Author: "Jérôme Mariette" - Searchworks@Jio Institute Digital Library Search Results

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1. Ten simple rules for switching from face-to-face to remote conference: An opportunity to estimate the reduction in GHG emissions.

Author: Valentin Guignon, Catherine Breton, Jérôme Mariette, François Sabot, Julien Fumey, Vincent Lefort, and Anna-Sophie Fiston-Lavier
Subjects: Biology (General), QH301-705.5
Abstract: In 2020, the world faced the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) pandemic that drastically altered people's lives. Since then, many countries have been forced to suspend public gatherings, leading to many conference cancellations, postponements, or reorganizations. Switching from a face-to-face to a remote conference became inevitable and the ultimate solution to sustain scientific exchanges at the national and the international levels. The same year, as a committee, we were in charge of organizing the major French annual conference that covers all computational biology areas: The "Journées Ouvertes en Biologie, Informatique et Mathématiques" (JOBIM). Despite the health crisis, we succeeded in changing the conference format from face to face to remote in a very short amount of time. Here, we propose 10 simple rules based on this experience to modify a conference format in an optimized and cost-effective way. In addition to the suggested rules, we decided to emphasize an unexpected benefit of this situation: a significant reduction in greenhouse gas (GHG) emissions related to travel for scientific conference attendance. We believe that even once the SARS-CoV-2 crisis is over, we collectively will have an opportunity to think about the way we approach such scientific events over the longer term.
Published: 2021
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2. An open-source tool to assess the carbon footprint of research

Author: Jérôme Mariette, Odile Blanchard, Olivier Berné, Olivier Aumont, Julian Carrey, AnneLaure Ligozat, Emmanuel Lellouch, Philippe-Emmanuel Roche, Gaël Guennebaud, Joel Thanwerdas, Philippe Bardou, Gérald Salin, Elise Maigne, Sophie Servan, and Tamara Ben-Ari
Subjects: carbon footprint, research sector, WEB application, Environmental sciences, GE1-350
Abstract: The scrutiny over the carbon footprint of research and higher education has increased rapidly in the last few years. This has resulted in a series of publications providing various estimates of the carbon footprint of one or several research activities, principally at the scale of a university or a research center or, more recently, a field of research. The variety of tools or methodologies on which these estimates rely unfortunately prevents any aggregation or direct comparison. This is because carbon footprint assessments are very sensitive to key parameters (e.g., emission factors) or hypotheses (e.g., scopes). Hence, it is impossible to address fundamental questions such as: is the carbon footprint of research structurally different between disciplines? Are plane trips a major source of carbon emissions in academic research? Massive collection and curation of carbon footprint data, across a large array of research situations and disciplines, is hence an important, timely and necessary challenge to answer these questions. This paper presents a framework to collect and analyse large amounts of homogeneous research carbon emission data in a network of research entities at the national scale. It relies on an open-source web application, GES 1point5 , designed to estimate the carbon footprint of a department, research lab or team in any country of the world. Importantly, GES 1point5 is also designed to aggregate all input data and corresponding GHG emissions estimates into a comprehensive database. GES 1point5 therefore enables (i) the identification of robust local or national determinants of the carbon footprint of research and (ii) the estimation of the carbon footprint of the entire research sector at national scale. A preliminary analysis of the carbon footprint of more than one hundred laboratories in France is presented to illustrate the potential of the framework. It shows that the average emissions are 479 t CO _2 e for a research lab and 3.6 t CO _2 e for an average lab member (respectively 404 and 3.1 t CO _2 e without accounting for the indirect radiative effects of aviation), with the current scope of GES 1point5 . Availability and implementation: GES 1point5 is available online at http://labos1point5.org/ges-1point5 http://labos1point5.org/ges-1point5 and its source code can be downloaded from the GitLab platform at https://framagit.org/labos1point5/l1p5-vuejs https://framagit.org/labos1point5/l1p5-vuejs .
Published: 2022
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3. The carbon footprint of scientific visibility

Author: Olivier Berné, Lydiane Agier, Antoine Hardy, Emmanuel Lellouch, Olivier Aumont, Jérôme Mariette, and Tamara Ben-Ari
Subjects: air travel, h-index, academia, GHG emissions, Environmental technology. Sanitary engineering, TD1-1066, Environmental sciences, GE1-350, Science, Physics, QC1-999
Abstract: In the face of global warming, academics have started to consider and analyze the environmental and carbon footprint associated with their professional activity. Among the several sources of greenhouse gas emissions from research activities, air travel—one of the most visible and unequal fractions of this footprint—has received much attention. Of particular interest is the question of how air travel may be related to scientific success or visibility as defined by current academic evaluation norms, notably bibliometric indicators. Existing studies, conducted over a small sample of individuals or within specific disciplines, have demonstrated that the number of citations may be related to air-travel frequency, but have failed to identify a link between air travel and publication rate or h -index. Here, using a comprehensive dataset aggregating the answers from over 6000 respondents to a survey sent to randomly selected scientists and staff across all research disciplines in France, we show that higher individual air travel is associated with a stronger publication rate and h -index. This relationship is robust to the inclusion of the effects of gender, career stage, and disciplines. Our analysis suggests that flying is a means for early-career scientists to obtain scientific visibility, and for senior scientists to maintain this visibility.
Published: 2022
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4. Novel Avian Coronavirus and Fulminating Disease in Guinea Fowl, France

Author: Etienne Liais, Guillaume Croville, Jérôme Mariette, Maxence Delverdier, Marie-Noëlle Lucas, Christophe Klopp, Jérôme Lluch, Cécile Donnadieu, James S. Guy, Léni Corrand, Mariette F. Ducatez, and Jean-Luc Guérin
Subjects: coronavirus, guinea fowl, metagenomics, next-generation sequencing, viruses, France, Medicine, Infectious and parasitic diseases, RC109-216
Abstract: For decades, French guinea fowl have been affected by fulminating enteritis of unclear origin. By using metagenomics, we identified a novel avian gammacoronavirus associated with this disease that is distantly related to turkey coronaviruses. Fatal respiratory diseases in humans have recently been caused by coronaviruses of animal origin.
Published: 2014
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5. RNAbrowse: RNA-Seq de novo assembly results browser.

Author: Jérôme Mariette, Céline Noirot, Ibounyamine Nabihoudine, Philippe Bardou, Claire Hoede, Anis Djari, Cédric Cabau, and Christophe Klopp
Subjects: Medicine, Science
Abstract: Transcriptome analysis based on a de novo assembly of next generation RNA sequences is now performed routinely in many laboratories. The generated results, including contig sequences, quantification figures, functional annotations and variation discovery outputs are usually bulky and quite diverse. This article presents a user oriented storage and visualisation environment permitting to explore the data in a top-down manner, going from general graphical views to all possible details. The software package is based on biomart, easy to install and populate with local data. The software package is available under the GNU General Public License (GPL) at http://bioinfo.genotoul.fr/RNAbrowse.
Published: 2014
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6. Assessment of replicate bias in 454 pyrosequencing and a multi-purpose read-filtering tool

Author: Klopp Christophe, Noirot Céline, and Jérôme Mariette
Subjects: Medicine, Biology (General), QH301-705.5, Science (General), Q1-390
Abstract: Abstract Background Roche 454 pyrosequencing platform is often considered the most versatile of the Next Generation Sequencing technology platforms, permitting the sequencing of large genomes, the analysis of variations or the study of transcriptomes. A recent reported bias leads to the production of multiple reads for a unique DNA fragment in a random manner within a run. This bias has a direct impact on the quality of the measurement of the representation of the fragments using the reads. Other cleaning steps are usually performed on the reads before assembly or alignment. Findings PyroCleaner is a software module intended to clean 454 pyrosequencing reads in order to ease the assembly process. This program is a free software and is distributed under the terms of the GNU General Public License as published by the Free Software Foundation. It implements several filters using criteria such as read duplication, length, complexity, base-pair quality and number of undetermined bases. It also permits to clean flowgram files (.sff) of paired-end sequences generating on one hand validated paired-ends file and the other hand single read file. Conclusions Read cleaning has always been an important step in sequence analysis. The pyrocleaner python module is a Swiss knife dedicated to 454 reads cleaning. It includes commonly used filters as well as specialised ones such as duplicated read removal and paired-end read verification.
Published: 2011
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7. An open-source tool to assess the carbon footprint of research.

Author: Jérôme Mariette, Odile Blanchard, Olivier Berné, and Tamara Ben Ari
Published: 2021

8. Unsupervised multiple kernel learning for heterogeneous data integration.

Author: Jérôme Mariette and Nathalie Villa-Vialaneix
Published: 2018
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9. Aggregating Self-Organizing Maps with Topology Preservation.

Author: Jérôme Mariette and Nathalie Villa-Vialaneix
Published: 2016
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10. Efficient interpretable variants of online SOM for large dissimilarity data.

Author: Jérôme Mariette, Madalina Olteanu, and Nathalie Villa-Vialaneix
Published: 2017
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11. Traveling for academic research : patterns, determinants and mitigation options

Author: Olivier Aumont, Tamara Ben Ari, Jérôme Mariette, Laurent Jeanneau, Aymeric Spiga, Gaelle Lefort, Philippe-e Roche, Alexandre Santerne, André Estevez-Torres, Nucleus for European Modeling of the Ocean (NEMO R&D ), Laboratoire d'Océanographie et du Climat : Expérimentations et Approches Numériques (LOCEAN), Muséum national d'Histoire naturelle (MNHN)-Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut Pierre-Simon-Laplace (IPSL (FR_636)), École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-École polytechnique (X)-Centre National d'Études Spatiales [Toulouse] (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité)-École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-École polytechnique (X)-Centre National d'Études Spatiales [Toulouse] (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité)-Muséum national d'Histoire naturelle (MNHN)-Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut Pierre-Simon-Laplace (IPSL (FR_636)), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-École polytechnique (X)-Centre National d'Études Spatiales [Toulouse] (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), Université de Montpellier (UM), Unité de Mathématiques et Informatique Appliquées de Toulouse (MIAT INRAE), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Géosciences Rennes (GR), Université de Rennes (UR)-Institut national des sciences de l'Univers (INSU - 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), Laboratoire de Météorologie Dynamique (UMR 8539) (LMD), Institut national des sciences de l'Univers (INSU - CNRS)-École polytechnique (X)-École des Ponts ParisTech (ENPC)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Département des Géosciences - ENS Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL), Université de Tours (UT), Hélium : du fondamental aux applications (NEEL - HELFA), Institut Néel (NEEL), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA), Aix Marseille Université (AMU), Laboratoire Jean Perrin (LJP), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut de Biologie Paris Seine (IBPS), Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), and European Geosciences Union
Subjects: [SDE]Environmental Sciences, determinants, academic research, Patterns
Abstract: International audience; In the midst of climate change, academic travels - one salient aspect of the carbon footprint of research activities - are at the center of a growing concern. Mitigation options often focus on two dimensions : (i) decreasing the frequency of attendance to conferences and (ii) modal shift in transport. Here, we analyze professional travel in academia from a unique database compiling about 100 000 travels from about 150 research labs across a large array of disciplines and localities in France to detail the structure, patterns and heterogeneity of national and international research travels for research purposes. We estimate the mitigation potential of a series of options encompassing but not limited to institutional options. We show that, if short distance traveling (typically below 1000 km) are largely dominant in number, their relative mitigation potential via modal shift is small (i.e., below 15%). On the other hand, long distance traveling, which is often associated with international collaborations or field work hold a much larger mitigation potential but question the very nature of research activities. We propose ambitious sobriety options to robustly decrease travel-induced GHG emissions in academia and discuss their acceptability in the context of the French public research system.
Published: 2023
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12. Bagged Kernel SOM.

Author: Jérôme Mariette, Madalina Olteanu, Julien Boelaert, and Nathalie Villa-Vialaneix
Published: 2014
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13. Accelerating stochastic kernel SOM.

Author: Jérôme Mariette, Fabrice Rossi, Madalina Olteanu, and Nathalie Villa-Vialaneix
Published: 2017

14. The BioMart community portal: an innovative alternative to large, centralized data repositories.

Author: Damian Smedley, Syed Haider, Steffen Durinck, Luca Pandini, Paolo Provero, James E. Allen, Olivier Arnaiz, Mohammad Hamza Awedh, Richard A. Baldock, Giulia Barbiera, Philippe Bardou, Tim Beck, Andrew Blake 0003, Merideth Bonierbale, Anthony J. Brookes, Gabriele Bucci, Iwan Buetti, Sarah W. Burge, Cédric Cabau, Joseph W. Carlson, Claude Chelala, Charalambos Chrysostomou, Davide Cittaro, Olivier Collin, Raul Cordova, Rosalind J. Cutts, Erik Dassi, Alex Di Génova, Anis Djari, Anthony Esposito, Heather Estrella, Eduardo Eyras, Julio Fernandez-Banet, Simon A. Forbes, Robert C. Free, Takatomo Fujisawa, Emanuela Gadaleta, Jose M. Garcia-Manteiga, David M. Goodstein, Kristian A. Gray, José Afonso Guerra-Assunção, Bernard Haggarty, Dongjin Han, Byungwoo Han, Todd W. Harris, Jayson Harshbarger, Robert K. Hastings, Richard D. Hayes, Claire Hoede, Shen Hu, Zhi-Liang Hu, Lucie N. Hutchins, Zhengyan Kan, Hideya Kawaji, Aminah-Olivia Keliet, Arnaud Kerhornou, Sunghoon Kim 0001, Rhoda Kinsella, Christophe Klopp, Lei Kong, Daniel Lawson, Dejan Lazarevic, Ji-Hyun Lee, Thomas Letellier, Chuan-Yun Li, Pietro Liò, Chu-Jun Liu, Jie Luo, Alejandro Maass, Jérôme Mariette, Thomas Maurel, Stefania Merella, Azza Mostafa Mohamed, François Moreews, Nabihoudine Ibouniyamine, Nelson Ndegwa, Céline Noirot, Christian Perez-Llamas, Michael Primig, Alessandro Quattrone, Hadi Quesneville, Davide Rambaldi, James M. Reecy, Michela Riba, Steven Rosanoff, Amna Ali Saddiq, Elisa Salas, Olivier Sallou, Rebecca Shepherd, Reinhard Simon, Linda Sperling, William Spooner, Daniel M. Staines, Delphine Steinbach, Kevin R. Stone, Elia Stupka, Jon W. Teague, Abu Z. Dayem Ullah, Jun Wang 0060, Doreen Ware, Marie Wong-Erasmus, Ken Youens-Clark, Amonida Zadissa, Shi-Jian Zhang, and Arek Kasprzyk
Published: 2015
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15. AN Automated Visual Event Detection System for Cabled Observatory Video.

Author: Danelle E. Cline, Duane R. Edgington, and Jérôme Mariette
Published: 2008

16. Jflow: a workflow management system for web applications.

Author: Jérôme Mariette, Frédéric Escudié, Philippe Bardou, Nabihoudine Ibouniyamine, Céline Noirot, Marie-Stéphane Trotard, Christine Gaspin, and Christophe Klopp
Published: 2016
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17. Des noyaux pour les omiques

Author: Jérôme MARIETTE and Nathalie VIALANEIX
Abstract: En biologie des systèmes, de nombreuses méthodes ont été développées pour intégrer les données -omiques et, parmi elles, les noyaux sont une approche couramment utilisée. Dans ce chapitre, nous présentons le cadre général des approches à noyau et leur utilité pour l'analyse de divers types de données biologiques avec un focus particulier sur les approches exploratoires.
Published: 2022
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18. Nuclear HMGB1 protects from nonalcoholic fatty liver disease through negative regulation of liver X receptor

Author: Jean Personnaz, Enzo Piccolo, Alizée Dortignac, Jason S. Iacovoni, Jérôme Mariette, Vincent Rocher, Arnaud Polizzi, Aurélie Batut, Simon Deleruyelle, Lucas Bourdens, Océane Delos, Lucie Combes-Soia, Romain Paccoud, Elsa Moreau, Frédéric Martins, Thomas Clouaire, Fadila Benhamed, Alexandra Montagner, Walter Wahli, Robert F. Schwabe, Armelle Yart, Isabelle Castan-Laurell, Justine Bertrand-Michel, Odile Burlet-Schiltz, Catherine Postic, Pierre-Damien Denechaud, Cédric Moro, Gaelle Legube, Chih-Hao Lee, Hervé Guillou, Philippe Valet, Cédric Dray, Jean-Philippe Pradère, Institut des Maladies Métaboliques et Casdiovasculaires (UPS/Inserm U1297 - I2MC), Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National de la Santé et de la Recherche Médicale (INSERM), Geroscience and rejuvenation research center (RESTORE), Université de Toulouse (UT)-Université de Toulouse (UT)-EFS-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Unité de Mathématiques et Informatique Appliquées de Toulouse (MIAT INRAE), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), SIAAP - Direction du Développement et de la Prospective, SIAAP, Toxicologie Intégrative & Métabolisme (ToxAlim-TIM), ToxAlim (ToxAlim), Université de Toulouse (UT)-Université de Toulouse (UT)-Ecole Nationale Vétérinaire de Toulouse (ENVT), Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Ecole d'Ingénieurs de Purpan (INP - PURPAN), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), MetaboHUB-MetaToul, MetaboHUB-Génopole Toulouse Midi-Pyrénées [Auzeville] (GENOTOUL), Université de Toulouse (UT)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Institut de pharmacologie et de biologie structurale (IPBS), Université de Toulouse (UT)-Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS), Plateforme Génome & Transcriptome (GET), Génopole Toulouse Midi-Pyrénées [Auzeville] (GENOTOUL), Unité de biologie moléculaire, cellulaire et du développement - UMR5077 (MCD), Centre de Biologie Intégrative (CBI), Université de Toulouse (UT)-Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS)-Université Toulouse III - Paul Sabatier (UT3), Institut Cochin (IC UM3 (UMR 8104 / U1016)), Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), Center for Integrative Genomics - Institute of Bioinformatics, Génopode (CIG), Swiss Institute of Bioinformatics [Lausanne] (SIB), Université de Lausanne = University of Lausanne (UNIL)-Université de Lausanne = University of Lausanne (UNIL), Lee Kong Chian School of Medicine, Nanyang Technological University [Singapour], Columbia University [New York], MetaToul FluxoMet (TBI-MetaToul), Université de Toulouse (UT)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-MetaboHUB-Génopole Toulouse Midi-Pyrénées [Auzeville] (GENOTOUL), Université de Toulouse (UT)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Toulouse Biotechnology Institute (TBI), Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Harvard T.H. Chan School of Public Health, This study was supported by grants from INSERM, Paul Sabatier University, the Agence Nationale de la Recherche (ANR-17-CE14-0016, to J.-P.P.), and the Région Occitanie and Association Française d’Etude et de Recherche sur l’Obésité (to J.-P.P.). J.P. was supported by a scholarship from Paul Sabatier University. E.P. was supported by a scholarship from Agence Nationale de la Recherche (ANR-17-CE14-0016). R.P. was supported by a scholarship from Région Midi-Pyrénées-INSERM (no. 15050341). O.B.-S. and L.C.-S. and the proteomic facility were supported, in part, by the Région Occitanie, European funds (Fonds Européens de Développement Régional, FEDER), Toulouse Métropole, and the French Ministry of Research with the Investissement d’Avenir Infrastructures Nationales en Biologie et Santé program (ProFI, Proteomics French Infrastructure project, ANR-10-INBS-08), ANR-17-CE14-0016,HOLISTic,Rôle de la protéine HMGB1 au cours du stress metabolique(2017), and Lee Kong Chian School of Medicine (LKCMedicine)
Subjects: Fatty Liver, Multidisciplinary, [SDV]Life Sciences [q-bio], Medicine [Science], chemical and pharmacologic phenomena, Lipid Metabolisms
Abstract: Dysregulations of lipid metabolism in the liver may trigger steatosis progression, leading to potentially severe clinical consequences such as nonalcoholic fatty liver diseases (NAFLDs). Molecular mechanisms underlying liver lipogenesis are very complex and fine-tuned by chromatin dynamics and multiple key transcription factors. Here, we demonstrate that the nuclear factor HMGB1 acts as a strong repressor of liver lipogenesis. Mice with liver-specific Hmgb1 deficiency display exacerbated liver steatosis, while Hmgb1-overexpressing mice exhibited a protection from fatty liver progression when subjected to nutritional stress. Global transcriptome and functional analysis revealed that the deletion of Hmgb1 gene enhances LXRα and PPARγ activity. HMGB1 repression is not mediated through nucleosome landscape reorganization but rather via a preferential DNA occupation in a region carrying genes regulated by LXRα and PPARγ. Together, these findings suggest that hepatocellular HMGB1 protects from liver steatosis development. HMGB1 may constitute a new attractive option to therapeutically target the LXRα-PPARγ axis during NAFLD. Published version This study was supported by grants from INSERM, Paul Sabatier University, the Agence Nationale de la Recherche (ANR-17-CE14-0016, to J.-P.P.), and the Région Occitanie and Association Française d’Etude et de Recherche sur l’Obésité (to J.-P.P.). J.P. was supported by a scholarship from Paul Sabatier University. E.P. was supported by a scholarship from Agence Nationale de la Recherche (ANR-17-CE14-0016). R.P. was supported by a scholarship from Région Midi-Pyrénées-INSERM (no. 15050341). O.B.-S. and L.C.-S. and the proteomic facility were supported, in part, by the Région Occitanie, European funds (Fonds Européens de Développement Régional, FEDER), Toulouse Métropole, and the French Ministry of Research with the Investissement d’Avenir Infrastructures Nationales en Biologie et Santé program (ProFI, Proteomics French Infrastructure project, ANR-10-INBS-08).
Published: 2022
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19. Feature selection for kernel methods in systems biology

Author: Céline Brouard, Jérôme Mariette, Rémi Flamary, and Nathalie Vialaneix
Abstract: The substantial development of high-throughput biotechnologies has rendered large-scale multi-omics datasets increasingly available. New challenges have emerged to process and integrate this large volume of information, often obtained from widely heterogeneous sources. Kernel methods have proven successful to handle the analysis of different types of datasets obtained on the same individuals. However, they usually suffer from a lack of interpretability since the original description of the individuals is lost due to the kernel embedding. We propose novel feature selection methods that are adapted to the kernel framework and go beyond the well-established work in supervised learning by addressing the more difficult tasks of unsupervised learning and kernel output learning. The method is expressed under the form of a non-convex optimization problem with a ℓ1 penalty, which is solved with a proximal gradient descent approach. It is tested on several systems biology datasets and shows good performances in selecting relevant and less redundant features compared to existing alternatives. It also proved relevant for identifying important governmental measures best explaining the time series of Covid-19 reproducing number evolution during the first months of 2020. The proposed feature selection method is embedded in the R package mixKernel version 0.8, published on CRAN. Installation instructions are available at http://mixkernel.clementine.wf/.
Published: 2022
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20. Ten simple rules for switching from face-to-face to remote conference: An opportunity to estimate the reduction in GHG emissions

Author: Anna-Sophie Fiston-Lavier, François Sabot, Julien Fumey, Vincent Lefort, Jérôme Mariette, Valentin Guignon, Catherine Breton, Alliance of Bioversity International and the International Center for Tropical Agriculture (CIAT) [Rome] (Alliance), Consultative Group on International Agricultural Research [CGIAR] (CGIAR), Unité de Mathématiques et Informatique Appliquées de Toulouse (MIAT INRAE), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Diversité, adaptation, développement des plantes (UMR DIADE), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud]), Société Française de BioInformatique (SFBI), Méthodes et Algorithmes pour la Bioinformatique (MAB), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Institut des Sciences de l'Evolution de Montpellier (UMR ISEM), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Montpellier (UM)-Institut de recherche pour le développement [IRD] : UR226-Centre National de la Recherche Scientifique (CNRS), Institut Universitaire de France (IUF), Ministère de l'Education nationale, de l’Enseignement supérieur et de la Recherche (M.E.N.E.S.R.), Unité de Mathématiques et Informatique Appliquées de Toulouse (MIAT INRA), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), École pratique des hautes études (EPHE), and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Montpellier (UM)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre National de la Recherche Scientifique (CNRS)-Institut de recherche pour le développement [IRD] : UR226
Subjects: RNA viruses, Viral Diseases, Atmospheric Science, Science and Technology Workforce, Epidemiology, Coronaviruses, Careers in Research, 01 natural sciences, UNEXPECTED BENEFIT, Database and Informatics Methods, Face-to-face, Medical Conditions, Medicine and Health Sciences, Biology (General), 010303 astronomy & astrophysics, Pathology and laboratory medicine, Simple (philosophy), Travel, 0303 health sciences, Ecology, Attendance, Software Engineering, Medical microbiology, GHG emissions, Chemistry, Professions, Editorial, Infectious Diseases, Computational Theory and Mathematics, Modeling and Simulation, Physical Sciences, Viruses, [SDE]Environmental Sciences, Engineering and Technology, France, SARS CoV 2, Pathogens, Computer and Information Sciences, SARS coronavirus, Coronavirus disease 2019 (COVID-19), QH301-705.5, Bioinformatics, Science Policy, Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), Research and Analysis Methods, Microbiology, Computer Software, Greenhouse Gases, 03 medical and health sciences, Cellular and Molecular Neuroscience, Gaz à effet de serre (GES), 0103 physical sciences, Genetics, Environmental Chemistry, Humans, Interpersonal Relations, Pandemics, Molecular Biology, Ecology, Evolution, Behavior and Systematics, 030304 developmental biology, Finance, SARS-CoV-2, business.industry, Ecology and Environmental Sciences, Teleworking, Organisms, Viral pathogens, Biology and Life Sciences, Computational Biology, COVID-19, Covid 19, Congresses as Topic, Microbial pathogens, Term (time), 13. Climate action, Atmospheric Chemistry, Greenhouse gas, People and Places, Earth Sciences, Videoconferencing, Scientists, Feasibility Studies, Population Groupings, business
Abstract: International audience; In 2020, the world faced the Severe Acute Respiratory Syndrome Coronavirus 2 (SAU : Pleasenotetha ARS-CoV-2) pandemic that drastically altered people's lives. Since then, many countries have been forced to suspend public gatherings, leading to many conference cancellations, postponements, or reorganizations. Switching from a face-to-face to a remote conference became inevitable and the ultimate solution to sustain scientific exchanges at the national and the international levels. The same year, as a committee, we were in charge of organizing the major French annual conference that covers all computational biology areas: The "Journé es Ouvertes en Biologie, Informatique et Mathé matiques" (JOBIM). Despite the health crisis, we succeeded in changing the conference format from face to face to remote in a very short amount of time. Here, we propose 10 simple rules based on this experience to modify a conference format in an optimized and cost-effective way. In addition to the suggested rules, we decided to emphasize an unexpected benefit of this situation: a significant reduction in greenhouse gas (GHG) emissions related to travel for scientific conference attendance. We believe that even once the SARS-CoV-2 crisis is over, we collectively will have an opportunity to think about the way we approach such scientific events over the longer term.
Published: 2021
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21. Nuclear HMGB1 protects from non-alcoholic fatty liver diseases through negative regulation of liver X receptor

Author: Romain Paccoud, Arnaud Polizzi, Isabelle Castan-Laurell, Jérôme Mariette, Robert F. Schwabe, W. Wahli, Frédéric Martins, Jason S. Iacovoni, Enzo Piccolo, Thomas Clouaire, Simon Deleruyelle, Chih-Hao Lee, Philippe Valet, Jean-Philippe Pradere, Gaëlle Legube, Fadila Benhamed, Hervé Guillou, Alexandra Montagner, Cedric Moro, Catherine Postic, Jean Personnaz, Elsa Moreau, Cédric Dray, Alizee Dortignac, Armelle Yart, and Aurélie Batut
Subjects: medicine.medical_specialty, Fatty liver, chemical and pharmacologic phenomena, Lipid metabolism, Biology, medicine.disease, Transcriptome, Endocrinology, Insulin resistance, Internal medicine, Lipogenesis, medicine, Steatosis, Liver X receptor, Transcription factor
Abstract: Dysregulations of lipid metabolism in the liver may trigger steatosis progression leading to potentially severe clinical consequences such as non-alcoholic fatty liver diseases (NAFLD). Molecular mechanisms underlying liver lipogenesis are very complex and fine-tuned by chromatin dynamics and the activity of multiple key transcription factors. Here, we demonstrate that the nuclear factor HMGB1 acts as a strong repressor of liver lipogenesis during metabolic stress in NAFLD. Mice with liver-specific Hmgb1-deficiency display exacerbated liver steatosis and hepatic insulin resistance when subjected to a high-fat diet or after fasting/refeeding. Global transcriptome and functional analysis revealed that the deletion of Hmgb1 gene enhances LXRα activity resulting in increased lipogenesis. HMGB1 repression is not mediated through nucleosome landscape re-organization but rather via a preferential DNA occupation in region carrying genes regulated by LXRα. Together these findings suggest that hepatocellular HMGB1 protects from liver steatosis development. HMGB1 may constitute a new attractive option to therapeutically target LXRα axis during NAFLD.
Published: 2021
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22. Kernel and dissimilarity methods for exploratory analysis in a social context

Author: Jérôme Mariette, Nathalie Vialaneix, Madalina Olteanu, Unité de Mathématiques et Informatique Appliquées de Toulouse (MIAT INRA), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Statistique, Analyse et Modélisation Multidisciplinaire (SAmos-Marin Mersenne) (SAMM), Université Paris 1 Panthéon-Sorbonne (UP1), and Vialaneix, Nathalie
Subjects: 0303 health sciences, [STAT.AP]Statistics [stat]/Applications [stat.AP], business.industry, Computer science, Context (language use), 02 engineering and technology, Extension (predicate logic), Machine learning, computer.software_genre, Set (abstract data type), 03 medical and health sciences, Kernel method, [STAT.AP] Statistics [stat]/Applications [stat.AP], Kernel (statistics), Similarity (psychology), 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Artificial intelligence, Focus (optics), business, Categorical variable, computer, 030304 developmental biology
Abstract: International audience; While most of statistical methods for prediction or data mining have been built for data made of independent observations of a common set of p numerical variables, many real-world applications do not fit in this framework. A more common and general situation is the case where a relevant similarity or dissimilarity can be computed between the observations, providing a summary of their relations to each other. This setting is related to the kernel framework that has allowed to extend most of standard statistical supervised and unsupervised methods to any type of data for which a relevant such kernel can be obtained. The present chapter aims at presenting kernel methods in general, with a specific focus on the less studied unsupervised framework. We illustrate its usefulness by describing the extension of self-organizing maps and by proposing an approach to combine kernels in an efficient way. The overall approach is illustrated on categorical time series in a social-science context and allows to illustrate how the choice of a given type of dissimilarity or group of dissimilarities can influence the output of the exploratory analysis.
Published: 2021

23. jvenn: an interactive Venn diagram viewer.

Author: Philippe Bardou, Jérôme Mariette, Frédéric Escudié, Christophe Djemiel, and Christophe Klopp
Published: 2014
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24. Efficient interpretable variants of online SOM for large dissimilarity data

Author: Madalina Olteanu, Jérôme Mariette, Nathalie Villa-Vialaneix, Unité de Mathématiques et Informatique Appliquées de Toulouse (MIAT INRA), Institut National de la Recherche Agronomique (INRA), Statistique, Analyse et Modélisation Multidisciplinaire (SAmos-Marin Mersenne) (SAMM), Université Panthéon-Sorbonne (UP1), and Université Paris 1 Panthéon-Sorbonne (UP1)
Subjects: 0209 industrial biotechnology, Similarity (geometry), Computer science, Cognitive Neuroscience, Feature vector, Stability (learning theory), Nyström, 02 engineering and technology, Machine learning, computer.software_genre, Kernel (linear algebra), 020901 industrial engineering & automation, Artificial Intelligence, 0202 electrical engineering, electronic engineering, information engineering, Sparse methods, [STAT.AP]Statistics [stat]/Applications [stat.AP], business.industry, Dimensionality reduction, K-PCA, Sparse approximation, SOM, Computer Science Applications, Kernel, ComputingMethodologies_PATTERNRECOGNITION, dissimilarity, 020201 artificial intelligence & image processing, Artificial intelligence, Data mining, business, computer, Curse of dimensionality
Abstract: International audience; Self-organizing maps (SOM) are a useful tool for exploring data. In its original version, the SOM algorithm was designed for numerical vectors. Since then, several extensions have been proposed to handle complex datasets described by (dis)similarities. Most of these extensions represent prototypes by a list of (dis)similarities with the entire dataset and suffer from several drawbacks: their complexity is increased-it becomes quadratic instead of linear-, the stability is reduced and the interpretability of the prototypes is lost. In the present article, we propose and compare two extensions of the stochastic SOM for (dis)similarity data: the first one takes advantage of the online setting in order to maintain a sparse representation of the prototypes at each step of the algorithm, while the second one uses a dimension reduction in a feature space defined by the (dis)similarity. Our contributions to the analysis of (dis)similarity data with topographic maps are thus twofolds: first, we present a new version of the SOM algorithm which ensures a sparse representation of the prototypes through online updates. Second, this approach is compared on several benchmarks to a standard dimension reduction technique (K-PCA), which is itself adapted to large datasets with the Nyström approximation. Results demonstrate that both approaches lead to reduce the prototypes dimensionality while providing accurate results in a reasonable computational time. Selecting one of these two strategies depends on the dataset size, the need to easily interpret the results and the computational facilities available. The conclusion tries to provide some recommendations to help the user making this choice.
Published: 2017
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25. Field Monitoring of Avian Influenza Viruses: Whole-Genome Sequencing and Tracking of Neuraminidase Evolution Using 454 Pyrosequencing

Author: Olivier Bouchez, Jérôme Mariette, Jean-Luc Guérin, Guillaume Croville, Johanna Barbieri, Christelle Camus-Bouclainville, Christophe Klopp, Sébastien M. Soubies, Interactions hôtes-agents pathogènes [Toulouse] (IHAP), Institut National de la Recherche Agronomique (INRA)-Ecole Nationale Vétérinaire de Toulouse (ENVT), Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, Unité de Biométrie et Intelligence Artificielle (UBIA), Institut National de la Recherche Agronomique (INRA), Laboratoire de Génétique Cellulaire (LGC), and This project was partly supported by French Region Midi-Pyrénées.
Subjects: Microbiology (medical), Turkeys, animal diseases, Molecular Sequence Data, Mutation, Missense, Hemagglutinins, Viral, Neuraminidase, Hemagglutinin (influenza), Genome, Viral, medicine.disease_cause, Genome, Disease Outbreaks, Evolution, Molecular, Viral Proteins, 03 medical and health sciences, symbols.namesake, Virology, medicine, Animals, Sequence Deletion, 030304 developmental biology, Whole genome sequencing, Genetics, Sanger sequencing, 0303 health sciences, [SDV.BA.MVSA]Life Sciences [q-bio]/Animal biology/Veterinary medicine and animal Health, biology, 030306 microbiology, virus diseases, Sequence Analysis, DNA, United States, Influenza A virus subtype H5N1, Ducks, Amino Acid Substitution, Influenza in Birds, Viral evolution, [SDV.MP.VIR]Life Sciences [q-bio]/Microbiology and Parasitology/Virology, biology.protein, symbols, RNA, Viral, Pyrosequencing, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie
Abstract: Adaptation of avian influenza viruses (AIVs) from waterfowl to domestic poultry with a deletion in the neuraminidase (NA) stalk has already been reported. The way the virus undergoes this evolution, however, is thus far unclear. We address this question using pyrosequencing of duck and turkey low-pathogenicity AIVs. Ducks and turkeys were sampled at the very beginning of an H6N1 outbreak, and turkeys were swabbed again 8 days later. NA stalk deletions were evidenced in turkeys by Sanger sequencing. To further investigate viral evolution, 454 pyrosequencing was performed: for each set of samples, up to 41,500 reads of ca. 400 bp were generated and aligned. Genetic polymorphisms between duck and turkey viruses were tracked on the whole genome. NA deletion was detected in less than 2% of reads in duck feces but in 100% of reads in turkey tracheal specimens collected at the same time. Further variations in length were observed in NA from turkeys 8 days later. Similarly, minority mutants emerged on the hemagglutinin (HA) gene, with substitutions mostly in the receptor binding site on the globular head. These critical changes suggest a strong evolutionary pressure in turkeys. The increasing performances of next-generation sequencing technologies should enable us to monitor the genomic diversity of avian influenza viruses and early emergence of potentially pathogenic variants within bird flocks. The present study, based on 454 pyrosequencing, suggests that NA deletion, an example of AIV adaptation from waterfowl to domestic poultry, occurs by selection rather than de novo emergence of viral mutants.
Published: 2012
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26. RNAspace.org: An integrated environment for the prediction, annotation, and analysis of ncRNA

Author: Hélène Touzet, Daniel Gautheret, Christine Gaspin, Benjamin Grenier-Boley, Marie-Josée Cros, Jérôme Mariette, Antoine de Monte, Philippe Bardou, Touzet, Helene, Gaspin, Christine, Unité de Biométrie et Intelligence Artificielle (ancêtre de MIAT) [UBIA], Laboratoire d'Informatique Fondamentale de Lille [LIFL], Système d'Information des GENomes des Animaux d'Elevage [SIGENAE], Institut de génétique et microbiologie [Orsay] [IGM], Unité de Biométrie et Intelligence Artificielle (UBIA), Institut National de la Recherche Agronomique (INRA), Bioinformatics and Sequence Analysis (BONSAI), Laboratoire d'Informatique Fondamentale de Lille (LIFL), Université de Lille, Sciences et Technologies-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lille, Sciences Humaines et Sociales-Centre National de la Recherche Scientifique (CNRS)-Université de Lille, Sciences et Technologies-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lille, Sciences Humaines et Sociales-Centre National de la Recherche Scientifique (CNRS)-Inria Lille - Nord Europe, Institut National de Recherche en Informatique et en Automatique (Inria), Système d'Information des GENomes des Animaux d'Elevage (SIGENAE), Institut de génétique et microbiologie [Orsay] (IGM), Université Paris-Sud - Paris 11 (UP11)-Centre National de la Recherche Scientifique (CNRS), This work was supported by the French 'Groupement d'Intérêt Scientifique Infrastructure, Biologie, Santé Agronomie.', Université de Lille, Sciences et Technologies-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lille, Sciences Humaines et Sociales-Centre National de la Recherche Scientifique (CNRS), This work was supported by the French 'Groupement d'Interet Scientifique Infrastructure, Biologie, Sante Agronomie.', and Unité de Biométrie et Intelligence Artificielle (ancêtre de MIAT) (UBIA)
Subjects: inorganic chemicals, RNA, Untranslated, Bioinformatics, Annotation, Bacterial genome size, Computational biology, Biology, complex mixtures, Genome, Noncoding RNA, DNA sequencing, 03 medical and health sciences, noncoding RNA, prediction, annotation, biochemistry, [SDV.BBM]Life Sciences [q-bio]/Biochemistry, Molecular Biology, bioinformatique, Molecular Biology, Gene, 030304 developmental biology, Genetics, Internet, 0303 health sciences, bioinformatic, Base Sequence, fungi, 030302 biochemistry & molecular biology, molecular biology, Genome project, equipment and supplies, Non-coding RNA, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], [SDV.MP]Life Sciences [q-bio]/Microbiology and Parasitology, ComputingMethodologies_PATTERNRECOGNITION, Eukaryotic chromosome fine structure, bacteria, Nucleic Acid Conformation, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], Databases, Nucleic Acid, Prediction, Software
Abstract: The annotation of noncoding RNA genes remains a major bottleneck in genome sequencing projects. Most genome sequences released today still come with sets of tRNAs and rRNAs as the only annotated RNA elements, ignoring hundreds of other RNA families. We have developed a web environment that is dedicated to noncoding RNA (ncRNA) prediction, annotation, and analysis and allows users to run a variety of tools in an integrated and flexible manner. This environment offers complementary ncRNA gene finders and a set of tools for the comparison, visualization, editing, and export of ncRNA candidates. Predictions can be filtered according to a large set of characteristics. Based on this environment, we created a public website located at http://RNAspace.org. It accepts genomic sequences up to 5 Mb, which permits for an online annotation of a complete bacterial genome or a small eukaryotic chromosome. The project is hosted as a Source Forge project (http://rnaspace.sourceforge.net/).
Published: 2011
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27. Using metabarcoding to reveal and quantify plant-pollinator interactions

Author: Roselyne Etienne, Christophe Andalo, Hélène Holota, Lucie Zinger, Aurélie Khimoun, Nathalie Escaravage, Charlène Pellizzari, Jérôme Mariette, Pierre Taberlet, Monique Burrus, André Pornon, Marie Vidal, Amaia Iribar, Peter Winterton, Evolution et Diversité Biologique ( EDB ), Institut de Recherche pour le Développement ( IRD ) -Université Paul Sabatier - Toulouse 3 ( UPS ) -Centre National de la Recherche Scientifique ( CNRS ), Biogéosciences [Dijon] ( BGS ), Université de Bourgogne ( UB ) -AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Centre National de la Recherche Scientifique ( CNRS ), Unité de Mathématiques et Informatique Appliquées de Toulouse ( MIAT INRA ), Institut National de la Recherche Agronomique ( INRA ), Laboratoire d'Ecologie Alpine ( LECA ), Université Joseph Fourier - Grenoble 1 ( UJF ) -Université Savoie Mont Blanc ( USMB [Université de Savoie] [Université de Chambéry] ) -Centre National de la Recherche Scientifique ( CNRS ), GeT PlaGe, Genotoul, Université Paul Sabatier - Toulouse 3 ( UPS ), Work supported by the French Laboratory of Excellence project 'TULIP' and by the Centre National de la Recherche Scientifique-INEE (ERESA program: DJ/ST/IP/2013/D-112)., ANR-11-IDEX-0002-02/10-LABX-0041,TULIP,Towards a Unified theory of biotic Interactions: the roLe of environmental ( 2011 ), ANR : ANR-11-IDEX-0002-02, Evolution et Diversité Biologique (EDB), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS), Biogéosciences [UMR 6282] [Dijon] (BGS), Centre National de la Recherche Scientifique (CNRS)-Université de Bourgogne (UB)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement, Unité de Mathématiques et Informatique Appliquées de Toulouse (MIAT INRA), Institut National de la Recherche Agronomique (INRA), Laboratoire d'Ecologie Alpine (LECA), Centre National de la Recherche Scientifique (CNRS)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Université Joseph Fourier - Grenoble 1 (UJF)-Université Grenoble Alpes (UGA), Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées, ANR-11-IDEX-0002-02/10-LABX-0041,TULIP,Towards a Unified theory of biotic Interactions: the roLe of environmental(2011), ANR: ANR-11-IDEX-0002-02, Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, Université de Bourgogne (UB)-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'Ecologie Alpine (LECA ), Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Génome et Transcriptome - Plateforme Génomique (GeT-PlaGe), Institut National de la Recherche Agronomique (INRA)-Plateforme Génome & Transcriptome (GET), Génopole Toulouse Midi-Pyrénées [Auzeville] (GENOTOUL), Université Fédérale Toulouse Midi-Pyrénées-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-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Génopole Toulouse Midi-Pyrénées [Auzeville] (GENOTOUL), Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), ANR-11-IDEX-0002,UNITI,Université Fédérale de Toulouse(2011), Université de Bourgogne (UB)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Centre National de la Recherche Scientifique (CNRS), Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Ecole Nationale Vétérinaire de Toulouse (ENVT), Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM)-Génopole Toulouse Midi-Pyrénées [Auzeville] (GENOTOUL), Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM), Pornon, André, Université de Toulouse (UT)-Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS), Biogéosciences [UMR 6282] (BGS), Université de Toulouse (UT)-Université de Toulouse (UT)-Ecole Nationale Vétérinaire de Toulouse (ENVT), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Génopole Toulouse Midi-Pyrénées [Auzeville] (GENOTOUL), Université de Toulouse (UT)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), and Université de Toulouse (UT)
Subjects: 0106 biological sciences, 0301 basic medicine, Insecta, DNA, Plant, Pollination, media_common.quotation_subject, Insect, Biology, medicine.disease_cause, 010603 evolutionary biology, 01 natural sciences, Article, 03 medical and health sciences, Species Specificity, Pollinator, Pollen, medicine, [ SDV.EE.IEO ] Life Sciences [q-bio]/Ecology, environment/Symbiosis, Animals, DNA Barcoding, Taxonomic, Plant Physiological Phenomena, media_common, [SDV.GEN]Life Sciences [q-bio]/Genetics, [ SDE.BE ] Environmental Sciences/Biodiversity and Ecology, Multidisciplinary, Community, Ecology, Plant community, Sequence Analysis, DNA, Plants, 15. Life on land, Ecological genetics, 030104 developmental biology, Taxon, [SDE.BE]Environmental Sciences/Biodiversity and Ecology, [ SDV.GEN ] Life Sciences [q-bio]/Genetics, [SDV.EE.IEO]Life Sciences [q-bio]/Ecology, environment/Symbiosis
Abstract: Given the ongoing decline of both pollinators and plants, it is crucial to implement effective methods to describe complex pollination networks across time and space in a comprehensive and high-throughput way. Here we tested if metabarcoding may circumvent the limits of conventional methodologies in detecting and quantifying plant-pollinator interactions. Metabarcoding experiments on pollen DNA mixtures described a positive relationship between the amounts of DNA from focal species and the number of trnL and ITS1 sequences yielded. The study of pollen loads of insects captured in plant communities revealed that as compared to the observation of visits, metabarcoding revealed 2.5 times more plant species involved in plant-pollinator interactions. We further observed a tight positive relationship between the pollen-carrying capacities of insect taxa and the number of trnL and ITS1 sequences. The number of visits received per plant species also positively correlated to the number of their ITS1 and trnL sequences in insect pollen loads. By revealing interactions hard to observe otherwise, metabarcoding significantly enlarges the spatiotemporal observation window of pollination interactions. By providing new qualitative and quantitative information, metabarcoding holds great promise for investigating diverse facets of interactions and will provide a new perception of pollination networks as a whole.
Published: 2016
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28. Jflow: a workflow management system for web applications

Author: Christine Gaspin, Philippe Bardou, Céline Noirot, Jérôme Mariette, Marie-Stéphane Trotard, Christophe Klopp, Ibouniyamine Nabihoudine, Frédéric Escudié, Unité de Mathématiques et Informatique Appliquées de Toulouse (MIAT INRA), Institut National de la Recherche Agronomique (INRA), Génétique Physiologie et Systèmes d'Elevage (GenPhySE ), École nationale supérieure agronomique de Toulouse [ENSAT]-Institut National de la Recherche Agronomique (INRA)-Ecole Nationale Vétérinaire de Toulouse (ENVT), Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National Polytechnique (Toulouse) (Toulouse INP), and Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées
Subjects: 0301 basic medicine, Statistics and Probability, medicine.medical_specialty, Databases, Factual, Computer science, Windows Workflow Foundation, [SDV]Life Sciences [q-bio], Information Storage and Retrieval, computer.software_genre, Biochemistry, Workflow engine, Workflow technology, XPDL, Workflow, 03 medical and health sciences, Upload, medicine, Web application, Humans, [INFO]Computer Science [cs], [MATH]Mathematics [math], Molecular Biology, Internet, business.industry, Computational Biology, Applications Notes, Computer Science Applications, Computational Mathematics, 030104 developmental biology, Computational Theory and Mathematics, Operating system, Database Management Systems, business, Web modeling, Workflow Management Coalition, computer, Sequence Analysis, Workflow management system, Software
Abstract: Summary: Biologists produce large data sets and are in demand of rich and simple web portals in which they can upload and analyze their files. Providing such tools requires to mask the complexity induced by the needed High Performance Computing (HPC) environment. The connection between interface and computing infrastructure is usually specific to each portal. With Jflow, we introduce a Workflow Management System (WMS), composed of jQuery plug-ins which can easily be embedded in any web application and a Python library providing all requested features to setup, run and monitor workflows. Availability and implementation: Jflow is available under the GNU General Public License (GPL) at http://bioinfo.genotoul.fr/jflow. The package is coming with full documentation, quick start and a running test portal. Contact: Jerome.Mariette@toulouse.inra.fr
Published: 2015
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29. Flight quotas outperform focused mitigation strategies in reducing the carbon footprint of academic travel

Author: Tamara Ben-Ari, Gaëlle Lefort, Jérome Mariette, Olivier Aumont, Laurent Jeanneau, Alexandre Santerne, Aymeric Spiga, and Philippe-e Roche
Subjects: carbon footprint of research, academic air travel, mitigation options, sufficiency, modal shift, Environmental technology. Sanitary engineering, TD1-1066, Environmental sciences, GE1-350, Science, Physics, QC1-999
Abstract: The carbon footprint of academia has become a prominent concern and a burgeoning research area, with a notable focus on greenhouse gas emissions (GHG) from research-related travels. Mitigation strategies often promote alternatives, such as developing virtual communication or adopting sustainable transportation modes for short distances. While more ambitious strategies involving the transformation of research practices are increasingly discussed, these mitigation solutions are rarely subjected to rigorous quantitative assessments or meaningful comparisons. This study analyzes a unique database of about 130 000 travel segments by car, train and plane in 159 research entities across a wide array of disciplines in France. We investigate the patterns and associated carbon footprint of these research travels and explore a diversity of mitigation options. Our analysis shows that air travel overwhelmingly outweighs the carbon footprint of research travel, representing more than 96% of GHG emissions. Intercontinental flights are infrequent (less than 10% of all plane trips) but dominate GHG travel emissions, accounting for over 64% of total emissions. In contrast, domestic and continental flights are the most common but their mitigation potential by modal shift to train is limited (e.g. less than 15% for trips under 1000 km). Similar reductions can be achieved by targeting a small subset of travels, for example by modulating the frequency of conference attendance. The greatest and possibly most robust mitigation potential lies in combining modal shift with moderating air mileage (e.g. reducing travelled distance or number of flights). Strategies focusing on electrification or modal shifts for cars, proposed in official guidelines, are found to have negligible impact. In the absence of low-carbon alternatives for long-haul flights, we contend that only comprehensive strategies and policies which include moderating air travel distance or frequency can achieve a robust significant reduction in the GHG emissions from academic travel.
Published: 2024
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30. Contrasted effects of natural complex mixtures of PAHs and metals on oxygen cycle in a microbial mat

Author: Marisol Goñi-Urriza, Olivier Pringault, Christophe Klopp, Pavel Senin, Johanne Aubé, Jérôme Mariette, Frédéric Escudié, Olivier Bouchez, Ecosystèmes lagunaires : organisation biologique et fonctionnement (ECOLAG), Université Montpellier 2 - Sciences et Techniques (UM2)-Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER)-Centre National de la Recherche Scientifique (CNRS), Institut des sciences analytiques et de physico-chimie pour l'environnement et les materiaux (IPREM), Université de Pau et des Pays de l'Adour (UPPA)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS), Unité de recherche Virologie et Immunologie Moléculaires (VIM (UR 0892)), Institut National de la Recherche Agronomique (INRA), Unité de Biométrie et Intelligence Artificielle de Toulouse [Castanet-Tolosan] (UBIA), Institut National de la Recherche Agronomique (INRA)-Plateforme bioinformatique du GIS GENOTOUL - Génopole Toulouse Midi-Pyrénées, Los Alamos National Laboratory (LANL), ANR-11-BSV7-0014,FUNHYMAT,Structure et fonctionnement de tapis microbiens contaminés avec des hydrocarbures(2011), Université de Pau et des Pays de l'Adour (UPPA)-Centre National de la Recherche Scientifique (CNRS), and Unité de recherche Virologie et Immunologie Moléculaires (VIM)
Subjects: Cyanobacteria, Environmental Engineering, Health, Toxicology and Mutagenesis, Heterotroph, Complex Mixtures, 010501 environmental sciences, 01 natural sciences, Carbon cycle, 03 medical and health sciences, Maturation, Environmental Chemistry, Autotroph, Microbial mat, Polycyclic Aromatic Hydrocarbons, Chemical composition, 030304 developmental biology, 0105 earth and related environmental sciences, Pollutant, 0303 health sciences, biology, Ecology, Respiration, Biofilm, Public Health, Environmental and Occupational Health, Production, General Medicine, General Chemistry, Contamination, biology.organism_classification, Pollution, [SDV.MP.BAC]Life Sciences [q-bio]/Microbiology and Parasitology/Bacteriology, 6. Clean water, Oxygen, Metals, 13. Climate action, Biofilms, Environmental chemistry, Water Microbiology, Microelectrodes, Water Pollutants, Chemical
Abstract: cited By 3; International audience; The contamination of polluted environments is often due to a complex mixture of pollutants sometimes at trace levels which nevertheless may have significant effects on the diversity and functioning of organisms. The aim of this study was to assess the functional responses of a microbial mat exposed to a natural complex mixture of PAHs and metals as a function of the maturation stage of the biofilm. Microbial mats sampled in a slightly polluted environment were exposed to contaminated water of a retention basin of an oil refinery. The responses of the microbial mats differed according to season. In spring 2012, strong inhibition of both oxygen production and respiration was observed relative to the control, with rates representing less than 5% of the control after 72. h of incubation. A decrease of microbial activities was followed by a decrease of the coupling between autotrophs and heterotrophs. In contrast, in autumn 2012, no significant changes for oxygen production and respiration were observed and the coupling between autotrophs and heterotrophs was not altered. The differences observed between the spring and autumn mats might be explained by the maturity of the microbial mat with dominance of heterotrophic bacteria in spring, and diatoms and cyanobacteria in autumn, as well as by the differences in the chemical composition of the complex mixture of PAHs and metals.
Published: 2015
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31. The BioMart community portal: an innovative alternative to large, centralized data repositories

Author: Byung Woo Han, Jose Manuel Garcia-Manteiga, Alejandro Maass, Jayson Harshbarger, Daniel M. Staines, Zhengyan Kan, Davide Rambaldi, Dong Jin Han, Richard Baldock, Ji Hyun Lee, Merideth Bonierbale, Hadi Quesneville, Anthony Esposito, Thomas Letellier, Jun Wang, Steven Rosanoff, Céline Noirot, Richard D. Hayes, Sarah W. Burge, Anthony J. Brookes, Gabriele Bucci, Giulia Barbiera, Elia Stupka, Olivier Arnaiz, Thomas Maurel, Shen Hu, Olivier Sallou, Emanuela Gadaleta, Jérôme Mariette, Rosalind J. Cutts, Joseph W. Carlson, Damian Smedley, Robert C. Free, James E. Allen, Simon A. Forbes, Kevin R. Stone, Jie Luo, Andrew Blake, Chu Jun Liu, Takatomo Fujisawa, Jon W. Teague, Cristian Perez-Llamas, Rebecca Shepherd, Julio Fernandez-Banet, Raul Cordova, David Goodstein, Shi Jian Zhang, Ken Youens-Clark, Cédric Cabau, José Afonso Guerra-Assunção, Iwan Buetti, Stefania Merella, Delphine Steinbach, Linda Sperling, Robert K. Hastings, Abu Z. Dayem Ullah, Claude Chelala, Erik Dassi, Eduardo Eyras, Sunghoon Kim, Kristian Gray, Dejan Lazarevic, Luca Pandini, Azza M. Mohamed, Doreen Ware, William Spooner, Alex Di Genova, Daniel Lawson, Alessandro Quattrone, Davide Cittaro, Heather Estrella, Rhoda Kinsella, Chuan-Yun Li, Christophe Klopp, Aminah Keliet, Michela Riba, Zhi-Liang Hu, Hideya Kawaji, Arnaud Kerhornou, James M. Reecy, Tim Beck, Charalambos Chrysostomou, François Moreews, Nelson Ndegwa, Arek Kasprzyk, Michael Primig, Claire Hoede, Ibounyamine Nabihoudine, Amonida Zadissa, Paolo Provero, Reinhard Simon, Todd W. Harris, Bernard Haggarty, Lucie N. Hutchins, Marie Wong-Erasmus, Philippe Bardou, Elisa Salas, Lei Kong, Anis Djari, Syed Haider, Steffen Durinck, Mohammad Awedh, Pietro Liò, Amna A. Saddiq, Olivier Collin, European Bioinformatics Institute [Hinxton] (EMBL-EBI), EMBL Heidelberg, Computer Laboratory [Cambridge], University of Cambridge [UK] (CAM), Genentech, Inc., Genentech, Inc. [San Francisco], San Raffaele Scientific Institute, Vita-Salute San Raffaele University and Center for Translational Genomics and Bioinformatics, Genetics, Biology and Biochemistry, Molecular Biotechnology Centre, Centre de génétique moléculaire (CGM), Université Paris-Sud - Paris 11 (UP11)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS), King Abdulaziz University, MRC Human Genetics Unit, University of Edinburgh-Western General Hospital, 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), Department of Genetics [Leicester], University of Leicester, Medical Research Council Harwell (Mammalian Genetics Unit and Mary Lyon Centre), Medical Research Counc, International Potato Center, Department of Energy / Joint Genome Institute (DOE), Los Alamos National Laboratory (LANL), Centre for Molecular Oncology and Imaging, Centre for Molecular Oncology and Imaging, Barts Cancer Institute, Plateforme bioinformatique GenOuest [Rennes], Inria Rennes – Bretagne Atlantique, 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 - 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), 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, University of Trento [Trento], University of Chile [Santiago], Unité de Biométrie et Intelligence Artificielle (UBIA), Institut National de la Recherche Agronomique (INRA), Pfizer Oncology, Institució Catalana de Recerca i Estudis Avançats (ICREA), Cancer Genome Project, The Wellcome Trust Sanger Institute [Cambridge], Kasuza DNA Research Institute, Seoul National University [Seoul] (SNU), Cold Spring Harbor Laboratory (CSHL), RIKEN - Institute of Physical and Chemical Research [Japon] (RIKEN), School of Dentistry and Dental Research Institute [UCLA], University of California [Los Angeles] (UCLA), University of California-University of California, Department of Animal Science and Center for Integrated Animal Genomics, Iowa State University (ISU), Mouse Genomic Informatics Group (MGI), The Jackson Laboratory, Unité de Recherche Génomique Info (URGI), Center for Bioinformatics [Pekin], Peking University [Beijing], Division of Industrial Ecology (KTH), Royal Institute of Technology [Stockholm] (KTH ), Institute of Molecular Medicine, Universidad de Santiago de Chile [Santiago] (USACH), Centro de Regulación Génica (CRG), Pontificia Universidad Católica de Chile (UC)-Universidad Andrés Bello [Santiago] (UNAB)-Universidad de Santiago de Chile [Santiago] (USACH), Centre de Modélisation Mathématique / Centro de Modelamiento Matemático (CMM), Centre National de la Recherche Scientifique (CNRS), Unité Mathématiques et Informatique Appliquées de Toulouse (MIAT), Physiologie, Environnement et Génétique pour l'Animal et les Systèmes d'Elevage [Rennes] (PEGASE), AGROCAMPUS OUEST-Institut National de la Recherche Agronomique (INRA), Unité de Mathématiques et Informatique Appliquées de Toulouse (MIAT INRA), Mathématiques et Informatique Appliquées Toulouse, Universitat Pompeu Fabra [Barcelona], Institut de recherche en santé, environnement et travail (Irset), Université d'Angers (UA)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-École des Hautes Études en Santé Publique [EHESP] (EHESP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Structure Fédérative de Recherche en Biologie et Santé de Rennes ( Biosit : Biologie - Santé - Innovation Technologique ), Department of Animal Science, Eagle Genomics Ltd, Eagle Genomics, State Key Laboratory of Lithospheric Evolution (SKL), Institute of Geology and Geophysics [Beijing] (IGG), Chinese Academy of Sciences [Beijing] (CAS)-Chinese Academy of Sciences [Beijing] (CAS), University of the Chinese Academy of Sciences, Ontario Institute for Cancer Research [Canada] (OICR), Ontario Institute for Cancer Research, Institut National de la Recherche Agronomique (INRA)-Ecole Nationale Vétérinaire de Toulouse (ENVT), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, International Potato Center [Lima] (CIP), Consultative Group on International Agricultural Research [CGIAR] (CGIAR), 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)-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)-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, Institut National de Recherche en Informatique et en Automatique (Inria), Universidad de Chile = University of Chile [Santiago] (UCHILE), The Jackson Laboratory [Bar Harbor] (JAX), Center for Bioinformatics [Peking], Pontificia Universidad Católica de Chile (UC)-Universidad Andrés Bello [Santiago] (UNAB), 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), Universitat Pompeu Fabra [Barcelona] (UPF), Structure Fédérative de Recherche en Biologie et Santé de Rennes ( Biosit : Biologie - Santé - Innovation Technologique )-Institut National de la Santé et de la Recherche Médicale (INSERM)-École des Hautes Études en Santé Publique [EHESP] (EHESP)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Université d'Angers (UA), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Université de Toulouse (UT), Université de Rennes (UR)-Plateforme Génomique Santé Biogenouest®-Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-GESTION DES DONNÉES ET DE LA CONNAISSANCE (IRISA-D7), 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)-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), 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), Unité de Biométrie et Intelligence Artificielle (ancêtre de MIAT) (UBIA), University of California (UC)-University of California (UC), Université d'Angers (UA)-Université de Rennes (UR)-École des Hautes Études en Santé Publique [EHESP] (EHESP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Structure Fédérative de Recherche en Biologie et Santé de Rennes ( Biosit : Biologie - Santé - Innovation Technologique ), Jonchère, Laurent, 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), 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, Lio, Pietro [0000-0002-0540-5053], and Apollo - University of Cambridge Repository
Subjects: Proteomics, Interface (Java), Data management, [SDV]Life Sciences [q-bio], génomique fonctionnelle, Biology, Ontology (information science), computer.software_genre, World Wide Web, Genomics, Humans, Internet, Neoplasms, Database Management Systems, Genetics, cancer, Web Server issue, protéomique, ontologie, ComputingMilieux_MISCELLANEOUS, base de données, business.industry, Toolbox, [SDV] Life Sciences [q-bio], espèce modèle, Scalability, The Internet, Web service, business, Host (network), computer
Abstract: International audience; The BioMart Community Portal (www.biomart.org) is a community-driven effort to provide a unified interface to biomedical databases that are distributed worldwide. The portal provides access to numerous database projects supported by 30 scientific organizations. It includes over 800 different biological datasets spanning genomics, proteomics, model organisms, cancer data, ontology information and more. All resources available through the portal are independently administered and funded by their host organizations. The BioMart data federation technology provides a unified interface to all the available data. The latest version of the portal comes with many new databases that have been created by our ever-growing community. It also comes with better support and extensibility for data analysis and visualization tools. A new addition to our toolbox, the enrichment analysis tool is now accessible through graphical and web service interface. The BioMart community portal averages over one million requests per day. Building on this level of service and the wealth of information that has become available, the BioMart Community Portal has introduced a new, more scalable and cheaper alternative to the large data stores maintained by specialized organizations.
Published: 2015
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32. Complete Genome Sequence of a Field Strain of Peste des Petits Ruminants Virus Isolated during 2010-2014 Epidemics in Senegal

Author: Yaya Thiongane, Jean-Luc Guérin, Guillaume Croville, Christophe Klopp, Olivier Kwiatek, Geneviève Libeau, Jérôme Mariette, Habib Salami, Sophie Valière, Moustapha Lo, Emmanuel Albina, Contrôle des maladies animales exotiques et émergentes (UMR CMAEE), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA), Interactions hôtes-agents pathogènes [Toulouse] (IHAP), Institut National de la Recherche Agronomique (INRA)-Ecole Nationale Vétérinaire de Toulouse (ENVT), Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, Unité de Mathématiques et Informatique Appliquées de Toulouse (MIAT INRA), Institut National de la Recherche Agronomique (INRA), Département de Génétique Animale [Toulouse], and Institut Sénégalais de Recherches Agricoles [Dakar] (ISRA)
Subjects: Séquence nucléotidique, Expérimentation in vivo, Biology, L73 - Maladies des animaux, Genome, Virus peste petits ruminants, Genetics, medicine, Surveillance épidémiologique, [INFO]Computer Science [cs], [MATH]Mathematics [math], Molecular Biology, Epizootic, Whole genome sequencing, Génome, Strain (biology), medicine.disease, Virology, 3. Good health, Épidémiologie, Peste-des-petits-ruminants virus, Viruses
Abstract: Peste des petits ruminants virus (PPRV) infection is expanding and results in regular epizootic activities in Africa, the Middle East, and Asia. Here, we report the complete genome sequence of a field strain of PPRV isolated in Senegal (SnDk11I13) in 2013.
Published: 2014
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33. jvenn: an interactive Venn diagram viewer

Author: Christophe Djemiel, Philippe Bardou, Frédéric Escudié, Christophe Klopp, Jérôme Mariette, Génétique Physiologie et Systèmes d'Elevage (GenPhySE ), École nationale supérieure agronomique de Toulouse [ENSAT]-Institut National de la Recherche Agronomique (INRA)-Ecole Nationale Vétérinaire de Toulouse (ENVT), Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, Plateforme SIGENAE, Institut National de la Recherche Agronomique (INRA), Unité de Mathématiques et Informatique Appliquées de Toulouse (MIAT INRA), and Plateforme Bio-Informatique - Génotoul
Subjects: JavaScript, Theoretical computer science, Edward-Venn, Computer science, [SDV]Life Sciences [q-bio], Jquery, JavaScript library, Biochemistry, Venn, Vizualisation, law.invention, Documentation, Structural Biology, law, conception informatique, Component (UML), Web page, Computer Graphics, [INFO]Computer Science [cs], [MATH]Mathematics [math], Molecular Biology, computer.programming_language, Internet, Information retrieval, Applied Mathematics, java, Computational Biology, Readability, Computer Science Applications, Venn diagram, computer, Software
Abstract: Venn diagrams are commonly used to display list comparison. In biology, they are widely used to show the differences between gene lists originating from different differential analyses, for instance. They thus allow the comparison between different experimental conditions or between different methods. However, when the number of input lists exceeds four, the diagram becomes difficult to read. Alternative layouts and dynamic display features can improve its use and its readability. jvenn is a new JavaScript library. It processes lists and produces Venn diagrams. It handles up to six input lists and presents results using classical or Edwards-Venn layouts. User interactions can be controlled and customized. Finally, jvenn can easily be embeded in a web page, allowing to have dynamic Venn diagrams. jvenn is an open source component for web environments helping scientists to analyze their data. The library package, which comes with full documentation and an example, is freely available at http://bioinfo.genotoul.fr/jvenn .
Published: 2014
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34. RNAbrowse: RNA-Seq de novo assembly results browser

Author: Christophe Klopp, Anis Djari, Philippe Bardou, Cédric Cabau, Jérôme Mariette, Céline Noirot, Claire Hoede, Ibounyamine Nabihoudine, Unité de Mathématiques et Informatique Appliquées de Toulouse (MIAT INRA), Institut National de la Recherche Agronomique (INRA), Plateforme Bio-Informatique - Génotoul, Génétique Physiologie et Systèmes d'Elevage (GenPhySE ), École nationale supérieure agronomique de Toulouse [ENSAT]-Institut National de la Recherche Agronomique (INRA)-Ecole Nationale Vétérinaire de Toulouse (ENVT), Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, and Plateforme SIGENAE
Subjects: Computer science, [SDV]Life Sciences [q-bio], Sequence assembly, lcsh:Medicine, RNA-Seq, 010501 environmental sciences, Bioinformatics, computer.software_genre, 01 natural sciences, Computer Applications, Transcriptome, User-Computer Interface, [MATH]Mathematics [math], lcsh:Science, 0303 health sciences, Multidisciplinary, Contig, Programming language, Software Engineering, Genome project, Genomics, Web-Based Applications, RNA extraction, Transcriptome Analysis, Research Article, Computer and Information Sciences, Sequence analysis, Gene prediction, Sequence alignment, 03 medical and health sciences, Genetics, Humans, [INFO]Computer Science [cs], 030304 developmental biology, 0105 earth and related environmental sciences, Internet, Sequence Assembly Tools, Software Tools, Sequence Analysis, RNA, Gene Expression Profiling, lcsh:R, RNA, Biology and Life Sciences, Computational Biology, Genome Analysis, Gene expression profiling, lcsh:Q, Genome Expression Analysis, computer, Software
Abstract: International audience; Transcriptome analysis based on a de novo assembly of next generation RNA sequences is now performed routinely in many laboratories. The generated results, including contig sequences, quantification figures, functional annotations and variation discovery outputs are usually bulky and quite diverse. This article presents a user oriented storage and visualisation environment permitting to explore the data in a top-down manner, going from general graphical views to all possible details. The software package is based on biomart, easy to install and populate with local data. The software package is available under the GNU General Public License (GPL) at http://bioinfo.genotoul.fr/RNAbrowse.
Published: 2014
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35. Microbial ecology of the rumen evaluated by 454 GS FLX pyrosequencing is affected by starch and oil supplementation of diets

Author: Olivier Bouchez, Jérôme Mariette, Francis Enjalbert, Sylvie Combes, Asma Zened, Annabelle Troegeler-Meynadier, Christophe Klopp, Laurent Cauquil, Ecole Nationale Vétérinaire de Toulouse - ENVT (FRANCE), Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE), Institut National de la Recherche Agronomique - INRA (FRANCE), Tissus animaux, nutrition, digestion, écosystème et métabolisme (TANDEM), 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)-École nationale supérieure agronomique de Toulouse [ENSAT], Unité de Mathématiques et Informatique Appliquées de Toulouse (MIAT INRA), 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), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, and Institut National Polytechnique de Toulouse - INPT (FRANCE)
Subjects: Rikenellaceae, Starch, Prevotellaceae, Applied Microbiology and Biotechnology, chemistry.chemical_compound, taxonomy, Sunflower Oil, 2. Zero hunger, 0303 health sciences, Silage, Diversity, Ecology, biology, food and beverages, Biodiversity, Dietary starch - Dietary oil, [SDV.MP]Life Sciences [q-bio]/Microbiology and Parasitology, Alimentation et Nutrition, Female, Ruminococcaceae, Rumen bacteria, food.ingredient, Rumen, dietary oil, Microbiology, Zea mays, diversity, 03 medical and health sciences, food, Animal science, Botany, dietary starch, Animals, Plant Oils, Dry matter, 030304 developmental biology, Taxonomy, Bacteria, 030306 microbiology, Sunflower oil, rumen bacteria, Sequence Analysis, DNA, biology.organism_classification, Médecine vétérinaire et santé animal, chemistry, Dietary Supplements, Cattle, 454 pyrosequencing
Abstract: Chantier qualité GA; International audience; To provide a comprehensive examination of the bacterial diversity in the rumen content of cows fed different diets, high-throughput 16S rRNA gene-based pyrosequencing was used. Four rumen fistulated nonlactating Holstein cows received 12 kg of dry matter per day of four diets based on maize silage during four periods: the low-starch diet (22% starch, 3% fat); the high-starch diet, supplemented with wheat plus barley (35% starch, 3% fat); the low-starch plus oil diet, supplemented with 5% of sunflower oil (20% starch, 7.6% fat) and the high-starch plus oil diet (33% starch, 7.3% fat). Samples were taken after 12 days of adaptation, 5 h postfeeding. Whatever the diet, bacterial community of sieved rumen contents was dominated by Firmicutes and Bacteroidetes. Lachnospiraceae, Ruminococcaceae, Prevotellaceae, and Rikenellaceae families were highly present and were clearly affected by cow diet. The highest abundance of Prevotellaceae and the lowest abundance of Ruminococcaceae and Rikenellaceae were found with the high-starch plus oil diet. Dietary starch increased the relative abundance of only three genera: Barnesiella, Oribacterium and Olsenella, but decreased the relative abundances of several genera, with very significant effects for Rikenellaceae_RC9 and Butyrivibrio-Pseudobutyrivibrio. Oil alone had a limited effect, but interestingly, starch plus oil addition differently affected the bacterial populations compared to starch addition without oil.
Published: 2013
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36. NG6: Integrated next generation sequencing storage and processing environment

Author: Jérôme Mariette, Frédéric Escudié, Christophe Klopp, Gerald Salin, Sylvain Thomas, Céline Noirot, Nicolas Allias, Unité de Biométrie et Intelligence Artificielle de Toulouse [Castanet-Tolosan] (UBIA), Institut National de la Recherche Agronomique (INRA)-Plateforme bioinformatique du GIS GENOTOUL - Génopole Toulouse Midi-Pyrénées, Génétique Cellulaire, BMC, Ed., Unité de Biométrie et Intelligence Artificielle (ancêtre de MIAT) (UBIA), Institut National de la Recherche Agronomique (INRA), and Mariette, Jérôme
Subjects: 0106 biological sciences, FASTQ format, lcsh:QH426-470, lcsh:Biotechnology, Interface (computing), Context (language use), Computational biology, Biology, computer.software_genre, 010603 evolutionary biology, 01 natural sciences, DNA sequencing, User-Computer Interface, 03 medical and health sciences, ng6, Software, ngs, lcsh:TP248.13-248.65, [SDV.BBM.GTP]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Genomics [q-bio.GN], Genetics, Information system, 030304 developmental biology, next generation sequencing, 0303 health sciences, Database, business.industry, Sequence Analysis, DNA, Grid, lcsh:Genetics, Workflow, [SDV.BBM.GTP] Life Sciences [q-bio]/Biochemistry, Molecular Biology/Genomics [q-bio.GN], data management, business, computer, Biotechnology
Abstract: Chantier qualité GA; International audience; ABSTRACT: BACKGROUND: Next generation sequencing platforms are now well implanted in sequencing centres and some laboratories. Upcoming smaller scale machines such as the 454 junior from Roche or the MiSeq from Illumina will increase the number of laboratories hosting a sequencer. In such a context, it is important to provide these teams with an easily manageable environment to store and process the produced reads. RESULTS: We describe a user-friendly information system able to manage large sets of sequencing data. It includes, on one hand, a workflow environment already containing pipelines adapted to different input formats (sff, fasta, fastq and qseq), different sequencers (Roche 454, Illumina HiSeq) and various analyses (quality control, assembly, alignment, diversity studies,...) and, on the other hand, a secured web site giving access to the results. The connected user will be able to download raw and processed data and browse through the analysis result statistics. The provided workflows can easily be modified or extended and new ones can be added. Ergatis is used as a workflow building, running and monitoring system. The analyses can be run locally or in a cluster environment using Sun Grid Engine. CONCLUSIONS: NG6 is a complete information system designed to answer the needs of a sequencing platform. It provides a user-friendly interface to process, store and download high-throughput sequencing data.
Published: 2012
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37. Metabolic adaptation to a high-fat diet is associated with a change in the gut microbiota

Author: Claire Cenac, Pierre Monsan, Aurélie Waget, Pascale Klopp, Philippe Valet, Jacques Amar, Jason S. Iacovoni, Jérôme Mariette, Rémy Burcelin, Mathieu Bergé, Jérôme Lluch, Audrey Baylac, Anne Bouloumié, Sandra Grès, Christophe Klopp, Françoise Ouarné, Christine Roques, Olivier Bouchez, Vassilia Theodorou, Matteo Serino, Elodie Luche, Institut des Maladies Métaboliques et Cardiovasculaires (I2MC), Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM), Adhésion Bacterienne et Formation de Biofilms, PRES Université de Toulouse, ToxAlim (ToxAlim), 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), Plateforme Bio-informatique Toulouse Genopole®, UBIA INRA, Toulouse Genopole, Génopole Toulouse Midi-Pyrénées [Auzeville] (GENOTOUL), Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Ecole Nationale Vétérinaire de Toulouse (ENVT), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de la Recherche Agronomique (INRA), CRITT Bio-Industrie, Ecole d'Ingénieurs de Purpan (INPT - EI Purpan), Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Recherche Agronomique (INRA)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA), Laboratoire d'Ingénierie des Systèmes Biologiques et des Procédés (LISBP), Centre National de la Recherche Scientifique (CNRS)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Service de Médecine Interne et Hypertension Artérielle [Toulouse], CHU Toulouse [Toulouse]-Hôpital de Rangueil, CHU Toulouse [Toulouse], his work was supported by grants from Agence Nationale pour la Recherche (ANR) to RB and collaborators (ANR-Florinflam and Transflora), in part, by the European Commission's Seventh Framework programme under grant agreement No 241913 (FLORINASH) to RB and by the Benjamin Delessert Foundation to MS., European Project: 241913,EC:FP7:HEALTH,FP7-HEALTH-2009-single-stage,FLORINASH(2010), Centre National de la Recherche Scientifique - CNRS (FRANCE), Ecole d'Ingénieurs de Purpan - EIP (FRANCE), Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE), Institut National de la Recherche Agronomique - INRA (FRANCE), Institut National des Sciences Appliquées de Toulouse - INSA (FRANCE), Institut National de la Santé et de la Recherche Médicale - INSERM (FRANCE), Institut universitaire de France - IUF (FRANCE), Université Toulouse III - Paul Sabatier - UT3 (FRANCE), Centre Hospitalier Universitaire de Toulouse - CHU Toulouse (FRANCE), ToxAlim (Toulouse, France), Laboratoire d'Ingénierie des Systèmes Biologiques et des Procédés - LISBP (Toulouse, France), Institut National Polytechnique de Toulouse - INPT (FRANCE), Institut National de la Recherche Agronomique (INRA)-Université Toulouse III - Paul Sabatier (UT3), 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)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM), Université Fédérale Toulouse Midi-Pyrénées, Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS), Neuro-Gastroentérologie & Nutrition (ToxAlim-NGN), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Recherche Agronomique (INRA)-Université Toulouse III - Paul Sabatier (UT3), Burcelin, Remy, Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National de la Santé et de la Recherche Médicale (INSERM), Université de Toulouse (UT)-Université de Toulouse (UT)-Ecole Nationale Vétérinaire de Toulouse (ENVT), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Ecole d'Ingénieurs de Purpan (INP - PURPAN), Université de Toulouse (UT)-Université de Toulouse (UT), Unité de Biométrie et Intelligence Artificielle (ancêtre de MIAT) (UBIA), Institut National de la Recherche Agronomique (INRA), Université de Toulouse (UT)-Institut National de la Santé et de la Recherche Médicale (INSERM), Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT), Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS), Service d'hypertension artérielle et thérapeutique [CHU Toulouse] (HTA et thérapeutique), Pôle Cardiovasculaire et Métabolique [CHU Toulouse], Centre Hospitalier Universitaire de Toulouse (CHU Toulouse)-Centre Hospitalier Universitaire de Toulouse (CHU Toulouse), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National de la Recherche Agronomique (INRA)-Université Toulouse III - Paul Sabatier (UT3), Simon, Marie Francoise, The role of intestinal microflora in non-alcoholic fatty liver disease (NAFLD) - FLORINASH - - EC:FP7:HEALTH2010-01-01 - 2014-12-31 - 241913 - VALID, Institut National des Sciences Appliquées (INSA)-Université Fédérale Toulouse Midi-Pyrénées-Institut National des Sciences Appliquées (INSA)-Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse III - Paul Sabatier (UT3), and Institut National des Sciences Appliquées (INSA)-Université Fédérale Toulouse Midi-Pyrénées-Institut National des Sciences Appliquées (INSA)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)
Subjects: Lipopolysaccharides, Male, mictobiota, White adipose tissue, [SDV.GEN] Life Sciences [q-bio]/Genetics, Gut flora, Fatty Acids, Nonesterified, Intestinal absorption, Cecum, Mice, 0302 clinical medicine, gut, metabolic, background, phenotype, diet, MESH: Diabetes Mellitus, Experimental, MESH: Animals, 2. Zero hunger, 0303 health sciences, Glucose tolerance test, MESH: Muscle, Skeletal, MESH: Cytokines, MESH: Fatty Acids, Nonesterified, medicine.diagnostic_test, biology, digestive, oral, and skin physiology, Gastroenterology, food and beverages, Phenotype, Adaptation, Physiological, Intestines, medicine.anatomical_structure, High-fat diet, Liver, 030220 oncology & carcinogenesis, MESH: Permeability, Cytokines, medicine.symptom, MESH: Intestines, medicine.medical_specialty, MESH: Glucose Tolerance Test, MESH: Intestinal Absorption, Médecine humaine et pathologie, Inflammation, Gut microbiota, Diet, High-Fat, MESH: Phenotype, digestive system, Permeability, Diabetes Mellitus, Experimental, 03 medical and health sciences, MESH: Mice, Inbred C57BL, Diabetes mellitus, Internal medicine, medicine, Animals, Muscle, Skeletal, MESH: Mice, 030304 developmental biology, [SDV.GEN]Life Sciences [q-bio]/Genetics, Metabolic diseases, MESH: Cecum, Glucose Tolerance Test, MESH: Metagenome, medicine.disease, biology.organism_classification, MESH: Adaptation, Physiological, MESH: Male, Mice, Inbred C57BL, MESH: Diet, High-Fat, Endocrinology, Intestinal Absorption, Immunology, Metagenome, MESH: Lipopolysaccharides, MESH: Liver
Abstract: Chantier qualité GA; International audience; OBJECTIVE: The gut microbiota, which is considered a causal factor in metabolic diseases as shown best in animals, is under the dual influence of the host genome and nutritional environment. This study investigated whether the gut microbiota per se, aside from changes in genetic background and diet, could sign different metabolic phenotypes in mice. METHODS: The unique animal model of metabolic adaptation was used, whereby C57Bl/6 male mice fed a high-fat carbohydrate-free diet (HFD) became either diabetic (HFD diabetic, HFD-D) or resisted diabetes (HFD diabetes-resistant, HFD-DR). Pyrosequencing of the gut microbiota was carried out to profile the gut microbial community of different metabolic phenotypes. Inflammation, gut permeability, features of white adipose tissue, liver and skeletal muscle were studied. Furthermore, to modify the gut microbiota directly, an additional group of mice was given a gluco-oligosaccharide (GOS)-supplemented HFD (HFD+GOS). RESULTS: Despite the mice having the same genetic background and nutritional status, a gut microbial profile specific to each metabolic phenotype was identified. The HFD-D gut microbial profile was associated with increased gut permeability linked to increased endotoxaemia and to a dramatic increase in cell number in the stroma vascular fraction from visceral white adipose tissue. Most of the physiological characteristics of the HFD-fed mice were modulated when gut microbiota was intentionally modified by GOS dietary fibres. CONCLUSIONS: The gut microbiota is a signature of the metabolic phenotypes independent of differences in host genetic background and diet.
Published: 2012
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38. Involvement of tissue bacteria in the onset of diabetes in humans: evidence for a concept

Author: Pascale Klopp, Jason S. Iacovoni, Joël Doré, Céline Lange, Olivier Bouchez, Rémy Burcelin, Jérôme Mariette, Jacques Amar, Olivier Lantieri, Marie-Aline Charles, Matteo Serino, L. Perez, Christophe Klopp, Michael Courtney, B. Balkau, Michel Marre, Chantal Chabo, Patricia Lepage, Stanislas Mondot, Epidémiologie et analyses en santé publique : risques, maladies chroniques et handicaps (LEASP), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, Service de Médecine Interne et Hypertension Artérielle [Toulouse], CHU Toulouse [Toulouse]-Hôpital de Rangueil, CHU Toulouse [Toulouse], Institut des Maladies Métaboliques et Cardiovasculaires (I2MC), Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM), Centre de recherche en épidémiologie et santé des populations (CESP), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Université Paris-Sud - Paris 11 (UP11)-Hôpital Paul Brousse-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), MICrobiologie de l'ALImentation au Service de la Santé (MICALIS), Institut National de la Recherche Agronomique (INRA)-AgroParisTech, Unité de Biométrie et Intelligence Artificielle de Toulouse [Castanet-Tolosan] (UBIA), Institut National de la Recherche Agronomique (INRA)-Plateforme bioinformatique du GIS GENOTOUL - Génopole Toulouse Midi-Pyrénées, Génopole Toulouse Midi-Pyrénées [Auzeville] (GENOTOUL), Institut National de la Recherche Agronomique (INRA)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-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-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM), Vaiomer, Déterminants génétiques du diabète de type 2 et de ses complications vasculaires ((U 695)), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Diderot - Paris 7 (UPD7), Service d'endocrinologie, diabétologie et nutrition [CHU Bichat], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-AP-HP - Hôpital Bichat - Claude Bernard [Paris], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Université Paris Diderot - Paris 7 (UPD7), Institut inter-Régional pour la SAnté (IRSA), This work was funded by the Agence Nationale de la Recherche (French national research agency) (to R. Burcelin) and by the Societe Francophone de Diabete (to B. Balkau). The D.E.S.I.R. study has been supported by Inserm contracts with CNAMTS, Lilly, Novartis Pharma and sanofi-aventis, by Inserm (Reseaux en Sante Publique, Interactions entre les determinants de la sante, Cohortes Sante TGIR 2008), the Association Diabete Risque Vasculaire, the Federation Francaise de Cardiologie, La Fondation de France, ALFEDIAM, ONIVINS, Societe Francophone du Diabete, Ardix Medical, Bayer Diagnostics, Becton Dickinson, Cardionics., Service d'hypertension artérielle et thérapeutique [CHU Toulouse] (HTA et thérapeutique), Pôle Cardiovasculaire et Métabolique [CHU Toulouse], Centre Hospitalier Universitaire de Toulouse (CHU Toulouse)-Centre Hospitalier Universitaire de Toulouse (CHU Toulouse), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Université Paris-Sud - Paris 11 (UP11)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpital Paul Brousse-Institut National de la Santé et de la Recherche Médicale (INSERM), Unité de Biométrie et Intelligence Artificielle (ancêtre de MIAT) (UBIA), Institut National de la Recherche Agronomique (INRA), Institut National de la Recherche Agronomique (INRA)-Université Toulouse III - Paul Sabatier (UT3), Université Paris Diderot - Paris 7 (UPD7)-Institut National de la Santé et de la Recherche Médicale (INSERM), Simon, Marie Francoise, Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Université Paris-Sud - Paris 11 (UP11)-Assistance publique - Hôpitaux de Paris (AP-HP) (APHP)-Hôpital Paul Brousse-Institut National de la Santé et de la Recherche Médicale (INSERM), Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de la Recherche Agronomique (INRA), VAIOMER SAS, VAIOMMER, Assistance publique - Hôpitaux de Paris (AP-HP) (APHP)-AP-HP - Hôpital Bichat - Claude Bernard [Paris]-Université Paris Diderot - Paris 7 (UPD7), and Plateforme bioinformatique du GIS GENOTOUL - Génopole Toulouse Midi-Pyrénées-Institut National de la Recherche Agronomique (INRA)
Subjects: Male, Endocrinology, Diabetes and Metabolism, [SDV]Life Sciences [q-bio], [MATH] Mathematics [math], 0302 clinical medicine, MESH: Metabolic Syndrome X, RNA, Ribosomal, 16S, MESH: Obesity, Longitudinal Studies, [MATH]Mathematics [math], MESH: Longitudinal Studies, Genetics, MESH: Aged, Metabolic Syndrome, 0303 health sciences, education.field_of_study, MESH: Middle Aged, biology, Middle Aged, MESH: Case-Control Studies, 3. Good health, [SDV] Life Sciences [q-bio], MESH: RNA, Ribosomal, 16S, Obesity, Abdominal, Female, France, MESH: Diabetes Mellitus, Type 2, Adult, Population, abdominal adiposity, 030209 endocrinology & metabolism, [INFO] Computer Science [cs], MESH: Obesity, Abdominal, 03 medical and health sciences, endocrinology, Diabetes mellitus, Internal Medicine, medicine, microbiota, Humans, [INFO]Computer Science [cs], Obesity, education, 16s rdna gene, 030304 developmental biology, Aged, MESH: Humans, Bacteria, Case-control study, MESH: Biological Markers, MESH: Adult, Human physiology, MESH: Metagenome, biology.organism_classification, medicine.disease, MESH: Male, MESH: France, MESH: Bacteria, Diabetes Mellitus, Type 2, Case-Control Studies, Immunology, Metagenome, metabolism, MESH: Female, Biomarkers
Abstract: Chantier qualité GA; International audience; AIMS/HYPOTHESIS: Evidence suggests that bacterial components in blood could play an early role in events leading to diabetes. To test this hypothesis, we studied the capacity of a broadly specific bacterial marker (16S rDNA) to predict the onset of diabetes and obesity in a general population. METHODS: Data from an Epidemiological Study on the Insulin Resistance Syndrome (D.E.S.I.R.) is a longitudinal study with the primary aim of describing the history of the metabolic syndrome. The 16S rDNA concentration was measured in blood at baseline and its relationship with incident diabetes and obesity over 9 years of follow-up was assessed. In addition, in a nested case-control study in which participants later developed diabetes, bacterial phylotypes present in blood were identified by pyrosequencing of the overall 16S rDNA gene content. RESULTS: We analysed 3,280 participants without diabetes or obesity at baseline. The 16S rDNA concentration was higher in those destined to have diabetes. No difference was observed regarding obesity. However, the 16S rDNA concentration was higher in those who had abdominal adiposity at the end of follow-up. The adjusted OR (95% CIs) for incident diabetes and for abdominal adiposity were 1.35 (1.11, 1.60), p = 0.002 and 1.18 (1.03, 1.34), p = 0.01, respectively. Moreover, pyrosequencing analyses showed that participants destined to have diabetes and the controls shared a core blood microbiota, mostly composed of the Proteobacteria phylum (85-90%). CONCLUSIONS/INTERPRETATION: 16S rDNA was shown to be an independent marker of the risk of diabetes. These findings are evidence for the concept that tissue bacteria are involved in the onset of diabetes in humans.
Published: 2011
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39. Assessment of replicate bias in 454 pyrosequencing and a multi-purpose read-filtering tool

Author: Christophe Klopp, Céline Noirot, Jérôme Mariette, Unité de Biométrie et Intelligence Artificielle (UBIA), Institut National de la Recherche Agronomique (INRA), and Mariette, Jérôme
Subjects: Medicine(all), Biochemistry, Genetics and Molecular Biology(all), Computer science, Fragment (computer graphics), [SHS.INFO]Humanities and Social Sciences/Library and information sciences, lcsh:R, 2 base encoding, lcsh:Medicine, Sequence assembly, General Medicine, Replicate, Library and information sciences, Sciences de l'information et de la communication, computer.software_genre, Genome, General Biochemistry, Genetics and Molecular Biology, DNA sequencing, lcsh:Biology (General), Technical Note, Pyrosequencing, Data mining, lcsh:Science (General), lcsh:QH301-705.5, computer, lcsh:Q1-390
Abstract: Background Roche 454 pyrosequencing platform is often considered the most versatile of the Next Generation Sequencing technology platforms, permitting the sequencing of large genomes, the analysis of variations or the study of transcriptomes. A recent reported bias leads to the production of multiple reads for a unique DNA fragment in a random manner within a run. This bias has a direct impact on the quality of the measurement of the representation of the fragments using the reads. Other cleaning steps are usually performed on the reads before assembly or alignment. Findings PyroCleaner is a software module intended to clean 454 pyrosequencing reads in order to ease the assembly process. This program is a free software and is distributed under the terms of the GNU General Public License as published by the Free Software Foundation. It implements several filters using criteria such as read duplication, length, complexity, base-pair quality and number of undetermined bases. It also permits to clean flowgram files (.sff) of paired-end sequences generating on one hand validated paired-ends file and the other hand single read file. Conclusions Read cleaning has always been an important step in sequence analysis. The pyrocleaner python module is a Swiss knife dedicated to 454 reads cleaning. It includes commonly used filters as well as specialised ones such as duplicated read removal and paired-end read verification.
Published: 2010
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40. An Automated Visual Event Detection System for Cabled Observatory Video

Author: D.R. Edgington, Danelle E. Cline, and Jérôme Mariette
Subjects: Event (computing), Computer science, Real-time computing, Monterey Accelerated Research System, Cabled observatory, Mars Exploration Program, Underwater, Remote sensing
Abstract: The permanent presence of underwater cameras on cabled observatories, such as the Victoria Experimental Network Under the Sea (VENUS) and Eye-In-The-Sea (EITS) on Monterey Accelerated Research System (MARS), will generate precious data that can move forward the boundaries of understanding the underwater world. However, sightings of underwater animal activities are rare, resulting in the recording of many hours of video with relatively few events of interest. Towards this end, an automated visual event detection system is in development at the Monterey Bay Aquarium Research Institute (MBARI) to address the problem of analyzing cabled observatory video. This paper describes the overall design of the development of a system to process video data and enable science users to analyze the results.
Published: 2007
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41. Detecting, Tracking and Classifying Animals in Underwater Observatory Video

Author: I. Kerkez, D.R. Edgington, Danelle E. Cline, and Jérôme Mariette
Subjects: Event (computing), Computer science, business.industry, Human video, Video tracking, Image processing, Computer vision, Artificial intelligence, Underwater, Remotely operated underwater vehicle, Mixture model, business, Object detection
Abstract: For oceanographic research, remotely operated underwater vehicles (ROVs) and underwater observatories routinely record several hours of video material every day. Manual processing of such large amounts of video has become a major bottleneck for scientific research based on this data. We have developed an automated system that detects, tracks, and classifies objects that are of potential interest for human video annotators. By pre-selecting salient targets for track initiation using a selective attention algorithm, we reduce the complexity of multi-target tracking. Then, if an object is tracked for several frames, a visual event is created and passed to a Bayesian classifier utilizing a Gaussian mixture model to determine the object class of the detected event.
Published: 2007
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42. Detecting, Tracking and Classifying Animals in Underwater Video

Author: D. Davis, Danelle E. Cline, I. Kerkez, Jérôme Mariette, and D.R. Edgington
Subjects: Naive Bayes classifier, Computer science, business.industry, Event (computing), Human video, Video tracking, Computer vision, Artificial intelligence, Underwater, Remotely operated underwater vehicle, Object (computer science), business, Mixture model
Abstract: For oceanographic research, remotely operated underwater vehicles (ROVs) and underwater observatories routinely record several hours of video material every day. Manual processing of such large amounts of video has become a major bottleneck for scientific research based on this data. We have developed an automated system that detects, tracks, and classifies objects that are of potential interest for human video annotators. By pre-selecting salient targets for track initiation using a selective attention algorithm, we reduce the complexity of multi-target tracking. Then, if an object is tracked for several frames, a visual event is created and passed to a Bayesian classifier utilizing a Gaussian mixture model to determine the object class of the detected event.
Published: 2006
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43. PREDICTIVE VALUE OF BLOOD BACTERIAL DNA ON THE ONSET OF TYPE 2 DIABETES FROM GENERAL POPULATION

Author: Laurence Perez, Céline Lange, M. Serino, P. Klopp, C. Chabot, R. Burcelin, Jérôme Mariette, M. A. Charles, M. Courntey, Jacques Amar, Michel Marre, B. Balkau, Joël Doré, O. Lantieri, and Olivier Bouchez
Subjects: education.field_of_study, Physiology, business.industry, Population, Type 2 diabetes, medicine.disease, Predictive value, Immunology, Internal Medicine, Medicine, Cardiology and Cardiovascular Medicine, business, education, Bacterial dna
Published: 2011
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44. O85 La concentration sanguine d’ADN bactérien prédit la survenue du diabète en population générale

Author: Céline Lange, Jason S. Iacovoni, M. Courtney, Patricia Lepage, Rémy Burcelin, Michel Marre, Jérôme Mariette, O. Lantieri, Pascale Klopp, Chantal Chabo, B. Balkau, Matteo Serino, M.-A. Charles, S. Mondo, L. Perez, and Jacques Amar
Subjects: Endocrinology, Endocrinology, Diabetes and Metabolism, Internal Medicine, General Medicine
Abstract: Objectif Des donnees experimentales suggerent que la presence de composants bacteriens dans le sang et les tissus est une des etapes initiales conduisant au diabete de type 2. L’objectif de l’etude est d’analyser la relation entre la concentration sanguine d’un gene hautement conserve au sein des especes bacteriennes : le gene 16S ARNr et la survenue du diabete dans une population generale. Patients et methodes L’etude DESIR est une etude de cohorte dont l’objectif etait de decrire l’histoire naturelle du syndrome metabolique. Les participants ont ete evalues a l’inclusion et a 3, 6 et 9 ans. La concentration sanguine du gene 16S ARNr a ete mesuree a l’inclusion. De plus nous avons realise dans un sous groupe de la population une etude cas temoin pour identifier par technique de pyrosequencage, l’ADN bacterien associe a la survenue du diabete. Resultats 3 650 participants indemnes de diabete a l’inclusion ont ete analyses. En reference au quartile de concentration sanguine d’ADN bacterien le plus faible et apres ajustement sur les facteurs confondants, l’odds ratio de developper un diabete pour la derniere periode de suivi etait de 1,92 (0,76–4,81) dans le quartile 2, 3.50 (1,42–8,62) dans le quartile 3 and 3,63 (1,52–8,70) dans le quartile 4. L’analyse par pyrosequencage de l’ADN bacterien a montre que les sujets destines a devenir diabetiques et les temoins partageaient le meme ensemble de genes bacteriens appartenant pour l’essentiel au phylum des proteobacteries avec des differences au niveau des genres bacteriens. Conclusion La concentration sanguine en genes bacteriens presents dans le sang est un marqueur de risque de developper un diabete. Le microbiome tissulaire pourrait etre une cible therapeutique pour prevenir les maladies metaboliques.
Published: 2011
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45. Assessment of replicate bias in 454 pyrosequencing and a multi-purpose read-filtering tool.

Author: Jérôme, Mariette, Noirot, Céline, and Klopp, Christophe
Abstract: Background: Roche 454 pyrosequencing platform is often considered the most versatile of the Next Generation Sequencing technology platforms, permitting the sequencing of large genomes, the analysis of variations or the study of transcriptomes. A recent reported bias leads to the production of multiple reads for a unique DNA fragment in a random manner within a run. This bias has a direct impact on the quality of the measurement of the representation of the fragments using the reads. Other cleaning steps are usually performed on the reads before assembly or alignment. Findings: PyroCleaner is a software module intended to clean 454 pyrosequencing reads in order to ease the assembly process. This program is a free software and is distributed under the terms of the GNU General Public License as published by the Free Software Foundation. It implements several filters using criteria such as read duplication, length, complexity, base-pair quality and number of undetermined bases. It also permits to clean flowgram files (.sff) of paired-end sequences generating on one hand validated paired-ends file and the other hand single read file. Conclusions: Read cleaning has always been an important step in sequence analysis. The pyrocleaner python module is a Swiss knife dedicated to 454 reads cleaning. It includes commonly used filters as well as specialised ones such as duplicated read removal and paired-end read verification. [ABSTRACT FROM AUTHOR]
Published: 2011
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