Search

Your search keyword '"Jean-François Gibrat"' showing total 88 results
Start Over Author "Jean-François Gibrat"
Sorry, I don't understand your search. ×
88 results on '"Jean-François Gibrat"'

1. A short note on dynamic programming in a band

Author

Jean-François Gibrat

Subjects
NGS, Read correction, Semi-global alignment, Computer applications to medicine. Medical informatics, R858-859.7, Biology (General), QH301-705.5
Abstract

Abstract Background Third generation sequencing technologies generate long reads that exhibit high error rates, in particular for insertions and deletions which are usually the most difficult errors to cope with. The only exact algorithm capable of aligning sequences with insertions and deletions is a dynamic programming algorithm. Results In this note, for the sake of efficiency, we consider dynamic programming in a band. We show how to choose the band width in function of the long reads’ error rates, thus obtaining an O(N32) $O(N^{\frac {3}{2}})$ algorithm in space and time. We also propose a procedure to decide whether this algorithm, when applied to semi-global alignments, provides the optimal score. Conclusions We suggest that dynamic programming in a band is well suited to the problem of aligning long reads between themselves and can be used as a core component of methods for obtaining a consensus sequence from the long reads alone. The function implementing the dynamic programming algorithm in a band is available, as a standalone program, at: https://forgemia.inra.fr/jean-francois.gibrat/BAND_DYN_PROG.git

Published
2018
Full Text
View/download PDF

2. On the use of algebraic topology concepts to check the consistency of genome assembly

Author

Jean-François Gibrat

Subjects
ngs technologies, olc assembly graphs, genomic repetitions, homology groups, betti numbers, Biology (General), QH301-705.5, Physiology, QP1-981, Physics, QC1-999
Abstract

This paper presents a preliminary work consisting of two contributions. The first one is the design of a very efficient algorithm based on an “Overlap-Layout-Consensus” (OLC) graph to assemble the long reads provided by 3rd generation technologies. The second concerns the analysis of this graph using algebraic topology concepts to determine, in advance, whether the assembly of the genome will be straightforward, i.e., whether it will lead to a pseudo-Hamiltonian path or cycle, or whether the results will need to be scrutinized. In the latter case, it will be necessary to look for “loops” in the OLC assembly graph caused by unresolved repeated genomic regions, and then try to untie the “knots” created by these regions.

Published
2019
Full Text
View/download PDF

3. Overview of a surface-ripened cheese community functioning by meta-omics analyses.

Author

Eric Dugat-Bony, Cécile Straub, Aurélie Teissandier, Djamila Onésime, Valentin Loux, Christophe Monnet, Françoise Irlinger, Sophie Landaud, Marie-Noëlle Leclercq-Perlat, Pascal Bento, Sébastien Fraud, Jean-François Gibrat, Julie Aubert, Frédéric Fer, Eric Guédon, Nicolas Pons, Sean Kennedy, Jean-Marie Beckerich, Dominique Swennen, and Pascal Bonnarme

Subjects
Medicine, Science
Abstract

Cheese ripening is a complex biochemical process driven by microbial communities composed of both eukaryotes and prokaryotes. Surface-ripened cheeses are widely consumed all over the world and are appreciated for their characteristic flavor. Microbial community composition has been studied for a long time on surface-ripened cheeses, but only limited knowledge has been acquired about its in situ metabolic activities. We applied metagenomic, metatranscriptomic and biochemical analyses to an experimental surface-ripened cheese composed of nine microbial species during four weeks of ripening. By combining all of the data, we were able to obtain an overview of the cheese maturation process and to better understand the metabolic activities of the different community members and their possible interactions. Furthermore, differential expression analysis was used to select a set of biomarker genes, providing a valuable tool that can be used to monitor the cheese-making process.

Published
2015
Full Text
View/download PDF

4. The arthrobacter arilaitensis Re117 genome sequence reveals its genetic adaptation to the surface of cheese.

Author

Christophe Monnet, Valentin Loux, Jean-François Gibrat, Eric Spinnler, Valérie Barbe, Benoit Vacherie, Frederick Gavory, Edith Gourbeyre, Patricia Siguier, Michaël Chandler, Rayda Elleuch, Françoise Irlinger, and Tatiana Vallaeys

Subjects
Medicine, Science
Abstract

Arthrobacter arilaitensis is one of the major bacterial species found at the surface of cheeses, especially in smear-ripened cheeses, where it contributes to the typical colour, flavour and texture properties of the final product. The A. arilaitensis Re117 genome is composed of a 3,859,257 bp chromosome and two plasmids of 50,407 and 8,528 bp. The chromosome shares large regions of synteny with the chromosomes of three environmental Arthrobacter strains for which genome sequences are available: A. aurescens TC1, A. chlorophenolicus A6 and Arthrobacter sp. FB24. In contrast however, 4.92% of the A. arilaitensis chromosome is composed of ISs elements, a portion that is at least 15 fold higher than for the other Arthrobacter strains. Comparative genomic analyses reveal an extensive loss of genes associated with catabolic activities, presumably as a result of adaptation to the properties of the cheese surface habitat. Like the environmental Arthrobacter strains, A. arilaitensis Re117 is well-equipped with enzymes required for the catabolism of major carbon substrates present at cheese surfaces such as fatty acids, amino acids and lactic acid. However, A. arilaitensis has several specificities which seem to be linked to its adaptation to its particular niche. These include the ability to catabolize D-galactonate, a high number of glycine betaine and related osmolyte transporters, two siderophore biosynthesis gene clusters and a high number of Fe(3+)/siderophore transport systems. In model cheese experiments, addition of small amounts of iron strongly stimulated the growth of A. arilaitensis, indicating that cheese is a highly iron-restricted medium. We suggest that there is a strong selective pressure at the surface of cheese for strains with efficient iron acquisition and salt-tolerance systems together with abilities to catabolize substrates such as lactic acid, lipids and amino acids.

Published
2010
Full Text
View/download PDF

5. The complete genome of Propionibacterium freudenreichii CIRM-BIA1, a hardy actinobacterium with food and probiotic applications.

Author

Hélène Falentin, Stéphanie-Marie Deutsch, Gwenaël Jan, Valentin Loux, Anne Thierry, Sandrine Parayre, Marie-Bernadette Maillard, Julien Dherbécourt, Fabien J Cousin, Julien Jardin, Patricia Siguier, Arnaud Couloux, Valérie Barbe, Benoit Vacherie, Patrick Wincker, Jean-François Gibrat, Claude Gaillardin, and Sylvie Lortal

Subjects
Medicine, Science
Abstract

BackgroundPropionibacterium freudenreichii is essential as a ripening culture in Swiss-type cheeses and is also considered for its probiotic use. This species exhibits slow growth, low nutritional requirements, and hardiness in many habitats. It belongs to the taxonomic group of dairy propionibacteria, in contrast to the cutaneous species P. acnes. The genome of the type strain, P. freudenreichii subsp. shermanii CIRM-BIA1 (CIP 103027(T)), was sequenced with an 11-fold coverage.Methodology/principal findingsThe circular chromosome of 2.7 Mb of the CIRM-BIA1 strain has a GC-content of 67% and contains 22 different insertion sequences (3.5% of the genome in base pairs). Using a proteomic approach, 490 of the 2439 predicted proteins were confirmed. The annotation revealed the genetic basis for the hardiness of P. freudenreichii, as the bacterium possesses a complete enzymatic arsenal for de novo biosynthesis of aminoacids and vitamins (except panthotenate and biotin) as well as sequences involved in metabolism of various carbon sources, immunity against phages, duplicated chaperone genes and, interestingly, genes involved in the management of polyphosphate, glycogen and trehalose storage. The complete biosynthesis pathway for a bifidogenic compound is described, as well as a high number of surface proteins involved in interactions with the host and present in other probiotic bacteria. By comparative genomics, no pathogenicity factors found in P. acnes or in other pathogenic microbial species were identified in P. freudenreichii, which is consistent with the Generally Recognized As Safe and Qualified Presumption of Safety status of P. freudenreichii. Various pathways for formation of cheese flavor compounds were identified: the Wood-Werkman cycle for propionic acid formation, amino acid degradation pathways resulting in the formation of volatile branched chain fatty acids, and esterases involved in the formation of free fatty acids and esters.Conclusions/significanceWith the exception of its ability to degrade lactose, P. freudenreichii seems poorly adapted to dairy niches. This genome annotation opens up new prospects for the understanding of the P. freudenreichii probiotic activity.

Published
2010
Full Text
View/download PDF

12. Protein Threading.

Author

Guillaume Collet, Rumen Andonov, Nicola Yanev, and Jean-François Gibrat

Published
2009

20. Proposal for a European Public Health Research Infrastructure for Sharing of health and Medical administrative data (PHRIMA).

Author

Anita Burgun, Dina V. Oksen, Wolfgang Kuchinke, Hans-Ulrich Prokosch, Thomas Ganslandt, Iain E. Buchan, Tjeerd Pieter van Staa, James A. Cunningham, Marianne L. Gjerstorff, Jean-Charles Dufour, Jean-François Gibrat, Macha Nikolski, Pierre Verger, Anne Cambon-Thomsen, Cristina Masella, Emanuele Lettieri, Paolo Bertele, Marjut Salokannel, Rodolphe Thiébaut, Charles Persoz, Geneviève Chêne, and Christian Ohmann

Published
2015
Full Text
View/download PDF

28. Ten steps to get started in Genome Assembly and Annotation

Author

Lieven Sterck, Brane Leskošek, Lucile Soler, Cederic Notredame, Erik Hjerde, Stéphanie Bocs, Mahesh Binzer-Panchal, Laurent Bouri, Henrik Lantz, Joelle Amselem, Christophe Klopp, Jean-François Gibrat, Olga Vinnere Pettersson, Salvadors Capella-Gutierrez, Anna Vlasova, Victoria Dominguez Del Angel, Institut Français de Bioinformatique, Université Paris-Saclay, Department of Chemistry, RIDER UNIVERSITY, Department of Plant Biotechnology and Bioinformatics, Ghent University [Belgium] (UGENT), Department of Plant Systems Biology, VIB, Spanish National Bioinformatics Institute (INB), Centro Nacional de Supercomputación, BSC Electronics, Centre for Genomic Regulation (CRG), Universitat Pompeu Fabra [Barcelona], Uppsala Genome Center, NGI/SciLifeLab - Department of Immunology, Genetics and Pathology, Uppsala University, Unité de Recherche Génomique Info (URGI), Institut National de la Recherche Agronomique (INRA), Université de Tunis [Tunis], Laboratory of Technologies of Information and Communication and Electrical Engineering, National Superior School of Engineers of Tunis, Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), South Green Bioinformatics Platform, Unité de Mathématiques et Informatique Appliquées de Toulouse (MIAT INRA), Mathématiques et Informatique Appliquées du Génome à l'Environnement [Jouy-En-Josas] (MaIAGE), Faculty of Medicine, Institute for Biostatistics and Medical Informatics, University of Ljubljana, IMBIM/NBIS/SciLifeLab, 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)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), Universiteit Gent = Ghent University [Belgium] (UGENT), Center for Plant Systems Biology (PSB Center), Vlaams Instituut voor Biotechnologie [Ghent, Belgique] (VIB), Barcelona Supercomputing Center - Centro Nacional de Supercomputacion (BSC - CNS), Universitat Pompeu Fabra [Barcelona] (UPF), Université de Tunis, Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Barcelona Supercomputing Center, 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), Universiteit Gent = Ghent University (UGENT), South Green Bioinformatics Platform [Montpellier], and Hjerde, Erik

Subjects
0301 basic medicine, Computer science, General assembly, Data management, [SDV]Life Sciences [q-bio], Annotation, Interoperability, Assembly, Sequence assembly, Bioinformatik och systembiologi, Genome, General Biochemistry, Genetics and Molecular Biology, F30 - Génétique et amélioration des plantes, Workflows, World Wide Web, 03 medical and health sciences, DNA--Analysis, Genomes, General Pharmacology, Toxicology and Pharmaceutics, ADN--Anàlisi, FAIR, Genome assembly, Bioinformatics and Systems Biology, General Immunology and Microbiology, business.industry, Biology and Life Sciences, General Medicine, Genome project, Articles, DNA, Opinion Article, L10 - Génétique et amélioration des animaux, ELIXIR-EXCELERATE, 030104 developmental biology, Workflow, NGS, business, Ciències de la salut [Àrees temàtiques de la UPC], Genome annotation
Abstract

As a part of the ELIXIR-EXCELERATE efforts in capacity building, we present here 10 steps to facilitate researchers getting started in genome assembly and genome annotation. The guidelines given are broadly applicable, intended to be stable over time, and cover all aspects from start to finish of a general assembly and annotation project. Intrinsic properties of genomes are discussed, as is the importance of using high quality DNA. Different sequencing technologies and generally applicable workflows for genome assembly are also detailed. We cover structural and functional annotation and encourage readers to also annotate transposable elements, something that is often omitted from annotation workflows. The importance of data management is stressed, and we give advice on where to submit data and how to make your results Findable, Accessible, Interoperable, and Reusable (FAIR). ELIXIR-EXCELERATE is funded by the European Commission within the Research Infrastructures Programme of Horizon 2020 [676559].

Published
2018
Full Text
View/download PDF

29. An economical security architecture for multi-cloud application deployments in federated environments

Author

Christophe Blanchet, Begüm İlke Zilci, Axel Küpper, Jean-François Gibrat, Yuri Demchenko, Fatih Turkmen, Mathias Slawik, Mathématiques et Informatique Appliquées du Génome à l'Environnement [Jouy-En-Josas] (MaIAGE), Institut National de la Recherche Agronomique (INRA), Technische Universität Berlin (TUB), University of Amsterdam, Centre National de la Recherche Scientifique (CNRS), UE, ProdInra, Archive Ouverte, Technische Universität Berlin (TU), and University of Amsterdam [Amsterdam] (UvA)

Subjects
Development environment, 021110 strategic, defence & security studies, Computer science, business.industry, [SDV]Life Sciences [q-bio], 0211 other engineering and technologies, Cloud computing, security architectures, 02 engineering and technology, Enterprise information security architecture, Architecture design, Computer security, computer.software_genre, Cloud federation, multi-cloud application, [SDV] Life Sciences [q-bio], federated Bioinformatics applications, Software, Risk analysis (engineering), 13. Climate action, 020204 information systems, 0202 electrical engineering, electronic engineering, information engineering, Architecture, business, computer
Abstract

Contemporary multi-cloud application deployments require increasingly complex security architectures, especially within federated environments. However, increased complexity often leads to higher efforts and raised costs for managing and securing those applications. This publication establishes an economical and comprehensive security architecture that is readily instantiable, pertinent to concrete users’ requirements, and builds upon up-to-date protocols and software. We highlight its feasibility by applying the architecture within the CYCLONE innovation action, deploying federated Bioinformatics applications within a cloud production environment. At last, we put special emphasis on the reduced management efforts to highlight the economic benefit of following our approach.

Published
2016

30. Protein domain assignment from the recurrence of locally similar structures

Author

Jean Garnier, Chin-Hsien Tai, Jean-François Gibrat, Peter J. Munson, Vichetra Sam, and Byungkook Lee

Subjects
Protein structure database, 0303 health sciences, Computer science, 030302 biochemistry & molecular biology, Protein domain, Structural alignment, Computational biology, Structural Classification of Proteins database, Bioinformatics, Biochemistry, Structural genomics, 03 medical and health sciences, Protein structure, Structural Biology, Threading (protein sequence), Molecular Biology, Protein secondary structure, 030304 developmental biology
Abstract

Domains are basic units of protein structure and essential for exploring protein fold space and structure evolution. With the structural genomics initiative, the number of protein structures in the Protein Databank (PDB) is increasing dramatically and domain assignments need to be done automatically. Most existing structural domain assignment programs define domains using the compactness of the domains and/or the number and strength of intra-domain versus inter-domain contacts. Here we present a different approach based on the recurrence of locally similar structural pieces (LSSPs) found by one-against-all structure comparisons with a dataset of 6373 protein chains from the PDB. Residues of the query protein are clustered using LSSPs via three different procedures to define domains. This approach gives results that are comparable to several existing programs that use geometrical and other structural information explicitly. Remarkably, most of the proteins that contribute the LSSPs defining a domain do not themselves contain the domain of interest. This study shows that domains can be defined by a collection of relatively small locally similar structural pieces containing, on average, four secondary structure elements. In addition, it indicates that domains are indeed made of recurrent small structural pieces that are used to build protein structures of many different folds as suggested by recent studies. Proteins 2011. Published 2010 Wiley-Liss, Inc.

Published
2010
Full Text
View/download PDF

31. Synchronized navigation and comparative analyses across Ensembl complete bacterial genomes with INSYGHT

Author

Annie Gendrault, Sylvie Thérond, Pierre Nicolas, Marc Rugeri, Valentin Loux, Jean-François Gibrat, Thomas Lacroix, Mathématiques et Informatique Appliquées du Génome à l'Environnement [Jouy-En-Josas] (MaIAGE), Institut National de la Recherche Agronomique (INRA), IDRIS, Institut du développement et des ressources en informatique scientifique (IDRIS), and Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)

Subjects
0301 basic medicine, Statistics and Probability, orthologs, [SDV]Life Sciences [q-bio], Interoperability, Genomics, Bacterial genome size, comparative genomics, Biology, computer.software_genre, Biochemistry, Genome, World Wide Web, 03 medical and health sciences, visualisation, Phylogenetics, Computer Graphics, Ensembl, [INFO]Computer Science [cs], [MATH]Mathematics [math], bacteria, Molecular Biology, Phylogeny, Internet, synteny, High-Throughput Nucleotide Sequencing, Genome Analysis, Applications Notes, Computer Science Applications, Visualization, Computational Mathematics, 030104 developmental biology, Computational Theory and Mathematics, Virtual machine, computer, Genome, Bacterial, Software
Abstract

Motivation: High-throughput sequencing technologies provide access to an increasing number of bacterial genomes. Today, many analyses involve the comparison of biological properties among many strains of a given species, or among species of a particular genus. Tools that can help the microbiologist with these tasks become increasingly important. Results: Insyght is a comparative visualization tool whose core features combine a synchronized navigation across genomic data of multiple organisms with a versatile interoperability between complementary views. In this work, we have greatly increased the scope of the Insyght public dataset by including 2688 complete bacterial genomes available in Ensembl thus vastly improving its phylogenetic coverage. We also report the development of a virtual machine that allows users to easily set up and customize their own local Insyght server. Availability and implementation: http://genome.jouy.inra.fr/Insyght Contact: Thomas.Lacroix@jouy.inra.fr

Published
2016
Full Text
View/download PDF

32. Mammalian olfactory receptors: molecular mechanisms of odorant detection, 3D-modeling, and structure-activity relationships

Author

Marie-Annick, Persuy, Guenhaël, Sanz, Anne, Tromelin, Thierry, Thomas-Danguin, Jean-François, Gibrat, and Edith, Pajot-Augy

Subjects
Structure-Activity Relationship, Imaging, Three-Dimensional, Olfactory Mucosa, Odorants, Animals, Humans, Receptors, Odorant
Abstract

This chapter describes the main characteristics of olfactory receptor (OR) genes of vertebrates, including generation of this large multigenic family and pseudogenization. OR genes are compared in relation to evolution and among species. OR gene structure and selection of a given gene for expression in an olfactory sensory neuron (OSN) are tackled. The specificities of OR proteins, their expression, and their function are presented. The expression of OR proteins in locations other than the nasal cavity is regulated by different mechanisms, and ORs display various additional functions. A conventional olfactory signal transduction cascade is observed in OSNs, but individual ORs can also mediate different signaling pathways, through the involvement of other molecular partners and depending on the odorant ligand encountered. ORs are engaged in constitutive dimers. Ligand binding induces conformational changes in the ORs that regulate their level of activity depending on odorant dose. When present, odorant binding proteins induce an allosteric modulation of OR activity. Since no 3D structure of an OR has been yet resolved, modeling has to be performed using the closest G-protein-coupled receptor 3D structures available, to facilitate virtual ligand screening using the models. The study of odorant binding modes and affinities may infer best-bet OR ligands, to be subsequently checked experimentally. The relationship between spatial and steric features of odorants and their activity in terms of perceived odor quality are also fields of research that development of computing tools may enhance.

Published
2015

33. Mammalian Olfactory Receptors

Author

Thierry Thomas-Danguin, Guenhaël Sanz, Marie-Annick Persuy, Anne Tromelin, Jean-François Gibrat, and Edith Pajot-Augy

Subjects
Virtual screening, Olfactory receptor, medicine.anatomical_structure, Odorant binding, Allosteric regulation, medicine, Computational biology, Signal transduction, Biology, Ligand (biochemistry), Bioinformatics, Function (biology), Sensory neuron
Abstract

This chapter describes the main characteristics of olfactory receptor (OR) genes of vertebrates, including generation of this large multigenic family and pseudogenization. OR genes are compared in relation to evolution and among species. OR gene structure and selection of a given gene for expression in an olfactory sensory neuron (OSN) are tackled. The specificities of OR proteins, their expression, and their function are presented. The expression of OR proteins in locations other than the nasal cavity is regulated by different mechanisms, and ORs display various additional functions. A conventional olfactory signal transduction cascade is observed in OSNs, but individual ORs can also mediate different signaling pathways, through the involvement of other molecular partners and depending on the odorant ligand encountered. ORs are engaged in constitutive dimers. Ligand binding induces conformational changes in the ORs that regulate their level of activity depending on odorant dose. When present, odorant binding proteins induce an allosteric modulation of OR activity. Since no 3D structure of an OR has been yet resolved, modeling has to be performed using the closest G-protein-coupled receptor 3D structures available, to facilitate virtual ligand screening using the models. The study of odorant binding modes and affinities may infer best-bet OR ligands, to be subsequently checked experimentally. The relationship between spatial and steric features of odorants and their activity in terms of perceived odor quality are also fields of research that development of computing tools may enhance.

Published
2015
Full Text
View/download PDF

34. Sheep cytosolic branched-chain amino acid aminotransferase: cDNA cloning, primary structure and molecular modelling and its unique expression in muscles

Author

Julien Bonfils, Magali Faure, Françoise Glomot, Isabelle Papet, Jean-François Gibrat, Unité de nutrition et métabolisme protéique, Institut National de la Recherche Agronomique (INRA), and Unité Mathématique Informatique et Génome (MIG)

Subjects
Models, Molecular, Protein Conformation, Transamination, [SDV]Life Sciences [q-bio], Molecular Sequence Data, Biophysics, Biology, medicine.disease_cause, Biochemistry, 03 medical and health sciences, Structural Biology, Complementary DNA, Escherichia coli, Genetics, medicine, Animals, [INFO]Computer Science [cs], Amino Acid Sequence, RNA, Messenger, Cloning, Molecular, Muscle, Skeletal, Transaminases, ComputingMilieux_MISCELLANEOUS, 030304 developmental biology, chemistry.chemical_classification, Cloning, 0303 health sciences, Sheep, Base Sequence, Molecular mass, EXPRESSION DU GENE, 030302 biochemistry & molecular biology, Protein primary structure, Brain, BIOLOGIE MOLECULAIRE, Molecular biology, Amino acid, Isoenzymes, Enzyme, chemistry, Organ Specificity, ADN-C, Sequence Alignment
Abstract

This paper presents the cloning and the molecular modelling of the cytosolic branched-chain amino acid aminotransferase (BCATc) from sheep brain. The sheep BCATc cDNA (3 kb) encodes a mature polypeptide of 385 amino acids with a calculated molecular mass of 43072.93 Da. The sequence of the sheep BCATc cDNA is more similar to other mammalian BCATc cDNAs (53-87% identical) than to the sheep mitochondrial branched-chain amino acid aminotransferase (52%). Sheep BCATc belongs to the IV Folding class of pyridoxal-5'-phosphate-depending enzymes. Based on the known structure of the branched-chain amino acid aminotransferase (BCAT) from Escherichia coli, a molecular model of sheep BCATc (amino acid residues 62-385) was built. This is the first three-dimensional model of any mammalian BCAT. It suggests that the enzymatic mechanism of sheep BCATc and likely of all mammalian BCAT is very similar to the mechanism of the E. coli BCAT and confirms the hypotheses regarding to the substrate binding sites of E. coli BCAT. Sheep skeletal muscle, which is the main in vivo site for transamination of branched-chain amino acids, exhibits an unique expression of BCATc.

Published
2000
Full Text
View/download PDF

35. Bacterial protein signals are associated with Crohn's disease

Author

Harry Sokol, Stanislas Mondot, Pascale Lepercq, Olivier David, Peter Sykacek, Florence Blon, Laurent Beaugerie, Brigitte Schaeffer, Véronique Monnet, David P. Kreil, Catherine Juste, Benoît Valot, S. Dusko Ehrlich, Patrice Martin, Alain Van Dorsselaer, Joël Doré, Wilfrid Carré, Valentin Loux, Christine Carapito, Jean-François Gibrat, Philippe Seksik, Christian Beauvallet, Alain Guillot, Sebastian Vaca, Nicolas Pons, Florence Levenez, Patricia Lepage, MICrobiologie de l'ALImentation au Service de la Santé (MICALIS), Institut National de la Recherche Agronomique (INRA)-AgroParisTech, Génétique Animale et Biologie Intégrative (GABI), Laboratoire de Spectrométrie de Masse BioOrganique [Strasbourg] (LSMBO), Département Sciences Analytiques et Interactions Ioniques et Biomoléculaires (DSA-IPHC), Institut Pluridisciplinaire Hubert Curien (IPHC), Université de Strasbourg (UNISTRA)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS)-Institut Pluridisciplinaire Hubert Curien (IPHC), Université de Strasbourg (UNISTRA)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS), Université Pierre et Marie Curie - Paris 6 (UPMC), Service de Gastroentérologie et nutrition [CHU Saint-Antoine], CHU Saint-Antoine [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), La plante et son environnement (PSE), Institut National de la Recherche Agronomique (INRA)-Université Paris-Sud - Paris 11 (UP11)-Institut National Agronomique Paris-Grignon (INA P-G)-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), Unité de Mathématique, Informatique et Génome (MIG), Institut National de la Recherche Agronomique (INRA), Espaces et Sociétés (ESO), Le Mans Université (UM)-Université de Caen Normandie (UNICAEN), Normandie Université (NU)-Normandie Université (NU)-Université d'Angers (UA)-AGROCAMPUS OUEST-Université de Rennes 2 (UR2), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Centre National de la Recherche Scientifique (CNRS)-Institut de Géographie et d'Aménagement Régional de l'Université de Nantes (IGARUN), Université de Nantes (UN)-Université de Nantes (UN), INRA, UR 0341 Mathématiques et Informatique Appliquées, 1Génétique Microbienne, INRA, Domaine de Vilvert, 78352 Jouy en Josas Cedex, Unité Mathématique Informatique et Génome (MIG), Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS)-Institut Pluridisciplinaire Hubert Curien (IPHC), Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), Université de Caen Normandie (UNICAEN), Normandie Université (NU)-Normandie Université (NU)-Le Mans Université (UM)-Université d'Angers (UA)-AGROCAMPUS OUEST-Université de Rennes 2 (UR2)-Centre National de la Recherche Scientifique (CNRS)-Institut de Géographie et d'Aménagement Régional de l'Université de Nantes (IGARUN), INRA - Mathématiques et Informatique Appliquées (Unité MIAJ), Unité Commune d'Expérimentation Animale de Vilvert (UCEA), Laboratoire de Spectrométrie de Masse BioOrganique, Département des Sciences Analytiques, Institut Pluridisciplinaire Hubert Curien, Strasbourg, France (LSMBO-DSA-IPHC), Centre National de la Recherche Scientifique (CNRS), Assistance publique - Hôpitaux de Paris (AP-HP) (APHP)-CHU Saint-Antoine [APHP], Normandie Université (NU)-Normandie Université (NU)-Le Mans Université (UM)-Université d'Angers (UA)-AGROCAMPUS OUEST-Université de Rennes 2 (UR2), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Centre National de la Recherche Scientifique (CNRS)-Institut de Géographie et d'Aménagement (IGARUN), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-CHU Saint-Antoine [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU), Institut de Géographie et d'Aménagement Régional de l'Université de Nantes (IGARUN), Université de Nantes (UN)-Université de Nantes (UN)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 2 (UR2), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-AGROCAMPUS OUEST, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Université d'Angers (UA)-Université de Caen Normandie (UNICAEN), and Normandie Université (NU)-Normandie Université (NU)-Le Mans Université (UM)

Subjects
Adult, Male, Firmicutes, Enteric Bacterial Microflora, Difference gel electrophoresis, Organic chemistry, Crohn's Disease, Microbiology, 03 medical and health sciences, 0302 clinical medicine, Bacterial Proteins, Crohn Disease, Sequence Analysis, Protein, Tandem Mass Spectrometry, protéine bactérienne, RNA, Ribosomal, 16S, Prevotella, Zymogen granule membrane, Humans, Electrophoresis, Gel, Two-Dimensional, 030304 developmental biology, 0303 health sciences, biology, Bacteria, [CHIM.ORGA]Chemical Sciences/Organic chemistry, Inflammatory Bowel Disease, Gastroenterology, maladie de crohn, biology.organism_classification, Mucus, Intestines, Chimie organique, Cross-Sectional Studies, Biochemistry, Proteome, 030211 gastroenterology & hepatology, Female, Cell envelope, Biomarkers, RC, Chromatography, Liquid
Abstract

International audience; OBJECTIVE: No Crohn's disease (CD) molecular maker has advanced to clinical use, and independent lines of evidence support a central role of the gut microbial community in CD. Here we explore the feasibility of extracting bacterial protein signals relevant to CD, by interrogating myriads of intestinal bacterial proteomes from a small number of patients and healthy controls. DESIGN: We first developed and validated a workflow-including extraction of microbial communities, two-dimensional difference gel electrophoresis (2D-DIGE), and LC-MS/MS-to discover protein signals from CD-associated gut microbial communities. Then we used selected reaction monitoring (SRM) to confirm a set of candidates. In parallel, we used 16S rRNA gene sequencing for an integrated analysis of gut ecosystem structure and functions. RESULTS: Our 2D-DIGE-based discovery approach revealed an imbalance of intestinal bacterial functions in CD. Many proteins, largely derived from Bacteroides species, were over-represented, while under-represented proteins were mostly from Firmicutes and some Prevotella members. Most overabundant proteins could be confirmed using SRM. They correspond to functions allowing opportunistic pathogens to colonise the mucus layers, breach the host barriers and invade the mucosae, which could still be aggravated by decreased host-derived pancreatic zymogen granule membrane protein GP2 in CD patients. Moreover, although the abundance of most protein groups reflected that of related bacterial populations, we found a specific independent regulation of bacteria-derived cell envelope proteins. CONCLUSIONS: This study provides the first evidence that quantifiable bacterial protein signals are associated with CD, which can have a profound impact on future molecular diagnosis.

Published
2014
Full Text
View/download PDF

36. Insyght: navigating amongst abundant homologues, syntenies and gene functional annotations in bacteria, it's that symbol!

Author

Valentin Loux, Jean-François Gibrat, Annie Gendrault, Thomas Lacroix, Mark Hoebeke, Unité Mathématique, Informatique et Génome (MIG), Institut National de la Recherche Agronomique (INRA), Station biologique de Roscoff [Roscoff] (SBR), Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS), ANR-PDR-080124-03-01, Lacroix, Thomas, and Unité Mathématique Informatique et Génome (MIG)

Subjects
archaea, synteny browser, logiciel, Genomics, Computational biology, annotation génomique, comparative genomics, Biology, Synteny, Genome, Genes, Archaeal, 03 medical and health sciences, ortholog browser, Sequence Homology, Nucleic Acid, [SDV.BBM.GTP]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Genomics [q-bio.GN], Computer Graphics, Genetics, Data Mining, 030304 developmental biology, Genomic organization, Comparative genomics, bactérie, 0303 health sciences, 030302 biochemistry & molecular biology, Molecular Sequence Annotation, bioinformatics, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], ComputingMethodologies_PATTERNRECOGNITION, Genes, Bacterial, logiciel libre, visualisation de données, Methods Online, Bio-informatique, Identification (biology), Phylogenetic profiling, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], Software
Abstract

article online : Received February 21, 2013; Revised August 28, 2014; Accepted September 10, 2014; International audience; High-throughput techniques have considerably increased the potential of comparative genomics whilst simultaneously posing many new challenges. One of those challenges involves efficiently mining the large amount of data produced and exploring the landscape of both conserved and idiosyncratic ge-nomic regions across multiple genomes. Domains of application of these analyses are diverse: identification of evolutionary events, inference of gene functions, detection of niche-specific genes or phy-logenetic profiling. Insyght is a comparative genomic visualization tool that combines three complementary displays: (i) a table for thoroughly browsing amongst homologues, (ii) a comparator of ortho-logue functional annotations and (iii) a genomic organization view designed to improve the legibility of rearrangements and distinctive loci. The latter display combines symbolic and proportional graphical paradigms. Synchronized navigation across multiple species and interoperability between the views are core features of Insyght. A gene filter mechanism is provided that helps the user to build a biologically relevant gene set according to multiple criteria such as presence/absence of homologues and/or various annotations. We illustrate the use of Insyght with scenarios. Currently, only Bacteria and Archaea are supported. A public instance is available at http://genome.jouy.inra.fr/Insyght. The tool is freely downloadable for private data set analysis.

Published
2014
Full Text
View/download PDF

37. Olfactory Receptor Proteins

Author

Jean-François Gibrat, Edith Pajot-Augy, Guenhaël Sanz, Neurobiologie de l'olfaction (NBO), Institut National de la Recherche Agronomique (INRA), and Unité Mathématique Informatique et Génome (MIG)

Subjects
Olfactory system, 0303 health sciences, Virtual screening, Olfactory receptor, bioelectronic nose, olfactory receptor, G protein, Computational biology, Biology, 3D structure, Transmembrane protein, 03 medical and health sciences, 0302 clinical medicine, medicine.anatomical_structure, 030220 oncology & carcinogenesis, medicine, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], protein, Olfactory epithelium, Integral membrane protein, 030304 developmental biology, G protein-coupled receptor
Abstract

Chapter 3; International audience; Bioelectronic noses can utilize olfactory receptors (ORs) as recognition elements. This chapter describes biochemical characteristics of these OR proteins. ORs being G protein-coupled receptors (GPCRs) are integral membrane proteins composed of seven transmembrane spanning helices. In mammals, there exist as many as 1,000 OR genes accounting for about 3% of the genome. Unfortunately, no three-dimensional (3D) strcture of OR is available and one must infer OR properties from those of better characterized GPCRs. The chapter offers a brief overview of the characteristics of known 3D structures of complexes of GPCRs with various types of ligands (agonists, inverse agonists, antagonists, etc.) and, in one case, also with a G protein. Based on these structural data, it then reviews hypotheses and experiments regarding the GPCR transduction mechanism. The chapter then describes how the set of known 3D structures (17 different GPCRs to date) can be used to model OR 3D structures that will be subsequently used as platform for ligand virtual screening. The following section examines the different mechanisms that regulate OR activity. Lastly, we focus on the use of OR proteins in bioelectronic noses.

Published
2014
Full Text
View/download PDF

38. E-Biothon : Une plate-forme pour accélérer les recherches en biologie, santé et environnement

Author

Nicolas Bard, Sylvie Boin, François Bothorel, Philippe Chaumeil, Philippe Collinet, Michel Daydé, Benjamin Depardon, Frédéric Desprez, Marie Flé, Alain Franc, Jean-Marc Frigerio, Olivier Gascuel, Stéphane Guindon, Jean-François Gibrat, Denis Girou, Pierre-François Lavallée, Vincent LEFORT, Gilles Lesage, Marc Rugeri, Evelyne Ruinet, Christophe Seguin, Sylvie Thérond, Laboratoire de l'Informatique du Parallélisme (LIP), École normale supérieure de Lyon (ENS de Lyon)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS), IBM France, IBM France (IBM), IBM-IBM, Biodiversité, Gènes & Communautés (BioGeCo), Institut National de la Recherche Agronomique (INRA)-Université de Bordeaux (UB), Institut du développement et des ressources en informatique scientifique (IDRIS), Centre National de la Recherche Scientifique (CNRS), Algorithmes Parallèles et Optimisation (IRIT-APO), Institut de recherche en informatique de Toulouse (IRIT), Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT)-Université de Toulouse (UT)-Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Toulouse Mind & Brain Institut (TMBI), Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT), Institut National Polytechnique (Toulouse) (Toulouse INP), SysFera [Villeurbanne], Algorithms and Software Architectures for Distributed and HPC Platforms (AVALON), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire de l'Informatique du Parallélisme (LIP), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure de Lyon (ENS de Lyon)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS), Méthodes et Algorithmes pour la Bioinformatique (MAB), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Unité Mathématique Informatique et Génome (MIG), Institut National de la Recherche Agronomique (INRA), Groupe d'Interet Scientifique (GIS). FRA., École normale supérieure - Lyon (ENS Lyon)-Université Claude Bernard Lyon 1 (UCBL), UMR BioGeCo, INRA & Université Bordeaux, Institut National de la Recherche Agronomique (INRA)-Université de Bordeaux (UB)-Institut National de la Recherche Agronomique (INRA)-Université de Bordeaux (UB), IDRIS, Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), Université Toulouse 1 Capitole (UT1)-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées, Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Lyon (ENS Lyon)-Université Claude Bernard Lyon 1 (UCBL), Mathématique Informatique et Génome, Institut National de la Recherche Agronomique (INRA)-Institut National de la Recherche Agronomique (INRA), Université Toulouse Le Mirail (Toulouse 2) (UTM), Institut National de Recherche en Informatique et en Automatique (Inria), Université Montpellier 2 - Sciences et Techniques (UM2), Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse 1 Capitole (UT1), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Centre National de la Recherche Scientifique (CNRS)-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École normale supérieure - Lyon (ENS Lyon), and Université de Lyon-École normale supérieure - Lyon (ENS Lyon)-Centre National de la Recherche Scientifique (CNRS)-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL)

Subjects
taxonomie, [SDV]Life Sciences [q-bio], plateforme informatique, épidémiologie, [INFO]Computer Science [cs], [MATH]Mathematics [math], [INFO.INFO-DC]Computer Science [cs]/Distributed, Parallel, and Cluster Computing [cs.DC], recherche, bioinformatique, biodiversité
Abstract

National audience; E-Biothon : Une plate-forme pour accélérer les recherches en biologie, santé et environnement

Published
2013

39. Automatic modeling of mammalian olfactory receptors and docking of odorants

Author

Guillaume Launay, Jean-François Gibrat, Guenhaël Sanz, Edith Pajot-Augy, Fallou Wade, Stéphane Téletchéa, Microbiologie moléculaire et biochimie structurale / Molecular Microbiology and Structural Biochemistry (MMSB), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS), Neurobiologie de l'Olfaction et de la Prise Alimentaire (NOPA), Institut National de la Recherche Agronomique (INRA), Mathématiques et Informatique Appliquées du Génome à l'Environnement [Jouy-En-Josas] (MaIAGE), Gibrat, Jean-Francois, Neurobiologie de l'Olfaction et Modélisation en Imagerie (NOeMI), Centre National de la Recherche Scientifique (CNRS). FRA., Unité Mathématique Informatique et Génome (MIG), Institut National de la Santé et de la Recherche Médicale (INSERM), Université de Nantes (UN), IFR 144 Neuro-Sud, and ProdInra, Migration

Subjects
Models, Molecular, Protein Folding, threating, receptors, Ligands, Receptors, Odorant, Biochemistry, Genome, 0302 clinical medicine, Databases, Protein, Receptor, 0303 health sciences, Chemistry, olfactory receptors, olfactory, ligand docking, Alimentation et Nutrition, Thermodynamics, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Protein Binding, Biotechnology, In silico, Molecular Sequence Data, Bioengineering, Computational biology, 3D modeling, 03 medical and health sciences, DOCK, Humans, Food and Nutrition, Computer Simulation, [SDV.NEU] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Amino Acid Sequence, Homology modeling, automates, gpcr sequence, Molecular Biology, G protein-coupled, 030304 developmental biology, G protein-coupled receptor, Sequence Homology, Amino Acid, Neurosciences, Reproducibility of Results, Docking (molecular), Neurons and Cognition, Odorants, Heterologous expression, Sequence Alignment, [SDV.AEN]Life Sciences [q-bio]/Food and Nutrition, 030217 neurology & neurosurgery
Abstract

We present a procedure that (i) automates the homology modeling of mammalian olfactory receptors (ORs) based on the six three-dimensional (3D) structures of G protein-coupled receptors (GPCRs) available so far and (ii) performs the docking of odorants on these models, using the concept of colony energy to score the complexes. ORs exhibit low-sequence similarities with other GPCR and current alignment methods often fail to provide a reliable alignment. Here, we use a fold recognition technique to obtain a robust initial alignment. We then apply our procedure to a human OR that we have previously functionally characterized. The analysis of the resulting in silico complexes, supported by receptor mutagenesis and functional assays in a heterologous expression system, suggests that antagonists dock in the upper part of the binding pocket whereas agonists dock in the narrow lower part. We propose that the potency of agonists in activating receptors depends on their ability to establish tight interactions with the floor of the binding pocket. We developed a web site that allows the user to upload a GPCR sequence, choose a ligand in a library and obtain the 3D structure of the free receptor and ligand-receptor complex (http://genome.jouy.inra.fr/GPCRautomodel).

Published
2012
Full Text
View/download PDF

40. Mapping reads on a genomic sequence: a practical comparative analysis

Author

Sophie Schbath, Veronique Martin, Matthias Zytnicki, Julien Fayolle, Valentin Loux, Jean-François Gibrat, Unité Mathématique Informatique et Génome (MIG), Institut National de la Recherche Agronomique (INRA), and Unité de Recherche Génomique Info (URGI)

Subjects
NGS, mapping reads, benchmark, ComputingMethodologies_PATTERNRECOGNITION, [SDV]Life Sciences [q-bio], ngs, benchmarking, short read alignment, burrows-wheeler transform, suffix tree, suffix array, hashing, spaced seed, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, [INFO]Computer Science [cs], [MATH]Mathematics [math], ComputingMilieux_MISCELLANEOUS
Abstract

National audience; Mapping short reads against a reference genome is classically the first step of many next-generation sequencing data analyses, and it should be as accurate as possible. Because of the large number of reads to handle, numerous sophisticated algorithms have been developped in the last 3 years to tackle this problem. In this article, we first review the underlying algorithms used in most of the existing mapping tools, and then we compare the performance of nine of these tools on a well controled benchmark built for this purpose. We built a set of reads that exist in single or multiple copies in a reference genome and for which there is no mismatch, and a set of reads with three mismatches. We considered as reference genome both the human genome and a concatenation of all complete bacterial genomes. On each dataset, we quantified the capacity of the different tools to retrieve all the occurrences of the reads in the reference genome. Special attention was paid to reads uniquely reported and to reads with multiple hits.

Published
2012

41. Genome Sequence of Corynebacterium casei UCMA 3821, Isolated from a Smear-Ripened Cheese

Author

Françoise Irlinger, Valentin Loux, Jean-François Gibrat, Christophe Monnet, Pascal Bonnarme, Pascal Bento, Cécile Straub, Sophie Landaud, Génie et Microbiologie des Procédés Alimentaires (GMPA), Institut National de la Recherche Agronomique (INRA)-AgroParisTech, Unité Mathématique, Informatique et Génome (MIG), and Institut National de la Recherche Agronomique (INRA)

Subjects
Surface, Annotation, Molecular Sequence Data, Biology, Corynebacterium, Microbiology, Database, 03 medical and health sciences, Cheese, Corynebacterium casei, bioinformatique, Molecular Biology, 030304 developmental biology, Whole genome sequencing, 0303 health sciences, STRUCTURE DU GENOME, Base Sequence, 030306 microbiology, Microbiology and Parasitology, Flore, Microbiologie et Parasitologie, Genome Announcements, RNA Genes, Biodiversité, Microflore, soft, [SDV.MP]Life Sciences [q-bio]/Microbiology and Parasitology, Genome, Bacterial
Abstract

Corynebacterium casei is one of the most prevalent species present on the surfaces of smear-ripened cheeses, where it contributes to the production of the desired organoleptic properties. Here, we report the draft genome sequence of Corynebacterium casei UCMA 3821 to provide insights into its physiology.

Published
2012
Full Text
View/download PDF

42. Statistical significance of threading scores

Author

Guillaume Launay, Afshin Fayyaz Movaghar, Sophie Schbath, François Rodolphe, Jean-François Gibrat, Unité Mathématique Informatique et Génome (MIG), Institut National de la Recherche Agronomique (INRA), University of Mazandaran, Université Claude Bernard Lyon 1 (UCBL), and Université de Lyon

Subjects
Computer science, Protein Conformation, Protein contact map, Thread (computing), 01 natural sciences, 010104 statistics & probability, 03 medical and health sciences, Statistics, COMPUTATIONAL MOLECULAR BIOLOGY, Genetics, Computer Simulation, Amino Acid Sequence, 0101 mathematics, Molecular Biology, Research Articles, 030304 developmental biology, Weibull distribution, 0303 health sciences, Models, Statistical, Stochastic process, Computational Biology, Proteins, food and beverages, MARKOV CHAINS, Random sequence, SEQUENCE ANALYSIS, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], STATISTICS, Computational Mathematics, Computational Theory and Mathematics, STOCHASTIC PROCESS, Modeling and Simulation, biologie moléculaire computationnelle, Generalized extreme value distribution, Probability distribution, Threading (protein sequence), Algorithm, Sequence Alignment, Algorithms, Statistical Distributions
Abstract

International audience; We present a general method for assessing threading score significance. The threading score of a protein sequence, thread onto a given structure, should be compared with the threading score distribution of a random amino-acid sequence, of the same length, thread on the same structure; small p-values point significantly high scores. We claim that, due to general protein contact map properties, this reference distribution is a Weibull extreme value distribution whose parameters depend on the threading method, the structure, the length of the query and the random sequence simulation model used. These parameters can be estimated off-line with simulated sequence samples, for different sequence lengths. They can further be interpolated at the exact length of a query, enabling the quick computation of the p-value.

Published
2012
Full Text
View/download PDF

43. Genome sequence of the persistent Salmonella enterica subsp enterica Serotype Senftenberg Strain SS209

Author

Olivier Grépinet, Philippe Velge, Zineb Boumart, Annie Gendrault, Isabelle Virlogeux-Payant, Hélène Chiapello, Jean-François Gibrat, Valentin Loux, Marianne Chemaly, Infectiologie et Santé Publique (UMR ISP), Institut National de la Recherche Agronomique (INRA)-Université de Tours, Unité Hygiène et Qualité des Produits Avicoles et Porcins, Agence nationale de sécurité sanitaire de l'alimentation, de l'environnement et du travail (ANSES), Unité Mathématique Informatique et Génome (MIG), Institut National de la Recherche Agronomique (INRA), Institut National de la Recherche Agronomique (INRA)-Université de Tours (UT), and Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)

Subjects
Serotype, DNA, Bacterial, animal structures, Molecular Sequence Data, education, Salmonella enteritica, Microbiology, Genome, 03 medical and health sciences, Molecular Biology, genome, 030304 developmental biology, 2. Zero hunger, Whole genome sequencing, 0303 health sciences, biology, 030306 microbiology, Strain (biology), génome, Animal production, Salmonella enterica, Gene Expression Regulation, Bacterial, Chromosomes, Bacterial, biology.organism_classification, [SDV.MP.BAC]Life Sciences [q-bio]/Microbiology and Parasitology/Bacteriology, Virology, Hatchery, 3. Good health, Genome Announcements, [SDV.MP]Life Sciences [q-bio]/Microbiology and Parasitology, Salmonella enterica subsp. enterica, Genome, Bacterial
Abstract

Salmonella enterica subsp. enterica serotype Senftenberg is an emerging serotype in poultry production which has been found to persist in animals and the farm environment. We report the genome sequence and annotation of the SS209 strain of S . Senftenberg, isolated from a hatchery, which was identified as persistent in broiler chickens.

Published
2012
Full Text
View/download PDF

44. Mapping reads on a genomic sequence: an algorithmic overview and a practical comparative analysis

Author

Véronique Martin, Valentin Loux, Jean-François Gibrat, Julien Fayolle, Sophie Schbath, Matthias Zytnicki, Unité Mathématique Informatique et Génome (MIG), Institut National de la Recherche Agronomique (INRA), Unité de Recherche Génomique Info (URGI), Schbath, Sophie, and Martin, Veronique

Subjects
Burrows–Wheeler transform, spaced seed, [SDV]Life Sciences [q-bio], Concatenation, Molecular Sequence Data, ngs, benchmarking, short read alignment, burrows-wheeler transform, suffix tree, suffix array, hashing, Hybrid genome assembly, Genomics, Bacterial genome size, Biology, computer.software_genre, Set (abstract data type), 03 medical and health sciences, Genetics, Humans, [INFO]Computer Science [cs], [MATH]Mathematics [math], Molecular Biology, Research Articles, 030304 developmental biology, 0303 health sciences, Bacteria, Base Sequence, Genome, Human, 030302 biochemistry & molecular biology, Chromosome Mapping, Sequence Analysis, DNA, analyse comparative, Computational Mathematics, ComputingMethodologies_PATTERNRECOGNITION, Computational Theory and Mathematics, Modeling and Simulation, Human genome, Data mining, computer, Sequence Alignment, algorithme, Algorithms, Genome, Bacterial, Software, Reference genome
Abstract

International audience; Mapping short reads against a reference genome is classically the first step of many next-generation sequencing data analyses, and it should be as accurate as possible. Because of the large number of reads to handle, numerous sophisticated algorithms have been developped in the last 3 years to tackle this problem. In this article, we first review the underlying algorithms used in most of the existing mapping tools, and then we compare the performance of nine of these tools on a well controled benchmark built for this purpose. We built a set of reads that exist in single or multiple copies in a reference genome and for which there is no mismatch, and a set of reads with three mismatches. We considered as reference genome both the human genome and a concatenation of all complete bacterial genomes. On each dataset, we quantified the capacity of the different tools to retrieve all the occurrences of the reads in the reference genome. Special attention was paid to reads uniquely reported and to reads with multiple hits.

Published
2012
Full Text
View/download PDF

45. Genome sequence of Staphylococcus equorum subsp equorum Mu2, Isolated from a french smear-ripened cheese

Author

Cécile Straub, Jean-François Gibrat, Françoise Irlinger, Christophe Monnet, Valentin Loux, Pascal Bonnarme, Sophie Landaud, Pascal Bento, Génie et Microbiologie des Procédés Alimentaires (GMPA), Institut National de la Recherche Agronomique (INRA)-AgroParisTech, Unité Mathématique, Informatique et Génome (MIG), and Institut National de la Recherche Agronomique (INRA)

Subjects
DNA, Bacterial, coagulase-negative staphylococci, [SDV.SA]Life Sciences [q-bio]/Agricultural sciences, fermented sausages, RNA genes, Molecular Sequence Data, medicine.disease_cause, Microbiology, 03 medical and health sciences, Cheese, medicine, Staphylococcus species, Molecular Biology, Fermentation in food processing, Aroma, 030304 developmental biology, 2. Zero hunger, Whole genome sequencing, 0303 health sciences, biology, 030306 microbiology, génome, food and beverages, Sequence Analysis, DNA, biology.organism_classification, Staphylococcus equorum, Genome Announcements, staphylococcus, Staphylococcus, Genome, Bacterial
Abstract

Staphylococcus equorumsubsp.equorumis a member of the coagulase-negative staphylococcus group and is frequently isolated from fermented food products and from food-processing environments. It contributes to the formation of aroma compounds during the ripening of fermented foods, especially cheeses and sausages. Here, we report the draft genome sequence ofStaphylococcus equorumsubsp.equorumMu2 to provide insights into its physiology and compare it with otherStaphylococcusspecies.

Published
2012
Full Text
View/download PDF

46. 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
Full Text
View/download PDF

47. A bioelectronic sensor nanoplatform based on olfactory receptors and electrochemical impedance spectroscopy

Author

Julien Daligault, Karine Badonnel, Aurelie Dewaele, Marie-Annick Persuy, Patrice Congar, Christine Baly, Roland Salesse, Guillaume Launay, Stéphane Téletchéa, Serigne Fallou Wade, Jean-François Gibrat, Guenhael Sanz, Nadia Zine, Abdelhamid Errachid, Nicole Jaffrezic-Renault, Edith Pajot-Augy, Neurobiologie de l'Olfaction et Modélisation en Imagerie (NOeMI), Institut National de la Recherche Agronomique (INRA), Institut Fédératif de Recherche 144 (NeuroSud Paris) (IFR 144), Unité Mathématique Informatique et Génome (MIG), Institut des Sciences Analytiques (ISA), Institut de Chimie du CNRS (INC)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS), and ProdInra, Migration

Subjects
[SDV] Life Sciences [q-bio], [SDV]Life Sciences [q-bio], [INFO]Computer Science [cs], [MATH] Mathematics [math], [INFO] Computer Science [cs], [MATH]Mathematics [math], ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
2011

48. Complete Genome Sequence of the Commensal Streptococcus salivarius Strain JIM8777

Author

Mathieu Almeida, Nicolas Sanchez, Pierre Renault, Valentin Loux, Nathalie Galleron, Arnaud Couloux, Eric Guédon, Céline Gautier, Corinne Cruaud, Maarten van de Guchte, Patrick Wincker, Sean Kennedy, Jean-François Gibrat, Nicolas Pons, Christine Delorme, S. Dusko Ehrlich, Séverine Layec, MICrobiologie de l'ALImentation au Service de la Santé (MICALIS), AgroParisTech-Université Paris-Saclay-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Génomique métabolique (UMR 8030), Genoscope - Centre national de séquençage [Evry] (GENOSCOPE), Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université d'Évry-Val-d'Essonne (UEVE)-Centre National de la Recherche Scientifique (CNRS), Mathématiques et Informatique Appliquées du Génome à l'Environnement [Jouy-En-Josas] (MaIAGE), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), INRA, Genoscope, Institut National de la Recherche Agronomique (INRA)-AgroParisTech, Unité Mathématique Informatique et Génome (MIG), Institut National de la Recherche Agronomique (INRA), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Centre National de la Recherche Scientifique (CNRS)-Université d'Évry-Val-d'Essonne (UEVE), Unité Mathématique, Informatique et Génome (MIG), Direction de Recherche Fondamentale (CEA) (DRF (CEA)), and Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Université d'Évry-Val-d'Essonne (UEVE)-Centre National de la Recherche Scientifique (CNRS)

Subjects
DNA, Bacterial, Sequence analysis, Molecular Sequence Data, Genomics, GENOMICS, COMPLETE GENOME, BACTERIA, génomique, medicine.disease_cause, Microbiology, Genome, 03 medical and health sciences, [SDV.BBM.GTP]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Genomics [q-bio.GN], medicine, Humans, Molecular Biology, 030304 developmental biology, bactérie, Genetics, Whole genome sequencing, Mouth, 0303 health sciences, biology, 030306 microbiology, Streptococcus, Strain (biology), Microbiology and Parasitology, Infant, streptococcus, Sequence Analysis, DNA, biology.organism_classification, [SDV.MP.BAC]Life Sciences [q-bio]/Microbiology and Parasitology/Bacteriology, Microbiologie et Parasitologie, Genome Announcements, 3. Good health, stomatognathic diseases, Streptococcus salivarius, [SDV.MP]Life Sciences [q-bio]/Microbiology and Parasitology, streptocoque, Oral ecology, Genome, Bacterial
Abstract

The commensal bacterium Streptococcus salivarius is a prevalent species of the human oropharyngeal tract with an important role in oral ecology. Here, we report the complete 2.2-Mb genome sequence and annotation of strain JIM8777, which was recently isolated from the oral cavity of a healthy, dentate infant.

Published
2011
Full Text
View/download PDF

49. CompaGB: An open framework for genome browsers comparison

Author

Annie Gendrault, Hélène Chiapello, Thomas Lacroix, Jean-François Gibrat, Valentin Loux, Lacroix, Thomas, Unité Mathématique Informatique et Génome (MIG), and Institut National de la Recherche Agronomique (INRA)

Subjects
fonction, [SDV]Life Sciences [q-bio], lcsh:Medicine, Genomics, Genome browser, Genome, browsers comparaison, General Biochemistry, Genetics and Molecular Biology, Task (project management), 03 medical and health sciences, 0302 clinical medicine, framework, compaGB, structure, Technical Note, Medicine, [INFO]Computer Science [cs], natural sciences, [MATH]Mathematics [math], lcsh:Science (General), lcsh:QH301-705.5, 030304 developmental biology, Medicine(all), 0303 health sciences, Biochemistry, Genetics and Molecular Biology(all), business.industry, génome, lcsh:R, General Medicine, Data science, Open framework, comparaison, ComputingMethodologies_PATTERNRECOGNITION, lcsh:Biology (General), 030220 oncology & carcinogenesis, business, lcsh:Q1-390
Abstract

Background Tools to visualize and explore genomes hold a central place in genomics and the diversity of genome browsers has increased dramatically over the last few years. It often turns out to be a daunting task to compare and choose a well-adapted genome browser, as multidisciplinary knowledge is required to carry out this task and the number of tools, functionalities and features are overwhelming. Findings To assist in this task, we propose a community-based framework based on two cornerstones: (i) the implementation of industry promoted software qualification method (QSOS) adapted for genome browser evaluations, and (ii) a web resource providing numerous facilities either for visualizing comparisons or performing new evaluations. We formulated 60 criteria specifically for genome browsers, and incorporated another 65 directly from QSOS's generic section. Those criteria aim to answer versatile needs, ranging from a biologist whose interest primarily lies into user-friendly and informative functionalities, a bioinformatician who wants to integrate the genome browser into a wider framework, or a computer scientist who might choose a software according to more technical features. We developed a dedicated web application to enrich the existing QSOS functionalities (weighting of criteria, user profile) with features of interest to a community-based framework: easy management of evolving data, user comments... Conclusions The framework is available at http://genome.jouy.inra.fr/CompaGB. It is open to anyone who wishes to participate in the evaluations. It helps the scientific community to (1) choose a genome browser that would better fit their particular project, (2) visualize features comparatively with easily accessible formats, such as tables or radar plots and (3) perform their own evaluation against the defined criteria. To illustrate the CompaGB functionalities, we have evaluated seven genome browsers according to the implemented methodology. A summary of the features of the compared genome browsers is presented and discussed.

Published
2011
Full Text
View/download PDF

50. Complete genome sequence of the clinical Streptococcus salivarius strain CCHSS3

Author

Christine Delorme, Corinne Cruaud, Hélène Chiapello, Nicolas Sanchez, Jean-François Gibrat, Valentin Loux, Claire Poyart, Eric Guédon, Céline Gautier, Mathieu Almeida, Pierre Renault, Sean Kennedy, S. Dusko Ehrlich, Arnaud Couloux, Patrick Wincker, Nicolas Pons, MICrobiologie de l'ALImentation au Service de la Santé (MICALIS), Institut National de la Recherche Agronomique (INRA)-AgroParisTech, Genoscope - Centre national de séquençage [Evry] (GENOSCOPE), Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Unité Mathématique, Informatique et Génome (MIG), Institut National de la Recherche Agronomique (INRA), Institut Cochin (IC UM3 (UMR 8104 / U1016)), Université Paris Descartes - Paris 5 (UPD5)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Université Paris Descartes - Faculté de Médecine (UPD5 Médecine), Université Paris Descartes - Paris 5 (UPD5), Département Microbiologie et Chaîne Alimentaire (MICA), and Unité Mathématique Informatique et Génome (MIG)

Subjects
DNA, Bacterial, Molecular Sequence Data, medicine.disease_cause, Microbiology, 03 medical and health sciences, Complete sequence, stomatognathic system, Sepsis, Streptococcal Infections, medicine, Humans, Endocarditis, Molecular Biology, COMPLETE, 030304 developmental biology, Whole genome sequencing, sang, 0303 health sciences, biology, 030306 microbiology, Streptococcus, COMPLETE GENOME, Sequence Analysis, DNA, biology.organism_classification, medicine.disease, Virology, Genome Announcements, 3. Good health, stomatognathic diseases, Blood, Streptococcus salivarius, [SDV.MP]Life Sciences [q-bio]/Microbiology and Parasitology, Bacteremia, BACTERIA, Meningitis, Genome, Bacterial, Bacteria
Abstract

Streptococcus salivarius is a commensal species commonly found in the human oral cavity and digestive tract, although it is also associated with human infections such as meningitis, endocarditis, and bacteremia. Here, we report the complete sequence of S. salivarius strain CCHSS3, isolated from human blood.

Published
2011
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results