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La connaissance de ces approches et des outils qui en\r\ndécoulent pour analyser la séquence et la structure des génomes, les annoter et caractériser\r\nleur diversité et leurs profils d’expression permet d’aborder des questions de recherche\r\nbiologique avancée sur la diversité et l’adaptation des plantes. Les espèces prises en\r\nconsidération sont des espèces phares des instituts de recherche agronomique de Montpellier\r\net font partie des cultures les plus importantes pour l’agriculture mondiale. Des plateformes\r\nd’outils bioinformatiques récents reposant sur des centres de calcul et de stockage haute\r\ncapacité, sont en place pour analyser des jeux de données originales permettant de mieux\r\ncomprendre comment les génomes de plantes évoluent et s’expriment. L’ensemble de ces\r\nconnaissances Findable, Accessible, Interoperable, Reusable car intégré dans des systèmes\r\nd’information peut soutenir l'identification de gènes responsables de caractères adaptatifs ou\r\nde production. La mobilisation de jeunes chercheurs sur ces sujets est primordiale tant la\r\ndemande est importante.\r\nLe module est structuré sous la forme de cours et de travaux tutorés avec la rencontre de\r\ngénéticiens et de bioinformaticiens permettant d’appréhender les formes variées des progrès\r\nen bioanalyse génomique. 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Cette édition de l’école aborde les nouveaux enjeux technologiques: elle s’articulera autour des différentes étapes qui mèneront à l’obtention d’un génome annoté à partir de données “long reads” et “hybride” : contrôle qualité des données, assemblage, scaffolding, polishing, annotation structurale et fonctionnelle (en session parallèle pour les procaryotes et les eucaryotes). \r\nL’école vise à introduire les concepts, à manipuler les outils informatiques et à en interpréter les résultats. Elle est basée sur une alternance de courtes sessions théoriques et d’ateliers pratiques. Les participants bénéficieront d’un tutorat personnalisé pour élaborer leur plan d’analyse, et effectuer les premières étapes de traitement de leurs propres données ou de celles de leur équipe.\r\nAttention : le tutorat n'a pas pour vocation de réaliser l’analyse complète des données des participants.\r\nPublic visé\r\nCette formation est destinée aux biologistes (ingénieurs, doctorants, chercheurs, enseignants-chercheurs, praticiens…) confrontés à l’analyse de données NGS, et qui ne disposent pas des compétences bioinformatiques suffisantes.","homepage":"https://www.france-bioinformatique.fr/formation/ebaii2022_genomique/","is_draft":false,"costs":[],"topics":[],"keywords":[],"prerequisites":[],"openTo":"Everyone","accessConditions":"","maxParticipants":40,"contacts":[],"elixirPlatforms":[],"communities":[],"sponsoredBy":[],"organisedByOrganisations":[],"organisedByTeams":[],"logo_url":"https://cvt.aviesan.fr/wp-content/themes/cvtaviesan/images/logo_cvt.png","updated_at":"2024-12-05T09:11:55.721141Z","audienceTypes":[],"audienceRoles":[],"difficultyLevel":"Novice","trainingMaterials":[],"learningOutcomes":"Initiation au traitement des données de génomique obtenues par séquençage à haut débit\r\nAssemblage et annotation de novo de génomes\r\nL’école vise à introduire les concepts, à manipuler les outils informatiques et à en interpréter les résultats. 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Remarkable strides were made in particular in the development of foundation models - AI models trained on extensive volumes of unlabelled data. Moreover, given the large amounts of omics data that are being generated and made accessible to researchers due to the drop in the cost of high-throughput technologies, analysing these complex high-volume data is not trivial, and the use of classical statistics can not explore their full potential. As such, Machine Learning (ML) and Artificial Intelligence (AI) have been recognized as key opportunity areas, as evidenced by a number of ongoing activities and efforts throughout the community.\r\n\r\nHowever, beyond the technological advances, it is equally important that the individual researchers acquire the necessary knowledge and skills to fully take advantage of Machine Learning. 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This workshop is tailored for data scientists, advanced Galaxy users, and team leaders who need to scale, automate, and publish their data analysis workflows for batch processing and production-level applications.\r\n\r\nOver five days, you’ll embark on a comprehensive journey through Galaxy’s advanced capabilities:\r\n\r\nMonday: Introduction & Workflow Development\r\n\r\nStart with a welcome and icebreaker to foster collaboration, followed by a brief overview of Galaxy and its workflow features. Dive into hands-on workflow development, where you’ll learn to design clean, efficient workflows, customize them with parameters, and generate user-friendly workflow reports—combining theory with practical application.\r\n\r\nTuesday: Workflow FAIRification, Documentation, and Export\r\n\r\nBegin with a recap of Day 1, then explore UseGalaxy.fr and its unique features. Learn to annotate workflows with metadata, apply best practices for FAIR compliance, and implement tests to ensure reliability. Publish your workflows to WorkflowHub and Dockstore via the IWC. Develop high-resolution workflow visualizations and create interactive tutorials using a “Choose Your Own Tutorial” approach. Finally, master workflow export by creating RO-Crates for reproducibility and submitting workflows to LifeMonitor for performance tracking.\r\n\r\nWednesday: Scaling Workflows & Galaxy Using Command-Line and API\r\n\r\nStart with a recap and real-world examples of large-scale Galaxy projects. Learn to execute workflows from the command line using Planemo, automate batch processing with shell scripts, and analyze performance for efficiency. Discover how to scale Galaxy use with BioBlend, designing Python scripts for batch workflow execution and evaluating scalability. The day concludes with an introduction to the “Bring Your Own Work” session.\r\n\r\nThursday: Bring Your Own Work (BYOW)\r\n\r\nDedicate the day to applying your new skills to your own projects. With guidance from trainers, refine your workflows, troubleshoot challenges, and implement solutions using your personal data. Collaborate with peers, document your progress, and optimize your workflows to leave with actionable results for your research.\r\n\r\nFriday: Storage, Data Management, Recap, and Closing\r\n\r\nThe final half-day begins with a recap of the week’s progress, followed by a session on “Bring Your Own Storage”, exploring how to integrate personal or institutional storage with Galaxy. Learn about managing databases in Galaxy and the IDC (Intergalactic Data Commission) effort for efficient data organization. The workshop concludes with a general recap, supplementary exercises, and feedback and closing remarks, ensuring you leave with a comprehensive understanding and resources for continued success.\r\n\r\nThis training will be conducted in French, while the materials (slides) will be in English.\r\n\r\nLearning Objectives\r\nAt the end of the workshop, you will be able to:\r\n\r\nWorkflow development\r\n    Understand the key aspects of workflows by identifying their core components and purpose.\r\n    Create clean, non-repetitive workflows by applying best practices for process design.\r\n    Use workflow parameters to customize and optimize workflows for specific tasks.\r\n    Generate user-friendly workflow reports to display workflow results in a structured way.\r\nWorkflow FAIRyfication\r\n    Annotate a Galaxy workflow with essential metadata to ensure it is findable and reusable.\r\n    Apply best practices to data analysis workflows to improve consistency and interoperability.\r\n    Implement robust tests to validate workflow reliability and accuracy.\r\n    Publish a Galaxy workflow on WorkflowHub and Dockstore via its integration into the IWC, demonstrating enhanced findability,accessibility, interroperability and usability for the scientific community.\r\nWorkflow Documentation\r\n    Design a high-resolution workflow image optimized for documentation and presentations.\r\n    Develop a hands-on tutorial with a “Choose Your Own Tutorial” approach, including:\r\n        A step-by-step tutorial with skeleton generation from the workflow.\r\n        A real-time tutorial that runs and explains the workflow interactively.\r\n    Produce a final documentation package that includes both tutorial formats and high-resolution visuals.\r\nWorkflow Export\r\n    Apply the process of creating a Galaxy Workflow Run RO-Crate by packaging a workflow with its metadata, inputs, and outputs, ensuring it is reproducible and FAIR-compliant.\r\n    Evaluate the completeness and accuracy of a Galaxy Workflow Run RO-Crate by reviewing its structure, metadata, and included files for adherence to best practices.\r\n    Submit a workflow to LifeMonitor, analyzing the platform’s feedback to assess workflow performance and improve its reliability for future use.\r\nWorkflow Scaling using command-line\r\nExecute workflows from the command line using the Planemo run subcommand, demonstrating the ability to run and monitor workflows outside the Galaxy interface.\r\nDevelop simple shell scripts to automate the execution of multiple workflows concurrently or sequentially, optimizing efficiency and scalability.\r\nAnalyze the performance and resource usage of workflows run via shell scripts, evaluating the effectiveness of scaling strategies for large-scale data processing.\r\nScaling Galaxy Use with the API and BioBlend\r\nUtilize the BioBlend library to programmatically interact with Galaxy, executing workflows, managing datasets, and automating repetitive tasks.\r\nDesign a Python script using BioBlend to scale Galaxy workflows for batch processing, ensuring efficient resource use and reproducibility.\r\nEvaluate the performance and scalability of workflows executed via BioBlend, comparing results with manual Galaxy interactions to identify improvements.\r\n“Bring Your Own Work”\r\nApply the concepts and tools learned during the training to develop or refine your own workflows using your personal data, with guidance from trainers.\r\nTroubleshoot challenges in your workflow or data analysis, implementing solutions with the support of trainers and peers.\r\nDemonstrate progress in your project by documenting your workflow, results, and any optimizations made during the sessions.\r\n\r\nRequirements\r\nPrior knowledge and experience using Galaxy\r\nPrior knowledge and experience using command line\r\nFluent in French (materials will be in English and discussions will happen in French)\r\nYour own computer\r\nOptional but encouraged: your own workflow and dataset for the Bring Your Own Work (BYOW) session. The workflow and the dataset must be shareable and non-sensitive (i.e., they must not contain any patient-related information or confidential data). 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workflows to improve consistency and interoperability.\r\nImplement robust tests to validate workflow reliability and accuracy.\r\nPublish a Galaxy workflow on WorkflowHub and Dockstore via its integration into the IWC, demonstrating enhanced findability, accessibility, interroperability and usability for the scientific community.\r\nWorkflow Documentation\r\nDesign a high-resolution workflow image optimized for documentation and presentations.\r\nDevelop a hands-on tutorial with a “Choose Your Own Tutorial” approach, including:\r\nA step-by-step tutorial with skeleton generation from the workflow.\r\nA real-time tutorial that runs and explains the workflow interactively.\r\nProduce a final documentation package that includes both tutorial formats and high-resolution visuals.\r\nWorkflow Export\r\nApply the process of creating a Galaxy Workflow Run RO-Crate by packaging a workflow with its metadata, inputs, and outputs, ensuring it is reproducible and FAIR-compliant.\r\nEvaluate the completeness and accuracy of a Galaxy Workflow Run RO-Crate by reviewing its structure, metadata, and included files for adherence to best practices.\r\nSubmit a workflow to LifeMonitor, analyzing the platform’s feedback to assess workflow performance and improve its reliability for future use.\r\nWorkflow Scaling using command-line\r\nExecute workflows from the command line using the Planemo run subcommand, demonstrating the ability to run and monitor workflows outside the Galaxy interface.\r\nDevelop simple shell scripts to automate the execution of multiple workflows concurrently or sequentially, optimizing efficiency and scalability.\r\nAnalyze the performance and resource usage of workflows run via shell scripts, evaluating the effectiveness of scaling strategies for large-scale data processing.\r\nScaling Galaxy Use with the API and BioBlend\r\nUtilize the BioBlend library to programmatically interact with Galaxy, executing workflows, managing datasets, and automating repetitive tasks.\r\nDesign a Python script using BioBlend to scale Galaxy workflows for batch processing, ensuring efficient resource use and reproducibility.\r\nEvaluate the performance and scalability of workflows executed via BioBlend, comparing results with manual Galaxy interactions to identify improvements.\r\n“Bring Your Own Work”\r\nApply the concepts and tools learned during the training to develop or refine your own workflows using your personal data, with guidance from trainers.\r\nTroubleshoot challenges in your workflow or data analysis, implementing solutions with the support of trainers and peers.\r\nDemonstrate progress in your project by documenting your workflow, results, and any optimizations made during the sessions.","hoursPresentations":null,"hoursHandsOn":null,"hoursTotal":null,"personalised":true,"event_set":["https://catalogue.france-bioinformatique.fr/api/event/802/?format=json","https://catalogue.france-bioinformatique.fr/api/event/803/?format=json"]},{"id":408,"name":"Introduction to R","shortName":"Intro R","description":"Objectives\r\n- Understand basic R commands\r\n- Learn how to use the RStudio interface\r\n- Understand the use of R functions\r\n- Be able to perform simple data manipulations\r\n- Be able to create basic visualizations\r\n\r\nCourse Content\r\nI. Introduction\r\n- Getting started with the RStudio environment\r\n- Programming best practices\r\n- Different types and classes of variables\r\n- Functions\r\n\r\nII. Data manipulation with the tidyverse\r\n- Logical operators\r\n- Working with data frames\r\n\r\nIII. Visualization with ggplot2\r\n- Principles\r\n- Simple examples","homepage":"https://pf-bird.univ-nantes.fr/training/r_lang/","is_draft":false,"costs":[],"topics":[],"keywords":[],"prerequisites":[],"openTo":"Everyone","accessConditions":"","maxParticipants":20,"contacts":[],"elixirPlatforms":[],"communities":[],"sponsoredBy":[],"organisedByOrganisations":[],"organisedByTeams":[{"id":16,"name":"BiRD","url":"https://catalogue.france-bioinformatique.fr/api/team/BiRD/?format=json"}],"logo_url":"https://bird.univ-nantes.io/website/images/logo/logo.svg","updated_at":"2026-03-26T14:31:34.767304Z","audienceTypes":[],"audienceRoles":[],"difficultyLevel":"","trainingMaterials":[],"learningOutcomes":"","hoursPresentations":null,"hoursHandsOn":null,"hoursTotal":null,"personalised":null,"event_set":[]},{"id":407,"name":"Optimal use of GLiCID HPC cluster","shortName":"GLiCID","description":"Objectives\r\n- understand the infrastructure of GLiCID HPC cluster\r\n- understand the different types of storage and computing nodes\r\n- launch computing tasks via the Slurm scheduler\r\n\r\nCourse Content\r\n- configuration of your account and connection with ssh on GLiCID (ssh keys)\r\n- navigate through the storage spaces\r\n- use slurm to launch a job\r\n- manage the software environments (micromamba, guix, modules)\r\n- use workflow managers on GLiCID","homepage":"https://pf-bird.univ-nantes.fr/training/cluster/","is_draft":false,"costs":["Free"],"topics":[],"keywords":[],"prerequisites":[],"openTo":"Everyone","accessConditions":"Prerequisites\r\n- Familiarity with basic Linux commands or have completed the training course “Introduction to the command-line interface.”\r\n- Have an account on the GLiCID cluster","maxParticipants":20,"contacts":[],"elixirPlatforms":[],"communities":[],"sponsoredBy":[],"organisedByOrganisations":[],"organisedByTeams":[{"id":16,"name":"BiRD","url":"https://catalogue.france-bioinformatique.fr/api/team/BiRD/?format=json"}],"logo_url":"https://bird.univ-nantes.io/website/images/logo/logo.svg","updated_at":"2026-03-02T16:36:38.694735Z","audienceTypes":["Professional (continued)"],"audienceRoles":["All"],"difficultyLevel":"","trainingMaterials":[],"learningOutcomes":"","hoursPresentations":null,"hoursHandsOn":null,"hoursTotal":null,"personalised":null,"event_set":["https://catalogue.france-bioinformatique.fr/api/event/794/?format=json"]},{"id":374,"name":"Best practices in Bioinformatics","shortName":"Best practices BiRD cluster","description":"Objectives\r\n- Understand and implement the principles of reproducible science in analysis and development projects\r\n- Acquire basic commands necessary for optimal use of a HPC cluster\r\n\r\nCourse Content\r\n- Introduction to reproducibility\r\n- Best practices on code history and sharing: Git\r\n- Software environment : conda/mamba\r\n- Presentation of a HPC cluster\r\n- Introduction to workflows using Snakemake","homepage":"https://pf-bird.univ-nantes.fr/training/cluster/","is_draft":false,"costs":["Free"],"topics":[],"keywords":[],"prerequisites":["Linux - Basic Knowledge"],"openTo":"Everyone","accessConditions":"- Familiarity with basic Linux commands or have completed the training course “Introduction to the command-line interface.”\r\n- Have an account on the GLiCID cluster.","maxParticipants":20,"contacts":["https://catalogue.france-bioinformatique.fr/api/userprofile/596/?format=json"],"elixirPlatforms":[],"communities":[],"sponsoredBy":[],"organisedByOrganisations":[],"organisedByTeams":[{"id":16,"name":"BiRD","url":"https://catalogue.france-bioinformatique.fr/api/team/BiRD/?format=json"}],"logo_url":"https://bird.univ-nantes.io/website/images/logo/logo.svg","updated_at":"2026-01-27T11:08:14.810708Z","audienceTypes":[],"audienceRoles":[],"difficultyLevel":"","trainingMaterials":[],"learningOutcomes":"","hoursPresentations":7,"hoursHandsOn":null,"hoursTotal":7,"personalised":null,"event_set":["https://catalogue.france-bioinformatique.fr/api/event/602/?format=json","https://catalogue.france-bioinformatique.fr/api/event/748/?format=json","https://catalogue.france-bioinformatique.fr/api/event/641/?format=json"]},{"id":310,"name":"Diplôme Universitaire en Bioinformatique Intégrative / University Diploma in Integrative Bioinformatics","shortName":"DUBii","description":"La bioinformatique est devenue une compétence incontournable pour l'analyse de données de natures diverses : génomes, transcriptomes, protéomes, métabolomes, structures macromoléculaires, réseaux d'interactions. L'appropriation par les biologistes des méthodes et des outils de biostatistique et bioinformatique intégrative est un enjeu majeur pour la montée en compétence des équipes de recherche et des plateformes de service.\r\n\r\nL'université de Paris propose en partenariat avec l'Institut Français de Bioinformatique (IFB) la troisième édition du Diplôme Universitaire en Bioinformatique intégrative (DUBii). Cette formation s’adresse en priorité à des biologistes ou à des médecins souhaitant évoluer en compétences ou envisager une reconversion professionnelle et ayant déjà acquis des compétences (formation courte, autoapprentissage, expérience de terrain) en informatique ou bioinformatique / biostatistique (environnement Unix, Python ou R ou autre langage de programmation). \r\n\r\nLe DUBii fournira une formation théorique et pratique, complétée par une période d'immersion de 20 jours sur l'une des plateformes régionales de l'IFB, qui mobilisera, dans le cadre d'un projet tutoré, l'ensemble des méthodes et outils appris durant les cours pour réaliser un projet personnel de bioinformatique intégrative. Ce projet combinera des données propres à chaque participant produites dans son laboratoire (principe BYOD : “Bring Your Own Data”) ou collectées à partir de bases de données publiques. \r\n\r\n Cette formation se déroulera pendant 8 semaines réparties entre :\r\nLes cours : 4 semaines à raison de 4 jours/semaine en présentiel (96h)\r\nLe projet tutoré : 20 jours sur l'une des plateformes bioinformatique de l'IFB","homepage":"https://odf.u-paris.fr/fr/offre-de-formation/diplome-d-universite-1/sciences-technologies-sante-STS/du-bioinformatique-integrative-dubii-DUSBIIN_118.html#programContent26c9751a-f434-491d-abe2-1151370851dc-1","is_draft":false,"costs":[],"topics":[],"keywords":[],"prerequisites":[],"openTo":"Everyone","accessConditions":"","maxParticipants":20,"contacts":[],"elixirPlatforms":[],"communities":[],"sponsoredBy":[],"organisedByOrganisations":[],"organisedByTeams":[],"logo_url":"","updated_at":"2022-09-15T12:11:46.140196Z","audienceTypes":[],"audienceRoles":[],"difficultyLevel":"Advanced","trainingMaterials":[],"learningOutcomes":"","hoursPresentations":null,"hoursHandsOn":null,"hoursTotal":null,"personalised":true,"event_set":["https://catalogue.france-bioinformatique.fr/api/event/466/?format=json","https://catalogue.france-bioinformatique.fr/api/event/414/?format=json","https://catalogue.france-bioinformatique.fr/api/event/400/?format=json"]},{"id":328,"name":"Workshop nf-core et sarek","shortName":"","description":"Dans le cadre du réseau métier ingénieur.e.s lillois, bilille organise un workshop de 2 jours autour de la communauté internationale et des pipelines de bioinformatique nf-core, les 8 et 9 Décembre sur le campus Cité Scientifique de l’Université de Lille, à Villeneuve d’Ascq.\r\n\r\nLe projet nf-core a été créé en 2018 afin de proposer et maintenir de manière collaborative des pipelines d’analyse de bioinformatique en Nextflow selon des standards stricts de qualité et de reproductibilité, tout en facilitant leur mise en œuvre sur la majorité des infrastructures de calcul. La communauté, très active, qui s’organise autour de cette collection de pipelines rassemble des scientifiques du monde entier, issus de parcours très divers.\r\n\r\nÀ l’occasion de cet atelier, nous accueillerons Maxime Garcia (Seqera labs, Stockholm), membre de l’équipe d’administration nf-core et développeur principal du pipeline d’analyse de variants génomique Sarek. Il présentera la communauté aux participant.e.s et les formera à l’utilisation de ces pipelines d’analyse, en alternant les présentations avec des mises en pratique. Il présentera également les outils de développement mis en place par nf-core pour permettre aux participant.e.s de contribuer aux outils existants et de proposer, si elles et ils le souhaitent, leurs propres pipelines selon les standards de la communauté.","homepage":"https://ums-plbs.univ-lille.fr/workshop-nf-core-et-sarek-avec-maxime-garcia","is_draft":false,"costs":["Free to academics"],"topics":[],"keywords":[],"prerequisites":[],"openTo":"Internal personnel","accessConditions":"Cet atelier s’adressant à un public averti en bioinformatique et/ou en biostatistiques, nous attendons des participant.e.s ayant déjà acquis une certaine familiarité avec les compétences suivantes :\r\n- Utilisation courante de la ligne de commande sous Unix\r\n- Utilisation des logiciels d’analyse de données de séquençage à haut débit\r\n- Utilisation de ressources de calcul intensif (cloud, cluster, …)\r\n- Connaissances de base sur les gestionnaires de workflow (Nextflow, SnakeMake, CWL, Galaxy,…)\r\n\r\nUne familiarité avec Nextflow, Conda et des gestionnaires de containers (Docker/Singularity) sera également utile, sans être toutefois obligatoire.","maxParticipants":20,"contacts":["https://catalogue.france-bioinformatique.fr/api/userprofile/757/?format=json"],"elixirPlatforms":[],"communities":[],"sponsoredBy":[],"organisedByOrganisations":[],"organisedByTeams":[{"id":3,"name":"Bilille","url":"https://catalogue.france-bioinformatique.fr/api/team/Bilille/?format=json"}],"logo_url":"https://bilille.univ-lille.fr/fileadmin/_processed_/9/2/csm_logo_bilille_complet_65be9bda8b.png","updated_at":"2024-12-09T17:41:31.307575Z","audienceTypes":[],"audienceRoles":[],"difficultyLevel":"","trainingMaterials":[],"learningOutcomes":"","hoursPresentations":null,"hoursHandsOn":null,"hoursTotal":null,"personalised":null,"event_set":["https://catalogue.france-bioinformatique.fr/api/event/538/?format=json"]},{"id":314,"name":"Workflow4metabolomics","shortName":"W4E","description":"Processing, statistical analysis, and annotation of metabolomics data is a complex task for experimenters since it involves many steps and requires a good knowledge of both the methodology and software tools. The Workflow4Metabolomics.org (W4M) online infrastructure provides a user-friendly and high-performance environment with advanced computational modules for building, running, and sharing complete workflows for LC-MS, GC-MS, FIA and NMR analysis. 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