST, also called antibiogram) is broadly used to test for antibiotic resistance in bacterial infections, it faces strong criticism because of inter-operator variability and the complexity of interpretative reading. Automatic reading systems address these issues, but are not always adapted or available to resource-limited settings. We present the first artificial intelligence (AI)-based, offline smartphone application for antibiogram analysis. The application captures images with the phone’s camera, and the user is guided throughout the analysis on the same device by a user-friendly graphical interface. An embedded expert system validates the coherence of the antibiogram data and provides interpreted results. The fully automatic measurement procedure of our application’s reading system achieves an overall agreement of 90% on susceptibility categorization against a hospital-standard automatic system and 98% against manual measurement (gold standard), with reduced inter-operator variability. The application’s performance showed that the automatic reading of antibiotic resistance testing is entirely feasible on a smartphone. Moreover our application is suited for resource-limited settings, and therefore has the potential to significantly increase patients’ access to AST worldwide.

Published
2020
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13. Learning the optimal scale for GWAS through hierarchical SNP aggregation

Author

Marie Szafranski, Florent Guinot, Christophe Ambroise, Franck Samson, Université d'Évry-Val-d'Essonne (UEVE), Mathématiques et Informatique Appliquées (MIA-Paris), AgroParisTech-Institut National de la Recherche Agronomique (INRA), AgroParisTech, Université Paris Saclay (COmUE), Laboratoire de Mathématiques et Modélisation d'Evry (LaMME), ENSIIE-Université d'Évry-Val-d'Essonne (UEVE)-Institut National de la Recherche Agronomique (INRA)-Centre National de la Recherche Scientifique (CNRS), UMR LaMME - UEVE, CNRS, ENSIIE, USC INRA, 23 bd de France, Evry, France, BIOptimize, Reims, France, Institut National de la Recherche Agronomique (INRA)-Université d'Évry-Val-d'Essonne (UEVE)-ENSIIE-Centre National de la Recherche Scientifique (CNRS), Unité Mathématique, Informatique et Génome (MIG), Institut National de la Recherche Agronomique (INRA), Guinot, Florent, Unité Mathématique Informatique et Génome (MIG), and Institut National de la Recherche Agronomique (INRA)-AgroParisTech

Subjects
FOS: Computer and information sciences, 0301 basic medicine, Genome-wide association study, Statistical genetics, Variable selection, Hierarchical clustering, 01 natural sciences, Biochemistry, Linkage Disequilibrium, 010104 statistics & probability, [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], Gene Frequency, Structural Biology, False positive paradox, [MATH]Mathematics [math], lcsh:QH301-705.5, Applied Mathematics, Methodology Article, 3. Good health, Computer Science Applications, Phenotype, Area Under Curve, lcsh:R858-859.7, [STAT.ME]Statistics [stat]/Methodology [stat.ME], Algorithms, Single-nucleotide polymorphism, Computational biology, Biology, lcsh:Computer applications to medicine. Medical informatics, Polymorphism, Single Nucleotide, Methodology (stat.ME), 03 medical and health sciences, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], SNP, Humans, [INFO]Computer Science [cs], Computer Simulation, Spondylitis, Ankylosing, 0101 mathematics, Molecular Biology, Statistics - Methodology, Statistical hypothesis testing, Genetic association, Univariate, Numerical Analysis, Computer-Assisted, 030104 developmental biology, [SDV.GEN.GH]Life Sciences [q-bio]/Genetics/Human genetics, lcsh:Biology (General), ROC Curve, Case-Control Studies, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie
Abstract

International audience; Motivation: Genome-Wide Association Studies (GWAS) seek to identify causal genomic variants associated with rare human diseases. The classical statistical approach for detecting these variants is based on univariate hypothesis testing, with healthy individuals being tested against affected individuals at each locus. Given that an individual's genotype is characterized by up to one million SNPs, this approach lacks precision, since it may yield a large number of false positives that can lead to erroneous conclusions about genetic associations with the disease. One way to improve the detection of true genetic associations is to reduce the number of hypotheses to be tested by grouping SNPs. Results: We propose a dimension-reduction approach which can be applied in the context of GWAS by making use of the haplotype structure of the human genome. We compare our method with standard univariate and multivariate approaches on both synthetic and real GWAS data, and we show that reducing the dimension of the predictor matrix by aggregating SNPs gives a greater precision in the detection of associations between the phenotype and genomic regions.

Published
2017
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14. Genome-wide study of structural variants in bovine Holstein, Montbéliarde and Normande dairy breeds

Author

Alain Pinton, Frédéric Escudié, Maria Bernard, Anis Djari, Alain Roulet, Mekki Boussaha, Franck Samson, Diane Esquerre, Cécile Grohs, Johanna Barbieri, Sébastien Fritz, Rabia Letaief, Christophe Klopp, Gerald Salin, Didier Boichard, Dominique Rocha, Génétique Animale et Biologie Intégrative (GABI), Institut National de la Recherche Agronomique (INRA)-AgroParisTech, Génétique Physiologie et Systèmes d'Elevage (GenPhySE ), École nationale supérieure agronomique de Toulouse [ENSAT]-Institut National de la Recherche Agronomique (INRA)-Ecole Nationale Vétérinaire de Toulouse (ENVT), Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, Unité de Biométrie et Intelligence Artificielle (UBIA), Institut National de la Recherche Agronomique (INRA), Union nationale des coopératives d’élevage et d’insémination animale (UNCEIA), Unité Mathématique, Informatique et Génome (MIG), ANR-Apisgene Cartoseq, Institut National de la Recherche Agronomique (INRA)-Ecole Nationale Vétérinaire de Toulouse (ENVT), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-École nationale supérieure agronomique de Toulouse [ENSAT], and Union Nationale des Coopératives Agricoles d'Elevage et d'Insémination Animale

Subjects
bovin, Genotype, variants structuraux, [SDV]Life Sciences [q-bio], Quantitative Trait Loci, Genomic Structural Variation, lcsh:Medicine, Single-nucleotide polymorphism, Genome-wide association study, Quantitative trait locus, Biology, Genome, Structural variation, Animals, Copy-number variation, lcsh:Science, 2. Zero hunger, Genetics, Multidisciplinary, génome, lcsh:R, Dairying, Bovine genome, lcsh:Q, Cattle, Animals, Inbred Strains, Genome-Wide Association Study, Research Article
Abstract

High-throughput sequencing technologies have offered in recent years new opportunities to study genome variations. These studies have mostly focused on single nucleotide polymorphisms, small insertions or deletions and on copy number variants. Other structural variants, such as large insertions or deletions, tandem duplications, translocations, and inversions are less well-studied, despite that some have an important impact on phenotypes. In the present study, we performed a large-scale survey of structural variants in cattle. We report the identification of 6,426 putative structural variants in cattle extracted from whole-genome sequence data of 62 bulls representing the three major French dairy breeds. These genomic variants affect DNA segments greater than 50 base pairs and correspond to deletions, inversions and tandem duplications. Out of these, we identified a total of 547 deletions and 410 tandem duplications which could potentially code for CNVs. Experimental validation was carried out on 331 structural variants using a novel high-throughput genotyping method. Out of these, 255 structural variants (77%) generated good quality genotypes and 191 (75%) of them were validated. Gene content analyses in structural variant regions revealed 941 large deletions removing completely one or several genes, including 10 single-copy genes. In addition, some of the structural variants are located within quantitative trait loci for dairy traits. This study is a pan-genome assessment of genomic variations in cattle and may provide a new glimpse into the bovine genome architecture. Our results may also help to study the effects of structural variants on gene expression and consequently their effect on certain phenotypes of interest.

Published
2015
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15. Identification of the putative ancestral allele of bovine single-nucleotide polymorphisms

Author

Coline Billerey, Dominique Rocha, Mekki Boussaha, Franck Samson, Didier Boichard, Génétique Animale et Biologie Intégrative (GABI), Institut National de la Recherche Agronomique (INRA)-AgroParisTech, Institut de génétique et microbiologie [Orsay] (IGM), Université Paris-Sud - Paris 11 (UP11)-Centre National de la Recherche Scientifique (CNRS), Unité Mathématique, Informatique et Génome (MIG), and Institut National de la Recherche Agronomique (INRA)

Subjects
Genotyping Techniques, [SDV]Life Sciences [q-bio], Population, Ancestral allele, Single-nucleotide polymorphism, comparative genomics, Biology, Polymorphism, Single Nucleotide, 03 medical and health sciences, Food Animals, Genetic variation, Databases, Genetic, single-nucleotide polymorphisms, SNP, Animals, Allele, education, Genotyping, 030304 developmental biology, Genetics, Comparative genomics, 0303 health sciences, education.field_of_study, 0402 animal and dairy science, 04 agricultural and veterinary sciences, General Medicine, 040201 dairy & animal science, SNP genotyping, cattle, sequence alignment, Animal Science and Zoology
Abstract

Summary Identifying the action of natural selection from patterns of standing genetic variation has long been of interest to the population genetic community. Thanks to the availability of large single-nucleotide polymorphism (SNP) data sets for many species and of high-throughput SNP genotyping methods, whole-genomic surveys to detect selective sweeps are now possible. Knowing the ancestral allele increases the power to detect selection. We present here a comparative genomic approach to determine the putative ancestral allele of bovine SNPs deposited in public databases. We analysed 19 551 488 SNPs and identified the putative ancestral allele for 14 339 107 SNPs. Our predicted ancestral alleles were in agreement with ancestral alleles detected by genotyping outgroup species for 97% SNPs from the BovineSNP50 BeadChip. This comparison indicates that our comparative genomic-based approach to identify putative ancestral alleles is reliable.

Published
2014
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16. YeastIP: a database for identification and phylogeny of Saccharomycotina yeasts

Author

David Navarro, Franck Samson, Stéphanie Weiss, Serge Casaregola, MICrobiologie de l'ALImentation au Service de la Santé (MICALIS), Institut National de la Recherche Agronomique (INRA)-AgroParisTech, AgroParisTech, Unité Mathématique, Informatique et Génome (MIG), Institut National de la Recherche Agronomique (INRA), Biodiversité et Biotechnologie Fongiques (BBF), Aix Marseille Université (AMU)-Institut National de la Recherche Agronomique (INRA)-École Centrale de Marseille (ECM), École Centrale de Marseille (ECM)-Aix Marseille Université (AMU)-Institut National de la Recherche Agronomique (INRA), Institut National de la Recherche Agronomique (INRA)-Aix Marseille Université (AMU)-École Centrale de Marseille (ECM), and Unité Mathématique Informatique et Génome (MIG)

Subjects
[SDV]Life Sciences [q-bio], Concatenation, Molecular Sequence Data, computer.software_genre, Applied Microbiology and Biotechnology, Microbiology, Genome, DNA, Ribosomal, Access to Information, 03 medical and health sciences, Species level, Ascomycota, Phylogenetics, Saccharomycotina, Good practice, DNA, Fungal, Phylogeny, 030304 developmental biology, 0303 health sciences, Internet, biology, Database, Base Sequence, 030306 microbiology, General Medicine, Sequence Analysis, DNA, biology.organism_classification, Taxonomy (biology), Genome, Fungal, Databases, Nucleic Acid, computer, Relevant information, yeast taxonomyascomycetous yeastsmultigenic analysissequence concatenationphylogenetic reconstruction
Abstract

Abbreviations: ITS, internal transcribed spacer; LSU, large subunit of the nuclear ribosomal DNA; SSU, small subunit of the nuclear ribosomal DNA; mtSSU, small subunit of the mitochondrial ribosomal DNA; With the advances in sequencing techniques, identification of ascomycetous yeasts to the species level and phylogeny reconstruction increasingly require curated and updated taxonomic information. A specific database with nucleotide sequences of the most common markers used for yeast taxonomy and phylogeny and a user-friendly interface allowing identification, taxonomy and phylogeny of yeasts species was developed. By the 1st of September 2012, the YeastIP database contained all the described Saccharomycotina species for which sequences used for taxonomy and phylogeny, such as D1/D2 rDNA and ITS, are available. The database interface was developed to provide a maximum of relevant information and data mining tools, including the following features: (1) the Blastn program for the sequences of the YeastIP database; (2) easy retrieval of selected sequences; (3) display of the available markers for each selected group of species; and (4) a tool to concatenate marker sequences, including those provided by the user. The concatenation tool allows phylogeny reconstruction through a direct link to the Phylogeny.fr platform. YeastIP is thus a unique database in that it provides taxonomic information and guides users in their taxonomic analyses. YeastIP facilitates multigenic analysis to encourage good practice in ascomycetous yeast phylogeny.

Published
2013
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17. DOMIRE : a web server for identifying structural domains and their neighbors in proteins

Author

Peter J. Munson, Byungkook Lee, Franck Samson, Richard Shrager, Jean-François Gibrat, Chin-Hsien Tai, Vichetra Sam, Jean Garnier, Unité Mathématique Informatique et Génome (MIG), Institut National de la Recherche Agronomique (INRA), Mathematical and Statistical Computing Laboratory (MSCL), Center for Information Technology, and National Cancer Institute

Subjects
Statistics and Probability, Protein structure database, Web server, Theoretical computer science, Computer science, [SDV]Life Sciences [q-bio], Structural alignment, Protein domain, Sequence alignment, computer.software_genre, Biochemistry, Genome, Structural genomics, Domain (software engineering), 03 medical and health sciences, 0302 clinical medicine, [INFO]Computer Science [cs], 030212 general & internal medicine, [MATH]Mathematics [math], Databases, Protein, Molecular Biology, 030304 developmental biology, 0303 health sciences, Internet, Proteins, Structural Classification of Proteins database, Computer Science Applications, Protein Structure, Tertiary, Computational Mathematics, Applications Note, ComputingMethodologies_PATTERNRECOGNITION, Computational Theory and Mathematics, proteine, structure, service online, Data mining, computer, Sequence Alignment, Algorithms, Software
Abstract

Summary: The DOMIRE web server implements a novel, automatic, protein structural domain assignment procedure based on 3D substructures of the query protein which are also found within structures of a non-redundant protein database. These common 3D substructures are transformed into a co-occurrence matrix that offers a global view of the protein domain organization. Three different algorithms are employed to define structural domain boundaries from this co-occurrence matrix. For each query, a list of structural neighbors and their alignments are provided. DOMIRE, by displaying the protein structural domain organization, can be a useful tool for defining protein common cores and for unravelling the evolutionary relationship between different proteins. Availability: http://genome.jouy.inra.fr/domire Contact: jean.garnier@jouy.inra.fr

Published
2012
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18. Exploration of plant genomes in the FLAGdb++ environment

Author

Franck Samson, Cécile Guichard, Jean-Charles Leplé, Philippe Label, Véronique Brunaud, Sébastien Aubourg, Sandra Derozier, Jean-Philippe Tamby, Alain Lecharny, Séverine Gagnot, Philippe Grevet, Unité Mathématique Informatique et Génome (MIG), Institut National de la Recherche Agronomique (INRA), Unité de recherche en génomique végétale (URGV), Institut National de la Recherche Agronomique (INRA)-Université d'Évry-Val-d'Essonne (UEVE)-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS), Unité de recherche Amélioration, Génétique et Physiologie Forestières (AGPF), ANR project, Genoplante project, and Aubourg, Sebastien

Subjects
0106 biological sciences, analyse de données, génomique fonctionnelle, Plant Science, computer.software_genre, 01 natural sciences, Genome, DATA INTEGRATION, GENOME DE LA PLANTE, FUNCTIONAL ANALYSIS, Génétique des plantes, GENOMICS, PLANT GENOME, BIOINFORMATICS, FUNCTIONAL GENOMICS, DATA ANALYSIS, STRUCTURAL ANNOTATION, SYSTEM BIOLOGY, COMPARATIVE GENOMICS, GENOMIQUE, ANALYSE FONCTIONNELLE, INTEGRATION DE DONNEES, ANNOTATION STRUCTURALE, lcsh:QH301-705.5, bioinformatique, computer.programming_language, génomique comparée, 0303 health sciences, food and beverages, analyse comparative, DECIPHER, Data mining, Functional genomics, Biotechnology, Data integration, biologie des systèmes, Java, Systems biology, Genomics, Computational biology, lcsh:Plant culture, Biology, Plants genetics, Database, [SDV.GEN.GPL]Life Sciences [q-bio]/Genetics/Plants genetics, 03 medical and health sciences, Genetics, lcsh:SB1-1110, 030304 developmental biology, Comparative genomics, lcsh:Biology (General), computer, 010606 plant biology & botany
Abstract

Background In the contexts of genomics, post-genomics and systems biology approaches, data integration presents a major concern. Databases provide crucial solutions: they store, organize and allow information to be queried, they enhance the visibility of newly produced data by comparing them with previously published results, and facilitate the exploration and development of both existing hypotheses and new ideas. Results The FLAGdb++ information system was developed with the aim of using whole plant genomes as physical references in order to gather and merge available genomic data from in silico or experimental approaches. Available through a JAVA application, original interfaces and tools assist the functional study of plant genes by considering them in their specific context: chromosome, gene family, orthology group, co-expression cluster and functional network. FLAGdb++ is mainly dedicated to the exploration of large gene groups in order to decipher functional connections, to highlight shared or specific structural or functional features, and to facilitate translational tasks between plant species (Arabidopsis thaliana, Oryza sativa, Populus trichocarpa and Vitis vinifera). Conclusion Combining original data with the output of experts and graphical displays that differ from classical plant genome browsers, FLAGdb++ presents a powerful complementary tool for exploring plant genomes and exploiting structural and functional resources, without the need for computer programming knowledge. First launched in 2002, a 15th version of FLAGdb++ is now available and comprises four model plant genomes and over eight million genomic features.

Published
2011
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19. BioWorkFlow: Web Services toolkit and workflow applications evaluation to deploy a confidence network

Author

Marc Wessner, Martin Senger, Franck Samson, Philippe Picouet, François Moreews, Hervé Ménager, Véronique Martin, Sébastien Letort, Catherine Letondal, Mark HOEBEKE, Jerome Gouzy, Jean-François Gibrat, Erwan Corre, Olivier Collin, Sébastien Carrere, Christophe Caron, Pierre Tufféry, Bertrand Néron, Unité de Biologie du développement et biotechnologies (JOUY BIOLOG DEVELOP BIOTECHNOL), Institut National de la Recherche Agronomique (INRA), Physiologie Moléculaire du Transport des Sucres chez les Végétaux (PhyMoTS), Université de Poitiers-Centre National de la Recherche Scientifique (CNRS), Unité Mathématique Informatique et Génome (MIG), Département Logique des Usages, Sciences sociales et Sciences de l'Information (LUSSI), Université européenne de Bretagne - European University of Brittany (UEB)-Télécom Bretagne-Institut Mines-Télécom [Paris] (IMT), Lab-STICC_TB_CID_DECIDE, Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance (Lab-STICC), Université européenne de Bretagne - European University of Brittany (UEB)-École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Télécom Bretagne-Institut Brestois du Numérique et des Mathématiques (IBNM), Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université européenne de Bretagne - European University of Brittany (UEB)-École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Télécom Bretagne-Institut Brestois du Numérique et des Mathématiques (IBNM), Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS), Biological systems and models, bioinformatics and sequences (SYMBIOSE), 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)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria), Institut Pasteur [Paris] (IP), Laboratoire Statistique et Génome (SG), Institut National de la Recherche Agronomique (INRA)-Université d'Évry-Val-d'Essonne (UEVE)-Centre National de la Recherche Scientifique (CNRS), Laboratoire des interactions plantes micro-organismes (LIPM), Institut National de la Recherche Agronomique (INRA)-Centre National de la Recherche Scientifique (CNRS), Végétaux marins et biomolécules, Station biologique de Roscoff [Roscoff] (SBR), Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-GOEMAR-Centre National de la Recherche Scientifique (CNRS), Bioinformatique génomique et moléculaire ((U 726)), Université Paris Diderot - Paris 7 (UPD7)-Institut National de la Santé et de la Recherche Médicale (INSERM), Modélisation mathématique et statistique en biologie et médecine, Ecologie et biologie des interactions (EBI), Institut Mines-Télécom [Paris] (IMT)-Télécom Bretagne-Université européenne de Bretagne - European University of Brittany (UEB), École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Télécom Bretagne-Institut Brestois du Numérique et des Mathématiques (IBNM), Université de Brest (UBO)-Université européenne de Bretagne - European University of Brittany (UEB)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Télécom Bretagne-Institut Brestois du Numérique et des Mathématiques (IBNM), Université de Brest (UBO)-Université européenne de Bretagne - European University of Brittany (UEB)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS), 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)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Inria Rennes – Bretagne Atlantique, Institut Pasteur [Paris], Centre National de la Recherche Scientifique (CNRS)-Institut National de la Recherche Agronomique (INRA), Unité Mathématique, Informatique et Génome (MIG), Unité de Biologie du développement et biotechnologies ( JOUY BIOLOG DEVELOP BIOTECHNOL ), Institut National de la Recherche Agronomique ( INRA ), Ecologie et biologie des interactions ( EBI ), Université de Poitiers-Centre National de la Recherche Scientifique ( CNRS ), Unité Mathématique Informatique et Génome ( MIG ), Département Logique des Usages, Sciences sociales et Sciences de l'Information ( LUSSI ), Université européenne de Bretagne ( UEB ) -Télécom Bretagne-Institut Mines-Télécom [Paris], Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance ( Lab-STICC ), École Nationale d'Ingénieurs de Brest ( ENIB ) -Université de Bretagne Sud ( UBS ) -Université de Brest ( UBO ) -Télécom Bretagne-Institut Brestois du Numérique et des Mathématiques ( IBNM ), Université de Brest ( UBO ) -Université européenne de Bretagne ( UEB ) -ENSTA Bretagne-Institut Mines-Télécom [Paris]-Centre National de la Recherche Scientifique ( CNRS ) -École Nationale d'Ingénieurs de Brest ( ENIB ) -Université de Bretagne Sud ( UBS ) -Université de Brest ( UBO ) -Télécom Bretagne-Institut Brestois du Numérique et des Mathématiques ( IBNM ), Université de Brest ( UBO ) -Université européenne de Bretagne ( UEB ) -ENSTA Bretagne-Institut Mines-Télécom [Paris]-Centre National de la Recherche Scientifique ( CNRS ), Biological systems and models, bioinformatics and sequences ( SYMBIOSE ), 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 de Recherche en Informatique et en Automatique ( Inria ) -Centre National de la Recherche Scientifique ( CNRS ) -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 de Recherche en Informatique et en Automatique ( Inria ) -Centre National de la Recherche Scientifique ( CNRS ) -Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique ( Inria ), Institut national de la recherche agronomique [Toulouse] ( INRA Toulouse ), Laboratoire Statistique et Génome ( SG ), Institut National de la Recherche Agronomique ( INRA ) -Université d'Évry-Val-d'Essonne ( UEVE ) -Centre National de la Recherche Scientifique ( CNRS ), Laboratoire des interactions plantes micro-organismes ( LIPMO ), Institut National de la Recherche Agronomique ( INRA ) -Centre National de la Recherche Scientifique ( CNRS ), Unité Mathématique, Informatique et Génome ( MIG ), Végétaux marins et biomolécules ( UMR7139 ), Université Pierre et Marie Curie - Paris 6 ( UPMC ) -GOEMAR-Centre National de la Recherche Scientifique ( CNRS ), Bioinformatique génomique et moléculaire, Université Paris Diderot - Paris 7 ( UPD7 ) -Institut National de la Santé et de la Recherche Médicale ( INSERM ), and Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Diderot - Paris 7 (UPD7)

Subjects
[ INFO.INFO-BI ] Computer Science [cs]/Bioinformatics [q-bio.QM], [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], [ SDV.BIBS ] Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], ComputingMilieux_MISCELLANEOUS
Abstract

National audience

Published
2008

20. FLAGdb++: a database for the functional analysis of the Arabidopsis genome

Author

Sylvain Duchêne, Michel Caboche, Yannick de Oliveira, Véronique Brunaud, Alain Lecharny, Franck Samson, Sébastien Aubourg, Unité de recherche en génomique végétale (URGV), Institut National de la Recherche Agronomique (INRA)-Université d'Évry-Val-d'Essonne (UEVE)-Centre National de la Recherche Scientifique (CNRS), and Centre National de la Recherche Scientifique (CNRS)-Université d'Évry-Val-d'Essonne (UEVE)-Institut National de la Recherche Agronomique (INRA)

Subjects
0106 biological sciences, génomique fonctionnelle, Information Storage and Retrieval, Locus (genetics), Computational biology, BASE DE DONNEES, ANALYSE FONCTIONNELLE, 01 natural sciences, Genome, Chromosomes, Plant, 03 medical and health sciences, User-Computer Interface, Arabidopsis, Databases, Genetic, Genetics, [SDV.BBM.BC]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Biochemistry [q-bio.BM], bioinformatique, 030304 developmental biology, 0303 health sciences, Internet, biology, génome, gène, Experimental data, Computational Biology, Graphical display, Genomics, Articles, biology.organism_classification, Visualization, [SDV.BBM.BC]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Biomolecules [q-bio.BM], arabidopsis, Arabidopsis genome, Genome, Plant, 010606 plant biology & botany
Abstract

FLAGdb++ is dedicated to the integration and visualization of data for high-throughput functional analysis of a fully sequenced genome, as illustrated for Arabidopsis. FLAGdb++ displays the predicted or experimental data in a position-dependent way and displays correlations and relationships between different features. FLAGdb++ provides for a given genome region, summarized characteristics of experimental materials like probe lengths, locations and specificities having an impact upon the confidence we will put in the experimental results. A selected subset of the available information is linked to a locus represented on an easy-to-interpret and memorable graphical display. Data are curated, processed and formatted before their integration into FLAGdb++. FLAGdb++ contains different options for easy back and forth navigation through many loci selected at the start of a session. It includes an original two-component visualization of the data, a genome-wide and a local view, which are permanently linked and display complementary information. Density curves along the chromosomes may be displayed in parallel for suggesting correlations between different structural and functional data. FLAGdb++ is fully accessible at http://genoplante-info.infobiogen.fr/FLAGdb/.

Published
2004
Full Text
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21. T-DNA integration into Arabidopsis genome depends on sequences of pre-insertion sites

Author

Loïc Lepiniec, Richard DeRose, Véronique Brunaud, Franck Samson, Alain Lecharny, Corinne Cruaud, Bertrand Dubreucq, Sandrine Balzergue, Michel Caboche, Sébastien Aubourg, Georges Pelletier, Nicole Bechtold, Stéphanie Chauvin, Unité de recherche en génomique végétale (URGV), Institut National de la Recherche Agronomique (INRA)-Université d'Évry-Val-d'Essonne (UEVE)-Centre National de la Recherche Scientifique (CNRS), Biologie des Semences (LBS), Institut National de la Recherche Agronomique (INRA)-Institut National Agronomique Paris-Grignon (INA P-G), Unité de recherche Génétique et amélioration des plantes (GAP), Institut National de la Recherche Agronomique (INRA), and Centre National de la Recherche Scientifique (CNRS)-Université d'Évry-Val-d'Essonne (UEVE)-Institut National de la Recherche Agronomique (INRA)

Subjects
0106 biological sciences, Base pair, 5' Flanking Region, [SDV]Life Sciences [q-bio], 5' flanking region, Scientific Report, Arabidopsis, Sequence Homology, Biology, 01 natural sciences, Biochemistry, 3' Flanking Region, 03 medical and health sciences, chemistry.chemical_compound, D-loop, Genetics, 5' REGION FLANQUANTE, DNA Integration, Molecular Biology, 3' REGION FLANQUANTE, 030304 developmental biology, 0303 health sciences, Gene Transfer Techniques, DNA, Plants, Genetically Modified, chemistry, T-DNA, Human genome, In vitro recombination, Genome, Plant, 010606 plant biology & botany
Abstract

A statistical analysis of 9000 flanking sequence tags characterizing transferred DNA (T-DNA) transformants in Arabidopsis sheds new light on T-DNA insertion by illegitimate recombination. T-DNA integration is favoured in plant DNA regions with an A-T-rich content. The formation of a short DNA duplex between the host DNA and the left end of the T-DNA sets the frame for the recombination. The sequence immediately downstream of the plant A-T-rich region is the master element for setting up the DNA duplex, and deletions into the left end of the integrated T-DNA depend on the location of a complementary sequence on the T-DNA. Recombination at the right end of the T-DNA with the host DNA involves another DNA duplex, 2–3 base pairs long, that preferentially includes a G close to the right end of the T-DNA.

Published
2002
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22. Improved PCR-walking for large-scale isolation of plant T-DNA borders

Author

S. Chauvin, R. de Rose, Franck Samson, Loïc Lepiniec, V. Biaudet, Corinne Cruaud, Michel Caboche, F. Le Boulaire, Bertrand Dubreucq, A. Lecharny, J. Weissenbach, Sandrine Balzergue, I. Le-Clainche, Unité de recherche en génomique végétale (URGV), Institut National de la Recherche Agronomique (INRA)-Université d'Évry-Val-d'Essonne (UEVE)-Centre National de la Recherche Scientifique (CNRS), Biologie des Semences (LBS), Institut National de la Recherche Agronomique (INRA)-Institut National Agronomique Paris-Grignon (INA P-G), ProdInra, Migration, and Centre National de la Recherche Scientifique (CNRS)-Université d'Évry-Val-d'Essonne (UEVE)-Institut National de la Recherche Agronomique (INRA)

Subjects
0106 biological sciences, Genetics, DNA, Bacterial, 0303 health sciences, Scale (ratio), [SDV]Life Sciences [q-bio], DNA, Biology, Isolation (microbiology), 01 natural sciences, Polymerase Chain Reaction, METODE, General Biochemistry, Genetics and Molecular Biology, law.invention, [SDV] Life Sciences [q-bio], 03 medical and health sciences, chemistry.chemical_compound, chemistry, law, Polymerase chain reaction, ComputingMilieux_MISCELLANEOUS, 030304 developmental biology, 010606 plant biology & botany, Biotechnology
Abstract

International audience

Published
2001

23. Computerized genetic map of Bacillus subtilis

Author

Stanislav Dusko Ehrlich, V Biaudet, Franck Samson, C Anagnostopoulos, Philippe Bessières, Unité de recherche Génétique Microbienne (UGM), Institut National de la Recherche Agronomique (INRA), and ProdInra, Migration

Subjects
Genetics, 0303 health sciences, Bacillaceae, biology, Databases, Factual, 030306 microbiology, Chromosome Mapping, Bacillus subtilis, biology.organism_classification, Microbiology, Bacillales, 03 medical and health sciences, User-Computer Interface, [SDV.MP]Life Sciences [q-bio]/Microbiology and Parasitology, Gene mapping, Genes, Bacterial, Gene, [SDV.MP] Life Sciences [q-bio]/Microbiology and Parasitology, Bacteria, Software, 030304 developmental biology, Genomic organization
Published
1996

24. A Web Site for Detecting Protein Structural Domain Neighbours

Author

Franck Samson, Jean Garnier, Vichetra Sam, Chin-Hsein Tai, Richard Shrager, Peter J. Munson, Jean-François Gibrat, Unité Mathématique Informatique et Génome (MIG), Institut National de la Recherche Agronomique (INRA), and National Institutes of Health

Subjects
0303 health sciences, Similarity (geometry), Matching (graph theory), Computer science, [SDV]Life Sciences [q-bio], Biophysics, Protein Data Bank (RCSB PDB), Sorting, Computational biology, Object (computer science), Domain (software engineering), 03 medical and health sciences, 0302 clinical medicine, Protein structure, [INFO]Computer Science [cs], [MATH]Mathematics [math], Peptide sequence, ComputingMilieux_MISCELLANEOUS, 030217 neurology & neurosurgery, 030304 developmental biology
Abstract

A query protein structure is compared with the VAST program to a database of target structures from the PDB (PDB40, list of protein structures having less than 40% of identical residues: 19 500 structures version 2011). The threshold of the VAST program is lowered in order to find the largest possible number of structures having a local similarity with the query protein. The purpose of the web site is to define structural domains in the query protein using the recurrence of these locally similar substructures. (http://genome.jouy.inra.fr/domire/). The list of matches is subsequently sorted according to two criteria: the number of aligned residues by VAST is at least 40% of the number of residues of the target, and 80% of the target length is aligned including gaps of non aligned residues if less than 40. Besides this list, a residue-residue alignment of the structural neighbour on the amino acid sequence of the query protein is provided together with a 3D view of their superposition. The object of this sorting is to help in detecting remote homologues and isolated protein structures matching the domain structures of a protein.

Published
2013
Full Text
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25. Analyse NGS sur infrastructure distribuée

Author

Martin, Tiphaine, Modèles et algorithmes pour la Bioinformatique et la Visualisation d'informations, (MABIOVIS), Laboratoire Bordelais de Recherche en Informatique (LaBRI), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB), GIS IBISA GRISBI, Christophe Blanchet and Christophe Caron and Olivier Collin and Stéphane Delmotte and Clément Gauthey and Tiphaine Martin and Franck Samson and Bruno Spataro, and Martin, Tiphaine

Subjects
[SDV.BIBS] Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], [INFO.INFO-DC] Computer Science [cs]/Distributed, Parallel, and Cluster Computing [cs.DC], [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], [INFO.INFO-DC]Computer Science [cs]/Distributed, Parallel, and Cluster Computing [cs.DC], [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], ComputingMilieux_MISCELLANEOUS, [INFO.INFO-BI] Computer Science [cs]/Bioinformatics [q-bio.QM]
Abstract

National audience; use case : New Generation Sequencing on GRID

Published
2011

26. Présentation des Centres Bioinformatiques Régionaux : RENABI-SO

Author

Martin, Tiphaine, Modèles et algorithmes pour la Bioinformatique et la Visualisation d'informations, (MABIOVIS), Laboratoire Bordelais de Recherche en Informatique (LaBRI), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB), GIS IBISA GRISBI, Christophe Blanchet and Christophe Caron and Olivier Collin and Stéphane Delmotte and Clément Gauthey and Tiphaine Martin and Franck Samson and Bruno Spataro, and Martin, Tiphaine

Subjects
[SDV.BIBS] Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], [INFO.INFO-DC] Computer Science [cs]/Distributed, Parallel, and Cluster Computing [cs.DC], [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], [INFO.INFO-DC]Computer Science [cs]/Distributed, Parallel, and Cluster Computing [cs.DC], [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], [INFO.INFO-BI] Computer Science [cs]/Bioinformatics [q-bio.QM]
Abstract

National audience; Description of RENABI-SO node within GRISBI

Published
2011

27. Présentation des Centres Bioinformatiques Régionaux : RENABI-GO

Author

Martin, Tiphaine, Caron, Christophe, Collin, Olivier, Modèles et algorithmes pour la Bioinformatique et la Visualisation d'informations, (MABIOVIS), Laboratoire Bordelais de Recherche en Informatique (LaBRI), Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS), Station biologique de Roscoff [Roscoff] (SBR), Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS), Biological systems and models, bioinformatics and sequences (SYMBIOSE), 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)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria), GIS IBISA GRISBI, Christophe Blanchet and Christophe Caron and Olivier Collin and Stéphane Delmotte and Clément Gauthey and Tiphaine Martin and Franck Samson and Bruno Spataro, Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB), 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)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), and Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Inria Rennes – Bretagne Atlantique

Subjects
[INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], [INFO.INFO-DC]Computer Science [cs]/Distributed, Parallel, and Cluster Computing [cs.DC], [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], ComputingMilieux_MISCELLANEOUS
Abstract

National audience

Published
2011

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