Handles creating, reading and updating training events.

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            "description": "\nObjectifs\n\nAller plus loin avec Perl afin d’être autonome pour des manipulations complexes visant à extraire et reformater des données issues de fichiers texte.\n\n \n \n \n \nProgramme\n\n- Expressions régulières\n- Fonctions\n- Prise en main de Bioperl\n \n \nIllustration avec des exercices de manipulation de fichiers de séquences et de fichiers de résultats d’outils bionformatiques.\n \n",
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            "description": "This course offers to develop and enhance advanced Linux shell command line and scripting skills for the processing and analysis of NGS data. We will work on a HPC server and use linux powerful commands to allow to analyze big amount of biological data.",
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            "description": "This course introduces the 2 commonly used workflow managers in the South Green Bioformatics platform, both in a theoretical and practical way, with hands-on practice sessions. It will help you to quickly develop and run your own pipelines using these tools through an graphical user or command-line interface.\n \nPrerequisites\nPrior knowledge of workflow managers not necessary Basic knowledge of Linux (Linux for dummies required) - TOGGLe practical\n\nProgram\nWhy using a workflow manager to analyse data ?\nHow to perform an analysis ?\nHow to create your own workflow ?\nHow to execute it?\nUse case\n\n\nLearning objectives\nExplaining what Workflow Managers are,\n\tin which way they differ from each other.\nHow you can use them in your research.\nCreating your own workflow\nAnalysing your NGS data with Galaxy and TOGGLe\n\n\nInstructors\nAlexis Dereeper (AD) - alexis.dereeper@ird.fr\nSebastien Ravel (SR) - sebastien.ravel@cirad.fr\nChristine Tranchant (CT) - christine.tranchant@ird.fr\n\n",
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            "description": "\nL'utilisation de plus en plus répandue de techniques d’imagerie et de séquençage à haut-débit en biologie est en train de révolutionner les sciences du vivant et de modifier en profondeur leurs pratiques. Dans ce contexte, des outils statistiques sont développés pour permettre d’analyser ces données de hautes dimensions, et la maîtrise de ces outils devient de plus en plus nécessaire pour produire des résultats de bonne qualité. Ce cours de 4 semaines couvrira les étapes nécessaires pour mettre en place un processus d’analyse de données, depuis la planification de l’expérience jusqu’à la fouille des données en passant par l’échantillonnage, les test d’hypothèses, la modélisation statistique etc.\nCe cours s’adresse en priorité aux étudiants de première année de thèse de l’Institut Pasteur. Tout étudiant en thèse sera automatiquement inscrit à ce cours, mais les élèves de 2e année, de 3e année ou les post-doctorants peuvent également s’inscrire, dans la limite des places disponibles. Il est à noter que le cours est obligatoire pour les étudiants de 1ère année. Des dispenses partielles ou totales sont possibles pour les étudiants qui ont déjà des connaissances en statistique, en mathématique ou en physique. Le cours déroulera sur 4 semaines, 4 jours par semaine, trois heures par jour. Chaque séance de trois heures alternera cours magistral et mise en pratique. Il y aura deux sessions : la première commencera le 22 octobre 2018 et la deuxième le 14 janvier 2019.\nChacune de ces deux sessions sera précédée d’une séance d’introduction à l’informatique. Cette séance proposera des notions d’architecture de l’ordinateur, de système d’organisation des fichiers et de format de fichiers. Chaque session sera également suivie d’un cours optionnel sur l’analyse et le traitement des images.\nPour plus d’information, ainsi que pour les inscriptions au module optionnel et les demandes d’exemption, rendez-vous sur la page du cours : https://c3bi.pasteur.fr/introduction-to-data-analysis-2018-19/\nThèmes abordés\nLe module d’analyse de données couvrira un large champ de notions nécessaires aux étudiants pour planifier leurs expériences, analyser et explorer leurs données, interpréter les résultats et générer des figures à des fins de publication. Il abordera des notions de base en statistique, dont les analyses uni- et multivariées, les analyses descriptives, les distributions statistiques usuelles utilisées en biologie, ainsi que les tests d’hypothèses. Les exercices et travaux pratiques seront réalisés avec R et RStudio. Plusieurs séances seront consacrées à une introduction à l’utilisation du langage de programmation R avant d’aborder les notions de statistiques et d’analyse de données.\nLe module d’analyse d’images introduira les principes de base de l’analyse d’image, et portera plus particulièrement sur l’extraction d’information quantitative d’images de microscopie. Ce cours est destiné aux personnes ayant peu ou pas d’expérience en analyse d’image. Il sera très orienté sur la pratique : des cours magistraux de courte durée seront immédiatement suivis de sessions pratiques. Il aidera à la fois les microscopistes débutants et experts qui n’ont jamais eu de formation concrète en analyse d’image.\n \n\n",
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            "description": "Program\r\n\r\n*  Handling mapping tools suitable for ILLUMINA and ONT data (bwa, minimap2)\r\n*  SNP detection from mapping of short reads against a reference genome: SNP calling, filters and SNP annotation. Examples of possible studies based on SNP arrays\r\n* Detecting Structural Variations (SV) in short and long reads (breakdancer, sniffle)\r\n* SV detection from genome assembly and comparison (minimap2, nucmer, assemblytics, siry)",
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            "name": "Bioinformatics of protein--protein interactions for wet lab scientists",
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            "description": "Understanding physical and functional interactions between molecules in living systems is crucial in many biological processes. Several powerful methods and techniques have been developed to generate molecular interaction data, focusing mainly on protein­-protein interactions (PPIs). In particular, PPIs involving partially or completely unstructured regions are building blocks of regulatory and signalling networks that control cell response to external and internal cues. Exploring these interactions may help understanding a protein’s function and behavior, predicting biological processes that a protein of unknown function is involved in, and characterising protein complexes that can be used to modulate or perturb known biological processes and pathways.\n",
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            "name": "« STATIMAGE», Statistiques pour l’imagerie de microscopie",
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            "description": "\n\n\nL’objectif de cette formation est de donner aux ingénieurs des plates-formes d’imagerie cellulaire et aux personnes qui utilisent ces technologies, des outils statistiques adaptés aux problématiques du domaine. Dans le but de traiter les grandes séries d’images acquises selon les diverses modalités, il est proposé de renforcer la maîtrise des outils statistiques pour une analyse pertinente mais aussi pour une optimisation des acquisitions.\nLa formation se déroulera du lundi midi au mercredi après-midi pour 2 demi-journées de cours (rappel des notions de bases, modélisation, plans d’expériences) et 3 demi-journées de travaux pratiques/dirigés.\n\n\n\n",
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            "description": "This training session is designed to help you to deal with small RNA sequences produced from the SGS (Second Generation Sequencing) technology particularly Illumina platforms (HiSeq). You will discover sequence file formats, learn about expression profiles of miRNA and other small non coding RNA and run different kind of analysis such as reads cleaning, alignment on a reference genome, detection and annotation of new and known miRNA, and expression quantification. Organized jointly by the Sigenae and bioinfo genotoul platforms.\n",
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            "name": "Analyses bioinformatique et statistiques de données ChIP-seq sous Unix",
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            "id": 256,
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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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            "id": 30,
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            "description": "\nObjectifs\n\nEtre autonome pour des manipulations simples visant à extraire, reformater des données issues de fichiers texte.\n \n\n \n \n \n \nProgramme\n\n- Expressions régulières\n- Gestion des erreurs\n- Biopython\n- Réalisation de programmes simples\n",
            "homepage": "http://migale.jouy.inra.fr/",
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            "id": 257,
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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",
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