Training Material List
Handles creating, reading and updating training materials.
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https://catalogue.france-bioinformatique.fr/api/trainingmaterial/?limit=20&offset=40&ordering=licence", "previous": "https://catalogue.france-bioinformatique.fr/api/trainingmaterial/?limit=20&ordering=licence", "results": [ { "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": [], "elixirPlatforms": [], "doi": null, "fileLocation": "http://www.france-bioinformatique.fr/sites/default/files/videos/scorms/metagenomics16/session_4/Soil_metagenomics_fundamental_and_applications/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/707/" ] }, { "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", "communities": [], "elixirPlatforms": [], "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", "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/708/" ] }, { "id": 81, "name": "Assessing microbial biogeography by using a metagenomic approach", "description": "Soils are highly complex ecosystems and are considered as one of the Earth’s main reservoirs of biological diversity. Bacteria account for a major part of this biodiversity, and it is now clear that such microorganisms have a key role in soil functioning processes. However, environmental factors regulating the diversity of below-ground bacteria still need to be investigated, which limits our understanding of the distribution of such bacteria at various spatial scales. The overall objectives of this study were: (i) to determine the spatial patterning of bacterial community diversity in soils at a broad scale, and (ii) to rank the environmental filters most influencing this distribution.\nThis study was performed at the scale of the France by using the French Soil Quality Monitoring Network. This network includes more than 2,200 soil samples along a systematic grid sampling. For each soil, bacterial diversity was characterized using a pyrosequencing approach targeting the 16S rRNA genes directly amplified from soil DNA, obtaining more than 18 million of high-quality sequences.\nThis study provides the first estimates of microbial diversity at the scale of France, with for example, bacterial richness ranging from 555 to 2,007 OTUs (on average: 1,289 OTUs). It also provides the first extensive map of bacterial diversity, as well as of major bacterial taxa, revealing a bacterial heterogeneous and spatially structured distribution at the scale of France. The main factors driving bacterial community distribution are the soil physico-chemical properties (pH, texture...) and land use (forest, grassland, crop system...), evidencing that bacterial spatial distribution at a broad scale depends on local filters such as soil characteristics and land use when regarding the community (quality, composition) as a whole. Moreover, this study also offers a better evaluation of the impact of land uses on soil microbial diversity and taxa, with consequences in terms of sustainability for agricultural systems.\n", "communities": [], "elixirPlatforms": [], "doi": null, "fileLocation": "http://www.france-bioinformatique.fr/sites/default/files/videos/scorms/metagenomics16/session_4/Assessing_microbial_biogeography_by_using_a_metagenomic_approach/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/709/" ] }, { "id": 82, "name": "Sequencing 6000 chloroplast genomes : the PhyloAlps project", "description": "Biodiversity is now commonly described by DNA based approches. Several actors are currently using DNA to describe biodiversity, and most of the time they use different genetic markers that is hampering an easy sharing of the accumulated knowledges. Taxonomists rely a lot on the DNA Barcoding initiative, phylogeneticists often prefer markers with better phylogenic properties, and ecologists, with the coming of the DNA metabarcoding, look for a third class of markers easiest to amplify from environmental DNA. Nevertheless they have all the same need of the knowledge accumulated by the others. But having different markers means that the sequecences have been got from different individuals in differente lab, following various protocoles. On that base, building a clean reference database, merging for each species all the available markers becomes a challenge. With the phyloAlps project we implement genome skimming at a large scale and propose it as a new way to set up such universal reference database usable by taxonomists, phylogeneticists, and ecologists. The Phyloalps project is producing for each species of the Alpine flora at least a genome skim composed of six millions of 100bp sequence reads. From such data it is simple to extract all chloroplastic, mitochondrial and nuclear rDNA markers commonely used. Moreover, most of the time we can get access to the complete chloroplast genome sequence and to a shallow sequencing of many nuclear genes. This methodes have already been successfully applied to algeae, insects and others animals. With the new single cell sequencing methods it will be applicable to most of the unicellular organisms. The good question is now : Can we consider the genome skimming as the next-generation DNA barcode ?\n", "communities": [], "elixirPlatforms": [], "doi": null, "fileLocation": "http://www.france-bioinformatique.fr/sites/default/files/videos/scorms/metagenomics16/session_3/Sequencing_6000_chloroplast_genomes_the_PhyloAlps_project/scormcontent/", "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/126/" ] }, { "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": [], "elixirPlatforms": [], "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" ], "audienceTypes": [], "audienceRoles": [], "difficultyLevel": "", "providedBy": [], "dateCreation": "2016-12-16", "dateUpdate": null, "licence": "CC BY-NC-ND", "maintainers": [ "https://catalogue.france-bioinformatique.fr/api/userprofile/123/" ] }, { "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", "communities": [], "elixirPlatforms": [], "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", "fileName": "missing.txt", "topics": [], "keywords": [ "Metagenomics" ], "audienceTypes": [], "audienceRoles": [], "difficultyLevel": "", "providedBy": [], "dateCreation": "2016-12-15", "dateUpdate": null, "licence": "CC BY-NC-ND", "maintainers": [ "https://catalogue.france-bioinformatique.fr/api/userprofile/710/" ] }, { "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": [], "elixirPlatforms": [], "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": [], "keywords": [ "Metagenomics" ], "audienceTypes": [], "audienceRoles": [], "difficultyLevel": "", "providedBy": [], "dateCreation": "2016-12-15", "dateUpdate": null, "licence": "CC BY-NC-ND", "maintainers": [ "https://catalogue.france-bioinformatique.fr/api/userprofile/711/" ] }, { "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" ], "audienceTypes": [], "audienceRoles": [], "difficultyLevel": "", "providedBy": [], "dateCreation": "2016-12-15", "dateUpdate": null, "licence": "CC BY-NC-ND", "maintainers": [ "https://catalogue.france-bioinformatique.fr/api/userprofile/712/" ] }, { "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": [], "elixirPlatforms": [], "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", "fileName": "missing.txt", "topics": [], "keywords": [ "Metagenomics" ], "audienceTypes": [], "audienceRoles": [], "difficultyLevel": "", "providedBy": [], "dateCreation": "2016-12-15", "dateUpdate": null, "licence": "CC BY-NC-ND", "maintainers": [ "https://catalogue.france-bioinformatique.fr/api/userprofile/713/" ] }, { "id": 88, "name": "From Samples to Data : Assuring Downstream Analysis with Upstream Planning", "description": "Metagenomic studies have gained increasing popularity in the years since the introduction of next generation sequencing. NGS allows for the production of millions of reads for each sample without the intermediate step of cloning. However, just as in the past, the quality of the data generate by this powerful technology depends on sample preparation, library construction and the selection of appropriate sequencing technology and sequencing depth. Here we explore the different variables involved in the process of preparing samples for sequencing analysis including sample collection, DNA extraction and library construction. We also examine the various sequencing technologies deployed for routine metagenomic analysis and considerations for their use in different model systems including humans, mouse and the environment. Future developments such as long-reads will also be discussed to provide a complete picture of important aspects prior to data analyses which play a critical role in the success of metagenomic studies.\n", "communities": [], "elixirPlatforms": [], "doi": null, "fileLocation": "http://www.france-bioinformatique.fr/sites/default/files/videos/scorms/metagenomics16/session_1/From_Samples_to_Data_Assuring_Downstream_Analysis_with_Upstream_Planning/scormcontent/index.html", "fileName": "missing.txt", "topics": [], "keywords": [ "Metagenomics" ], "audienceTypes": [], "audienceRoles": [], "difficultyLevel": "", "providedBy": [], "dateCreation": null, "dateUpdate": null, "licence": "CC BY-NC-ND", "maintainers": [ "https://catalogue.france-bioinformatique.fr/api/userprofile/714/" ] }, { "id": 30, "name": "Welcome message", "description": "Presentation of the workshop (Chairman: Claudine Médigue)\n", "communities": [], "elixirPlatforms": [], "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": [], "audienceRoles": [], "difficultyLevel": "", "providedBy": [], "dateCreation": "2016-12-15", "dateUpdate": null, "licence": "CC BY-NC-ND", "maintainers": [ "https://catalogue.france-bioinformatique.fr/api/userprofile/187/", "https://catalogue.france-bioinformatique.fr/api/userprofile/664/", "https://catalogue.france-bioinformatique.fr/api/userprofile/431/" ] }, { "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": [], "elixirPlatforms": [], "doi": null, "fileLocation": "https://urgi.versailles.inra.fr/Tools/PASTEClassifier/PASTEClassifier-tuto", "fileName": "missing.txt", "topics": [], "keywords": [ "genomics", "Transposons" ], "audienceTypes": [], "audienceRoles": [], "difficultyLevel": "", "providedBy": [], "dateCreation": null, "dateUpdate": null, "licence": "CeCILL", "maintainers": [] }, { "id": 100, "name": "REPET: TEdannot Tutorial", "description": "TEannot is able to annote a genome using DNA sequences library. This library can be a predicted TE library built by TEdenovo\n", "communities": [], "elixirPlatforms": [], "doi": null, "fileLocation": "https://urgi.versailles.inra.fr/Tools/REPET/TEannot-tuto", "fileName": "missing.txt", "topics": [], "keywords": [ "genomics", "Annotation" ], "audienceTypes": [], "audienceRoles": [], "difficultyLevel": "", "providedBy": [], "dateCreation": null, "dateUpdate": null, "licence": "CeCILL", "maintainers": [] }, { "id": 101, "name": "REPET: TEdenovo tutorial", "description": "The TEdenovo pipeline follows a philosophy in three first steps:\nDetection of repeated sequences (potential TE)\nClustering of these sequences\nGeneration of consensus sequences for each cluster, representing the ancestral TE\n", "communities": [], "elixirPlatforms": [], "doi": null, "fileLocation": "https://urgi.versailles.inra.fr/Tools/REPET/TEdenovo-tuto", "fileName": "missing.txt", "topics": [], "keywords": [ "genomics", "Annotation" ], "audienceTypes": [], "audienceRoles": [], "difficultyLevel": "", "providedBy": [], "dateCreation": null, "dateUpdate": null, "licence": "CeCILL", "maintainers": [] }, { "id": 130, "name": "Taxonomic Profiling and Visualization of Metagenomic Data", "description": "This tutorial covers the questions:\r\n- Which species (or genera, families, …) are present in my sample?\r\n- What are the different approaches and tools to get the community profile of my sample?\r\n- How can we visualize and compare community profiles?\r\n\r\nAt the end of the tutorial, learners would be able to:\r\n- Explain what taxonomic assignment is\r\n- Explain how taxonomic assignment works\r\n- Apply Kraken and MetaPhlAn to assign taxonomic labels\r\n- Apply Krona and Pavian to visualize results of assignment and understand the output\r\n- Identify taxonomic classification tool that fits best depending on their data", "communities": [], "elixirPlatforms": [], "doi": null, "fileLocation": "https://training.galaxyproject.org/training-material/topics/microbiome/tutorials/taxonomic-profiling/tutorial.html", "fileName": "taxonomic-profiling", "topics": [ "http://edamontology.org/topic_3697", "http://edamontology.org/topic_3174", "http://edamontology.org/topic_0637" ], "keywords": [ "Galaxy" ], "audienceTypes": [ "Undergraduate", "Graduate", "Professional (initial)", "Professional (continued)" ], "audienceRoles": [ "Researchers", "Life scientists", "Biologists" ], "difficultyLevel": "Novice", "providedBy": [], "dateCreation": null, "dateUpdate": null, "licence": "CC-BY-4.0", "maintainers": [ "https://catalogue.france-bioinformatique.fr/api/userprofile/677/" ] }, { "id": 131, "name": "16S Microbial Analysis with mothur", "description": "This tutorial covers the questions:\r\n- What is the effect of normal variation in the gut microbiome on host health?\r\n\r\nAt the end of the tutorial, learners would be able to:\r\n- Analyze of 16S rRNA sequencing data using the mothur toolsuite in Galaxy\r\n- Using a mock community to assess the error rate of your sequencing experiment\r\n- Visualize sample diversity using Krona and Phinch", "communities": [], "elixirPlatforms": [], "doi": null, "fileLocation": "https://training.galaxyproject.org/training-material/topics/microbiome/tutorials/mothur-miseq-sop-short/tutorial.html", "fileName": "mothur-miseq-sop-short", "topics": [ "http://edamontology.org/topic_3697", "http://edamontology.org/topic_0637" ], "keywords": [ "Galaxy", "Metabarcoding" ], "audienceTypes": [ "Undergraduate", "Graduate", "Professional (initial)", "Professional (continued)" ], "audienceRoles": [ "Researchers", "Life scientists", "Biologists" ], "difficultyLevel": "Novice", "providedBy": [], "dateCreation": null, "dateUpdate": null, "licence": "CC-BY-4.0", "maintainers": [ "https://catalogue.france-bioinformatique.fr/api/userprofile/677/" ] }, { "id": 126, "name": "Galaxy 101 for everyone", "description": "This practical aims at familiarizing you with the Galaxy user interface. It will teach you how to perform basic tasks such as importing data, running tools, working with histories, creating workflows and sharing your work. Not everyone has the same background and that’s ok!", "communities": [], "elixirPlatforms": [], "doi": null, "fileLocation": "https://training.galaxyproject.org/training-material/topics/introduction/tutorials/galaxy-intro-101-everyone/tutorial.html", "fileName": "galaxy-intro-101-everyone", "topics": [], "keywords": [], "audienceTypes": [], "audienceRoles": [], "difficultyLevel": "", "providedBy": [], "dateCreation": null, "dateUpdate": null, "licence": "CC-BY-4.0", "maintainers": [ "https://catalogue.france-bioinformatique.fr/api/userprofile/677/" ] }, { "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", "communities": [], "elixirPlatforms": [], "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" ], "keywords": [ "Quality Control" ], "audienceTypes": [ "Graduate", "Professional (initial)", "Professional (continued)" ], "audienceRoles": [ "Researchers", "Life scientists", "Biologists" ], "difficultyLevel": "Novice", "providedBy": [], "dateCreation": null, "dateUpdate": null, "licence": "CC-BY-4.0", "maintainers": [ "https://catalogue.france-bioinformatique.fr/api/userprofile/677/" ] }, { "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", "communities": [], "elixirPlatforms": [], "doi": null, "fileLocation": "https://training.galaxyproject.org/training-material/topics/microbiome/tutorials/metatranscriptomics/tutorial.html", "fileName": "metatranscriptomics", "topics": [ "http://edamontology.org/topic_3941", "http://edamontology.org/topic_1775" ], "keywords": [ "Galaxy" ], "audienceTypes": [ "Undergraduate", "Graduate", "Professional (initial)", "Professional (continued)" ], "audienceRoles": [ "Researchers", "Life scientists", "Biologists" ], "difficultyLevel": "Novice", "providedBy": [], "dateCreation": null, "dateUpdate": null, "licence": "CC-BY-4.0", "maintainers": [ "https://catalogue.france-bioinformatique.fr/api/userprofile/677/" ] }, { "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", "communities": [], "elixirPlatforms": [], "doi": null, "fileLocation": "https://training.galaxyproject.org/training-material/topics/sequence-analysis/tutorials/mapping/tutorial.html", "fileName": "mapping", "topics": [ "http://edamontology.org/topic_0102" ], "keywords": [ "Mapping" ], "audienceTypes": [ "Undergraduate", "Graduate", "Professional (initial)", "Professional (continued)" ], "audienceRoles": [ "Researchers", "Life scientists", "Biologists" ], "difficultyLevel": "Novice", "providedBy": [], "dateCreation": null, "dateUpdate": null, "licence": "CC-BY-4.0", "maintainers": [ "https://catalogue.france-bioinformatique.fr/api/userprofile/677/" ] } ] }{ "count": 144, "next": "