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Moreover, given the large amounts of omics data that are being generated and made accessible to researchers due to the drop in the cost of high-throughput technologies, analysing these complex high-volume data is not trivial, and the use of classical statistics can not explore their full potential. As such, Machine Learning (ML) and Artificial Intelligence (AI) have been recognized as key opportunity areas, as evidenced by a number of ongoing activities and efforts throughout the community.\r\n\r\nHowever, beyond the technological advances, it is equally important that the individual researchers acquire the necessary knowledge and skills to fully take advantage of Machine Learning. 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Cette mise en oeuvre permettra de balayer l’ensemble des points d’attention d’une analyse intégrative,  de la préparation des données jusqu’à l’interprétation des résultats,\r\n- de créer, améliorer et partager les ressources pédagogiques (supports de formation, jeux de données, tutoriels) sur le thème de la bioinformatique intégrative.\r\n\r\nA la fin de cette formation les participants :\r\n- auront acquis un socle de connaissances générales en bioinformatique intégrative, \r\n- auront mis en oeuvre une analyse intégrative depuis la préparation des données jusqu’à l’analyse critique de résultats sur un/des jeux de données proposés lors de la formation,\r\n- auront contribué à constituer du matériel pédagogique partagé sur le sujet.\r\n\r\nPré-requis\r\n- Connaissances de base en Unix/shell, R et/ou Python \r\n- Autonomie dans la gestion de son poste de travail (installation de librairies et maîtrise des environnements de packaging type conda)","homepage":"https://www.france-bioinformatique.fr/formation/etbii/","is_draft":false,"costs":["770 TTC pour les académiques  et 1540 TTC pour les privés"],"topics":["http://edamontology.org/topic_3391","http://edamontology.org/topic_3366","http://edamontology.org/topic_0091"],"keywords":["Methodology","Biostatistics","Biological network inference and analysis","Dimension reduction","Semantic web","Integration of heterogeneous data","Data Integration","Tool integration"],"prerequisites":["Linux and knowledge of NGS formats","Basic knowledge of R"],"openTo":"Everyone","accessConditions":"Cette formation est ouverte à toute la communauté mais cette première édition s’adresse en priorité à des bioinformaticien·ne·s des plateformes membres et équipes associées IFB souhaitant contribuer à la constitution de matériel pédagogique pour se préparer au montage de futures formations sur ce thème.","maxParticipants":30,"contacts":["https://catalogue.france-bioinformatique.fr/api/userprofile/762/?format=json"],"elixirPlatforms":[],"communities":[],"sponsoredBy":[{"id":1,"name":"CNRS - IFB","url":"https://catalogue.france-bioinformatique.fr/api/eventsponsor/CNRS%20-%20IFB/?format=json"}],"organisedByOrganisations":[{"id":4,"name":"IFB - ELIXIR-FR","url":"https://catalogue.france-bioinformatique.fr/api/organisation/IFB%20-%20ELIXIR-FR/?format=json"}],"organisedByTeams":[{"id":29,"name":"IFB Core","url":"https://catalogue.france-bioinformatique.fr/api/team/IFB%20Core/?format=json"}],"logo_url":"https://drive.google.com/file/d/1a_fuOgqOU812GRJLApGMofJ87WTlxDI0/view?usp=sharing","updated_at":"2024-12-03T15:46:48.157120Z","audienceTypes":["Professional (continued)"],"audienceRoles":["Life scientists","Computer scientists","Bioinformaticians"],"difficultyLevel":"Novice","trainingMaterials":[],"learningOutcomes":"A la fin de cette formation les participants :\r\n- auront acquis un socle de connaissances générales  en bioinformatique intégrative, \r\n- auront mis en oeuvre une analyse intégrative depuis la préparation des données jusqu’à l’analyse critique de résultats sur un/des jeux de données proposés lors de la formation,\r\n- auront contribué à constituer du matériel pédagogique partagé sur le sujet.","hoursPresentations":null,"hoursHandsOn":null,"hoursTotal":null,"personalised":false,"event_set":["https://catalogue.france-bioinformatique.fr/api/event/740/?format=json","https://catalogue.france-bioinformatique.fr/api/event/489/?format=json","https://catalogue.france-bioinformatique.fr/api/event/505/?format=json"]},{"id":275,"name":"Single-Cell : Transcriptomics, Spatial and Long reads","shortName":"SincellTE","description":"This workshop focuses on the large-scale study of heterogeneity across individual cells from a genomic, transcriptomic and epigenomic point of view. New technological developments enable the characterization of molecular information at a single cell resolution for large numbers of cells. The high dimensional omics data that these technologies produce raise novel methodological challenges for the analysis. In this regard, dedicated bioinformatics and statistical methods have been developed in order to extract robust information.\r\n\r\nThe workshop aims to provide such methods for engineers and researchers directly involved in functional genomics projects making use of single-cell technologies. A wide range of single cell topics will be covered in lectures, demonstrations and practical classes. Among others, the areas and issues to be addressed will include the choice of the most appropriate single-cell sequencing technology, the experimental design and the bioinformatics and statistical methods and pipelines. For this edition, new courses/practicals will focus on spatial transcriptomics, cell phenotyping and additional multi-omics.\r\n\r\nA wide range of single cell topics will be covered in lectures, demonstrations and practical classes. Among others, the areas and issues to be addressed will include the choice of the most appropriate single-cell sequencing technology, the experimental design and the bioinformatics and statistical methods and pipelines. For this edition, new courses/practicals will focus on spatial transcriptomics, cell phenotyping and additional multi-omics.\r\n\r\nRequirements : Participants must have prior experience on NGS data analysis  with everyday use of R and good knowledge of Unix command line. Before the training, participants will be asked to familiarize themselves with the processing and primary analyses steps of scRNA-seq datasets with provided pedagogic material.\r\n\r\nIt is not necessary to have personal single-cell data to analyse.","homepage":"","is_draft":false,"costs":["Priced"],"topics":[],"keywords":[],"prerequisites":["Master","Autre (Diplôme universitaire, école d'ingénieur ...)"],"openTo":"Everyone","accessConditions":"Participants must have prior experience on NGS data analysis with everyday use of R and/or Python and good knowledge of Unix command line. 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Cette édition de l’école aborde les nouveaux enjeux technologiques: elle s’articulera autour des différentes étapes qui mèneront à l’obtention d’un génome annoté à partir de données “long reads” et “hybride” : contrôle qualité des données, assemblage, scaffolding, polishing, annotation structurale et fonctionnelle (en session parallèle pour les procaryotes et les eucaryotes). \r\nL’école vise à introduire les concepts, à manipuler les outils informatiques et à en interpréter les résultats. Elle est basée sur une alternance de courtes sessions théoriques et d’ateliers pratiques. Les participants bénéficieront d’un tutorat personnalisé pour élaborer leur plan d’analyse, et effectuer les premières étapes de traitement de leurs propres données ou de celles de leur équipe.\r\nAttention : le tutorat n'a pas pour vocation de réaliser l’analyse complète des données des participants.\r\nPublic visé\r\nCette formation est destinée aux biologistes (ingénieurs, doctorants, chercheurs, enseignants-chercheurs, praticiens…) confrontés à l’analyse de données NGS, et qui ne disposent pas des compétences bioinformatiques suffisantes.","homepage":"https://www.france-bioinformatique.fr/formation/ebaii2022_genomique/","is_draft":false,"costs":[],"topics":[],"keywords":[],"prerequisites":[],"openTo":"Everyone","accessConditions":"","maxParticipants":40,"contacts":[],"elixirPlatforms":[],"communities":[],"sponsoredBy":[],"organisedByOrganisations":[],"organisedByTeams":[],"logo_url":"https://cvt.aviesan.fr/wp-content/themes/cvtaviesan/images/logo_cvt.png","updated_at":"2024-12-05T09:11:55.721141Z","audienceTypes":[],"audienceRoles":[],"difficultyLevel":"Novice","trainingMaterials":[],"learningOutcomes":"Initiation au traitement des données de génomique obtenues par séquençage à haut débit\r\nAssemblage et annotation de novo de génomes\r\nL’école vise à introduire les concepts, à manipuler les outils informatiques et à en interpréter les résultats. Elle est basée sur une alternance de courtes sessions théoriques et d’ateliers pratiques. Les participants bénéficieront d’un tutorat personnalisé pour élaborer leur plan d’analyse, et effectuer les premières étapes de traitement de leurs propres données ou de celles de leur équipe.","hoursPresentations":null,"hoursHandsOn":null,"hoursTotal":null,"personalised":true,"event_set":["https://catalogue.france-bioinformatique.fr/api/event/463/?format=json"]},{"id":365,"name":"BIGomics, Génomique Comparative","shortName":"BOGC","description":"Ce module vise à fournir une expérience d’analyse de données de génomique.\r\nLes technologies Next Generation Sequencing (NGS) ont conduit à une production massive de\r\ndonnées « Omiques » pour les plantes cultivées majeures, ce qui demande de nouvelles\r\napproches d’analyses haut débit. La connaissance de ces approches et des outils qui en\r\ndécoulent pour analyser la séquence et la structure des génomes, les annoter et caractériser\r\nleur diversité et leurs profils d’expression permet d’aborder des questions de recherche\r\nbiologique avancée sur la diversité et l’adaptation des plantes. Les espèces prises en\r\nconsidération sont des espèces phares des instituts de recherche agronomique de Montpellier\r\net font partie des cultures les plus importantes pour l’agriculture mondiale. Des plateformes\r\nd’outils bioinformatiques récents reposant sur des centres de calcul et de stockage haute\r\ncapacité, sont en place pour analyser des jeux de données originales permettant de mieux\r\ncomprendre comment les génomes de plantes évoluent et s’expriment. 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