{"count":391,"next":"https://catalogue.france-bioinformatique.fr/api/training/?format=json&limit=20&offset=20&ordering=-id","previous":null,"results":[{"id":414,"name":"Sciences Reproductibles : git et Quarto, du code à la présentation des résultats","shortName":"SciRepro - Git&Quarto","description":"The aim of this course is to go a step further than the introductory course offered as part of the FAIR-Bioinfo programme, which focuses on familiarising participants with best practices in bioinformatics in order to ensure the long-term sustainability and reproducibility of their research work. 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This workshop is tailored for data scientists, advanced Galaxy users, and team leaders who need to scale, automate, and publish their data analysis workflows for batch processing and production-level applications.\r\n\r\nOver five days, you’ll embark on a comprehensive journey through Galaxy’s advanced capabilities:\r\n\r\nMonday: Introduction & Workflow Development\r\n\r\nStart with a welcome and icebreaker to foster collaboration, followed by a brief overview of Galaxy and its workflow features. Dive into hands-on workflow development, where you’ll learn to design clean, efficient workflows, customize them with parameters, and generate user-friendly workflow reports—combining theory with practical application.\r\n\r\nTuesday: Workflow FAIRification, Documentation, and Export\r\n\r\nBegin with a recap of Day 1, then explore UseGalaxy.fr and its unique features. Learn to annotate workflows with metadata, apply best practices for FAIR compliance, and implement tests to ensure reliability. Publish your workflows to WorkflowHub and Dockstore via the IWC. Develop high-resolution workflow visualizations and create interactive tutorials using a “Choose Your Own Tutorial” approach. Finally, master workflow export by creating RO-Crates for reproducibility and submitting workflows to LifeMonitor for performance tracking.\r\n\r\nWednesday: Scaling Workflows & Galaxy Using Command-Line and API\r\n\r\nStart with a recap and real-world examples of large-scale Galaxy projects. Learn to execute workflows from the command line using Planemo, automate batch processing with shell scripts, and analyze performance for efficiency. Discover how to scale Galaxy use with BioBlend, designing Python scripts for batch workflow execution and evaluating scalability. 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. The workflow and the dataset must be shareable and non-sensitive (i.e., they must not contain any patient-related information or confidential data). 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workflows to improve consistency and interoperability.\r\nImplement robust tests to validate workflow reliability and accuracy.\r\nPublish 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\nDesign a high-resolution workflow image optimized for documentation and presentations.\r\nDevelop a hands-on tutorial with a “Choose Your Own Tutorial” approach, including:\r\nA step-by-step tutorial with skeleton generation from the workflow.\r\nA real-time tutorial that runs and explains the workflow interactively.\r\nProduce a final documentation package that includes both tutorial formats and high-resolution visuals.\r\nWorkflow Export\r\nApply 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\nEvaluate the 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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.","hoursPresentations":null,"hoursHandsOn":null,"hoursTotal":null,"personalised":true,"event_set":["https://catalogue.france-bioinformatique.fr/api/event/802/?format=json","https://catalogue.france-bioinformatique.fr/api/event/803/?format=json"]},{"id":412,"name":"Construction and analysis of eukaryotic pangenome graphs","shortName":"","description":"This training session is organized by the Genotoul-Bioinfo platform. This 2 days long course is dedicated to the construction and the analysis of eukaryotic pangenome graphs.\r\n\r\nWe will first present the concept of graph-based pangenome, then build one. We will then apply several tools for its analysis: use annotation, call variants, extract sub-graphs, visualize the graph, map reads, genotype individuals, and perform a GWAS on the graph. The different formats will also be presented.\r\n\r\nBy the end of the course, trainees will be familiar with the topic, and able to run the major tools made to build an exploit a pangenome graph.","homepage":"https://bioinfo.genotoul.fr/index.php/events/pangenome/","is_draft":false,"costs":["Non-academic for non-academic: 1100€ + 20% taxes (TVA)","INRAE for INRAE's staff: 300 € no VAT charged","Academic non-INRAE for academic but non-INRAE: 340 € + 20% taxes 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taxonomiques tels l’ADN ribosomique, les gènes COI/COX ou les ITS a ouvert de nouveaux horizons dans l’étude des communautés de macro et micro-organismes et l’étude des écosystèmes.\r\n\r\nLe but de cette formation est, d’une part, d’introduire les concepts clés liés aux analyses de metabarcoding et de les illustrer au moyen de cas concrets d’analyse et, d’autre part, de former les utilisateurs aux traitements de données de metabarcoding au travers de l’usage du logiciel SAMBA (Noël et al., in 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Introduction\r\n- Single-cell RNA sequencing\r\n- From raw sequencing data to count matrices\r\n- Software tools\r\n\r\nII. Preprocessing of the expression matrix (Theory and Practice)\r\n- Quality control\r\n- Normalization\r\n- Dimensionality reduction (HVG, PCA, UMAP)\r\n- Detection of expression biases\r\n\r\nIII. Annotation (Theory and Practice)\r\n- Clustering\r\n- Marker genes\r\n- Cell type identification\r\n- Analysis of marker gene lists with the R package ClusterProfiler\r\n\r\nIV. Practical Workshop “Bring your own data”\r\n- Semi-autonomous execution of primary analysis on learners’ own data","homepage":"https://pf-bird.univ-nantes.fr/training/singlecell/","is_draft":false,"costs":["Priced"],"topics":[],"keywords":[],"prerequisites":[],"openTo":"Everyone","accessConditions":"1. Good knowledge of R or having completed the training “Bioanalysis Training: Introduction to the R Programming Language.”\r\n\r\n2. 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