Handles creating, reading and updating training events.

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            "name": "RNASeq Analysis",
            "shortName": "RNASeq Analysis",
            "description": "Objectives\r\n- Understand the key steps in RNASeq data analysis for a differential expression study\r\n- Know how to perform command-line analysis using Snakemake.\r\n\r\nPedagogical Content\r\nDay 1\r\n- Principle of RNASeq technology: objectives and experimental design.\r\n- Data quality assessment (FastQC, MultiQC).\r\n- Sequence alignment to a reference genome (STAR).\r\n\r\nDay 2\r\n- Differential gene expression analysis (HTSeqCount, DESeq2).\r\n- Functional annotation (GO, Kegg).\r\n- Using the Snakemake workflow system.\r\n- Comparison between RNASeq and 3’SRP methods.\r\n\r\nThe theoretical part is followed by a pipeline run step-by-step on a test dataset. \r\nIt will be possible to start an analysis on your own data.",
            "homepage": "https://pf-bird.univ-nantes.fr/training/rnaseq/",
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            "accessConditions": "- Be comfortable with basic Linux commands or have completed the training course “Introduction to the command-line interface.”\r\n- Be familiar with the use of a computing cluster, conda/mamba et snakemake or have completed the training course “Best practices in bioinformatics.”",
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            "updated_at": "2026-03-02T16:30:29.225935Z",
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            "name": "Analyse de données de métabarcoding",
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            "description": "Cette formation est dédiée à l’analyse de données de type “metabarcoding” issues de la technologie de séquençage Illumina. Nous aborderons les différentes étapes bioinformatiques nécessaires pour transformer les données de séquençage brutes en table d’abondances. Nous présenterons également les outils et méthodologies classiquement utilisés pour décrire la diversité observée et comparer les échantillons.\r\n\r\nA l’issue des 4 jours de formation, les stagiaires connaîtront le périmètre, les avantages et limites des analyses de données de séquençage amplicons (métabarcoding). Ils seront capables d’utiliser les outils de FROGS sur les jeux de données de la formation (16S et ITS) et sauront utiliser l’application Easy16S.\r\n\r\nIls seront capables d’identifier les outils et méthodes adaptées au cadre de leurs analyses. S’ils ont en leur possession un jeu de données à analyser, ils sont encouragés à venir avec celui- ci.\r\n\r\nProgramme :\r\n\r\n\r\nAnalyses bioinformatiques sous Galaxy\r\n\r\n    Introduction générale sur les données amplicons\r\n    Présentation et mise en application avec la suite FROGS du nettoyage des données, du clustering, de la détection de chimères, de l’assignation taxonomique et des étapes annexes\r\n    Conclusion, limite des méthodes, outils compagnons\r\n\r\nAnalyses statistiques avec Easy16S\r\n\r\n    Introduction générale\r\n    Import, manipulation et visualisation des données\r\n    Mesure de diversités : Unifrac, Bray-Curtis, etc.\r\n    Ordination et réduction de dimension : MDS\r\n    Clustering et Heatmap\r\n    Comparaison d’échantillons : PERMANOVA, adonis\r\n\r\nMise en application sur données personnelles ou publiques",
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            "updated_at": "2026-02-12T10:53:42.895487Z",
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            "description": "This training session is organized by the Genotoul bioinfo platform. This course is dedicated to the analysis of prokaryotic shotgun metagenomic data from Illumina and Pacbio HiFi sequencing technology. \r\n\r\nAfter an overview of metagenomics and the biases and limitations of analyses, we will look at the main steps involved in analysing metagenomic data and launch independent tools on the genobioinfo cluster.\r\nLearners will then test a workflow to automate processing on a test dataset (metagWGS ).\r\nOn the third day, learners will choose which analysis strategy to start with according to their experimental design and launch the first stage of metagWGS on their own data.\r\nBy the end of the course, trainees will be familiar with the scope, advantages and limitations of shotgun sequencing data analysis and will have started the analysis on their own data.\r\n\r\ncalendar\r\n \r\n\r\nThis training is focused on practice. It consists of several modules with a large variety of exercises:\r\n\r\nFirst Day\r\nStart at 09:00 am\r\nTour de table\r\nIntroduction to metagenomics, Illumina and Pacbio data, analysis stages, analysis limits, etc.\r\nPresentation of some key tools for each stage\r\nPractical work on the main stages launched independently\r\nEnd at 17:00 pm\r\nSecond Day\r\nStart at 09:00 am\r\nIntroduction to the advantages and disadvantages of workflows and containers\r\nLaunch of the data cleansing stage\r\nLaunch of the rest of the workflow and analysis of the multiQC report\r\nEnd at 17:00 pm\r\nThird Day – BYOD\r\nStart at 09:00 am\r\nDefine the analysis strategy and launch the start of the analysis of your own data.\r\nEnd at 17:00 pm maximum",
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                "INRAE for INRAE's staff: 450 € no VAT charged"
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            "id": 320,
            "name": "Ecole Thématique de Bioinformatique Intégrative / Integrative Bioinformatics Training School",
            "shortName": "ETBII",
            "description": "Dans l’objectif de développer et fédérer des compétences en bioinformatique intégrative au sein de la communauté, l’IFB propose une nouvelle école thématique ayant un double objectif :\r\n- une montée en compétences théoriques et pratiques des bioinformaticiens,\r\n- la constitution de matériel pédagogique partagé sur ce sujet.\r\n\r\nCette école rassemble une équipe pédagogique de 10 personnes et pourra accueillir 30 participants pour sa première édition.\r\nL’ensemble de la formation reposera sur l’utilisation des ressources de calcul et de la plateforme pédagogique de l’Institut Français de Bioinformatique.\r\n\r\nObjectifs pédagogiques \r\n\r\nLa formation a pour but :\r\n- d’introduire les concepts de bases et les différents types d’approches utilisées en bioinformatique intégrative,\r\n- de proposer un approfondissement et une mise en pratique d’une de ces approches sur un/des jeux de données intégrant différents types de données omiques. Cette mise en oeuvre permettra de balayer l’ensemble des points d’attention d’une analyse intégrative,  de la préparation des données jusqu’à l’interprétation des résultats,\r\n- de créer, améliorer et partager les ressources pédagogiques (supports de formation, jeux de données, tutoriels) sur le thème de la bioinformatique intégrative.\r\n\r\nA la fin de cette formation les participants :\r\n- auront acquis un socle de connaissances générales en bioinformatique intégrative, \r\n- auront mis en oeuvre une analyse intégrative depuis la préparation des données jusqu’à l’analyse critique de résultats sur un/des jeux de données proposés lors de la formation,\r\n- auront contribué à constituer du matériel pédagogique partagé sur le sujet.\r\n\r\nPré-requis\r\n- Connaissances de base en Unix/shell, R et/ou Python \r\n- Autonomie dans la gestion de son poste de travail (installation de librairies et maîtrise des environnements de packaging type conda)",
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            "id": 413,
            "name": "Galaxy Beyond Basics: Mastering Workflows, Automation, and Scalability",
            "shortName": "Galaxy avancée",
            "description": "Join us for an intensive, week-long, in-person training designed to elevate your Galaxy expertise to new heights. 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. 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            "homepage": "https://training.galaxyproject.org/training-material/events/2026-10-12-Advanced-Galaxy-Training.html#overview",
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            "id": 395,
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            "shortName": "AlphaFold",
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            "id": 316,
            "name": "IMGT® standards, databases, tools and web resources",
            "shortName": "IMGT workshop",
            "description": "Presentation of IMGT® patterns and resources for the study of genes, expressed repertoires and three-dimensional structures of immunoglobulins (antibodies) and T cell receptors.",
            "homepage": "https://www.imgt.org/",
            "is_draft": false,
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                "http://edamontology.org/topic_3930"
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                "Immune repertoire analysis",
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            "id": 275,
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            "description": "This workshop focuses on the large-scale study of heterogeneity across individual cells from a genomic, transcriptomic and epigenomic point of view. New technological developments enable the characterization of molecular information at a single cell resolution for large numbers of cells. The high dimensional omics data that these technologies produce raise novel methodological challenges for the analysis. In this regard, dedicated bioinformatics and statistical methods have been developed in order to extract robust information.\r\n\r\nThe workshop aims to provide such methods for engineers and researchers directly involved in functional genomics projects making use of single-cell technologies. A wide range of single cell topics will be covered in lectures, demonstrations and practical classes. Among others, the areas and issues to be addressed will include the choice of the most appropriate single-cell sequencing technology, the experimental design and the bioinformatics and statistical methods and pipelines. For this edition, new courses/practicals will focus on spatial transcriptomics, cell phenotyping and additional multi-omics.\r\n\r\nA wide range of single cell topics will be covered in lectures, demonstrations and practical classes. Among others, the areas and issues to be addressed will include the choice of the most appropriate single-cell sequencing technology, the experimental design and the bioinformatics and statistical methods and pipelines. For this edition, new courses/practicals will focus on spatial transcriptomics, cell phenotyping and additional multi-omics.\r\n\r\nRequirements : Participants must have prior experience on NGS data analysis  with everyday use of R and good knowledge of Unix command line. Before the training, participants will be asked to familiarize themselves with the processing and primary analyses steps of scRNA-seq datasets with provided pedagogic material.\r\n\r\nIt is not necessary to have personal single-cell data to analyse.",
            "homepage": "",
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            "id": 412,
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            "description": "This training session is organized by the Genotoul-Bioinfo platform. This 2 days long course is dedicated to the construction and the analysis of eukaryotic pangenome graphs.\r\n\r\nWe will first present the concept of graph-based pangenome, then build one. We will then apply several tools for its analysis: use annotation, call variants, extract sub-graphs, visualize the graph, map reads, genotype individuals, and perform a GWAS on the graph. The different formats will also be presented.\r\n\r\nBy the end of the course, trainees will be familiar with the topic, and able to run the major tools made to build an exploit a pangenome graph.",
            "homepage": "https://bioinfo.genotoul.fr/index.php/events/pangenome/",
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                "INRAE for INRAE's staff: 300 € no VAT charged",
                "Academic non-INRAE for academic but non-INRAE: 340 € + 20% taxes (TVA)"
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                "http://edamontology.org/topic_0625"
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                "Cluster"
            ],
            "openTo": "Everyone",
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            "maxParticipants": 12,
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                    "id": 88,
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                    "id": 37,
                    "name": "MIAT - Mathématiques et Informatique Appliquées de Toulouse",
                    "url": "https://catalogue.france-bioinformatique.fr/api/organisation/MIAT%20-%20Math%C3%A9matiques%20et%20Informatique%20Appliqu%C3%A9es%20de%20Toulouse/?format=api"
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            "logo_url": "https://bioinfo.genotoul.fr/wp-content/uploads/bioinfo_logo-rvb-petit.png",
            "updated_at": "2026-04-20T08:06:46.497837Z",
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            "difficultyLevel": "Intermediate",
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        {
            "id": 350,
            "name": "Formation Principes FAIR dans un projet de bioinformatique",
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            "description": "Cette formation sur 3 jours est destinée à des bioinformaticiens et biostatisticiens souhaitant acquérir des compétences théoriques et pratiques sur les principes \"FAIR\" (Facile à trouver, Accessible, Interopérable, Réutilisable) appliqués à un projet d'analyse et/ou de développement.",
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            "keywords": [
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                "Snakemake",
                "Docker"
            ],
            "prerequisites": [
                "Linux - Basic Knowledge"
            ],
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            "accessConditions": "Academics",
            "maxParticipants": 14,
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                    "id": 83,
                    "name": "IGBMC",
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            "updated_at": "2023-12-20T15:44:00.254606Z",
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            "learningOutcomes": "A l'issue de cette formation, les participants pourront mettre en oeuvre les principes de la science reproductible : encapsuler un environnement de travail (Docker, Singularity), concevoir et exécuter des workflows (Snakemake), gérer des versions de code (Git), passer à l’échelle sur un cluster de calcul (Slurm), gérer des environnements logiciels (Conda) et assurer la traçabilité de leur analyse à l’aide de Notebooks (Jupyter).",
            "hoursPresentations": 10,
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            "id": 344,
            "name": "Analyses Single Cell RNA-seq (ScRNA-seq) avec R",
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            "description": "Cette formation introduira notamment la librairie Seurat permettant la manipulation et l'analyse de données Single Cell RNA-seq ainsi que la visualisation des résultats d'analyse\r\n\r\n- Rappels des concepts du séquençage Single Cell RNA-seq\r\n- Importation des données Single Cell dans R\r\n- Intégration de données Single Cell multiples\r\n- Quality Check et pré-traitement des données\r\n- Normalisation de données\r\n- Identification de marqueurs\r\n- Clustering et assignation cellulaire\r\n- Analyse différentielle des groupes cellulaires\r\n- Savoir intégrer les données de spatialisation\r\n- Savoir intégrer les données de trajectoire\r\n- Savoir intégrer les données de communication cellulaire\r\n- Savoir intégrer les données d'épigénétique (ATAC-seq)",
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                "R",
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                "R programming"
            ],
            "openTo": "Everyone",
            "accessConditions": "Maîtrise du langage R\r\nAvoir suivi le stage \"Langage R : introduction\" ou niveau équivalent.\r\nAfin de vérifier que votre maîtrise du langage R est suffisante pour pouvoir suivre ce stage, nous vous invitons à effectuer et à renvoyer le test téléchargeable\r\nhttps://cnrsformation.cnrs.fr/data/STG_23294_55153.docx",
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                    "id": 6,
                    "name": "CNRS formation entreprise",
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            "updated_at": "2023-08-31T09:19:56.754683Z",
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        },
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            "id": 290,
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            "shortName": "NGS-analysis-cli",
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                "http://edamontology.org/topic_3168",
                "http://edamontology.org/topic_2269",
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            "id": 377,
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            "description": "The Toulouse Genotoul bioinformatics platform, in collaboration with the Genotoul Biostatistics platform, and the MIAT unit, organize a 3,5 days long training course for bio-informaticians and biologists aiming at learning sequence analysis. It focuses on (protein coding) gene expression analysis using reads produced by ‘RNA-Seq’. This training session is designed to introduce sequences from ‘NGS’ (Next Generation Sequencing), particularly Illumina platforms (HiSeq). You will discover the standards file formats, learn about the usual biases of this type of data and run different kinds of analyses, such as spliced alignment on a reference genome, novel gene and transcript discovery, expression quantification of coding genes and transcripts. Finally you will be able to extract the differentially expressed genes.",
            "homepage": "https://bioinfo.genotoul.fr/index.php/events/rnaseq-alignment-transcripts-assemblies-statistics/",
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                "Non-academic: 550€ + 20% taxes (TVA)",
                "Academic but non-INRAE: 170 € + 20% taxes (TVA)",
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            ],
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                "http://edamontology.org/topic_3308"
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            "id": 360,
            "name": "Modélisation in silico de structures 3D de protéines. Prédiction de mutations, de fixation de ligands",
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            "description": "Objectifs pédagogiques\r\nA l’issue de la formation, les stagiaires connaîtront les principales fonctionnalités du logiciel PyMOL. Ils seront capables de les appliquer pour visualiser leur système biologique d’intérêt, et d’effectuer des commandes basiques d’identification de poches catalytiques, de profilage de surface électrostatique, et de mutations d’acides aminés.\r\n\r\nAussi, ils connaîtront les bases et les outils de bioinformatique structurale et seront autonomes pour effectuer des modèles de protéines par prédiction (Alphafold2), calculer les meilleures poses de fixation de leur(s) ligand(s) (Autodock4) et reconstruire l’éventuel assemblage biologique.\r\n\r\nBonus : Ils s’approprieront ces outils avec une demi-journée dédiée à la modélisation de leur système d’étude : protéines, interactions protéines/ADN, arrimage de ligand, etc.\r\n\r\nProgramme\r\nVisualiser :\r\n* Maîtriser les bases de la visualisation des protéines en 3D avec PyMOL.\r\nComprendre :\r\n* Analyser des structures 3D de protéines (RX ou RMN).\r\n* Identifier des homologues avec HHpred.\r\n* Modéliser par prédiction sa protéine d’intérêt avec Alphafold2.\r\nPrédire :\r\n* Savoir calculer des meilleures poses de ligands avec Autodock.\r\n* Prédir et modéliser les mutations in silico.\r\n\r\n- Points forts et limites des différents outils\r\n- ️“hand- on tutorials”\r\n- Plus une session dédiée : «bring your own protein»",
            "homepage": "https://documents.migale.inrae.fr/trainings.html",
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            ],
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            ],
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                "2D/3D",
                "Protein/protein interaction modelisation"
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            "audienceRoles": [
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                "Bioinformaticians"
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            "learningOutcomes": "A l’issue de la formation, les stagiaires connaîtront les principales fonctionnalités du logiciel PyMOL. Ils seront capables de les appliquer pour visualiser leur système biologique d’intérêt, et d’effectuer des commandes basiques d’identification de poches catalytiques, de profilage de surface électrostatique, et de mutations d’acides aminés.\r\n\r\nAussi, ils connaîtront les bases et les outils de bioinformatique structurale et seront autonomes pour effectuer des modèles de protéines par prédiction (Alphafold2), calculer les meilleures poses de fixation de leur(s) ligand(s) (Autodock4) et reconstruire l’éventuel assemblage biologique.\r\n\r\nBonus : Ils s’approprieront ces outils avec une demi-journée dédiée à la modélisation de leur système d’étude : protéines, interactions protéines/ADN, arrimage de ligand, etc.",
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            "id": 319,
            "name": "Exploration de la Diversité Taxonomique  des Ecosystèmes par Metabarcoding",
            "shortName": "",
            "description": "En matière de prospectives scientifiques, l’INSU OA, le CNRS et l’IRD ambitionnent de caractériser la biodiversité environnementale afin d’étudier l’impact du changement global sur les milieux et de l’anthropisation de la planète. Ces enjeux nécessitent l’acquisition de connaissances sur la biodiversité pour répondre aux grands défis planétaires (e.g. modéliser, anticiper, prévenir les catastrophes écologiques), aux objectifs de développement durable, et contribuer aux grandes transitions de la société dans un contexte de changement climatique.\r\n\r\nLe metabarcoding est aujourd’hui une des approches incontournable dans la description des écosystèmes pour répondre à ces enjeux scientifiques; elle offre une caractérisation exhaustive de la diversité taxonomique (composition en espèces et abondances) d’un écosystème via le séquençage massif de marqueurs d’intérêts (e.g. ARN ribosomaux 16S, 18S, gène COX, …) et le post-traitement bio-informatique des données générées.\r\n\r\nL’Action Nationale de Formation CNRS-INSU MetaBioDiv, portée par l’Institut Méditerranéen d’Océanologie (Armougom F., MIO) et la Délégation Régionale Côte d’Azur CNRS (DR20, Pierrette Finsac), propose à la communauté scientifique une formation sur la caractérisation de la biodiversité taxonomique d’écosystèmes (procaryotes et micro-eucaryotes) par le prisme du séquençage haut-débit Illumina (Miseq) et du traitement bio-informatique associé (outils R sous Rstudio).",
            "homepage": "https://anfmetabiodiv.mio.osupytheas.fr",
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            "description": "In an effort to inform members of the research community about our annotation methods, to provide training for collaborators and other scientists who use the MicroScope platfom, and to inform scientific public on the analysis available in PkGDB (Prokaryotic Genome DataBase), we have developed a 4.5-day course in Microbial Genome Annotation and Comparative Analysis using the MaGe graphical interfaces.\r\n\r\nThis course will familiarize attendees with LABGeM’s annotation pipeline and the manual annotation software MaGe (Magnifying Genome) . No specific bioinformatics skill is required: detailed instruction on the algorithm developed in each annotation methods can be found in specific training courses on «Genomic sequences analysis». Here we focus on the general idea behind each method and, above all, the way you can interpret the corresponding results and combine them with other evidences in order to change or correct the current automatic functional annotation of a given gene, if necessary.\r\n\r\nThis course will also describe how to perform effective searches and analysis of procaryotic data using the graphical functionalities of the MaGe’s interfaces. Because of the numerous pre-computation available in our system (results of “common” annotation tools, synteny with all complete bacterial genomes, metabolic pathway reconstruction, fusion/fission events, genomic islands, …), many practical exercises allow attendees to get familiar with the use the MaGe graphical interfaces in order to efficiently explore these sets of results.",
            "homepage": "https://labgem.genoscope.cns.fr/professional-trainings/microscope-professional-trainings/training-annotation-analysis-of-prokaryotic-genomes-using-the-microscope-platform/",
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            ],
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                "http://edamontology.org/topic_3301",
                "http://edamontology.org/topic_0797"
            ],
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            "prerequisites": [
                "Licence"
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                    "id": 15,
                    "name": "Laboratory of Bioinformatics Analyses for Genomics and Metabolism",
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            ],
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                    "id": 67,
                    "name": "University Paris-Saclay",
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            "updated_at": "2025-12-09T09:10:02.012461Z",
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                "Life scientists",
                "Biologists",
                "Curators"
            ],
            "difficultyLevel": "Intermediate",
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            "hoursPresentations": null,
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                "https://catalogue.france-bioinformatique.fr/api/event/745/?format=api",
                "https://catalogue.france-bioinformatique.fr/api/event/507/?format=api",
                "https://catalogue.france-bioinformatique.fr/api/event/577/?format=api",
                "https://catalogue.france-bioinformatique.fr/api/event/576/?format=api",
                "https://catalogue.france-bioinformatique.fr/api/event/659/?format=api",
                "https://catalogue.france-bioinformatique.fr/api/event/658/?format=api",
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        },
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            "id": 301,
            "name": "MIAPPE, Minimum Information About Plant Phenotyping Experiments",
            "shortName": "MIAPPE",
            "description": "The Minimal Information About Plant Phenotyping Experiments (MIAPPE, www.miappe.org) standard has been designed by ELIXIR, EMPHASIS and Bioversity international to guide plant scientist in the management of experimental data. Furthermore, since genetic studies relies on the integration and the linking between phenotype and genotype datasets, relevant section of MIAPPE are beginning to be used for genotyping standards.\r\nThis formation will cover a general introduction of the MIAPPE principles and some examples to illustrate different use cases on the usage of MIAPPE for plant phenotyping data standardization.",
            "homepage": "",
            "is_draft": false,
            "costs": [
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            ],
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                "http://edamontology.org/topic_3572",
                "http://edamontology.org/topic_0219",
                "http://edamontology.org/topic_0625",
                "http://edamontology.org/topic_0780"
            ],
            "keywords": [
                "Données"
            ],
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            ],
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                    "name": "URGI - US1164",
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