Handles creating, reading and updating training events.

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            "description": " \n\nLe programme de cette introduction à Galaxy est le suivant : présentation de Galaxy, se connecter à l’instance toulousaine, commencer à utiliser certains outils bioinformatiques standards, la gestion des fichiers dans galaxy. Découvrir les bonnes pratiques dans Galaxy. Organisée en collaboration avec la plateforme Bioinfo Genotoul.\n\n\n",
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            "description": "This course offers an introduction on how to work with HPC Southgreen clusters. It is intended for new users, with the goals of improving user productivity and minimizing the obstacles. The HPC Southgreen cluster are presented, together with the tools to be able to use it. Module load notion, interactive usage and batch jobs submittions will be developped.\nPrerequisites\nLinux Basics\n\nProgram\nIntroduction to HPCs architecture\nDiscover Sun grid Engine (SGE)\nData Management on clusters\nInteractive Usage\nSubmit batch jobs\n\n\nLearning objectives\nAfter this course, participants should be able to:\nUse the HPC resources interactively\nManage data copies\nUse module load\nsubmit batch jobs\n\n\nInstructors\n\n\nNdomassi Tando (NT) - ndomassi.tando@ird.fr\nBertrand Pitollat(BP) - bertrand.pitollat@cirad.fr\nAlexis Dereeper (AD) - alexis.dereeper@ird.fr​\n\n",
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            "description": "L’objectif est de se familiariser avec l’interface utilisateur de Galaxy. \r\n\r\nAprès une introduction à Galaxy, une session pratique sur la plateforme Galaxy couvrira comment :\r\n- Importer des données\r\n- Identifier des outils\r\n- Faire une analyse\r\n- Gérer un historique\r\n- Créer un workflow",
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            "openTo": "Internal personnel",
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            "updated_at": "2024-02-08T10:47:23.782242Z",
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            ],
            "learningOutcomes": "At the end of the tutorial, learners would be able to:\r\n- Assess short reads FASTQ quality using FASTQE 🧬😎 and FastQC\r\n- Assess long reads FASTQ quality using Nanoplot and PycoQC\r\n- Perform quality correction with Cutadapt (short reads)\r\n-  Summarise quality metrics MultiQC\r\n- Process single-end and paired-end data\r\n- Define what mapping is\r\n- Perform mapping of reads on a reference genome\r\n- Evaluate the mapping output",
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        {
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            "name": "Metabarcoding analyses (using FROGS in Galaxy and Phyloseq)",
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            "description": "This course offers an introduction to metabarcoding analyses at two different levels/steps: bioinformatics with FROGS pipeline in the Galaxy environment, biostatistics with PhyloSeq R package. This includes preprocessing, clustering and OTU picking, taxonomic assignation, estimation of diversity, visualization of statistics results.\r\nPrerequisites\r\nGalaxy, R knowledge\r\n\r\nProgram\r\nIntroduction to metagenomics and metabarcoding\r\nPre-processing, Clustering, taxonomic affiliation (FROGS)\r\nHandling and visualizing OTU table using PhyloSeq R package (PhyloSeq)\r\n\r\n\r\nLearning objectives\r\nManipulate tools available for metabarcoding analysis\r\nStudy sample diversity by using NGS and post-NGS analysis tools\r\nVisualize diversity metrics in metabarcoding approach​\r\n\r\n\r\nInstructors\r\nJulie Orjuela - julie.orjuela@ird.fr\r\nFlorentin Constancias - florentin.constancias@cirad.fr\r\nAlexis Dereeper - alexis.dereeper@ird.fr",
            "homepage": "https://southgreenplatform.github.io/trainings//metabarcoding/",
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