Handles creating, reading and updating training materials.

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            "name": "Chip-seq: Discovering motifs in peaks with RSAT",
            "description": "Read mapping: from raw reads to aligned reads.\nPeak calling: from aligned reads to regions/peaks of high read density.\nChIP-seq annotation\nIdentification of genes related to the peaks.\nProfiles of ChIP-seq reads around reference points (TSS, histone marks,).\nFunctional enrichment of the genes related to the peaks.\n\n",
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            "name": "Chip-seq: Motif Analysis Tutorial",
            "description": "Introduction\nGoal\nThe aim is to :\nGet familiar with motif analysis of ChIP-seq data.\nLearn de novo motif discovery methods.\nIn practice :\nMotif discovery with peak-motifs\nDifferential analysis\nRandom controls\n",
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            "description": "Introduction to RADSeq through STACKS on Galaxy\n",
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            "fileLocation": "http://www.france-bioinformatique.fr/sites/default/files/V08_Yvan%20Le%20Bras%20-%20Training%20RADSeq_0.pdf",
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            "name": "Visualization of NGS data with IGV",
            "description": "Visualisation of next-gen sequencing data with Integrative Genomics Viewer\n",
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            "fileLocation": "http://ecole-bioinfo-aviesan.sb-roscoff.fr/sites/ecole-bioinfo-aviesan.sb-roscoff.fr/files/files/IGV-Roscoff-Septembre2015.pdf",
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            "name": "RNA-Seq: isoform detection and quantification",
            "description": "transcriptome from new condition\ntissue-speci c transcriptome\ndifferent development stages\ntranscriptome from non model organism\ncancer cell\nRNA maturation mutant\n \nHow to manage RNA-Seq data with genes subjected to di erential\nsplicing?\nIs it possible to discover new isoforms?\nIs it possible to quantify abundance of each isoform\n",
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            "doi": null,
            "fileLocation": "http://ecole-bioinfo-aviesan.sb-roscoff.fr/sites/ecole-bioinfo-aviesan.sb-roscoff.fr/files/files/toffano-TP_RNASeq_Isoform.pdf",
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            "id": 54,
            "name": "Transcriptome de novo assembly",
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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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            "id": 105,
            "name": "Exploring Microscope Platform",
            "description": "\n \n\nHow to use the Microscope Platform to annotate and analyze microbial genomes.\n \n",
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            "name": "RNA-Seq: Differential Expression Analysis",
            "description": "\n \n\nBe careful about experimental design : avoid putting all the\nreplicates in the same lane, using the same barcode for the\nreplicates, putting different number of samples in lanes etc...\nNon- uniformity of the per base read distribution (Illumina Random\nHexamer Priming bias visible on the 13 first bases)\nBias hierarchy : biological condition >> concentration > run/flowcell> lane\nAt equivalent expression level, a long gene will have more reads than a short one.\nNon random coverage along the transcript.\nMultiple hit for some reads alignments.\n \n",
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            "id": 97,
            "name": "Analysis of community composition data using phyloseq",
            "description": "Learn about and become familiar with phyloseq R package for the analysis of microbial census data\n",
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            "doi": null,
            "fileLocation": "http://genoweb.toulouse.inra.fr/~formation/15_FROGS/April2016/R_phyloseq/phyloseq_formation_toulouse_201604.pdf",
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            "id": 52,
            "name": "Statistics with RStudio",
            "description": "Introduction to statistics with R\n",
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            "fileLocation": "http://jvanheld.github.io/stats_avec_RStudio_EBA/",
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            "name": "A Quick and focused overview of R data types and ggplot2 syntax",
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            "id": 109,
            "name": "RNA-Seq: Differential Expression Analysis",
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            "name": "Differential gene expression analysis : Practical part",
            "description": "RNA-seq: Differential gene expression analysis practical session\n",
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            "id": 62,
            "name": "RNA-seq: Differential gene expression analysis",
            "description": "Transcriptome analysis provides information about the identity and quantity of all RNA molecules\n",
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            "doi": null,
            "fileLocation": "http://www.france-bioinformatique.fr/sites/default/files/R01_EBA206_RNAseq_ref.pdf",
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            "id": 144,
            "name": "Reference-based RNA-Seq data analysis with Galaxy",
            "description": "This tutorial covers the questions:\r\n- What are the steps to process RNA-Seq data?\r\n- How to identify differentially expressed genes across multiple experimental conditions?\r\n- What are the biological functions impacted by the differential expression of genes?\r\n\r\nAt the end of the tutorial, learners would be able to:\r\n- Check a sequence quality report generated by FastQC for RNA-Seq data\r\n- Explain the principle and specificity of mapping of RNA-Seq data to an eukaryotic reference genome\r\n- Select and run a state of the art mapping tool for RNA-Seq data\r\n- Evaluate the quality of mapping results\r\n- Describe the process to estimate the library strandness\r\n- Estimate the number of reads per genes\r\n- Explain the count normalization to perform before sample comparison\r\n- Construct and run a differential gene expression analysis\r\n- Analyze the DESeq2 output to identify, annotate and visualize differentially expressed genes\r\n- Perform a gene ontology enrichment analysis\r\n- Perform and visualize an enrichment analysis for KEGG pathways",
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            "doi": null,
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            "id": 135,
            "name": "Training RNASeq - bioinfo part - Genotoul-bioinfo",
            "description": "Cette formation a pour but de vous aider à traiter les séquences courtes issues des plates-formes de séquençage Illumina. Vous y découvrirez les formats de séquences et d’alignement les biais connus et mettrez en œuvre des logiciels d'alignement épissé sur génome de référence, la recherche de nouveaux gènes, de nouveaux transcrits et la quantification de l'expression de ces gènes et transcrits.",
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        {
            "id": 53,
            "name": "RNA - Seq de novo",
            "description": "\n \n\nPractical session on transciptome de novo assembly\n \n",
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            "elixirPlatforms": [],
            "doi": null,
            "fileLocation": "http://ressources.france-bioinformatique.fr/sites/default/files/A01b_Galaxy_RNASeq_denovo_ITMO2016_TP_v2red.pdf",
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            "id": 51,
            "name": "Eukaryotic small RNA",
            "description": "\n \n\nSmall RNAseq data analysis for miRNA identification\n \n",
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