Handles creating, reading and updating training materials.

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            "id": 10,
            "name": "JSON schema validation with ontologies",
            "description": "\n \n\nJSON schema validation with ontologies\n \n",
            "communities": [],
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            "doi": null,
            "fileLocation": "http://ressources.france-bioinformatique.fr/sites/default/files/videos/scorms/json-schema-validation_deb6/scormcontent/index.html",
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                "biohackaton 2018"
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        },
        {
            "id": 18,
            "name": "Development of BioJS components",
            "description": "\n \n\nDevelopment of BioJS components\n \n",
            "communities": [],
            "elixirPlatforms": [],
            "doi": null,
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            "id": 139,
            "name": "Cluster Slides",
            "description": "Slides for Cluster training (bioinfo genotoul facility)",
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            "doi": null,
            "fileLocation": "https://bioinfo.genotoul.fr/wp-content/uploads/Formation_cluster_SLURM_2024.pdf",
            "fileName": "Formation_cluster_SLURM_2024.pdf",
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            "keywords": [
                "Cluster"
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                    "id": 22,
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            "id": 98,
            "name": "A Quick and focused overview of R data types and ggplot2 syntax",
            "description": "R and RStudio overview.\n",
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            "doi": null,
            "fileLocation": "http://genoweb.toulouse.inra.fr/~formation/15_FROGS/April2016/R_phyloseq/ModuleIntroInitR.pdf",
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                "Statistics",
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            "id": 64,
            "name": "Variants: alignment and pre-treatment; GATK",
            "description": "DNA-seq Bioinformatics Analysis: from raw sequences to processed alignments\n",
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            "doi": null,
            "fileLocation": "http://www.france-bioinformatique.fr/sites/default/files/V01_ITMO_2016_EG_from_fastq_to_mapping_1.pdf",
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            "topics": [],
            "keywords": [
                "genomics",
                "DNA-seq"
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            "dateCreation": "2016-11-22",
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            "id": 104,
            "name": "Exploring microbiomes with the MicroScope Platform",
            "description": "This module is separated in different courses:\nMicroScope: General overview, Keyword search and gene cart functionalities\n\n\n\n\n\n\n\n\n\n\n\n\nFunctional annotation of microbial genomes\n\n\n\n\n\n\n\n\nFunctional annotation of microbial genomes: Prediction of enzymatic functions\n\n\n\n\n\n\n\n\nRelational annotation of bacterial genomes: synteny\n\n\n\n\n\n\n\n\nAutomatic functional assignation and expert annotation of genes\n\n\n\n\n\n\n\n\nRelational annotation of bacterial genomes: phylogenetic profiles\n\n\n\n\n\n\n\n\nRelational annotation of bacterial genomes: pan-genome analysis\n\n\n\n\n\n\n\n\nRelational annotation of bacterial genomes: metabolic pathways\n\n\n\n\nSyntactic re-annotation of public microbial genomes\n\n\n\n\nSyntactic annotation of microbial genomes\n\n\n\n\n \n",
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            "keywords": [
                "genomics",
                "Annotation",
                "Transcriptomics",
                "Microbial evolution",
                "Metabolomics"
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            "id": 71,
            "name": "Holistic metagenomics in marine communities",
            "description": "Complex microscopic communities are composed of species belonging to all life realms, from single-cell prokaryotes to multicellular eukaryotes of small size. Each component of a community needs to be studied for a full understanding of the functions performed by the whole assemblage, however methods to investigate microbiomes are generally restricted to a single kingdom. Using examples from the Tara Oceans project, we will show how size fractionation and use of varied metabarcoding, metagenomics and metatranscriptomics approaches can help studying the marine plankton community as a whole, in a wide geographic space.\n",
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            "doi": null,
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            "id": 2,
            "name": "Using blockchain in biomedical provenance, the identifiers use case",
            "description": "\n \n\nUsing blockchain in biomedical provenance, the identifiers use case\n \n",
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            "doi": null,
            "fileLocation": "http://ressources.france-bioinformatique.fr/sites/default/files/videos/scorms/using-blockchain_039d/scormcontent/index.html",
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            "topics": [],
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        {
            "id": 72,
            "name": "Hidden in the permafrost",
            "description": "The last decade witnessed the discovery of four families of giant viruses infecting Acanthamoeba. They have genome encoding from 500 to 2000 genes, a large fraction of which encoding proteins of unknown origin. These unique proteins meant to recognize and manipulate the same building blocks as cells raise the question on their origin as well as the role viruses played in the cellular word evolution. The Mimiviridae and the Pandoraviridae are increasingly populated by members from very diverse habitats and are ubiquitous on the planet. After prospecting the space, we went back in the past and isolated two other giant virus families from a 30,000 years old permafrost sample, Pithovirus and Mollivirus sibericum. A metagenomics study of the sample was performed to inventory its biodiversity and assess to what extend the host and the viruses were dominant. I will describe the two sequencing approaches which have been used and compare the results.\n1: Raoult D, Audic S, Robert C, Abergel C, Renesto P, Ogata H, La Scola B, Suzan M, Claverie JM. The 1.2-megabase genome sequence of Mimivirus. Science. 2004 Nov 19;306(5700):1344-50.\n2: Philippe N, Legendre M, Doutre G, Couté Y, Poirot O, Lescot M, Arslan D, Seltzer V, Bertaux L, Bruley C, Garin J, Claverie JM, Abergel C. Pandoraviruses: amoeba viruses with genomes up to 2.5 Mb reaching that of parasitic eukaryotes. Science. 2013 Jul 19;341(6143):281-6. \n3: Legendre M, Bartoli J, Shmakova L, Jeudy S, Labadie K, Adrait A, Lescot M, Poirot O, Bertaux L, Bruley C, Couté Y, Rivkina E, Abergel C, Claverie JM. Thirty-thousand-year-old distant relative of giant icosahedral DNA viruses with a pandoravirus morphology. Proc Natl Acad Sci U S A. 2014 Mar 18;111(11):4274-9.\n4: Legendre M, Lartigue A, Bertaux L, Jeudy S, Bartoli J, Lescot M, Alempic JM, Ramus C, Bruley C, Labadie K, Shmakova L, Rivkina E, Couté Y, Abergel C, Claverie JM. In-depth study of Mollivirus sibericum, a new 30,000-y-old giant virus infecting Acanthamoeba. Proc Natl Acad Sci U S A. 2015 Sep 22;112(38):E5327-35.\n",
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            "doi": null,
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        {
            "id": 47,
            "name": "ToolShed upload and tool-iuc PR",
            "description": "Questions\nWhat is a Tool Shed?\nHow to install tools and workflows from a Tool Shed into a Galaxy instance?\nWhat are the Tool Shed repository types?\nHow to publish with Planemo?\nObjectives\nDiscover what is a Tool Shed\nBe able to install tools and workflows from a Tool Shed into a Galaxy instance\nBe able to publish tools with Planemo\n",
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            "doi": null,
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                "Galaxy"
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            "dateCreation": "2017-01-17",
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                "https://catalogue.france-bioinformatique.fr/api/userprofile/362/?format=api"
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        {
            "id": 46,
            "name": "BioBlend API",
            "description": "BioBlend module, a python library to use Galaxy API\n",
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            "doi": null,
            "fileLocation": "http://galaxyproject.github.io/training-material/Dev-Corner/slides/bioblend_api.html",
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            "topics": [],
            "keywords": [
                "Galaxy",
                "Python",
                "API"
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            "audienceTypes": [],
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            "dateCreation": "2017-01-18",
            "dateUpdate": null,
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                "https://catalogue.france-bioinformatique.fr/api/userprofile/563/?format=api"
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        {
            "id": 83,
            "name": "Rationale and Tools to look for the unknown in (metagenomic) sequence data",
            "description": "The interpretation of metagenomic data (environmental, microbiome, etc, ...) usually involves the recognition of sequence similarity with previously identified (micro-organisms). This is for instance the main approach to taxonomical assignments and a starting point to most diversity analyses. When exploring beyond the frontier of known biology, one should expect a large proportion of environmental sequences not exhibiting any significant similarity with known organisms. Notably, this is the case for eukaryotic viruses belonging to new families, for which the proportion of \"no match\" could reach 90%. Most metagenomics studies tend to ignore this large fraction of sequences that might be the equivalent of \"black matter\" in Biology. We will present some of the ideas and tools we are using to extract that information from large metagenomics data sets in search of truly unknown microorganisms.\nOne of the tools, \"Seqtinizer\", an interactive contig selection/inspection interface will also be presented in the context of \"pseudo-metagenomic\" projects, where the main organism under genomic study (such as sponges or corals) turns out to be (highly) mixed with an unexpected population of food, passing-by, or symbiotic microorganisms.\n",
            "communities": [],
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            "doi": null,
            "fileLocation": "http://www.france-bioinformatique.fr/sites/default/files/videos/scorms/metagenomics16/session_3/Rational_and_tools_to_look_for_the_unknown_in_sequence_data/scormcontent/index.html",
            "fileName": "missing.txt",
            "topics": [],
            "keywords": [
                "Metagenomics"
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            "audienceTypes": [],
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            "dateCreation": "2016-12-16",
            "dateUpdate": null,
            "licence": "CC BY-NC-ND",
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                "https://catalogue.france-bioinformatique.fr/api/userprofile/123/?format=api"
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        },
        {
            "id": 15,
            "name": "From Biotea to Bioschemas: definition of profiles required to represent scholarly publications",
            "description": "\n \n\nFrom Biotea to Bioschemas: definition of profiles required to represent scholarly publications\n \n",
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            "doi": null,
            "fileLocation": "http://ressources.france-bioinformatique.fr/sites/default/files/videos/scorms/from-biotea-to-bioschemas_28f9/scormcontent/index.html",
            "fileName": "missing.txt",
            "topics": [],
            "keywords": [
                "biohackaton 2018"
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            "dateCreation": "2019-03-21",
            "dateUpdate": null,
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        },
        {
            "id": 77,
            "name": "Prokaryotic Phylogeny on the Fly: databases and tools for online taxonomic identification",
            "description": "PPF (Prokaryotic Phylogeny on the Fly) is an automated pipeline allowing to compute molecular phylogenies for prokarotic organisms. It is based on a set of specialized databases devoted to SSU rRNA, the most commonly used marker for bacterial txonomic identification. Those databases are splitted into different subsets using phylogenetic information.   The procedure for computing a phylogeny is completely automated. Homologous sequence are first recruited through a BLAST search performed on a sequence (or a set of sequences). Then the homologous sequences detected are aligned using one of the multiple sequence alignment programs provided in the pipeline (MAFFT, MUSCLE or CLUSTALO). The alignment is then filtered using BMGE and a Maximum Likelihood (ML) tree is computed using the program FastTree. The tree can be rooted with an outgroup provided by the user and its leaves are coloured with a scheme related to the taxonomy of the sequences.  The main advantage provided by PPF is that its databases are generated using a phylogeny-oriented procedure and and therefore much more efficient for phylogentic analyses that \"generic\" collections such as SILVA (in the case SSU rRNA) por GenBank. It is therefore much more suited to compute prokaryotic molecular phylogenies than related systems such as the Phylogeny.fr online system.  PPF can be accessed online at https://umr5558-bibiserv.univ-lyon1.fr/lebibi/PPF-in.cgi\n",
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            "doi": null,
            "fileLocation": "http://www.france-bioinformatique.fr/sites/default/files/videos/scorms/metagenomics16/session_5/Procaryotic_phylogenu_on_the_fly/scormcontent/index.html",
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            "topics": [],
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                "Metagenomics"
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            "dateCreation": "2016-12-16",
            "dateUpdate": null,
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        {
            "id": 140,
            "name": "TP Cluster",
            "description": "TP for cluster training (bioinfo genotoul facility)",
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            "doi": null,
            "fileLocation": "https://genotoul-bioinfo.pages.mia.inra.fr/linux-cluster/",
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            "keywords": [
                "Cluster"
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            "dateCreation": null,
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        {
            "id": 73,
            "name": "200 billion sequences and counting: analysis, discovery and exploration of datasets with EBI Metagenomics",
            "description": "EBI metagenomics (EMG, https://www.ebi.ac.uk/metagenomics/) is a freely available hub for the analysis and exploration of metagenomic, metatranscriptomic, amplicon and assembly data. The resource provides rich functional and taxonomic analyses of user-submitted sequences, as well as analysis of publicly available metagenomic datasets held within the European Nucleotide Archive (ENA). EMG has recently undergone rapid expansion, with an over 10-fold increase in data volumes in the first 5 months of 2016. It now houses ~ 50k publicly available data sets, and represents one of the largest collections of analysed metagenomic data. As its data content has grown, EMG has increasingly become a platform for data discovery. To support this process, we have made a series of user-interface improvements, including the classification of projects by biome, presentation of results data for better visualisation and more convenient download, and provision of project level summary files. More recently, we have indexed project metadata for use with the EBI search engine, enabling exploration across different datasets. For example, users are able to search with a particular taxonomic lineage or protein function and discover the projects, samples and sequencing runs in which that lineage or function is found. This functionality allows users to explore associations between biomes, environmental conditions and organisms and functions (e.g., discovering protein coding sequences that correspond to certain enzyme families found in aquatic environments at a given temperature range). Here, we give an overview of the EMG data analysis pipeline and web site, and illustrate the use of the new search facility for data discovery.\n",
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            "doi": null,
            "fileLocation": "http://www.france-bioinformatique.fr/sites/default/files/videos/scorms/metagenomics16/session_7/200_billions_sequences_and_counting_discovery_and_exploration_of_datasets_with_EBI_metagenomics/scormcontent/index.html",
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        },
        {
            "id": 56,
            "name": "RADSeq Data Analysis",
            "description": "Introduction to RADSeq through STACKS on Galaxy\n",
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            "doi": null,
            "fileLocation": "http://www.france-bioinformatique.fr/sites/default/files/V08_Yvan%20Le%20Bras%20-%20Training%20RADSeq_0.pdf",
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            "topics": [],
            "keywords": [
                "NGS"
            ],
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            "dateCreation": "2016-11-23",
            "dateUpdate": null,
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        },
        {
            "id": 40,
            "name": "Galactic Database",
            "description": "How to choose a database for Galaxy and configure it\n",
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            "doi": null,
            "fileLocation": "https://igbmc.github.io/egdw2017/day4/admin/02-database/index.html",
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                "Galaxy"
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            "dateCreation": "2017-01-19",
            "dateUpdate": null,
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        },
        {
            "id": 123,
            "name": "IFB Cloud tutorial: Gene regulation",
            "description": "1. Using the Gene-regulation appliance\n1.1 Requirements\n1.2 Virtual disk creation\n1.3 Creation of an instance\n1.4 Connection to the device\n1.5 Download source data\n1.6 Execute workflow\n\n2. Visualizing results\n2.1 Install and run the X2Go client on your host computer\n2.2 Visualize results\nFastQC\nIGV\n\n\n3. Create your own Gene-regulation appliance\nCreation of an instance\nInstalling programs and dependencies\nGet the gene-regulation repository\nRun makefile to install the dependencies\n\n",
            "communities": [],
            "elixirPlatforms": [],
            "doi": null,
            "fileLocation": "https://github.com/rioualen/gene-regulation/blob/master/doc/gene-regulation_tutorials/gene-regulation_with_IFB_cloud.Rmd",
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            "topics": [],
            "keywords": [
                "Gene regulation",
                "Cloud Computing"
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            "dateCreation": "2016-05-31",
            "dateUpdate": null,
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                "https://catalogue.france-bioinformatique.fr/api/userprofile/624/?format=api"
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        },
        {
            "id": 67,
            "name": "Galaxy: Initiation II",
            "description": "Galaxy II: common tools, quality control; alignment; data managment\n",
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            "doi": null,
            "fileLocation": "http://www.france-bioinformatique.fr/sites/default/files/G045_16-11-21-galaxy-tp-initiation-II-0.3.pdf",
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