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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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The goal is to make you self-sufficient in using R for your own analyses.\r\n\r\n\r\nKey Highlights:\r\n\r\nSmall group sessions for interactive and personalized learning.\r\nHybrid mode with 3 in-person sessions and 7 remote sessions.\r\nHands-on practice with an individualized project presented at the end of each training course.\r\nTailored feedback on your own data.\r\nLimited spots available, registration is now open.","homepage":"https://inforbio.github.io/ioc_r_scrnaseq.html","is_draft":false,"costs":["Priced"],"topics":[],"keywords":[],"prerequisites":["none"],"openTo":"Everyone","accessConditions":"","maxParticipants":16,"contacts":["https://catalogue.france-bioinformatique.fr/api/userprofile/809/?format=json"],"elixirPlatforms":[],"communities":[],"sponsoredBy":[{"id":18,"name":"IBiSA","url":"https://catalogue.france-bioinformatique.fr/api/eventsponsor/IBiSA/?format=json"},{"id":19,"name":"Sorbonne Université","url":"https://catalogue.france-bioinformatique.fr/api/eventsponsor/Sorbonne%20Universit%C3%A9/?format=json"}],"organisedByOrganisations":[],"organisedByTeams":[],"logo_url":"https://github.com/InforBio/InforBio.github.io/blob/main/images/logoInforBio_fond_blanc.png?raw=true","updated_at":"2025-09-11T14:23:22.054252Z","audienceTypes":[],"audienceRoles":[],"difficultyLevel":"Novice","trainingMaterials":[],"learningOutcomes":"","hoursPresentations":30,"hoursHandsOn":null,"hoursTotal":null,"personalised":null,"event_set":[]},{"id":400,"name":"Interactive Online Companionship - SingleCell RNAseq Analysis with R Seurat 2026","shortName":"IOC - SingleCell","description":"InforBio offers online bioinformatics training tailored to the needs of research labs, with small group sessions to ensure personalized learning. 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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.","hoursPresentations":12,"hoursHandsOn":12,"hoursTotal":24,"personalised":null,"event_set":["https://catalogue.france-bioinformatique.fr/api/event/792/?format=json"]},{"id":345,"name":"Graphiques sous R avec ggplot2 / Graphics with R-ggplot2","shortName":"ggplot2","description":"Objectifs pédagogiques :\r\nÀ l’issue de la formation, les stagiaires connaîtront les principales fonctionnalités du package R « ggplot2 » et la démarche sous-jacente pour construire un graphique à partir d’un tableau de données. Ils seront capables de réaliser plusieurs types de représentations graphiques, telles que des nuages de points, des courbes, des histogrammes, des diagrammes en bâtons, des boxplots, des heatmaps, etc.  Les stagiaires pourront apporter leur propre tableau de données et pratiquer dessus en fin de formation. \r\n\r\nProgramme :\r\n- Principes généraux liés au package ggplot2 \r\n- Principales fonctions graphiques pour réaliser des nuages de points, des histogrammes, des boxplots, etc. \r\n- Principales fonctions pour jouer sur les coloriages en fonction d’une variable, sur les échelles de couleurs, sur les graduations, sur les représentations multiples, etc.","homepage":"https://migale.inrae.fr/trainings","is_draft":false,"costs":["Priced"],"topics":["http://edamontology.org/topic_0605","http://edamontology.org/topic_0091","http://edamontology.org/topic_2269"],"keywords":["Représentations graphiques"],"prerequisites":["Langage R de base"],"openTo":"Everyone","accessConditions":"","maxParticipants":10,"contacts":["https://catalogue.france-bioinformatique.fr/api/userprofile/769/?format=json"],"elixirPlatforms":[],"communities":[],"sponsoredBy":[],"organisedByOrganisations":[{"id":82,"name":"INRAE","url":"https://catalogue.france-bioinformatique.fr/api/organisation/INRAE/?format=json"},{"id":88,"name":"BioinfOmics","url":"https://catalogue.france-bioinformatique.fr/api/organisation/BioinfOmics/?format=json"}],"organisedByTeams":[{"id":10,"name":"MIGALE","url":"https://catalogue.france-bioinformatique.fr/api/team/MIGALE/?format=json"}],"logo_url":"https://migale.inrae.fr/sites/default/files/migale-orange_0.png","updated_at":"2024-01-18T12:50:47.605879Z","audienceTypes":[],"audienceRoles":[],"difficultyLevel":"Intermediate","trainingMaterials":[],"learningOutcomes":"À l’issue de la formation, les stagiaires connaîtront les principales fonctionnalités du package R « ggplot2 » et la démarche sous-jacente pour construire un graphique à partir d’un tableau de données. Ils seront capables de réaliser plusieurs types de représentations graphiques, telles que des nuages de points, des courbes, des histogrammes, des diagrammes en bâtons, des boxplots, des heatmaps, etc.","hoursPresentations":1,"hoursHandsOn":5,"hoursTotal":6,"personalised":null,"event_set":["https://catalogue.france-bioinformatique.fr/api/event/554/?format=json","https://catalogue.france-bioinformatique.fr/api/event/569/?format=json","https://catalogue.france-bioinformatique.fr/api/event/683/?format=json","https://catalogue.france-bioinformatique.fr/api/event/778/?format=json"]},{"id":357,"name":"Manipulation de données avec R, introduction à tidyverse","shortName":"Introduction à tidyverse","description":"Objectifs pédagogiques\r\nA l’issue de la formation, les stagiaires seront capables de :\r\n* utiliser les principales fonctions des packages dplyr et tidyr de l’écosystème du « tidyverse »\r\n* lire les données et les ranger dans un format « tidy »\r\n* manipuler les données : filtrer, sélectionner, trier, produire des résultats par groupe, fusionner plusieurs tables\r\n* mettre en forme et pivoter les tables de données\r\n\r\nProgramme\r\n* Principes du tidyverse\r\n* Principales fonctions de manipulation de données du package dplyr : ajouter de nouvelles variables, sélectionner des colonnes, filtrer des lignes, trier, grouper, fusionner des tables\r\n* Enchaînements des opérations à l’aide de « pipe »\r\n* Mise en forme, jointure et pivot de données avec le package tidyr\r\n* Mise en application sur un exemple d’analyse de données de transcriptomique.","homepage":"https://documents.migale.inrae.fr/trainings.html","is_draft":false,"costs":["Priced"],"topics":["http://edamontology.org/topic_0605"],"keywords":["R Language","Tidyverse"],"prerequisites":["Basic knowledge of R"],"openTo":"Everyone","accessConditions":"","maxParticipants":10,"contacts":["https://catalogue.france-bioinformatique.fr/api/userprofile/769/?format=json"],"elixirPlatforms":[],"communities":[],"sponsoredBy":[],"organisedByOrganisations":[{"id":82,"name":"INRAE","url":"https://catalogue.france-bioinformatique.fr/api/organisation/INRAE/?format=json"},{"id":88,"name":"BioinfOmics","url":"https://catalogue.france-bioinformatique.fr/api/organisation/BioinfOmics/?format=json"}],"organisedByTeams":[{"id":10,"name":"MIGALE","url":"https://catalogue.france-bioinformatique.fr/api/team/MIGALE/?format=json"}],"logo_url":"https://migale.inrae.fr/sites/default/files/migale-orange_0.png","updated_at":"2024-01-18T13:15:41.633863Z","audienceTypes":[],"audienceRoles":[],"difficultyLevel":"Intermediate","trainingMaterials":[],"learningOutcomes":"A l’issue de la formation, les stagiaires seront capables de :\r\n\r\nutiliser les principales fonctions des packages dplyr et tidyr de l’écosystème du « tidyverse »\r\nlire les données et les ranger dans un format « tidy »\r\nmanipuler les données : filtrer, sélectionner, trier, produire des résultats par groupe, fusionner plusieurs tables\r\nmettre en forme et pivoter les tables de données","hoursPresentations":2,"hoursHandsOn":10,"hoursTotal":12,"personalised":null,"event_set":["https://catalogue.france-bioinformatique.fr/api/event/700/?format=json","https://catalogue.france-bioinformatique.fr/api/event/579/?format=json","https://catalogue.france-bioinformatique.fr/api/event/782/?format=json"]},{"id":359,"name":"Comparaison de génomes microbiens","shortName":"Comparaison de génomes microbiens","description":"Objectifs pédagogiques\r\nConnaître les concepts et les principales méthodes bioinformatiques pour comparer un jeu de données de génomes microbiens. Construire et évaluer la qualité d’un jeu de données. Savoir mettre en œuvre une comparaison de génomes et en interpréter les résultats.\r\n\r\nProgramme\r\n* Construction d’un jeu de données :\r\n* Téléchargement de données publiques\r\n* Evaluation de la qualité\r\n* Caractérisation de la diversité génomique\r\n* Stratégies de comparaison :\r\n* Construction de famille de protéines\r\n* Alignement de génomes complets\r\n* Analyse des résultats :\r\n   o Notion de core et pan-génome\r\n   o Notions élémentaires de phylogénomique\r\n   o Visualisation et interprétation des résultats\r\n* Mise en pratique sur un jeu de données bactériens, utilisation des logiciels dRep et Roary sous Galaxy.","homepage":"https://documents.migale.inrae.fr/trainings.html","is_draft":false,"costs":["Priced"],"topics":["http://edamontology.org/topic_3299","http://edamontology.org/topic_0622"],"keywords":["Comparative genomics"],"prerequisites":[],"openTo":"Everyone","accessConditions":"","maxParticipants":10,"contacts":["https://catalogue.france-bioinformatique.fr/api/userprofile/769/?format=json"],"elixirPlatforms":[],"communities":[],"sponsoredBy":[],"organisedByOrganisations":[{"id":82,"name":"INRAE","url":"https://catalogue.france-bioinformatique.fr/api/organisation/INRAE/?format=json"},{"id":88,"name":"BioinfOmics","url":"https://catalogue.france-bioinformatique.fr/api/organisation/BioinfOmics/?format=json"}],"organisedByTeams":[{"id":10,"name":"MIGALE","url":"https://catalogue.france-bioinformatique.fr/api/team/MIGALE/?format=json"}],"logo_url":"https://migale.inrae.fr/sites/default/files/migale-orange_0.png","updated_at":"2024-01-18T14:13:49.810934Z","audienceTypes":["Professional (continued)"],"audienceRoles":["Biologists","Bioinformaticians"],"difficultyLevel":"Novice","trainingMaterials":[],"learningOutcomes":"Connaître les concepts et les principales méthodes bioinformatiques pour comparer un jeu de données de génomes microbiens. \r\nConstruire et évaluer la qualité d’un jeu de données. \r\nSavoir mettre en œuvre une comparaison de génomes et en interpréter les résultats.","hoursPresentations":3,"hoursHandsOn":3,"hoursTotal":6,"personalised":null,"event_set":["https://catalogue.france-bioinformatique.fr/api/event/584/?format=json","https://catalogue.france-bioinformatique.fr/api/event/696/?format=json"]},{"id":353,"name":"Analyse de données métagénomiques shotgun / shotgun metagenomics","shortName":"Shotgun metagenomics","description":"Objectifs pédagogiques\r\n\r\nCette formation est dédiée à l’analyse de données métagénomiques procaryotes de type « shotgun » issues de la technologie de séquençage Illumina. Nous présenterons les étapes bioinformatiques nécessaires pour nettoyer les données brutes et les caractériser d’un point de vue taxonomique. Nous aborderons ensuite les différentes stratégies à employer pour assembler les reads et obtenir des comptages sur des gènes prédits. Enfin nous présenterons quelques outils pour obtenir une annotation fonctionnelle des échantillons. A l’issue des 2 jours de formation, les stagiaires connaîtront le périmètre, les avantages et limites des analyses de données de séquençage shotgun. Ils seront capables d’utiliser les outils présentés sur les jeux de données de la formation. L’ensemble des TP se déroulera sur l’infrastructure de Migale et nécessite une pratique courante de la ligne de commande.\r\n\r\nProgramme\r\n\r\nIntroduction générale sur les données métagénomiques\r\nAssignation taxonomique\r\nNettoyage des données brutes\r\nAssemblage / Binning\r\nPrédiction de gènes procaryotes\r\nAnnotation fonctionnelle\r\nConclusion, limites des méthodes","homepage":"https://documents.migale.inrae.fr/trainings.html","is_draft":false,"costs":["Priced"],"topics":["http://edamontology.org/topic_3697"],"keywords":["Metagenomics"],"prerequisites":["Linux/Unix","Cluster"],"openTo":"Everyone","accessConditions":"","maxParticipants":10,"contacts":["https://catalogue.france-bioinformatique.fr/api/userprofile/769/?format=json"],"elixirPlatforms":[],"communities":[],"sponsoredBy":[],"organisedByOrganisations":[{"id":82,"name":"INRAE","url":"https://catalogue.france-bioinformatique.fr/api/organisation/INRAE/?format=json"},{"id":88,"name":"BioinfOmics","url":"https://catalogue.france-bioinformatique.fr/api/organisation/BioinfOmics/?format=json"}],"organisedByTeams":[{"id":10,"name":"MIGALE","url":"https://catalogue.france-bioinformatique.fr/api/team/MIGALE/?format=json"}],"logo_url":"https://migale.inrae.fr/sites/default/files/migale-orange_0.png","updated_at":"2024-01-18T13:16:49.313752Z","audienceTypes":[],"audienceRoles":[],"difficultyLevel":"Intermediate","trainingMaterials":[],"learningOutcomes":"Cette formation est dédiée à l’analyse de données métagénomiques procaryotes de type « shotgun » issues de la technologie de séquençage Illumina. Nous présenterons les étapes bioinformatiques nécessaires pour nettoyer les données brutes et les caractériser d’un point de vue taxonomique. Nous aborderons ensuite les différentes stratégies à employer pour assembler les reads et obtenir des comptages sur des gènes prédits. Enfin nous présenterons quelques outils pour obtenir une annotation fonctionnelle des échantillons. A l’issue des 2 jours de formation, les stagiaires connaîtront le périmètre, les avantages et limites des analyses de données de séquençage shotgun. Ils seront capables d’utiliser les outils présentés sur les jeux de données de la formation. L’ensemble des TP se déroulera sur l’infrastructure de Migale et nécessite une pratique courante de la ligne de commande.","hoursPresentations":5,"hoursHandsOn":7,"hoursTotal":12,"personalised":null,"event_set":["https://catalogue.france-bioinformatique.fr/api/event/572/?format=json","https://catalogue.france-bioinformatique.fr/api/event/780/?format=json","https://catalogue.france-bioinformatique.fr/api/event/694/?format=json"]}]}