Handles creating, reading and updating training materials.

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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",
            "fileName": "missing.txt",
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            "keywords": [
                "NGS"
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            "dateCreation": "2016-11-23",
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        },
        {
            "id": 127,
            "name": "Quality Control with Galaxy",
            "description": "This tutorial covers the questions:\r\n- How to perform quality control of NGS raw data?\r\n- What are the quality parameters to check for a dataset?\r\n- How to improve the quality of a dataset?\r\n\r\nAt the end of the tutorial, learners would be able to:\r\n- Assess short reads FASTQ quality using FASTQE 🧬😎 and FastQC\r\n- Assess long reads FASTQ quality using Nanoplot and PycoQC\r\n- Perform quality correction with Cutadapt (short reads)\r\n-  Summarise quality metrics MultiQC\r\n- Process single-end and paired-end data",
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            "doi": null,
            "fileLocation": "https://training.galaxyproject.org/training-material/topics/sequence-analysis/tutorials/quality-control/tutorial.html",
            "fileName": "quality-control",
            "topics": [
                "http://edamontology.org/topic_3168",
                "http://edamontology.org/topic_0091"
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            "keywords": [
                "Quality Control"
            ],
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                "Professional (continued)"
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            "audienceRoles": [
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            "difficultyLevel": "Novice",
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            "dateCreation": null,
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        {
            "id": 5,
            "name": "Putting structured data into individual entry pages in biological database",
            "description": "\n \n\nPutting structured data into individual entry pages in biological database\n \n",
            "communities": [],
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            "doi": null,
            "fileLocation": "http://ressources.france-bioinformatique.fr/sites/default/files/videos/scorms/putting-structured_bf31/scormcontent/index.html",
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            "topics": [],
            "keywords": [
                "biohackaton 2018"
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            "dateCreation": "2019-03-21",
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            "id": 38,
            "name": "(Proxy) Web Server Choices and Configuration",
            "description": "Installation and configuration of NGiNX for Galaxy\n",
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            "doi": null,
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            "keywords": [
                "Galaxy",
                "NGiNX"
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            "dateCreation": "2017-01-19",
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            "id": 6,
            "name": "ProtVista (protein annotation viewer) extension using Bioschemas data",
            "description": "\n \n\nProtVista (protein annotation viewer) extension using Bioschemas data\n \n",
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            "doi": null,
            "fileLocation": "http://ressources.france-bioinformatique.fr/sites/default/files/videos/scorms/protvista-protein_2cd8/scormcontent/index.html",
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                "biohackaton 2018"
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            "id": 7,
            "name": "Prototyping the new PSICQUIC 2-0",
            "description": "\n \n\nPrototyping the new PSICQUIC 2.0\n \n",
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            "doi": null,
            "fileLocation": "http://ressources.france-bioinformatique.fr/sites/default/files/videos/scorms/prototyping_353b/scormcontent/index.html",
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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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            "name": "Processing_large_files_with_sed_awk_2024",
            "description": "This “Sed and AWK to modify large text files” training session is organized by the Genotoul bioinfo platform.\r\n\r\nThe Linux sed command is a powerful and very fast text editor without an interface. Sed can select, substitute, add, delete, and modify text in files and streams. Sed relies heavily on regular expressions for pattern matching and text selection. We’ll manipulate regexes and the sed command to modify and filter several type of file often used in bioinformatics.\r\n\r\nAWK enables to easily process columns in large text files but is also a quite powerfull programming language. This training session aims at introducing you AWK principles. You will learn about variables, operators and functions useful to manipulate very large files. \r\n\r\nFor example you can use AWK to generate your unix command lines to be launched on the cluster. AWK enables to process millions of lines in text files. The course includes short feature presentations between long hands-on sessions in which you will be able to understand the global ideas as well as details.",
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            "doi": null,
            "fileLocation": "https://web-genobioinfo.toulouse.inrae.fr/~klopp/SedAwk2024/Processing_large_files_with_sed_awk_2024.pdf",
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            "audienceRoles": [
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                "Bioinformaticians"
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            "difficultyLevel": "Intermediate",
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                    "id": 22,
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            "dateCreation": null,
            "dateUpdate": "2024-03-01",
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        },
        {
            "id": 8,
            "name": "Pathway effect prediction for protein targets",
            "description": "\n \n\nPathway effect prediction for protein targets\n \n",
            "communities": [],
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            "doi": null,
            "fileLocation": "http://ressources.france-bioinformatique.fr/sites/default/files/videos/scorms/pathway-effect_b1e2/scormcontent/index.html",
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            "dateCreation": "2019-03-21",
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        },
        {
            "id": 99,
            "name": " PASTEClassifier Tutorial",
            "description": "The PASTEClassifier (Pseudo Agent System for Transposable Elements Classification) is a transposable element (TE) classifier searching for structural features and similarity to classify TEs (  Hoede C. et al. 2014 )\n",
            "communities": [],
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            "doi": null,
            "fileLocation": "https://urgi.versailles.inra.fr/Tools/PASTEClassifier/PASTEClassifier-tuto",
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            "topics": [],
            "keywords": [
                "genomics",
                "Transposons"
            ],
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            "dateCreation": null,
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            "licence": "CeCILL",
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        },
        {
            "id": 9,
            "name": "OmicsPath: Finding Relevant omics datasets using pathway information",
            "description": "\n \n\nOmicsPath: Finding Relevant omics datasets using pathway information\n \n",
            "communities": [],
            "elixirPlatforms": [],
            "doi": null,
            "fileLocation": "http://ressources.france-bioinformatique.fr/sites/default/files/videos/scorms/omicspath-finding_b649/scormcontent/index.html",
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            "topics": [],
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                "biohackaton 2018"
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            "dateCreation": "2019-03-21",
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        },
        {
            "id": 134,
            "name": "Nucleoli segmentation and feature extraction using CellProfiler",
            "description": "This tutorial covers the following questions:\r\n- How do I run an image analysis pipeline on public data using CellProfiler?\r\n- How do I analyse the DNA channel of fluorescence siRNA screens?\r\n- How do I download public image data into my history?\r\n- How do I segment and label cell nuclei?\r\n- How do I segment nucleoli (as the absence of DNA)?\r\n- How do I combine nuclei and nucleoli into one segmentation mask?\r\n- How do I extract the background of an image?\r\n- How do I relate the nucleoli to their parent nucleus?\r\n- How do I measure the image and object features?\r\n- How do I measure the image quality?\r\n\r\nAt the end of the tutorial, learners would be able to:\r\n- How to download images from a public image repository.\r\n- How to segment cell nuclei using CellProfiler in Galaxy.\r\n- How to segment cell nucleoli using CellProfiler in Galaxy.\r\n- How to extract features for images, nuclei and nucleoli.",
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            "doi": null,
            "fileLocation": "https://training.galaxyproject.org/training-material/topics/imaging/tutorials/tutorial-CP/tutorial.html",
            "fileName": "tutorial-CP",
            "topics": [
                "http://edamontology.org/topic_3383",
                "http://edamontology.org/topic_3382"
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                "Galaxy"
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        {
            "id": 69,
            "name": "New perspectives on nitrite-oxidizing bacteria - linking genomes to physiology",
            "description": "It is a generally accepted characteristic of the biogeochemical nitrogen cycle that nitrification is catalyzed by two distinct clades of microorganisms. First, ammonia-oxidizing bacteria and archaea convert ammonia to nitrite, which subsequently is oxidized to nitrate by nitrite-oxidizing bacteria (NOB). The latter were traditionally perceived as physiologically restricted organisms and were less intensively studied than other nitrogen-cycling microorganisms. This picture is contrasted by new discoveries of an unexpected high diversity of mostly uncultured NOB and a great physiological versatility, which includes complex microbe-microbe interactions and lifestyles outside the nitrogen cycle. Most surprisingly, close relatives to NOB perform complete nitrification (ammonia oxidation to nitrate), a process that had been postulated to occur under conditions selecting for low growth rates but high growth yields.\nThe existence of Nitrospira species that encode all genes required for ammonia and nitrite oxidation was first detected by metagenomic analyses of an enrichment culture for nitrogen-transforming microorganisms sampled from the anoxic compartment of a recirculating aquaculture system biofilter. Batch incubations and FISH-MAR experiments showed that these Nitrospira indeed formed nitrate from the aerobic oxidation of ammonia, and used the energy derived from complete nitrification for carbon fixation, thus proving that they indeed represented the long-sought-after comammox organisms. Their ammonia monooxygenase (AMO) enzymes were distinct from canonical AMOs, therefore rendering recent horizontal gene transfer from known ammonia-oxidizing microorganisms unlikely. Instead, their AMO displayed highest similarities to the “unusual” particulate methane monooxygenase from Crenothrix polyspora, thus shedding new light onto the function of this sequence group. This recognition of a novel AMO type indicates that a whole group of ammonia-oxidizing microorganisms has been overlooked, and will improve our understanding of the environmental abundance and distribution of this functional group. Data mining of publicly available metagenomes already indicated a widespread occurrence in natural and engineered environments like aquifers and paddy soils, and drinking and wastewater treatment systems.\n",
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            "doi": null,
            "fileLocation": "http://www.france-bioinformatique.fr/sites/default/files/videos/scorms/metagenomics16/session_8/New_perspectives_on_nitrite_xidizing_bacteria/scormcontent/index.html",
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        {
            "id": 80,
            "name": "Multiple Comparative Metagenomics using Multiset k-mer Counting",
            "description": "Large scale metagenomic projects aim to extract biodiversity knowledge between different environmental conditions. Current methods for comparing microbial communities face important limitations. Those based on taxonomical or functional assignation rely on a small subset of the sequences that can be associated to known organisms. On the other hand, de novo methods, that compare the whole set of sequences, do not scale up on ambitious metagenomic projects.\nThese limitations motivated the development of a new de novo metagenomic comparative method, called Simka. This method computes a large collection of standard ecology distances by replacing species counts by k-mer counts. Simka scales-up today metagenomic projects thanks to a new parallel k-mer counting strategy on multiple datasets.\nExperiments on public Human Microbiome Project datasets demonstrate that Simka captures the essential underlying biological structure. Simka was able to compute in a few hours both qualitative and quantitative ecology distances on hundreds of metagenomic samples (690 samples, 32 billions of reads). We also demonstrate that analyzing metagenomes at the k-mer level is highly correlated with extremely precise de novo comparison techniques which rely on all-versus-all sequences alignment strategy.\n",
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            "doi": null,
            "fileLocation": "http://www.france-bioinformatique.fr/sites/default/files/videos/scorms/metagenomics16/session_4/Multiple_comparative_metagenomics_using_multiset_k_mer_couting/scormcontent/index.html",
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        {
            "id": 74,
            "name": " MG-RAST  -  experiences from processing a quarter million metagenomic data sets",
            "description": "MG-RAST has been offering metagenomic analyses since 2007. Over 20,000 researchers have submitted data. I will describe the current MG-RAST implementation and demonstrate some of its capabilities. In the course of the presentation I will highlight several metagenomic pitfalls. MG-RAST: http://metagenomics.anl.gov MG-RAST-APP: http://api.metagenomics.anl.gov/api.html\n",
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            "doi": null,
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            "dateCreation": "2016-12-16",
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        },
        {
            "id": 132,
            "name": "Metatranscriptomics analysis using microbiome RNA-seq data",
            "description": "This tutorial covers the questions:\r\n- How to analyze metatranscriptomics data?\r\n- What information can be extracted of metatranscriptomics data?\r\n- How to assign taxa and function to the identified sequences?\r\n\r\nAt the end of the tutorial, learners would be able to:\r\n- Choose the best approach to analyze metatranscriptomics data\r\n- Understand the functional microbiome characterization using metatranscriptomic results\r\n- Understand where metatranscriptomics fits in ‘multi-omic’ analysis of microbiomes\r\n- Visualise a community structure",
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            "fileLocation": "https://training.galaxyproject.org/training-material/topics/microbiome/tutorials/metatranscriptomics/tutorial.html",
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                "Galaxy"
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        {
            "id": 128,
            "name": "Mapping with Galaxy",
            "description": "This tutorial covers the questions:\r\n- What is mapping?\r\n- What two things are crucial for a correct mapping?\r\n- What is BAM?\r\n\r\nAt the end of the tutorial, learners would be able to:\r\n- Define what mapping is\r\n- Perform mapping of reads on a reference genome\r\n- Evaluate the mapping output",
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            "doi": null,
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        {
            "id": 138,
            "name": "Linux TP",
            "description": "TP for linux training (genotoul bioinfo facility)",
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            "doi": null,
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            "id": 137,
            "name": "Linux slides",
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            "doi": null,
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            "fileName": "Formation_LINUX_GenoToul_2024.pdf",
            "topics": [],
            "keywords": [
                "Operating systems"
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        },
        {
            "id": 10,
            "name": "JSON schema validation with ontologies",
            "description": "\n \n\nJSON schema validation with ontologies\n \n",
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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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