Handles creating, reading and updating training events.

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            "name": "Improve your command line skills by learning a few words of Perl",
            "shortName": "",
            "description": "This “Perl one-liners” training session is organized by the Sigenae platform. Perl one-liners are small and awesome Perl programs that fit in a single line of code and perform many operations such as replacing of text, spacing, deleting, calculation, manipulation in files and many more. This training will allow you to discover the power of Perl on the command line and learn how to use it to automate your file manipulations and command line generation with classical file formats such as tabulated text, fastq, sam/bam, and vcf.\r\n\r\nThis training lasts one day and is focused on practice. It consists of 3 parts with a large variety of exercises:\r\n\r\nIntroduction to Perl and its characteristics: Perl is a widely used programming language for data processing and task automation. We will introduce the main characteristics of Perl and discuss why it is particularly suited for biologists who want to manipulate files and generate command lines.\r\nPerl on the command line: we will show how to use Perl on the command line to perform common tasks, such as searching and replacing strings, merging files, and loop over lists of files.\r\nConcrete examples: we will present several concrete examples drawn from biology, such as extracting information from genomic sequence files, converting files between different formats, and generating command lines for data biology tools.\r\n \r\nThe session will take place in the room ‘salle de formation MIAT’ at INRAE center of Toulouse-Auzeville.",
            "homepage": "https://bioinfo.genotoul.fr/index.php/events/onelineperl/",
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                "Non-academic: 550€ + 20% taxes (TVA)",
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            "name": "HOW TO RUN A NF-CORE NEXTFLOW WORKFLOW ON GENOTOUL ?",
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            "description": "This training session is organized by the Genotoul bioinfo platform and aims at learning nf-core workflow submission, error understanding, resuming jobs and ressource reservation. We will present and practice:\r\n\r\nthe Nextflow software\r\nthe nf-core community and pipelines\r\nWhat is a singularity image ?\r\nWhere are installed the nf-core workflows ? Which version do I use ?\r\nHow to run a workflow and which config file is used ?\r\nWhich kind of error I can get ?\r\nHow to resume failed jobs?\r\nHow to handle genome indexes ?\r\nHow to monitor my process and then well configure my workflow ?\r\nHow do you best adjust CPU and RAM reservations?\r\nThis is NOT a bioinformatic training on a particular workflow or a training on how to develop a workflow.\r\n\r\nThis training is focused on practice. It consists of several modules with a large variety of exercises:\r\n\r\nStart at 09:00 am\r\nEnd at 17:00 pm",
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            "name": "Annotation and analysis of prokaryotic genomes using the MicroScope platform",
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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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                    "name": "Laboratory of Bioinformatics Analyses for Genomics and Metabolism",
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            "updated_at": "2025-12-09T09:10:02.012461Z",
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            "name": "NGS data analysis on the command line",
            "shortName": "NGS-analysis-cli",
            "description": "This hands-on course will teach bioinformatic approaches for analyzing Illumina sequencing data. Our goal is to introduce the command line skills you need to make the most of your NGS data. \r\nDuring this 4-day training we will first introduce the Linux environment, shell commands and basic R scripting.  And then we will focus on two NGS data analyses -- small RNA-seq and RNA-seq -- based on published datasets from the model organism Arabidopsis thaliana",
            "homepage": "https://www.ibmp.cnrs.fr/bioinformatics-trainings/",
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            "updated_at": "2024-01-22T14:51:37.215331Z",
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            "learningOutcomes": "Applied Knowledge (Know-how):\r\n- Basic proficiency at the Linux command line prompt\r\n- Basic proficiency of R (environment, objects, graphs) \r\n- Next generation sequencing (NGS) file formats; reference genomes - Mapping NGS read data to reference genomes (bowtie, samtools)\r\n- Small RNA-seq analysis; epigenomics applications (ShortStack)\r\n- RNA-seq for transcriptomics; differential gene expression analysis (HISAT2, DESeq2) - Data wrangling and visualization in R (Rstudio, ggplot2)",
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            "id": 271,
            "name": "Bioinformatique pour le traitement de données de séquençage (NGS) : analyse de transcriptome",
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            "description": "OBJECTIFS\r\n- Comprendre les principes des méthodes d'analyse de données de séquençage à haut débit\r\n- Comprendre les résultats obtenus, les paramètres et leurs impacts sur les analyses\r\n- Savoir choisir et utiliser les principaux outils d'analyse\r\n- Être autonome pour utiliser un pipeline d'analyse\r\n- Savoir manipuler les fichiers de séquences : préparation et filtration\r\n- Savoir évaluer la qualité des données\r\n- Savoir analyser les résultats avec ou sans génome de référence\r\n\r\nPRÉREQUIS\r\n- Notions de base en informatique : fichiers, répertoire...\r\n- Notions du système linux et des lignes de commande\r\n- Niveau master \r\n\r\nPROGRAMME\r\n- Linux : commandes de base\r\n- Les données NGS : fichiers, manipulation de base, nettoyage\r\n- Mapping : principaux outils et pratique\r\n- Transcriptomique :\r\n. analyse de RNA-seq : expression différentielle des gènes / des ARNs (comptage et DESeq2) ; comparaison d'échantillons issus de conditions différentes\r\n. post-analyse : analyse GO, interrogation bases de connaissances (ex : KEGG), création de graphique (en R)\r\n. analyse couplée transcriptome / traductome",
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            "id": 332,
            "name": "SHORT-READ ALIGNMENT AND SMALL SIZE VARIANTS CALLING",
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            "description": "This training session, organized jointly with the Sigenae platform, is designed to introduce NGS data, in particular Illumina Solexa technologies with command line. You will discover the new sequence formats, the assembly formats and the known biases of these technologies. You will use mapping on reference genome software, polymorphisms detection with the GATK pipeline and alignment visualization software.\r\n\r\nThis training is focused on the practice. It consists of modules with a large variety of exercises:\r\n\r\nDay 1 (09:00 am to 12:30 am): Fastq format / Sequence quality. Read mapping.\r\nDay 1 (14:00 pm to 17:00 pm): SAM format. Visualisation.\r\nDay 2 (09:00 am to 17:00 am): Variant calling. VCF format. Variant annotation (SNPeff / SNPsift).\r\n \r\nThe session will take place in the room ‘salle de formation’ at INRAE center of Toulouse-Auzeville.\r\n\r\nPrerequisites: ability to use a Unix environment (see Unix training) and Cluster (see Cluster training).\r\n \r\nTool box: FastQC, BWA, Samtools, Picard tools, GATK, SnpSift / SnpEff, IGV.",
            "homepage": "https://bioinfo.genotoul.fr/index.php/events/alignment-and-small-size-variants-calling/",
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            "description": "This training session is designed to help you deal with the platform compute cluster and data banks. You will launch your first processing batch on the cluster and will learn how to track and manage them. Organized jointly by the Sigenae and bioinfo genotoul platforms.",
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                "https://catalogue.france-bioinformatique.fr/api/event/610/?format=api",
                "https://catalogue.france-bioinformatique.fr/api/event/753/?format=api",
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                "https://catalogue.france-bioinformatique.fr/api/event/668/?format=api",
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            "name": "Molecular Phylogeny - Level 1",
            "shortName": "Phylogénie moléculaire - Niveau 1",
            "description": "OBJECTIF\r\n- Savoir inférer un arbre phylogénétique et l'interpréter\r\n\r\nPRÉREQUIS\r\n- Savoir ce à quoi correspondent des séquences génétiques homologues\r\n- Avoir déjà utilisé les logiciels de base en bioinformatique\r\n- Connaître les notions de base en statistiques (tests, lois probabilistes usuelles, méthodes simples d'estimation de paramètres)\r\n- Avoir des notions de programmation\r\n\r\nPROGRAMME\r\n- Lignes de commandes Linux\r\n- Le format Newick\r\n- Dessin d'arbres\r\n- Alignements multiples et nettoyage\r\n- Modèles d'évolution\r\n- Choix de modèles\r\n- Définitions et propriétés des arbres\r\n- Méthodes de parcimonie\r\n- Méthodes de distance\r\n- Maximum de vraisemblance\r\n- Reconstruction phylogénétique Bayésienne\r\n- Bootstraps et autres supports de branches",
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            "description": "Présentation de la formation\r\nA la demande du laboratoire d'Ecologie Microbienne de Lyon, l'équipe Formation de l'IFB organise une session de formation de deux jours sous Galaxy pour l'analyse de données de métagénomique et métatranscriptomique.\r\n\r\nObjectifs pédagogiques\r\nA la fin de cette formation, les participants auront \r\n\r\n- acquis des connaissances théoriques et pratiques sur les méthodes et objectifs d'une analyse en métagénomique et métatranscriptomique\r\n\r\n - réalisé une analyse de données de données métataxonomique, métagénomique shotgun et métatranscriptomique sous l'environnement Galaxy et sur des données fournies par l'équipe pédagogique\r\n\r\n- choisi et initié une analyse sur un jeu de données de leur choix en bénéficiant de l'encadrement de l'équipe pédagogique (Bring Your Own Data sessions)",
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                "http://edamontology.org/topic_3941",
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            "id": 272,
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            "shortName": "Phylogénie moléculaire - Niveau 2",
            "description": "OBJECTIF\r\n- Être capable de tester des hypothèses et d'ajuster des modèles permettant de comprendre l'évolution à l'échelle moléculaire\r\n\r\nPRÉREQUIS\r\n- Avoir déjà utilisé les logiciels de base en phylogénie moléculaire\r\n- Maîtriser les notions de base en statistiques (tests statistiques, principe du bootstrap, intervalles de confiances, etc.) et de probabilités (probabilités jointes / conditionnelles, théorème de Bayes, etc.)\r\n- Maîtriser un langage de programmation\r\n- Notions de phylogénie moléculaire\r\nAvoir suivi le stage \"Phylogénie moléculaire - formation de base\" ou niveau équivalent \r\n\r\nPROGRAMME\r\n- Phylogénétique et génétique des populations\r\n- Détection de sélection positive au sein de séquences codantes\r\n- Datation moléculaire : intégrer fossiles et molécules\r\n- Phylogénomique\r\n- Super-arbres et super-matrices, réconciliations d'arbres\r\n- Visualisation de l'information en phylogénie\r\n- Placement phylogénétique\r\n- Bases d'épidémiologie (modèles en compartiments, ODE, applications, etc)\r\n- Simulations selon une variété de modèles épidémiologiques\r\n- Phylodynamique : combiner épidémiologie et évolution",
            "homepage": "",
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            "costs": [
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                "1200 €"
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            "keywords": [
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                "Phylogenomics"
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            "id": 344,
            "name": "Analyses Single Cell RNA-seq (ScRNA-seq) avec R",
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                "R",
                "NGS Sequencing Data Analysis"
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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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            "name": "Pipelines et méthodes bioinformatiques pour l'analyse de données de séquençage (NGS)",
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            "description": "Bilille propose des formations en partenariat avec CNRS Formation Entreprises à destination des chercheur-euse-s, enseignant-e-s-chercheur-euse-s, ingénieur-e-s, technicien-ne-s en biologie et médecine. \r\n\r\nObjectifs :\r\n- Comprendre les principes des méthodes d'analyse de données de séquençage à haut débit (NGS)\r\n- Comprendre les paramètres des méthodes et leur impact sur les résultats\r\n- Apprendre à identifier les outils d'analyse en fonction du jeu de données\r\n- Être autonome pour analyser des données dans un gestionnaire de workflow comme Galaxy\r\n- Savoir manipuler les fichiers de lecture de séquençage : extraction, préparation, filtrage / nettoyage\r\n- Savoir évaluer la qualité des données de séquençage\r\n- Savoir analyser des données de séquençage de génomes (avec ou sans génome de référence) et prendre du recul sur le protocole expérimental\r\n- Savoir analyser des données de RNA-seq (avec ou sans génome de référence) et prendre du recul sur le protocole expérimental",
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