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3. A project-scale map of metadata to improve future data management

Author

Pétéra, Mélanie, Cabasson, Cecile, Comte, Blandine, Duperier, Christophe, Filangi, Olivier, Prigent, Sylvain, Pujos-Guillot, Estelle, Giacomoni, Franck, Plateforme Exploration du Métabolisme (PFEM), Institut National de la Recherche Agronomique (INRA)-Université Blaise Pascal - Clermont-Ferrand 2 (UBP)-MetaboHUB-Clermont, MetaboHUB-MetaboHUB, Biologie du fruit et pathologie (BFP), Université de Bordeaux (UB)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Plateforme Bordeaux Metabolome, Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-MetaboHUB-Bordeaux, Institut de Génétique, Environnement et Protection des Plantes (IGEPP), Université de Rennes (UR)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro Rennes Angers, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), ANR-11-INBS-0010,METABOHUB,Développement d'une infrastructure française distribuée pour la métabolomique dédiée à l'innovation(2011), Petera, Mélanie, and Développement d'une infrastructure française distribuée pour la métabolomique dédiée à l'innovation - - METABOHUB2011 - ANR-11-INBS-0010 - INBS - VALID

Subjects
[CHIM.OTHE] Chemical Sciences/Other, Gestion de données de la recheche, [CHIM.OTHE]Chemical Sciences/Other, Métabolomique, FAIR, Méta-données
Abstract

International audience; Today, the intra-lab application of best practices in the metabolomics field usually guarantees an adequate data exploitation within a single lab. However, the growing interest in multi-analyses designs (e.g. complementary analytical platforms, variety of matrices, multi-omics), as well as the need of data sharing and reuse, increase the difficulty of data management. Indeed, managing the multiplicity and the heterogeneity of information involved is required to achieve relevant knowledge extraction from metabolomics data. Within the MetaboHUB national infrastructure, one objective is to optimize data handling, especially metadata, to facilitate large-scale analyses, multi-platforms studies, and data FAIRisation (Findability, Accessibility, Interoperability, Reusability). In particular, this fits in the MetaboHUB scientific roadmap that promotes the open science development in the field of metabolomics.In the context of metabolomic and lipidomic studies, data production and analysis come along with a large diversity of metadata (data of the data). To identify clearly-defined bottlenecks and targets for future improvement in data management, the objective of this work was to build a metadata map at the scale of a scientific project. Aiming for completeness, this map was constructed in a collaborative and multidisciplinary way involving chemists, biologists, data stewards as well as computer scientists, combining their respective experience and knowledge. Based on the resulting metadata map, targets (areas and topics) to be further investigated were identified, enabling the construction of transversal working groups at the consortium scale. In particular, this work enables to focus efforts on clearly defined issues to improve standardisation of practices regarding data management and metadata documentation. In conclusion, this collaborative map construction has been shown to be an efficient tool to draw a clear « where do we stand / where do we go » picture inside a national infrastructure like MetaboHUB regarding project-scale metadata. This facilitates the definition of a precise data management. Such an approach could be translated within other infrastructures, consortia and/or communities.

Published
2022

4. Metabolite reporting in large-scale studies within different metabolomics communities: DO WE SPEAK THE SAME LANGUAGE?

Author

Hajjar, Ghina, Benaben, David, Paulhe, Nils, Duperier, Christophe, Filangi, Olivier, Giacomoni, Franck, Comte, Blandine, Pujos-Guillot, Estelle, Plateforme Exploration du Métabolisme (PFEM), Institut National de la Recherche Agronomique (INRA)-Université Blaise Pascal - Clermont-Ferrand 2 (UBP)-MetaboHUB-Clermont, MetaboHUB-MetaboHUB, Biologie du fruit et pathologie (BFP), Université de Bordeaux (UB)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Institut de Génétique, Environnement et Protection des Plantes (IGEPP), Université de Rennes (UR)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro Rennes Angers, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), and Hajjar, Ghina

Subjects
[SDV.AEN] Life Sciences [q-bio]/Food and Nutrition, [CHIM.ANAL] Chemical Sciences/Analytical chemistry, [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], [SDV.SPEE] Life Sciences [q-bio]/Santé publique et épidémiologie, [CHIM.ANAL]Chemical Sciences/Analytical chemistry, [INFO.INFO-DB] Computer Science [cs]/Databases [cs.DB], [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], [SDV.AEN]Life Sciences [q-bio]/Food and Nutrition, [INFO.INFO-BI] Computer Science [cs]/Bioinformatics [q-bio.QM]
Abstract

International audience; Since the emergence of high throughput metabolomics, there has been a growing number of scientific communities performing metabolomic studies. Therefore, it has become crucial to standardize reporting and sharing of metabolites. Although minimum reporting standards for analytical practices and data processing are available, there are no established standards for metabolite reporting. In this context, our objective was to review the existing practices in terms of metabolite reporting in different scientific communities both in published results and across databases.In this context, we considered plasma metabolites reported in human large-scale studies from different communities, namely analytical chemistry, medicine and epidemiology. We focused only on metabolites reported as level 1 identification according to the Metabolomics Standard Initiative. We applied a data curation workflow on the list of annotated metabolites given by the authors. First, we performed a manual curation that included the addition of missing identifiers and the editing of some incoherent metadata. Second, we applied an automatic query algorithm in order to obtain additional information from available databases such as the compact hash code of the IUPAC International Chemical Identifier “InChIKey”. Identified metabolites were then compared between the selected studies using either the names given by the authors or the InChIKeys added after data curation. Regular inconsistencies were observed in metabolite reporting both in published results and across different databases. In the former, incoherence was observed in the metabolite information (identifiers not referring to the same isomer, metabolite name not corresponding to the molecular formula). Besides, isomers were listed with their corresponding retention times, yet without any indication of the isomers’ identity. On the other hand, cross-linking provided across databases presented some incoherent information regarding nomenclatures, optical isomerism, stereochemistry of asymmetric carbons, and molecular structure (acid/base; zwitterionic or canonical forms, molecules with a permanent charge) in addition to a mismatch between two structurally different compounds. The evaluation of metabolite reporting across different databases for instance HMDB, PubChem and ChEBI was performed with the help of the Metabolomics Semantic DataLake (MSD) team. Information was calculated from latest public versions of the aforementioned databases, under a Big Data infrastructure (Apache Spark) and Scala programming language. Based on the InChIKey, we were able to identify all incorrect metabolite matches in HMDB, PubChem and ChEBI and to categorize them into “structurally different compounds”, “optical isomerism” or “structural isomerism”.Although not yet required, the InChIKey was found to be the most suitable identifier for comparing reported metabolites between studies and across databases. It is therefore recommended either to use this identifier or to perform a deep data curation when reporting identified metabolites. This work will allow providing guidelines for a more effective and reproducible metabolomics data sharing.

Published
2022

6. Evaluation of GC/MS-Based 13 C-Positional Approaches for TMS Derivatives of Organic and Amino Acids and Application to Plant 13 C-Labeled Experiments.

Author

Dellero, Younès, Filangi, Olivier, and Bouchereau, Alain

Subjects
AMINO acid derivatives, KREBS cycle, ORGANIC acids, METABOLISM, PLANT metabolism, MASS spectrometry
Abstract

Analysis of plant metabolite 13C-enrichments with gas-chromatography mass spectrometry (GC/MS) has gained interest recently. By combining multiple fragments of a trimethylsilyl (TMS) derivative, 13C-positional enrichments can be calculated. However, this new approach may suffer from analytical biases depending on the fragments selected for calculation leading to significant errors in the final results. The goal of this study was to provide a framework for the validation of 13C-positional approaches and their application to plants based on some key metabolites (glycine, serine, glutamate, proline, α-alanine and malate). For this purpose, we used tailor-made 13C-PT standards, harboring known carbon isotopologue distributions and 13C-positional enrichments, to evaluate the reliability of GC-MS measurements and positional calculations. Overall, we showed that some mass fragments of proline_2TMS, glutamate_3TMS, malate_3TMS and α-alanine_2TMS had important biases for 13C measurements resulting in significant errors in the computational estimation of 13C-positional enrichments. Nevertheless, we validated a GC/MS-based 13C-positional approach for the following atomic positions: (i) C1 and C2 of glycine_3TMS, (ii) C1, C2 and C3 of serine_3TMS, and (iii) C1 of malate_3TMS and glutamate_3TMS. We successfully applied this approach to plant 13C-labeled experiments for investigating key metabolic fluxes of plant primary metabolism (photorespiration, tricarboxylic acid cycle and phosphoenolpyruvate carboxylase activity). [ABSTRACT FROM AUTHOR]

Published
2023
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7. Validation of carbon isotopologue distribution measurements by GC-MS and application to 13C-metabolic flux analysis of the tricarboxylic acid cycle in Brassica napus leaves.

Author

Dellero, Younès, Berardocco, Solenne, Berges, Cécilia, Filangi, Olivier, and Bouchereau, Alain

Subjects
KREBS cycle, RAPESEED, PYRUVATE dehydrogenase complex, ACID analysis, GAS chromatography/Mass spectrometry (GC-MS), ORGANIC acids, CITRATES
Abstract

The estimation of metabolic fluxes in photosynthetic organisms represents an important challenge that has gained interest over the last decade with the development of 13C-Metabolic Flux Analysis at isotopically non-stationary steady-state. This approach requires a high level of accuracy for the measurement of Carbon Isotopologue Distribution in plant metabolites. But this accuracy has still not been evaluated at the isotopologue level for GC-MS, leading to uncertainties for the metabolic fluxes calculated based on these fragments. Here, we developed a workflow to validate the measurements of CIDs from plant metabolites with GC-MS by producing tailor-made E. coli standard extracts harboring a predictable binomial CID for some organic and amino acids. Overall, most of our TMS-derivatives mass fragments were validated with these standards and at natural isotope abundance in plant matrices. Then, we applied this validated MS method to investigate the light/ dark regulation of plant TCA cycle by incorporating U-13C-pyruvate to Brassica napus leaf discs. We took advantage of pathway-specific isotopologues/ isotopomers observed between two and six hours of labeling to show that the TCA cycle can operate in a cyclic manner under both light and dark conditions. Interestingly, this forward cyclic flux mode has a nearly four-fold higher contribution for pyruvate-to-citrate and pyruvate-to-malate fluxes than the phosphoenolpyruvate carboxylase (PEPc) flux reassimilating carbon derived from some mitochondrial enzymes. The contribution of stored citrate to the mitochondrial TCA cycle activity was also questioned based on dynamics of 13C-enrichment in citrate, glutamate and succinate and variations of citrate total amounts under light and dark conditions. Interestingly, there was a light-dependent 13C-incorporation into glycine and serine showing that decarboxylations from pyruvate dehydrogenase complex and TCA cycle enzymes were actively reassimilated and could represent up to 5% to net photosynthesis. [ABSTRACT FROM AUTHOR]

Published
2023
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8. Suggesting disease associations for overlooked metabolites using literature from metabolic neighbors.

Author

Delmas, Maxime, Filangi, Olivier, Duperier, Christophe, Paulhe, Nils, Vinson, Florence, Rodriguez-Mier, Pablo, Giacomoni, Franck, Jourdan, Fabien, and Frainay, Clément

Subjects
*SCIENTIFIC literature, *LATENT structure analysis, *METABOLITES, *SCIENCE databases, *DATABASES, *METABOLOMICS
Abstract

In human health research, metabolic signatures extracted from metabolomics data have a strong added value for stratifying patients and identifying biomarkers. Nevertheless, one of the main challenges is to interpret and relate these lists of discriminant metabolites to pathological mechanisms. This task requires experts to combine their knowledge with information extracted from databases and the scientific literature. However, we show that most compounds (>99%) in the PubChem database lack annotated literature. This dearth of available information can have a direct impact on the interpretation of metabolic signatures, which is often restricted to a subset of significant metabolites. To suggest potential pathological phenotypes related to overlooked metabolites that lack annotated literature, we extend the "guilt-by-association" principle to literature information by using a Bayesian framework. The underlying assumption is that the literature associated with the metabolic neighbors of a compound can provide valuable insights, or an a priori , into its biomedical context. The metabolic neighborhood of a compound can be defined from a metabolic network and correspond to metabolites to which it is connected through biochemical reactions. With the proposed approach, we suggest more than 35,000 associations between 1,047 overlooked metabolites and 3,288 diseases (or disease families). All these newly inferred associations are freely available on the FORUM ftp server (see information at https://github.com/eMetaboHUB/Forum-LiteraturePropagation). [ABSTRACT FROM AUTHOR]

Published
2023
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9. FORUM: building a Knowledge Graph from public databases and scientific literature to extract associations between chemicals and diseases

Author

Delmas, Maxime, primary, Filangi, Olivier, additional, Paulhe, Nils, additional, Vinson, Florence, additional, Duperier, Christophe, additional, Garrier, William, additional, Saunier, Paul-Emeric, additional, Pitarch, Yoann, additional, Jourdan, Fabien, additional, Giacomoni, Franck, additional, and Frainay, Clément, additional

Published
2021
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10. Comparison of analyses of the XVth QTLMAS common dataset III: Genomic Estimations of Breeding Values

Author

Demeure Olivier, Le Roy Pascale, Filangi Olivier, and Elsen Jean-Michel

Subjects
Medicine, Science
Abstract

Abstract Background The QTLMAS XVth dataset consisted of pedigree, marker genotypes and quantitative trait performances of animals with a sib family structure. Pedigree and genotypes concerned 3,000 progenies among those 2,000 were phenotyped. The trait was regulated by 8 QTLs which displayed additive, imprinting or epistatic effects. The 1,000 unphenotyped progenies were considered as candidates to selection and their Genomic Estimated Breeding Values (GEBV) were evaluated by participants of the XVth QTLMAS workshop. This paper aims at comparing the GEBV estimation results obtained by seven participants to the workshop. Methods From the known QTL genotypes of each candidate, two "true" genomic values (TV) were estimated by organizers: the genotypic value of the candidate (TGV) and the expectation of its progeny genotypic values (TBV). GEBV were computed by the participants following different statistical methods: random linear models (including BLUP and Ridge Regression), selection variable techniques (LASSO, Elastic Net) and Bayesian methods. Accuracy was evaluated by the correlation between TV (TGV or TBV) and GEBV presented by participants. Rank correlation of the best 10% of individuals and error in predictions were also evaluated. Bias was tested by regression of TV on GEBV. Results Large differences between methods were found for all criteria and type of genetic values (TGV, TBV). In general, the criteria ranked consistently methods belonging to the same family. Conclusions Bayesian methods - A

Published
2012
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12. Structural and functional evolutionary dynamics of duplicated genes and genomes in nascent and natural B. napus

Author

Rousseau-Gueutin, Mathieu, Lucas, Jérémy, Denoeud, France, Ferreira de Carvalho, Julie, He, Zhesi, Boutte, Julien, Deniot, Gwenaëlle, Falentin, Cyril, Filangi, Olivier, Gilet, Marie-Madeleine, Legeai, Fabrice, Lodé-Taburel, Maryse, Morice, Jérôme, Trotoux, Gwenn, Aury, Jean-Marc, Barbe, Valérie, Snowdon, Rod J., Wincker, Patrick, Bancroft, Ian, Chèvre, Anne-Marie, Institut de Génétique, Environnement et Protection des Plantes (IGEPP), Institut National de la Recherche Agronomique (INRA)-Université de Rennes (UR)-AGROCAMPUS OUEST, 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), University of York [York, UK], Institut für Insektenbiotechnologie [Justus-Liebig-Universität Gießen], Justus-Liebig-Universität Gießen = Justus Liebig University (JLU), Rousseau-Gueutin, Mathieu, Université de Rennes 1 (UR1), 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)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), and Justus-Liebig-Universität Gießen (JLU)

Subjects
[SDV.GEN.GPL]Life Sciences [q-bio]/Genetics/Plants genetics, [SDV.GEN.GPL] Life Sciences [q-bio]/Genetics/Plants genetics, [SDV.BID.EVO]Life Sciences [q-bio]/Biodiversity/Populations and Evolution [q-bio.PE], [SDV.BID.EVO] Life Sciences [q-bio]/Biodiversity/Populations and Evolution [q-bio.PE], ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
2019

13. Statistical properties of interval mapping methods on quantitative trait loci location: impact on QTL/eQTL analyses

Author

Wang Xiaoqiang, Gilbert Hélène, Moreno Carole, Filangi Olivier, Elsen Jean-Michel, and Le Roy Pascale

Subjects
QTL, linkage analysis, QTL location, bias, Genetics, QH426-470
Abstract

Abstract Background Quantitative trait loci (QTL) detection on a huge amount of phenotypes, like eQTL detection on transcriptomic data, can be dramatically impaired by the statistical properties of interval mapping methods. One of these major outcomes is the high number of QTL detected at marker locations. The present study aims at identifying and specifying the sources of this bias, in particular in the case of analysis of data issued from outbred populations. Analytical developments were carried out in a backcross situation in order to specify the bias and to propose an algorithm to control it. The outbred population context was studied through simulated data sets in a wide range of situations. The likelihood ratio test was firstly analyzed under the "one QTL" hypothesis in a backcross population. Designs of sib families were then simulated and analyzed using the QTL Map software. On the basis of the theoretical results in backcross, parameters such as the population size, the density of the genetic map, the QTL effect and the true location of the QTL, were taken into account under the "no QTL" and the "one QTL" hypotheses. A combination of two non parametric tests - the Kolmogorov-Smirnov test and the Mann-Whitney-Wilcoxon test - was used in order to identify the parameters that affected the bias and to specify how much they influenced the estimation of QTL location. Results A theoretical expression of the bias of the estimated QTL location was obtained for a backcross type population. We demonstrated a common source of bias under the "no QTL" and the "one QTL" hypotheses and qualified the possible influence of several parameters. Simulation studies confirmed that the bias exists in outbred populations under both the hypotheses of "no QTL" and "one QTL" on a linkage group. The QTL location was systematically closer to marker locations than expected, particularly in the case of low QTL effect, small population size or low density of markers, i.e. designs with low power. Practical recommendations for experimental designs for QTL detection in outbred populations are given on the basis of this bias quantification. Furthermore, an original algorithm is proposed to adjust the location of a QTL, obtained with interval mapping, which co located with a marker. Conclusions Therefore, one should be attentive when one QTL is mapped at the location of one marker, especially under low power conditions.

Published
2012
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14. Cytonuclear interactions overcome inter-genomic conflict resulting from interspecific hybridization and genome doubling

Author

Ferreira De Carvalho, Julie, Lucas, Jérémy, Falentin, Cyril, Deniot, Gwenaëlle, Filangi, Olivier, Gilet, Marie-Madeleine, Legeai, Fabrice, Lodé-Taburel, Maryse, Morice, Jérôme, Trotoux, Gwenn, Aury, Jean-Marc, Barbe, Valérie, Keller, Jean, Snowdon, Rod J., He, Zhesi, Denoeud, France, Wincker, Patrick, Bancroft, Ian, Chèvre, Anne-Marie, Rousseau-Gueutin, Mathieu, Institut de Génétique, Environnement et Protection des Plantes (IGEPP), Université de Rennes 1 (UR1), 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)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), 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), Ecosystèmes, biodiversité, évolution [Rennes] (ECOBIO), Centre National de la Recherche Scientifique (CNRS)-Observatoire des Sciences de l'Univers de Rennes (OSUR)-Institut Ecologie et Environnement (INEE), Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES), Justus-Liebig-Universität Gießen (JLU), University of York [York, UK], Institut National de la Recherche Agronomique (INRA)-Université de Rennes (UR)-AGROCAMPUS OUEST, Université de Rennes (UR)-Institut Ecologie et Environnement (INEE), Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Observatoire des Sciences de l'Univers de Rennes (OSUR), Université de Rennes (UR)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Rennes 2 (UR2)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Rennes 2 (UR2)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Centre National de la Recherche Scientifique (CNRS), Justus-Liebig-Universität Gießen = Justus Liebig University (JLU), and Rousseau-Gueutin, Mathieu

Subjects
[SDV.GEN.GPL]Life Sciences [q-bio]/Genetics/Plants genetics, [SDV.GEN.GPL] Life Sciences [q-bio]/Genetics/Plants genetics, [SDV.BID.EVO]Life Sciences [q-bio]/Biodiversity/Populations and Evolution [q-bio.PE], [SDV.BID.EVO] Life Sciences [q-bio]/Biodiversity/Populations and Evolution [q-bio.PE], ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
2018

15. From diploids to a huge diversity ready-to-use for oilseed rape breeding

Author

Paillard, Sophie, Lodé-Taburel, Maryse, Trotoux, Gwenn, Eber, Frederique, Gilet, Marie-Madeleine, Morice, Jérôme, Filangi, Olivier, Legeai, Fabrice, Laperche, Anne, Rousseau-Gueutin, Mathieu, Chèvre, Anne-Marie, Institut de Génétique, Environnement et Protection des Plantes (IGEPP), Institut National de la Recherche Agronomique (INRA)-Université de Rennes 1 (UR1), 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), and Institut National de la Recherche Agronomique (INRA)-Université de Rennes (UR)-AGROCAMPUS OUEST

Subjects
[SDV.BV]Life Sciences [q-bio]/Vegetal Biology, ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
2018

16. Cytonuclear interactions remain stable during allopolyploid evolution despite repeated whole‐genome duplications in Brassica

Author

Ferreira de Carvalho, Julie, primary, Lucas, Jérémy, additional, Deniot, Gwenaëlle, additional, Falentin, Cyril, additional, Filangi, Olivier, additional, Gilet, Marie, additional, Legeai, Fabrice, additional, Lode, Maryse, additional, Morice, Jérôme, additional, Trotoux, Gwenn, additional, Aury, Jean‐Marc, additional, Barbe, Valérie, additional, Keller, Jean, additional, Snowdon, Rod, additional, He, Zhesi, additional, Denoeud, France, additional, Wincker, Patrick, additional, Bancroft, Ian, additional, Chèvre, Anne‐Marie, additional, and Rousseau‐Gueutin, Mathieu, additional

Published
2019
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17. Complex trait subtypes identification using transcriptome profiling reveals an interaction between two QTL affecting adiposity in chicken

Author

Blum Yuna, Le Mignon Guillaume, Causeur David, Filangi Olivier, Désert Colette, Demeure Olivier, Le Roy Pascale, and Lagarrigue Sandrine

Subjects
Biotechnology, TP248.13-248.65, Genetics, QH426-470
Abstract

Abstract Background Integrative genomics approaches that combine genotyping and transcriptome profiling in segregating populations have been developed to dissect complex traits. The most common approach is to identify genes whose eQTL colocalize with QTL of interest, providing new functional hypothesis about the causative mutation. Another approach includes defining subtypes for a complex trait using transcriptome profiles and then performing QTL mapping using some of these subtypes. This approach can refine some QTL and reveal new ones. In this paper we introduce Factor Analysis for Multiple Testing (FAMT) to define subtypes more accurately and reveal interaction between QTL affecting the same trait. The data used concern hepatic transcriptome profiles for 45 half sib male chicken of a sire known to be heterozygous for a QTL affecting abdominal fatness (AF) on chromosome 5 distal region around 168 cM. Results Using this methodology which accounts for hidden dependence structure among phenotypes, we identified 688 genes that are significantly correlated to the AF trait and we distinguished 5 subtypes for AF trait, which are not observed with gene lists obtained by classical approaches. After exclusion of one of the two lean bird subtypes, linkage analysis revealed a previously undetected QTL on chromosome 5 around 100 cM. Interestingly, the animals of this subtype presented the same q paternal haplotype at the 168 cM QTL. This result strongly suggests that the two QTL are in interaction. In other words, the "q configuration" at the 168 cM QTL could hide the QTL existence in the proximal region at 100 cM. We further show that the proximal QTL interacts with the previous one detected on the chromosome 5 distal region. Conclusion Our results demonstrate that stratifying genetic population by molecular phenotypes followed by QTL analysis on various subtypes can lead to identification of novel and interacting QTL.

Published
2011
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18. Detection of QTL with effects on osmoregulation capacities in the rainbow trout (Oncorhynchus mykiss)

Author

Pottinger Thomas G, Bovenhuis Henk, Boussaha Mekki, Guyomard René, Filangi Olivier, Krieg Francine, Dechamp Nicolas, Le Bras Yvan, Prunet Patrick, Le Roy Pascale, and Quillet Edwige

Subjects
Genetics, QH426-470
Abstract

Abstract Background There is increasing evidence that the ability to adapt to seawater in teleost fish is modulated by genetic factors. Most studies have involved the comparison of species or strains and little is known about the genetic architecture of the trait. To address this question, we searched for QTL affecting osmoregulation capacities after transfer to saline water in a nonmigratory captive-bred population of rainbow trout. Results A QTL design (5 full-sib families, about 200 F2 progeny each) was produced from a cross between F0 grand-parents previously selected during two generations for a high or a low cortisol response after a standardized confinement stress. When fish were about 18 months old (near 204 g body weight), individual progeny were submitted to two successive hyper-osmotic challenges (30 ppt salinity) 14 days apart. Plasma chloride and sodium concentrations were recorded 24 h after each transfer. After the second challenge, fish were sacrificed and a gill index (weight of total gill arches corrected for body weight) was recorded. The genome scan was performed with 196 microsatellites and 85 SNP markers. Unitrait and multiple-trait QTL analyses were carried out on the whole dataset (5 families) through interval mapping methods with the QTLMap software. For post-challenge plasma ion concentrations, significant QTL (P < 0.05) were found on six different linkage groups and highly suggestive ones (P < 0.10) on two additional linkage groups. Most QTL affected concentrations of both chloride and sodium during both challenges, but some were specific to either chloride (2 QTL) or sodium (1 QTL) concentrations. Six QTL (4 significant, 2 suggestive) affecting gill index were discovered. Two were specific to the trait, while the others were also identified as QTL for post-challenge ion concentrations. Altogether, allelic effects were consistent for QTL affecting chloride and sodium concentrations but inconsistent for QTL affecting ion concentrations and gill morphology. There was no systematic lineage effect (grand-parental origin of QTL alleles) on the recorded traits. Conclusions For the first time, genomic loci associated with effects on major physiological components of osmotic adaptation to seawater in a nonmigratory fish were revealed. The results pave the way for further deciphering of the complex regulatory mechanisms underlying seawater adaptation and genes involved in osmoregulatory physiology in rainbow trout and other euryhaline fishes.

Published
2011
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19. A fast algorithm for estimating transmission probabilities in QTL detection designs with dense maps

Author

Gilbert Hélène, Filangi Olivier, Elsen Jean-Michel, Le Roy Pascale, and Moreno Carole

Subjects
Animal culture, SF1-1100, Genetics, QH426-470
Abstract

Abstract Background In the case of an autosomal locus, four transmission events from the parents to progeny are possible, specified by the grand parental origin of the alleles inherited by this individual. Computing the probabilities of these transmission events is essential to perform QTL detection methods. Results A fast algorithm for the estimation of these probabilities conditional to parental phases has been developed. It is adapted to classical QTL detection designs applied to outbred populations, in particular to designs composed of half and/or full sib families. It assumes the absence of interference. Conclusion The theory is fully developed and an example is given.

Published
2009
Full Text
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20. AgroDataRing

Author

Adenot, Pierre, Bansard, Stéphane, Benaben, David, Brunaud, Veronique, Caron, Christophe, Dehne Garcia, Alexandre, Duperier, Christophe, Falce, Adrien, Filangi, Olivier, Giacomoni, Franck, Granier, Fabienne, Grevet, Philippe, Guilhot, Nicolas, Hofstetter, Annie, Joets, Johann, Hotelier, Thierry, Langella, Olivier, Legrand, Ludovic, Loaec, Mikaël, Lollier, Virginie, Moreau, Patrick, Morin, Emmanuelle, Quesneville, Hadi, Rabemanantsoa, Tovo, Salin, Gerald, and Tessier, Dominique

Subjects
partage des données, stockage de données, gestion de données, données de la recherche, données, stockage, pérennisation, mutualisation, partage, communautés, banque de données, traitement de données
Abstract

Les communautés scientifiques se trouvent aujourd’hui confrontées à un changement de paradigme autour de la gestion des données, nécessitant une meilleure gestion du cycle de vie des données avec notamment leur traitement et intégration, et leur partage. À la suite du chantier « Data Partage » lancé dès 2012 à l’Inra, le groupe de travail « e-infra Storage » a initié en 2016 une réflexion collective autour des besoins de l’Institut en matière de dispositif de stockage des données patrimoniales scientifiques qui a abouti à la co-construction d’une infrastructure partagée et mutualisée : AgroDataRing.

Published
2018

21. How a polyploid becomes a new species: example from the Brassica model

Author

Rousseau-Gueutin, Mathieu, Ferreira De Carvalho, Julie, Lucas, J., Denoued, F., Legeai, Fabrice, He, Z., Morice, Jérôme, Lodé-Taburel, Maryse, Boutte, Julien, Deniot, Gwenaëlle, Falentin, Cyril, Filangi, Olivier, Trotoux, Gwenn, Gilet, Marie-Madeleine, Coriton, Olivier, Huteau, Virginie, Aury, J.M., Barbe, Valérie, Salse, Jérôme, Winckler, P., Snowdon, R., Bancroft, Ian, Chèvre, Anne-Marie, Institut de Génétique, Environnement et Protection des Plantes (IGEPP), Institut National de la Recherche Agronomique (INRA)-Université de Rennes (UR)-AGROCAMPUS OUEST, Rousseau-Gueutin, Mathieu, Institut National de la Recherche Agronomique (INRA)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-AGROCAMPUS OUEST, and Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)

Subjects
[SDV.GEN.GPL]Life Sciences [q-bio]/Genetics/Plants genetics, [SDV.BV.AP]Life Sciences [q-bio]/Vegetal Biology/Plant breeding, [SDV.GEN.GPL] Life Sciences [q-bio]/Genetics/Plants genetics, [SDV.BID.EVO]Life Sciences [q-bio]/Biodiversity/Populations and Evolution [q-bio.PE], [SDV.BID.EVO] Life Sciences [q-bio]/Biodiversity/Populations and Evolution [q-bio.PE], [SDV.BV.AP] Life Sciences [q-bio]/Vegetal Biology/Plant breeding, ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
2018

22. AskOmics, a web tool to integrate and query biological data using semantic web technologies

Author

Garnier, Xavier, Bretaudeau, Anthony, Filangi, Olivier, Legeai, Fabrice, Siegel, Anne, Dameron, Olivier, Dynamics, Logics and Inference for biological Systems and Sequences (Dyliss), Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-GESTION DES DONNÉES ET DE LA CONNAISSANCE (IRISA-D7), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes 1 (UR1), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Plateforme bioinformatique GenOuest [Rennes], Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Plateforme Génomique Santé Biogenouest®-Inria Rennes – Bretagne Atlantique, Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut de Génétique, Environnement et Protection des Plantes (IGEPP), Institut National de la Recherche Agronomique (INRA)-Université de Rennes 1 (UR1), 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), Scalable, Optimized and Parallel Algorithms for Genomics (GenScale), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Université de Rennes (UR)-Plateforme Génomique Santé Biogenouest®-Inria Rennes – Bretagne Atlantique, Institut National de la Recherche Agronomique (INRA)-Université de Rennes (UR)-AGROCAMPUS OUEST, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), and SIEGEL, Anne

Subjects
[INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], [INFO.INFO-BI] Computer Science [cs]/Bioinformatics [q-bio.QM]
Abstract

National audience; A web tool to integrate and query biological data using semantic web technologies

Published
2017

23. Integration of Linked Data into Galaxy using Askomics

Author

Garnier, Xavier, Dameron, Olivier, Filangi, Olivier, Legeai, Fabrice, Bretaudeau, Anthony, Dynamics, Logics and Inference for biological Systems and Sequences (Dyliss), Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-GESTION DES DONNÉES ET DE LA CONNAISSANCE (IRISA-D7), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-CentraleSupélec-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Institut de Génétique, Environnement et Protection des Plantes (IGEPP), Institut National de la Recherche Agronomique (INRA)-Université de Rennes 1 (UR1), 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), Plateforme bioinformatique GenOuest [Rennes], Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Plateforme Génomique Santé Biogenouest®-Inria Rennes – Bretagne Atlantique, Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Scalable, Optimized and Parallel Algorithms for Genomics (GenScale), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut National de la Recherche Agronomique (INRA)-Université de Rennes (UR)-AGROCAMPUS OUEST, Université de Rennes (UR)-Plateforme Génomique Santé Biogenouest®-Inria Rennes – Bretagne Atlantique, Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Legeai, Fabrice, Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes 1 (UR1), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), and Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA)

Subjects
Askomics Linked data SparQL Galaxy, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], ComputingMilieux_MISCELLANEOUS, [INFO.INFO-BI] Computer Science [cs]/Bioinformatics [q-bio.QM]
Abstract

International audience

Published
2017

24. Comparative genomic analysis of Clubroot resistance in the Brassicaceae family

Author

Evrard, Aurélie, Bettembourg, Charles, Dameron, Olivier, Filangi, Olivier, Bretaudeau, Anthony, Legeai, Fabrice, Delourme, Régine, Manzanares-Dauleux, Maria, Jubault, Mélanie, Institut de Génétique, Environnement et Protection des Plantes (IGEPP), Institut National de la Recherche Agronomique (INRA)-Université de Rennes 1 (UR1), 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), Dynamics, Logics and Inference for biological Systems and Sequences (Dyliss), Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-GESTION DES DONNÉES ET DE LA CONNAISSANCE (IRISA-D7), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), CentraleSupélec-Télécom Bretagne-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Rennes (ENS Rennes)-Université de Bretagne Sud (UBS)-Centre National de la Recherche Scientifique (CNRS)-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)-CentraleSupélec-Télécom Bretagne-Université de Rennes 1 (UR1), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-École normale supérieure - Rennes (ENS Rennes)-Université de Bretagne Sud (UBS)-Centre National de la Recherche Scientifique (CNRS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES), Institut National de la Recherche Agronomique (INRA)-Université de Rennes (UR)-AGROCAMPUS OUEST, Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-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)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Télécom Bretagne-CentraleSupélec-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)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Université de Bretagne Sud (UBS)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-CentraleSupélec-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Université de Rennes (UNIV-RENNES)-CentraleSupélec-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Université de Bretagne Sud (UBS)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Rennes (ENS Rennes)-Télécom Bretagne-Université de Rennes 1 (UR1), and Université de Rennes (UNIV-RENNES)-CentraleSupélec

Subjects
fungi, food and beverages, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM]
Abstract

International audience; Clubroot, caused by the obligate biotroph Plasmodiophora brassicae, is one of theeconomically most important diseases of Brassica crops in the world including B. oleracea(coles), B. napus (oilseed rape) and B. rapa (turnip). The breeding of resistant cultivars iscurrently a main goal to control this disease in all Brassica crops. Very few resistant varietiesare currently available especially for coles and oilseed rape. The model plant Arabidopsisthaliana is also a host for clubroot and shows natural variation in the responses to clubrootinfection. Genetical genomics analyses of the plant response to clubroot infection are inprogress in our team in order to determine which structural and functional characteristics ofthe resistance factors have to be taken into account to build resistant varieties in a complexenvironment. The aim of the present work was to investigate synteny of the regions carryingresistance factors to clubroot through a comparative structural study in the three Brassicacrops (B. napus, B. oleracea and B. rapa) and in the model species A. thaliana and to identifyunderlying candidate genes.In the past, genetic analyses for resistance to various P. brassicae isolates have beenperformed in these four species and major genes and quantitative trait loci (QTL) with high ormoderate effect have been identified. Thanks to the recent availability of the genomesequence for these species, the genes underneath the detected regions and their orthologs weredetermined for each species and compared. Results showed that some QTL genomic regionswere syntenic between these species and common underlying genes were identified. Severalother regions/genes were specific to one plant species.The existence of common and specific regions in these Brassicaeae species is discussed in thelight of both evolutionary aspects and implications for the construction of durable resistantvarieties.

Published
2016

25. Integration and query of biological datasets with Semantic Web technologies: AskOmics

Author

Evrard, Aurélie, Bettembourg, Charles, Jubault, Mélanie, Dameron, Olivier, Filangi, Olivier, Bretaudeau, Anthony, Legeai, Fabrice, Institut de Génétique, Environnement et Protection des Plantes (IGEPP), Institut National de la Recherche Agronomique (INRA)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-AGROCAMPUS OUEST, Dynamics, Logics and Inference for biological Systems and Sequences (Dyliss), Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-GESTION DES DONNÉES ET DE LA CONNAISSANCE (IRISA-D7), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Université de Bretagne Sud (UBS)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-CentraleSupélec-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Université de Rennes (UNIV-RENNES)-CentraleSupélec-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Université de Bretagne Sud (UBS)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Rennes (ENS Rennes)-Télécom Bretagne-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-CentraleSupélec, Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES), Plateforme bioinformatique GenOuest [Rennes], Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Plateforme Génomique Santé Biogenouest®-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Rennes (ENS Rennes)-Télécom Bretagne-CentraleSupélec, Scalable, Optimized and Parallel Algorithms for Genomics (GenScale), GESTION DES DONNÉES ET DE LA CONNAISSANCE (IRISA-D7), Université de Rennes (UNIV-RENNES)-CentraleSupélec-Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria), 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), CentraleSupélec-Télécom Bretagne-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Rennes (ENS Rennes)-Université de Bretagne Sud (UBS)-Centre National de la Recherche Scientifique (CNRS)-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)-CentraleSupélec-Télécom Bretagne-Université de Rennes 1 (UR1), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-École normale supérieure - Rennes (ENS Rennes)-Université de Bretagne Sud (UBS)-Centre National de la Recherche Scientifique (CNRS)-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 des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Plateforme Génomique Santé Biogenouest®-Inria Rennes – Bretagne Atlantique, Institut National de la Recherche Agronomique (INRA)-Université de Rennes (UR)-AGROCAMPUS OUEST, Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-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)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), Université de Rennes (UR), Université de Rennes (UR)-Plateforme Génomique Santé Biogenouest®-Inria Rennes – Bretagne Atlantique, Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-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)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Plateforme Génomique Santé Biogenouest®-Inria Rennes – Bretagne Atlantique, and Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Inria Rennes – Bretagne Atlantique

Subjects
Askomics, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], SPARQL, data integration, RDF, Semantic Web
Abstract

National audience; Over the past few years, research programs involving genetic, genomic and post-genomic sequencing of various living organisms have become fast growing areas of biology. Once the computational challenges of processing datasets have been dealt with; large and complex biological data still remain in the hands of biologists for interpretation. Projects such as Biomart and Intermine have been developed for the international community to facilitate exchange and comparison of complex biological data. However for non-model organisms, large heterogeneous biological datasets can be difficult to associate in order to obtain a comprehensive view. Overall, access and interrogation remain time consuming for biologists and integrating publicly available data is still an open challenge. Linked data and Semantic Web technologies benefit to biologists. Using RDF (Reference Description Framework); biological data can be described using triples that associate an entity (called subject), a relation (called property) and a value for the relation (called object). Data from different datasets can be integrated and the SPARQL query language support their analysis. Nevertheless; understanding and acquiring the query language can be a daunting task for biologists. Here we present AskOmics, a tool supporting both intuitive data integration and querying while shielding the user from most of the technical difficulties underlying RDF and SPARQL. The virtualization-based deployment of AskOmics makes the tool easy to manage, reliable and simple to install. For data integration, the user loads his data as tabulation-separated files structured according to simple principles. This structure allows AskOmics to generate automatically the corresponding RDF triples, and to store them into a triplestore such as Fuseki or Virtuoso. At this point, the user’s data are available just like in any SPARQL endpoint. AskOmics automatically generates an abstract representation of the dataset based on the types of the subject and object of its triples. For data querying, AskOmics provides a visually intuitive interface compatible with any SPARQL endpoint (that is one generated by AskOmics data generation function, or any regular triplestore). The user can then select a sequence of nodes in this simplified view, and AskOmics generates the corresponding SPARQL query that can be executed on the original dataset. For example; it could be difficult for biologists to identify features such as genes underlying localised genomic regions limited by genetic markers as it requires the users to combine different files. Tabulation-separated files containing genes and genetic markers could be uploaded in AskOmics with the following criteria: genetic markers and genes identified as entity, each entity is related to a chromosome and a position start and end with numerical values. AskOmics interface allows the user, without knowledge in SPARQL language, to either select genomic regions with distinct markers or simply provide numerical values as the lower and upper position. The intersection with additional features could be computed for producing lists of features such as genes underlying specific genomic regions. The result can then be downloaded as a tabulation-separated file. Currently under development, AskOmics will also support the integration of external databases to compare or complete new findings.AskOmics’ principle is generic. It has been applied successfully to the analysis of large scale datasets including genetic, epigenomic, transcriptomic profiles and orthologous relationships to identify genomic regions that are involved in the variability of Brassicaceae (Arabidopsis, cabbage, turnip and oilseed rape) in response to clubroot disease. About 2.6 millions of triples were stored from 370 000 uploaded entities corresponding to genomic positions of genes amongst the four species of the Brassicaeae family as well as relationship data (orthology and transcriptomics). The fast queries allowed to identify lists of genes with specific expression profiles and their corresponding orthologs in the three others species.

Published
2016

26. Logo et site web de la plateforme MEANS

Author

Bitteur, Sylvaine, Filangi, Olivier, and ProdInra, Migration

Subjects
[SDV] Life Sciences [q-bio], [SDE] Environmental Sciences, [SDV.BV] Life Sciences [q-bio]/Vegetal Biology
Published
2013

27. Finding gene to genome epistatic effects

Author

Filangi, Olivier, Bacciu, Nicola, Demeure, Olivier, Legarra, Andres, Elsen, Jean Michel, Le Roy, Pascale, Physiologie, Environnement et Génétique pour l'Animal et les Systèmes d'Elevage [Rennes] (PEGASE), AGROCAMPUS OUEST-Institut National de la Recherche Agronomique (INRA), Station d'Amélioration Génétique des Animaux (SAGA), Institut National de la Recherche Agronomique (INRA), 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)-Institut National de la Recherche Agronomique (INRA), Ville service., and ProdInra, Archive Ouverte

Subjects
[SDV] Life Sciences [q-bio], [SDV]Life Sciences [q-bio]
Abstract

absent

Published
2012

28. QGP: Quantitative Genetics Platform. A high performance computing solution for quantitative genetics software

Author

Monsoor, Misharl, Neau, Andre, Souchal, Martin, Nugier, Sylvie, Laperruque, Francois, Iannuccelli, Eddie, Le Roy, Pascale, Ricard, Edmond, Robelin, David, Filangi, Olivier, Physiologie, Environnement et Génétique pour l'Animal et les Systèmes d'Elevage [Rennes] (PEGASE), Institut National de la Recherche Agronomique (INRA)-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), Génétique Animale et Biologie Intégrative (GABI), Institut National de la Recherche Agronomique (INRA)-AgroParisTech, Centre de Traitement de l'Information Génétique (CTIG), Institut National de la Recherche Agronomique (INRA), Station d'Amélioration Génétique des Animaux (SAGA), Laboratoire de Génétique Cellulaire (LGC), 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), and Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées

Subjects
analyse de données, Bioinformatics, génotypage, plateforme informatique, Bio-informatique, biologie intégrative, animal, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], ComputingMilieux_MISCELLANEOUS
Abstract

National audience

Published
2012

29. Linkage analysis of the XVIth QTLMAS simulated dataset using QTLMAP

Author

Demeure, Olivier, Filangi, Olivier, Gilbert, Hélène, Moreno-Romieux, Carole, Legarra, Andres, Elsen, Jean Michel, Le Roy, Pascale, Physiologie, Environnement et Génétique pour l'Animal et les Systèmes d'Elevage [Rennes] (PEGASE), AGROCAMPUS OUEST-Institut National de la Recherche Agronomique (INRA), Laboratoire de Génétique Cellulaire (LGC), 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-Institut National de la Recherche Agronomique (INRA), Station d'Amélioration Génétique des Animaux (SAGA), Institut National de la Recherche Agronomique (INRA), Institut National de la Recherche Agronomique (INRA)-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), Institut National de la Recherche Agronomique (INRA)-Ecole Nationale Vétérinaire de Toulouse (ENVT), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, AGRIS. Algero, ITA. Università degli Studi di Sassari (UniSS), ITA., AGROCAMPUS OUEST, and Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de la Recherche Agronomique (INRA)

Subjects
[SDV]Life Sciences [q-bio], cartographie qtl, marqueur génomique, food and beverages, analyse de liaison, animal d'élevage
Abstract

Session : Common data set: methods for QTL detection and association analysis; absent

Published
2012

30. Integrating QTL controlling fatness, lipid metabolites and gene expressions to genetically dissect the adiposity complex trait in a meat chicken cross

Author

Blum, Yuna, Demeure, Olivier, Désert, Colette, Guillou, Hervé, Bertrand-Michel, J, Filangi, Olivier, Le Roy, Pascale, Causeur, David, Lagarrigue, Sandrine, Génétique Animale (GARen), Institut National de la Recherche Agronomique (INRA)-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)-Ecole Nationale Supérieure Agronomique de Rennes, AGROCAMPUS OUEST, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Toxicologie Intégrative & Métabolisme (ToxAlim-TIM), ToxAlim (ToxAlim), Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Ecole Nationale Vétérinaire de Toulouse (ENVT), Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Ecole d'Ingénieurs de Purpan (INPT - EI Purpan), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Recherche Agronomique (INRA)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Recherche Agronomique (INRA), and Institut National de la Santé et de la Recherche Médicale (INSERM)

Subjects
QTL, chicken, [SDV]Life Sciences [q-bio], ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
2011

31. Genome-Wide QTL detection for growth, body composition and quality related traits in chicken

Author

Demeure, Olivier, Duclos, Michel Jacques, Berri, Cécile, Bacciu, Nicola, Pitel, Frédérique, Le Mignon, Guillaume, Filangi, Olivier, Cogburn, Larry A., Lagarrigue, Sandrine, Le Roy, Pascale, Le Bihan-Duval, Elisabeth, Génétique Animale (GARen), Institut National de la Recherche Agronomique (INRA)-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)-Ecole Nationale Supérieure Agronomique de Rennes, Unité de Recherches Avicoles (URA), Institut National de la Recherche Agronomique (INRA), Laboratoire de Génétique Cellulaire (LGC), 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, University of Delaware [Newark], and ProdInra, Migration

Subjects
[SDV] Life Sciences [q-bio], [SDV]Life Sciences [q-bio], food and beverages, [INFO]Computer Science [cs], [INFO] Computer Science [cs]
Abstract

International audience; During the past decades, genetic improvement for growth, body composition and quality related traits in chicken was obtained through a strong selection based on the phenotype. Since 20 years, the genetic variability has been studied through quantitative trait loci (QTL) detection in many different breeds or selected lines. This approach has led to the identifi cation of many QTLs but with localisation intervals too large to allow marker assisted selection. Since the sequencing of complete genomes, thousands of single nucleotide polymorphisms (SNPs) were identified. Therefore, regions unexplored before can be studied and QTL locations refi ned. The aim of this study was to fi ne map QTLs affecting 26 economically important traits by genotyping 1536 SNPs and 127 microsatellites on 579 F2 animals obtained by crossing divergently selected fat and lean lines. QTL interval mapping was performed with QTLMap software which was developed for populations containing a mixture of full and half-sib families. In addition to single QTL mapping, hypotheses such as the presence of two linked QTLs infl uencing the same trait were tested. A total of 57 QTLs was detected at the 5% chromosome wide level and 28 QTLs were suggested at the 10% chromosome wide level. A further list of 13 QTLs was detected by multi-QTL approach. Our results confi rmed some QTLs previously identifi ed with the set of microsatellite markers and refi ned their position. Interestingly, additional QTLs were identifi ed mostly because this study provided a better coverage of the chicken genome (28 chromosomes), including chromosomal regions which had never been thoroughly studied.

Published
2010

32. The repercussions of statistical properties of interval mapping methods on eQTL detection

Author

Wang, Xiaoqiang, Elsen, Jean Michel, Gilbert, Hélène, Moreno-Romieux, Carole, Filangi, Olivier, Le Roy, Pascale, Génétique Animale (GARen), Institut National de la Recherche Agronomique (INRA)-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)-Ecole Nationale Supérieure Agronomique de Rennes, Station d'Amélioration Génétique des Animaux (SAGA), Institut National de la Recherche Agronomique (INRA), Génétique Animale et Biologie Intégrative (GABI), Institut National de la Recherche Agronomique (INRA)-AgroParisTech, Institut National de la Recherche Agronomique (INRA)-AGROCAMPUS OUEST-Ecole Nationale Supérieure Agronomique de Rennes-IFR140, AgroParisTech-Institut National de la Recherche Agronomique (INRA), and IFR140-Ecole Nationale Supérieure Agronomique de Rennes-AGROCAMPUS OUEST-Institut National de la Recherche Agronomique (INRA)

Subjects
INTERVAL MAPPING, MARKERS, QTL, TRANSCRIPTOMIC, [SDV]Life Sciences [q-bio], DETECTION
Abstract

absent

Published
2009

34. Detection of QTL with effects on osmoregulation capacities in the rainbow trout (Oncorhynchus mykiss)

Author

Le Bras, Yvan, Dechamp, Nicolas, Krieg, Francine, Filangi, Olivier, Guyomard, Rene, Boussaha, Mekki, Bovenhuis, Henk, Pottinger, Thomas G., Prunet, Patrick, Le Roy, Pascale, Quillet, Edwige, Le Bras, Yvan, Dechamp, Nicolas, Krieg, Francine, Filangi, Olivier, Guyomard, Rene, Boussaha, Mekki, Bovenhuis, Henk, Pottinger, Thomas G., Prunet, Patrick, Le Roy, Pascale, and Quillet, Edwige

Abstract

Background There is increasing evidence that the ability to adapt to seawater in teleost fish is modulated by genetic factors. Most studies have involved the comparison of species or strains and little is known about the genetic architecture of the trait. To address this question, we searched for QTL affecting osmoregulation capacities after transfer to saline water in a nonmigratory captive-bred population of rainbow trout. Results A QTL design (5 full-sib families, about 200 F2 progeny each) was produced from a cross between F0 grand-parents previously selected during two generations for a high or a low cortisol response after a standardized confinement stress. When fish were about 18 months old (204 g body weight), individual progeny were submitted to two successive hyper-osmotic challenges (30g of salt/L) at a 14 d interval. Plasma chloride and sodium concentrations were recorded 24h after each transfer. After the second challenge, fish were sacrificed and gill index (weight of total gill arches corrected for body weight) was recorded. The genome scan was performed using 200 microsatellites and 88 SNP markers. Unitrait and multitrait QTL analyses evidenced a total of 15 and 7 different QTL (P<0.10) for plasma ion concentrations and gill index respectively. Among the most significant QTL, three affected concentrations of both chloride and sodium during both challenges, two were specific to either chloride or sodium concentrations, three QTL were specific to gill index, and three affected both gill index and ionic concentrations in plasma. Altogether, allelic effects were consistent for QTL affecting chloride and sodium concentrations but inconsistent for QTL affecting ionic concentrations and gill morphology. There was no systematic lineage effect (grand-parental origin of QTL alleles) on the recorded traits. Conclusions For the first time, genomic loci associated with effects on major physiological components of osmotic adaptation to seawater in a nonmigratory f

Published
2011

35. Genome-wide interval mapping using SNPs identifies new QTL for growth, body composition and several physiological variables in an F2 intercross between fat and lean chicken lines

Author

Demeure, Olivier, primary, Duclos, Michel J, additional, Bacciu, Nicola, additional, Le Mignon, Guillaume, additional, Filangi, Olivier, additional, Pitel, Frédérique, additional, Boland, Anne, additional, Lagarrigue, Sandrine, additional, Cogburn, Larry A, additional, Simon, Jean, additional, Le Roy, Pascale, additional, and Le Bihan-Duval, Elisabeth, additional

Published
2013
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39. XVth QTLMAS: simulated dataset

Author

Elsen, Jean-Michel, primary, Tesseydre, Simon, additional, Filangi, Olivier, additional, Le Roy, Pascale, additional, and Demeure, Olivier, additional

Published
2012
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43. QTL detection for coccidiosis (Eimeria tenella) resistance in a Fayoumi × Leghorn F2 cross, using a medium-density SNP panel.

Author

Bacciu, Nicola, Bed'Hom, Bertrand, Filangi, Olivier, Romé, Hélène, Gourichon, David, Répérant, Jean-Michel, Le Roy, Pascale, Pinard-van der Laan, Marie-Hélène, and Demeure, Olivier

Subjects
COCCIDIOIDES, PARASITIC diseases, VETERINARY parasitology, POULTRY industry, ANIMAL genetics
Abstract

Background: Coccidiosis is a major parasitic disease that causes huge economic losses to the poultry industry. Its pathogenicity leads to depression of body weight gain, lesions and, in the most serious cases, death in affected animals. Genetic variability for resistance to coccidiosis in the chicken has been demonstrated and if this natural resistance could be exploited, it would reduce the costs of the disease. Previously, a design to characterize the genetic regulation of Eimeria tenella resistance was set up in a Fayoumi × Leghorn F2 cross. The 860 F2 animals of this design were phenotyped for weight gain, plasma coloration, hematocrit level, intestinal lesion score and body temperature. In the work reported here, the 860 animals were genotyped for a panel of 1393 (157 microsatellites and 1236 single nucleotide polymorphism (SNP) markers that cover the sequenced genome (i.e. the 28 first autosomes and the Z chromosome). In addition, with the aim of finding an index capable of explaining a large amount of the variance associated with resistance to coccidiosis, a composite factor was derived by combining the variables of all these traits in a single variable. QTL detection was performed by linkage analysis using GridQTL and QTLMap. Single and multi-QTL models were applied. Results: Thirty-one QTL were identified i.e. 27 with the single-QTL model and four with the multi-QTL model and the average confidence interval was 5.9 cM. Only a few QTL were common with the previous study that used the same design but focused on the 260 more extreme animals that were genotyped with the 157 microsatellites only. Major differences were also found between results obtained with QTLMap and GridQTL. Conclusions: The medium-density SNP panel made it possible to genotype new regions of the chicken genome (including micro-chromosomes) that were involved in the genetic control of the traits investigated. This study also highlights the strong variations in QTL detection between different models and marker densities. [ABSTRACT FROM AUTHOR]

Published
2014
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44. Genome-wide interval mapping using SNPs identifies new QTL for growth, body composition and several physiological variables in an F2 intercross between fat and lean chicken lines.

Author

Demeure, Olivier, Duclos, Michel J., Bacciu, Nicola, Le Mignon, Guillaume, Filangi, Olivier, Pitel, Frédérique, Boland, Anne, Lagarrigue, Sandrine, Cogburn, Larry A., Simon, Jean, Le Roy, Pascale, and Le Bihan-Duval, Elisabeth

Subjects
BODY composition, CHICKENS, GENETIC correlations, MICROSATELLITE repeats, SINGLE nucleotide polymorphisms, BODY weight
Abstract

Background: For decades, genetic improvement based on measuring growth and body composition traits has been successfully applied in the production of meat-type chickens. However, this conventional approach is hindered by antagonistic genetic correlations between some traits and the high cost of measuring body composition traits. Marker-assisted selection should overcome these problems by selecting loci that have effects on either one trait only or on more than one trait but with a favorable genetic correlation. In the present study, identification of such loci was done by genotyping an F2 intercross between fat and lean lines divergently selected for abdominal fatness genotyped with a medium-density genetic map (120 microsatellites and 1302 single nucleotide polymorphisms). Genome scan linkage analyses were performed for growth (body weight at 1, 3, 5, and 7 weeks, and shank length and diameter at 9 weeks), body composition at 9 weeks (abdominal fat weight and percentage, breast muscle weight and percentage, and thigh weight and percentage), and for several physiological measurements at 7 weeks in the fasting state, i.e. body temperature and plasma levels of IGF-I, NEFA and glucose. Interval mapping analyses were performed with the QTLMap software, including single-trait analyses with single and multiple QTL on the same chromosome. Results: Sixty-seven QTL were detected, most of which had never been described before. Of these 67 QTL, 47 were detected by single-QTL analyses and 20 by multiple-QTL analyses, which underlines the importance of using different statistical models. Close analysis of the genes located in the defined intervals identified several relevant functional candidates, such as ACACA for abdominal fatness, GHSR and GAS1 for breast muscle weight, DCRX and ASPSCR1 for plasma glucose content, and ChEBP for shank diameter. Conclusions: The medium-density genetic map enabled us to genotype new regions of the chicken genome (including micro-chromosomes) that influenced the traits investigated. With this marker density, confidence intervals were sufficiently small (14 cM on average) to search for candidate genes. Altogether, this new information provides a valuable starting point for the identification of causative genes responsible for important QTL controlling growth, body composition and metabolic traits in the broiler chicken. [ABSTRACT FROM AUTHOR]

Published
2013
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45. Comparison of analyses of the XVth QTLMAS common dataset III: Genomic Estimations of Breeding Values.

Author

Le Roy, Pascale, Filangi, Olivier, Demeure, Olivier, and Elsen, Jean-Michel

Subjects
GENOMICS, ADULT education workshops, LINEAR statistical models, BAYESIAN analysis, CHROMOSOMES
Abstract

Background: The QTLMAS XVth dataset consisted of pedigree, marker genotypes and quantitative trait performances of animals with a sib family structure. Pedigree and genotypes concerned 3,000 progenies among those 2,000 were phenotyped. The trait was regulated by 8 QTLs which displayed additive, imprinting or epistatic effects. The 1,000 unphenotyped progenies were considered as candidates to selection and their Genomic Estimated Breeding Values (GEBV) were evaluated by participants of the XVth QTLMAS workshop. This paper aims at comparing the GEBV estimation results obtained by seven participants to the workshop. Methods: From the known QTL genotypes of each candidate, two “true” genomic values (TV) were estimated by organizers: the genotypic value of the candidate (TGV) and the expectation of its progeny genotypic values (TBV). GEBV were computed by the participants following different statistical methods: random linear models (including BLUP and Ridge Regression), selection variable techniques (LASSO, Elastic Net) and Bayesian methods. Accuracy was evaluated by the correlation between TV (TGV or TBV) and GEBV presented by participants. Rank correlation of the best 10% of individuals and error in predictions were also evaluated. Bias was tested by regression of TV on GEBV. Results: Large differences between methods were found for all criteria and type of genetic values (TGV, TBV). In general, the criteria ranked consistently methods belonging to the same family. Conclusions: Bayesian methods - A

Published
2012
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46. XVth QTLMAS: simulated dataset.

Author

Elsen, Jean-Michel, Tesseydre, Simon, Filangi, Olivier, Le Roy, Pascale, and Demeure, Olivier

Subjects
GENOTYPE-environment interaction, ADULT education workshops, CHROMOSOMES, HERITABILITY, RUMINANTS
Abstract

Background: Our aim was to simulate the data for the QTLMAS2011 workshop following a pig-type family structure under an oligogenic model, each QTL being specific. Results: The population comprised 3000 individuals issued from 20 sires and 200 dams. Within each family, 10 progenies belonged to the experimental population and were assigned phenotypes and marker genotypes and 5 belonged to the selection population, only known on their marker genotypes. A total of 10,000 SNPs carried by 5 chromosomes of 1 Morgan each were simulated. Eight QTL were created (1 quadri-allelic, 2 linked in phase, 2 linked in repulsion, 1 imprinted and 2 epistatic). Random noise was added giving an heritability of 0.30. The marker density, LD and MAF were similar to real life parameters [ABSTRACT FROM AUTHOR]

Published
2012
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47. Validation of carbon isotopologue distribution measurements by GC-MS and application to 13 C-metabolic flux analysis of the tricarboxylic acid cycle in Brassica napus leaves.

Author

Dellero Y, Berardocco S, Berges C, Filangi O, and Bouchereau A

Abstract

The estimation of metabolic fluxes in photosynthetic organisms represents an important challenge that has gained interest over the last decade with the development of 13 C-Metabolic Flux Analysis at isotopically non-stationary steady-state. This approach requires a high level of accuracy for the measurement of Carbon Isotopologue Distribution in plant metabolites. But this accuracy has still not been evaluated at the isotopologue level for GC-MS, leading to uncertainties for the metabolic fluxes calculated based on these fragments. Here, we developed a workflow to validate the measurements of CIDs from plant metabolites with GC-MS by producing tailor-made E. coli standard extracts harboring a predictable binomial CID for some organic and amino acids. Overall, most of our TMS-derivatives mass fragments were validated with these standards and at natural isotope abundance in plant matrices. Then, we applied this validated MS method to investigate the light/dark regulation of plant TCA cycle by incorporating U- 13 C-pyruvate to Brassica napus leaf discs. We took advantage of pathway-specific isotopologues/isotopomers observed between two and six hours of labeling to show that the TCA cycle can operate in a cyclic manner under both light and dark conditions. Interestingly, this forward cyclic flux mode has a nearly four-fold higher contribution for pyruvate-to-citrate and pyruvate-to-malate fluxes than the phosphoenolpyruvate carboxylase (PEPc) flux reassimilating carbon derived from some mitochondrial enzymes. The contribution of stored citrate to the mitochondrial TCA cycle activity was also questioned based on dynamics of 13 C-enrichment in citrate, glutamate and succinate and variations of citrate total amounts under light and dark conditions. Interestingly, there was a light-dependent 13 C-incorporation into glycine and serine showing that decarboxylations from pyruvate dehydrogenase complex and TCA cycle enzymes were actively reassimilated and could represent up to 5% to net photosynthesis., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The handling editor is currently organizing a Research Topic with the author YD., (Copyright © 2023 Dellero, Berardocco, Berges, Filangi and Bouchereau.)

Published
2023
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