Handles creating, reading and updating training materials.

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            "name": "Connecting Galaxy to a compute cluster",
            "description": "Problems\nRunning jobs on the Galaxy server negatively impacts Galaxy UI performance\nEven adding one other host helps\nCan restart Galaxy without interrupting jobs\nSolution:\nConnecter Galaxy to a computing cluster\n",
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            "description": "Opening an x2go session to the IFBcloud\n",
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            "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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            "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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            "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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            "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",
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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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        {
            "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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            "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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            "id": 76,
            "name": " Reconstructing genomes from metagenomes: The holy grail of microbiology",
            "description": "Shotgun metagenomics provides insights into a larger context of naturally occurring microbial genomes when short reads are assembled into contiguous DNA segments (contigs). Contigs are often orders of magnitude longer than individual sequences, offering improved annotations, and key information about the organization of genes in cognate genomes. Several factors affect the assembly performance, and the feasibility of the assembly-based approaches varies across environments. However, increasing read lengths, novel experimental approaches, advances in computational tools and resources, and improvements in assembly algorithms and pipelines render the assembly-based metagenomic workflow more and more accessible. The utility of metagenomic assembly remarkably increases when contigs are organized into metagenome-assembled genomes (MAGs). Often-novel MAGs frequently provide deeper insights into bacterial lifestyles that would otherwise remain unknown as evidenced by recent discoveries. The increasing rate of the recovery of MAGs presents new opportunities to link environmental distribution patterns of microbial populations and their functional potential, and transforms the field of microbiology by providing a more complete understanding of the microbial life, ecology, and evolution.\n",
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            "id": 96,
            "name": "Training on Galaxy : Metagenomics",
            "description": "Find Rapidly OTU with Galaxy Solution\n",
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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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            "licence": "CC BY-NC-ND",
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        },
        {
            "id": 70,
            "name": "Revealing and analyzing microbial networks: from topology to functional behaviors",
            "description": "Understanding the interactions between microbial communities and their environment well enough to be able to predict diversity on the basis of physicochemical parameters is a fundamental pursuit of microbial ecology that still eludes us. However, modeling microbial communities is a complicated task, because (i) communities are complex, (ii) most are described qualitatively, and (iii) quantitative understanding of the way communities interacts with their surroundings remains incomplete. Within this seminar, we will illustrate two complementary approaches that aim to overcome these points in different manners.\n",
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            "doi": null,
            "fileLocation": "http://www.france-bioinformatique.fr/sites/default/files/videos/scorms/metagenomics16/session_8/Revealing_and_analyzing%20microbial_networks_from_topology_to_functional_behaviors/scormcontent/index.html",
            "fileName": "missing.txt",
            "topics": [],
            "keywords": [
                "Metagenomics"
            ],
            "audienceTypes": [],
            "audienceRoles": [],
            "difficultyLevel": "",
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            "dateCreation": "2016-12-16",
            "dateUpdate": null,
            "licence": "CC BY-NC-ND",
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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",
            "communities": [],
            "elixirPlatforms": [],
            "doi": null,
            "fileLocation": "http://www.france-bioinformatique.fr/sites/default/files/videos/scorms/metagenomics16/session_7/Holistic_metagenomics_in_marine_communities/scormcontent/index.html",
            "fileName": "missing.txt",
            "topics": [],
            "keywords": [
                "Metagenomics"
            ],
            "audienceTypes": [],
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            "dateCreation": "2016-12-16",
            "dateUpdate": null,
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        },
        {
            "id": 95,
            "name": "Exercices on Galaxy: metagenomics",
            "description": "Find Rapidly OTU with Galaxy Solution\n",
            "communities": [],
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            "doi": null,
            "fileLocation": "http://genoweb.toulouse.inra.fr/~formation/15_FROGS/April2016/FROGS_april_2016_Pratice.pdf",
            "fileName": "missing.txt",
            "topics": [],
            "keywords": [
                "Metagenomics",
                "Galaxy"
            ],
            "audienceTypes": [],
            "audienceRoles": [],
            "difficultyLevel": "",
            "providedBy": [],
            "dateCreation": "2016-04-01",
            "dateUpdate": null,
            "licence": 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": [],
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                "Metagenomics"
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            "dateCreation": "2016-12-16",
            "dateUpdate": null,
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        }
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}