Handles creating, reading and updating training events.

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            "name": "RNASeq analyses (using Galaxy and TOGGLe)",
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            "description": "This course offers an introduction to RNASeq analyses using two different workflow management systems: Galaxy and TOGGLe. This includes reference-based mapping, estimates of transcript levels, differential expression (DE) analyses, visualization of statistics results.\r\nPrerequisites\r\nWorkflow management system (Galaxy, TOGGLe)\r\n\r\nProgram\r\nMapping of RNASeq against a transcriptome reference with kallisto (Galaxy)\r\nMapping of RNASeq against an annotated genome reference with TopHat (TOGGLe)\r\nDifferential expression analysis using EdgeR and DESeq2\r\nPlots, clustering, co-expression network: degust, WGCNA\r\n\r\n\r\nLearning objectives\r\nManipulate packages/tools available for searching DE genes\r\nThink about different normalisation methods\r\nDetect differentially expressed genes\r\nCompare results between two approaches\r\n\r\n\r\nInstructors\r\nAlexis Dereeper - alexis.dereeper@ird.fr\r\nSebastien Cunnac - sebastien.cunnac@ird.fr\r\nSebastien Ravel - sebastien.ravel@cirad.fr\r\nChristine Tranchant  - christine.tranchant@ird.fr",
            "homepage": "https://southgreenplatform.github.io/trainings//rnaseq/",
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                "Free"
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            "id": 29,
            "name": "Initiation à Python",
            "shortName": "",
            "description": "\nObjectifs\n\nInitiation à la programmation.\nRéalisation de tâches simples d'extractions d'informations.\nIdentifier les possibilités offertes par l'écriture de quelques lignes de codes.\n \n\n \n \n \n \nProgramme\n\n• Présentation de Python\n• Variables Python\n• Structures de contrôle\n• Réalisation de programmes simples\n• Gestion de fichiers\n• Fonctions\nIllustration avec des exercices de manipulation de fichiers de séquences\n",
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            "id": 79,
            "name": "Phylogénie moléculaire",
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            "description": "\nLes objectifs sont :\n1. Acquérir des connaissances théoriques et pratiques en phylogénie moléculaire.\n2. Être autonome dans la conduite d'une analyse phylogénétique.\n3. Maîtriser le choix, le paramétrage et l'exploitation des résultats des programmes de phylogénie.\nhttps://cnrsformation.cnrs.fr/\n\n",
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                "Evolution and Phylogeny",
                "Molecular evolution",
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                "Selection Detection",
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                "Phylogenomics",
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            "name": "Initiation à R",
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            "description": "\nObjectifs\n\n- Présenter le langage de programmation R et ses principes.\n- Utiliser les principales fonctionnalités de ce langage pour effectuer des calculs mathématiques, statistiques ou des représentations graphiques.\n- Attention : ce module n'est ni un module de statistique, ni un module d'analyse statistique des données.\n\nProgramme\n\n- Structures et manipulation de données.\n- Principaux éléments du langage de programmation (boucle, fonctions…).\n- Différentes représentations graphiques de données/résultats (plot, histogramme, boxplot).\n",
            "homepage": "http://migale.jouy.inra.fr/",
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            "name": "Formation de chercheurs",
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            "description": " lors de Workshop (organisation de séances de travaux pratiques)\n pour des formations individuelles\n",
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                "Metagenomics"
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            "name": "Master Bioinfo Toulouse ",
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            "homepage": "",
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            "id": 349,
            "name": "Reproducible Research",
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            "description": "The following topics and tools are covered in the course:\r\n\r\n    Data management\r\n    Project organisation\r\n    Git\r\n    Conda\r\n    Snakemake\r\n    Nextflow\r\n    R Markdown\r\n    Jupyter\r\n    Docker\r\n    Singularity\r\n\r\nAt the end of the course, students should be able to:\r\n\r\n    Use good practices for data analysis and management\r\n    Clearly organise their bioinformatic projects\r\n    Use the version control system Git to track and collaborate on code\r\n    Use the package and environment manager Conda\r\n    Use and develop workflows with Snakemake and Nextflow\r\n    Use R Markdown and Jupyter Notebooks to document and generate automated reports for their analyses\r\n    Use Docker and Singularity to distribute containerized computational environments",
            "homepage": "https://southgreenplatform.github.io/training_reproducible_research/",
            "is_draft": false,
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            "name": "Introduction à l'analyse de données de séquençage avec contrôle qualité et alignement sur un génome de référence avec Galaxy",
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            "description": "L’objectif de cette formation est de se familiariser avec les premières étapes communes à toutes les analyses de données de séquençage : le contrôle qualité des données et l’alignement sur un génome de référence. Nous proposons au personnel non-bioinformaticien de les accompagner dans la prise en main des ces étapes d’analyses en utilisant la plateforme de bio-analyse Galaxy. \r\n\r\nAprès une introduction aux données de séquençage, une session pratique sur la plateforme Galaxy couvrira comment :\r\n- évaluer la qualité de données de séquençage,\r\n- améliorer la qualité de données de séquençage\r\n- aligner des données sur un génome de référence",
            "homepage": "",
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                "Free to academics"
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                "http://edamontology.org/topic_0091",
                "http://edamontology.org/topic_0102"
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                "Galaxy",
                "Mapping"
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            "prerequisites": [
                "Galaxy - Basic usage"
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            "openTo": "Internal personnel",
            "accessConditions": "Formation ouverte au personnel de l’UCA & Associés\r\nAvoir un ordinateur portable et un accès wifi eduroam\r\nAvoir un compte sur la plateforme Galaxy (Faire une demande le cas échéant sur hub.mesocentre.uca.fr)\r\nÊtre familier avec Galaxy",
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                    "id": 96,
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                    "id": 127,
                    "name": "Quality Control with Galaxy",
                    "url": "https://catalogue.france-bioinformatique.fr/api/trainingmaterial/Quality%20Control%20with%20Galaxy/?format=api"
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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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            "name": "Python avancé",
            "shortName": "",
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            "name": "Galaxy: metagenomic: sequence analysis of amplicons from MiSeq and 454 sequencing with FROGS with Galaxy first step and statistics",
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            "description": "This training session, organized by Bioinfo Genotoul, Sigenae, NED (GenPhySE) and TWB, is designed to help you to deal with NGS data of 16S, 18S ... DNA produced with MiSeq from Illumina and Roche 454 technologies in the Galaxy workbench.\nYou will discover how to use our Galaxy instance, clean reads, clusterize them, do the taxonomic affiliation and perform statistics to interpret your results.\nPrerequisites: knowledge of R or in another programming language\n",
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            "id": 73,
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            "description": "As the command line training but with Galaxy. Organized jointly by the Sigenae and the Bioinfo Genotoul platforms.\n",
            "homepage": "http://bioinfo.genotoul.fr/index.php/events/rnaseq-alignment-and-transcripts-ass…",
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            "id": 111,
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            "id": 72,
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            "homepage": "http://bioinfo.genotoul.fr/index.php/events/reads-alignment-and-small-size-varia…",
            "is_draft": false,
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                "Priced"
            ],
            "topics": [],
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                "NGS Data Analysis",
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                "Genomics (DNA-seq)",
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            "topics": [],
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            "id": 110,
            "name": "Galaxy : first step",
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            "id": 78,
            "name": "Formation des post-doctorants",
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