Handles creating, reading and updating training materials.

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            "id": 53,
            "name": "RNA - Seq de novo",
            "description": "\n \n\nPractical session on transciptome de novo assembly\n \n",
            "communities": [],
            "elixirPlatforms": [],
            "doi": null,
            "fileLocation": "http://ressources.france-bioinformatique.fr/sites/default/files/A01b_Galaxy_RNASeq_denovo_ITMO2016_TP_v2red.pdf",
            "fileName": "missing.txt",
            "topics": [],
            "keywords": [
                "RNA-seq"
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            "audienceTypes": [],
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            "dateCreation": "2016-11-23",
            "dateUpdate": null,
            "licence": "CC BY-NC-SA",
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        },
        {
            "id": 52,
            "name": "Statistics with RStudio",
            "description": "Introduction to statistics with R\n",
            "communities": [],
            "elixirPlatforms": [],
            "doi": null,
            "fileLocation": "http://jvanheld.github.io/stats_avec_RStudio_EBA/",
            "fileName": "missing.txt",
            "topics": [],
            "keywords": [
                "R"
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            "dateCreation": "2016-11-23",
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        },
        {
            "id": 54,
            "name": "Transcriptome de novo assembly",
            "description": "Not available\n",
            "communities": [],
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            "doi": null,
            "fileLocation": "http://www.france-bioinformatique.fr/sites/default/files/A01_Galaxy_RNASeq_denovo_ITMO2016_0.pdf",
            "fileName": "missing.txt",
            "topics": [],
            "keywords": [
                "Transcriptomics"
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            "dateCreation": "2016-11-23",
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        },
        {
            "id": 55,
            "name": "x2Go",
            "description": "Opening an x2go session to the IFBcloud\n",
            "communities": [],
            "elixirPlatforms": [],
            "doi": null,
            "fileLocation": "http://www.france-bioinformatique.fr/sites/default/files/x2go_to_IFB-VM.pdf",
            "fileName": "missing.txt",
            "topics": [],
            "keywords": [
                "Cloud"
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            "dateCreation": "2016-11-23",
            "dateUpdate": null,
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        },
        {
            "id": 51,
            "name": "Eukaryotic small RNA",
            "description": "\n \n\nSmall RNAseq data analysis for miRNA identification\n \n",
            "communities": [],
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            "doi": null,
            "fileLocation": "http://ressources.france-bioinformatique.fr/sites/default/files/sRNA-Seq.pdf",
            "fileName": "missing.txt",
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                "RNA-seq"
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            "dateCreation": "2016-11-23",
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        },
        {
            "id": 60,
            "name": "Differential analysis of RNA-Seq data",
            "description": "Design, describe, explore and model\n",
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            "doi": null,
            "fileLocation": "http://www.france-bioinformatique.fr/sites/default/files/R02-R03_slidesRoscoff_stats_HVaret.pdf",
            "fileName": "missing.txt",
            "topics": [],
            "keywords": [
                "genomics",
                "RNA-seq"
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            "dateCreation": "2016-11-23",
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        },
        {
            "id": 56,
            "name": "RADSeq Data Analysis",
            "description": "Introduction to RADSeq through STACKS on Galaxy\n",
            "communities": [],
            "elixirPlatforms": [],
            "doi": null,
            "fileLocation": "http://www.france-bioinformatique.fr/sites/default/files/V08_Yvan%20Le%20Bras%20-%20Training%20RADSeq_0.pdf",
            "fileName": "missing.txt",
            "topics": [],
            "keywords": [
                "NGS"
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            "dateCreation": "2016-11-23",
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        },
        {
            "id": 87,
            "name": "Deciphering the human intestinal tract microbiome using metagenomic computational methods",
            "description": "In 2010, the MetaHIT consortium published a 3.3M microbiota gene catalog generated by whole genome shotgun metagenomic sequencing, representing a mixture of bacteria, archaea, parasites and viruses coming from 124 human stool metagenomic samples [Qin et al, Nature 2010].\nHowever most of the genes were fragmented, taxonomically and functionally unknown, making it difficult to define and select biomarkers of interest for genome-wide association studies.\nSince that, this human gene catalog was improved multiple times, with the last update by [Li et al, Nature Biotechnology, 2014], which generated a 10M gene catalog using more than 1000 metagenomic samples and including some prevalent human microbe genome available at that time. Along with the catalog update, the scientific community developed new tools to challenge the complexity of this dataset and provided new ways to assemble, annotate, quantify and classify the genes coming from these catalogs.\nIn this talk we will discuss the main approaches related to the computational treatment of the different gene catalog other the time, illustrated by the different papers that deciphered step by step the hidden information of our microbiota and his link with our health.\n",
            "communities": [],
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            "doi": null,
            "fileLocation": "http://www.france-bioinformatique.fr/sites/default/files/videos/scorms/metagenomics16/session_1/Deciphering_the_human_intestinal_tract_microbiome_using_metagenomic_computational_methods/scormcontent/index.html",
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            "topics": [],
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            "dateCreation": "2016-12-15",
            "dateUpdate": null,
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        {
            "id": 84,
            "name": "Who is doing what on the cheese surface? Overview of the cheese microbial ecosystem functioning by metatranscriptomic analyses",
            "description": "Cheese ripening is a complex biochemical process driven by microbial communities composed of both eukaryotes and prokaryotes. Surface-ripened cheeses are widely consumed all over the world and are appreciated for their characteristic flavor. Microbial community composition has been studied for a long time on surface-ripened cheeses, but only limited knowledge has been acquired about its in situ metabolic activities. We used an iterative sensory procedure to select a simplified microbial consortium, composed of only nine species (three yeasts and six bacteria), producing the odor of Livarot-type cheese when inoculated in a sterile cheese curd. All the genomes were sequenced in order to determine the functional capacities of the different species and facilitate RNA-Seq data analyses. We followed the ripening process of experimental cheeses made using this consortium during four weeks, by metatranscriptomic and biochemical analyses. By combining all of the data, we were able to obtain an overview of the cheese maturation process and to better understand the metabolic activities of the different community members and their possible interactions. We next applied the same approach to investigate the activity of the microorganisms in real cheeses, namely Reblochon-style cheeses. This provided useful insights into the physiological changes that occur during cheese ripening, such as changes in energy substrates, anabolic reactions, or stresses.\n",
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            "doi": null,
            "fileLocation": "http://www.france-bioinformatique.fr/sites/default/files/videos/scorms/metagenomics16/session_2/Who_is_doing_what_on_the_cheese_surface_Overview_of_the_cheese%20microbial_ecosystem_functioning_by_metatranscriptomic_analyses/scormcontent/index.html",
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            "dateCreation": "2016-12-15",
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        },
        {
            "id": 85,
            "name": "Exploiting collisions between DNA molecules to characterize the genomic structures of complex communities",
            "description": "Meta3C is an experimental and computational approach that exploits the physical contacts experienced by DNA molecules sharing the same cellular compartments. These collisions provide a quantitative information that allows interpreting and phasing the genomes present within complex mixes of species without prior knowledge. Not only the exploitation of chromosome physical 3D signatures hold interesting premises regarding solving the genome sequences from discrete species, but it also allows assigning mobile elements such as plasmids or phages to their hosts.\n",
            "communities": [],
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            "doi": null,
            "fileLocation": "http://www.france-bioinformatique.fr/sites/default/files/videos/scorms/metagenomics16/session_2/Exploiting_collisions_between_DNA_molecules_to_characterize_the_genomic_structures_of_complex_communities/scormcontent/",
            "fileName": "missing.txt",
            "topics": [],
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            "dateCreation": "2016-12-15",
            "dateUpdate": null,
            "licence": "CC BY-NC-ND",
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        },
        {
            "id": 86,
            "name": "Gut metagenomics in cardiometabolic diseases",
            "description": "Cardio-metabolic and Nutrition-related diseases (CMDs) represent an enormous burden for health care. They are characterized by very heterogeneous phenotypes progressing with time. It is virtually impossible to predict who will or will not develop cardiovascular comorbidities. There is a clear need to intervene earlier in the natural cycle of the disease, before irreversible tissue damages develop. Predictive tools still remain elusive and environmental factors (food, nutrition, physical activity and psychosocial factors) play major roles in the development of these interrelated pathologies. Poor nutritional environment and lifestyle also promote health deterioration resulting in CMD progression. In the last few years, the characterization of the gut microbiome (i.e. collective bacteria genome) and gut-derived molecules (i.e. metabolites, lipids, inflammatory molecules) has opened up new avenues for the generation of fundamental knowledge regarding putative shared pathways in CMD. The gut microbiome is likely to have an even greater impact than genetic factors given its close relationship with environmental factors. In metabolic disorders, the discoveries that low bacterial gene richness associates with cardiovascular risks stimulate encourage these developments. Due to the complexity of the gut microbiome, and its interactions with human (host) metabolism as well as with the immune system, it is only through integrative analyses where metabolic network models are used as scaffold for analysis that it will be possible to identify markers and shared pathways, which will contribute to improve patient stratification and develop new modes of patient care.\n",
            "communities": [],
            "elixirPlatforms": [],
            "doi": null,
            "fileLocation": "http://www.france-bioinformatique.fr/sites/default/files/videos/scorms/metagenomics16/session_2/Gut_metagenomics_in_cardiometabolic_diseases/scormcontent/index.html",
            "fileName": "missing.txt",
            "topics": [],
            "keywords": [
                "Metagenomics"
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            "dateCreation": "2016-12-15",
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        },
        {
            "id": 30,
            "name": "Welcome message",
            "description": "Presentation of the workshop (Chairman: Claudine Médigue)\n",
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            "doi": null,
            "fileLocation": "http://www.france-bioinformatique.fr/sites/default/files/videos/scorms/metagenomics16/session_1/welcome_message/scormcontent/index.html",
            "fileName": "missing.txt",
            "topics": [],
            "keywords": [
                "Metagenomics",
                "biohackaton"
            ],
            "audienceTypes": [],
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            "dateCreation": "2016-12-15",
            "dateUpdate": null,
            "licence": "CC BY-NC-ND",
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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",
            "communities": [],
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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",
            "fileName": "missing.txt",
            "topics": [],
            "keywords": [
                "Metagenomics"
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            "dateCreation": "2016-12-16",
            "dateUpdate": null,
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        },
        {
            "id": 79,
            "name": "Soil metagenomics, potential and pitfalls",
            "description": "The soil microorganisms are responsible for a range of critical functions including those that directly affect our quality of life (e.g., antibiotic production and resistance – human and animal health, nitrogen fixation -agriculture, pollutant degradation – environmental bioremediation). Nevertheless, genome structure information has been restricted by a large extent to a small fraction of cultivated species. This limitation can be circumvented now by modern alternative approaches including metagenomics or single cell genomics.  Metagenomics includes the data treatment of DNA sequences from many members of the microbial community, in order to either extract a specific microorganism’s genome sequence or to evaluate the community function based on the relative quantities of different gene families. In my talk I will show how these metagenomic datasets can be used to estimate and compare the functional potential of microbial communities from various environments with a special focus on antibiotic resistance genes. However, metagenomic datasets can also in some cases be partially assembled into longer sequences representing microbial genetic structures for trying to correlate different functions to their co-location on the same genetic structure. I will show how the microbial community composition of a natural grassland soil characterized by extremely high microbial diversity could be managed for sequentially attempt to reconstruct some bacterial genomes.\nMetagenomics can also be used to exploit the genetic potential of environmental microorganisms. I will present an integrative approach coupling rrs phylochip and high throughput shotgun sequencing to investigate the shift in bacterial community structure and functions after incubation with chitin. In a second step, these functions of potential industrial interest can be discovered by using hybridization of soil metagenomic DNA clones spotted on high density membranes by a mix of oligonucleotide probes designed to target genes encoding for these enzymes. After affiliation of the positive hybridizing spots to the corresponding clones in the metagenomic library the inserts are sequenced, DNA assembled and annotated leading to identify new coding DNA sequences related to genes of interest with a good coverage but a low similarity against closest hits in the databases confirming novelty of the detected and cloned genes.\n",
            "communities": [],
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            "doi": null,
            "fileLocation": "http://www.france-bioinformatique.fr/sites/default/files/videos/scorms/metagenomics16/session_4/Soil_metagenomics_fundamental_and_applications/scormcontent/index.html",
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            "topics": [],
            "keywords": [
                "Metagenomics"
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            "dateCreation": "2016-12-16",
            "dateUpdate": null,
            "licence": "CC BY-NC-ND",
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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",
            "communities": [],
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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": [],
            "keywords": [
                "Metagenomics"
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            "dateCreation": "2016-12-16",
            "dateUpdate": null,
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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,
            "fileLocation": "http://www.france-bioinformatique.fr/sites/default/files/videos/scorms/metagenomics16/session_6/MG_RSAT/scormcontent/index.html",
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            "topics": [],
            "keywords": [
                "Metagenomics"
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            "dateCreation": "2016-12-16",
            "dateUpdate": null,
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        },
        {
            "id": 75,
            "name": "Fast filtering, mapping and assembly of 16S ribosomal RNA",
            "description": "The application of next-generation sequencing technologies to RNA or DNA directly extracted from a community of organisms yields a mixture of nucleotide fragments. The task to distinguish amongst these and to further categorize the families of ribosomal RNAs (or any other given marker) is an important step for examining the phylogenetic classification of the constituting species. In this perspective, we have developed  a complete bioinformatics suite, called MATAM, capable of handling large sets of  reads in a fast and accurate way. MATAM covers all steps of the analysis, from the identification of reads of interest in the raw sequencing data to the reconstruction of the  full-length sequences of the marker and alignment to a reference database for taxonomic assignment. Part of MATAM is based on the SortMeRNA software, also developed by the team.\n",
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            "doi": null,
            "fileLocation": "http://www.france-bioinformatique.fr/sites/default/files/videos/scorms/metagenomics16/session_6/Fast_filtering_mapping_and_assembly_of_16S_rRNA/scormcontent/index.html",
            "fileName": "missing.txt",
            "topics": [],
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            "dateCreation": "2016-12-16",
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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": 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",
            "communities": [],
            "elixirPlatforms": [],
            "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",
            "fileName": "missing.txt",
            "topics": [],
            "keywords": [
                "Metagenomics"
            ],
            "audienceTypes": [],
            "audienceRoles": [],
            "difficultyLevel": "",
            "providedBy": [],
            "dateCreation": "2016-12-16",
            "dateUpdate": null,
            "licence": "CC BY-NC-ND",
            "maintainers": [
                "https://catalogue.france-bioinformatique.fr/api/userprofile/704/"
            ]
        },
        {
            "id": 78,
            "name": "Dr Jekyll and Mr Hyde: The dual face of metagenomics in phylogenetic analysis",
            "description": "The aim of this lecture is to present the impact of metagenomics and single-cell genomics on public databases. These new powerful approches allow us to have access to the diversity of life on our planet. However, care has to be taken when using these data for posterior analyses, such as phylogenetic studies, as critical errors can still be present in the databases. This course will incorporate examples taken from real studies, and we will investigate methods used for error detection.\n",
            "communities": [],
            "elixirPlatforms": [],
            "doi": null,
            "fileLocation": "http://www.france-bioinformatique.fr/sites/default/files/videos/scorms/metagenomics16/session_5/Dr_Jekyll_and_Mr_Hyde_The_dual_face_of_metagenomics_in_phylogenetic_analysis/scormcontent/index.html",
            "fileName": "missing.txt",
            "topics": [],
            "keywords": [
                "Metagenomics"
            ],
            "audienceTypes": [],
            "audienceRoles": [],
            "difficultyLevel": "",
            "providedBy": [],
            "dateCreation": "2016-12-16",
            "dateUpdate": null,
            "licence": "CC BY-NC-ND",
            "maintainers": [
                "https://catalogue.france-bioinformatique.fr/api/userprofile/706/"
            ]
        }
    ]
}