Training Material List
Handles creating, reading and updating training materials.
GET /api/trainingmaterial/?format=api&offset=120&ordering=-audienceRoles
{ "count": 144, "next": "https://catalogue.france-bioinformatique.fr/api/trainingmaterial/?format=api&limit=20&offset=140&ordering=-audienceRoles", "previous": "https://catalogue.france-bioinformatique.fr/api/trainingmaterial/?format=api&limit=20&offset=100&ordering=-audienceRoles", "results": [ { "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", "communities": [], "elixirPlatforms": [], "doi": null, "fileLocation": "http://www.france-bioinformatique.fr/sites/default/files/videos/scorms/metagenomics16/session_5/Reconstructing_genomes_from_metagenomes_the_holy_grail_of_microbiology/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/665/?format=api" ] }, { "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": [], "elixirPlatforms": [], "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" ], "audienceTypes": [], "audienceRoles": [], "difficultyLevel": "", "providedBy": [], "dateCreation": "2016-12-16", "dateUpdate": null, "licence": "CC BY-NC-ND", "maintainers": [ "https://catalogue.france-bioinformatique.fr/api/userprofile/700/?format=api" ] }, { "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/?format=api" ] }, { "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/?format=api" ] }, { "id": 42, "name": "Galaxy Installation", "description": "How to install a local instance of Galaxy\n", "communities": [], "elixirPlatforms": [], "doi": null, "fileLocation": "https://igbmc.github.io/egdw2017/day4/admin/00-installation/index.html", "fileName": "missing.txt", "topics": [], "keywords": [ "Galaxy" ], "audienceTypes": [], "audienceRoles": [], "difficultyLevel": "", "providedBy": [], "dateCreation": "2017-01-19", "dateUpdate": null, "licence": null, "maintainers": [ "https://catalogue.france-bioinformatique.fr/api/userprofile/563/?format=api" ] }, { "id": 90, "name": "A Simple Phylogenetic Tree Construction (part 2)", "description": "Understand the method used in identifying an unknown sequence.\nUnderstand the limitations of this method\nGet to grips with various software (CLUSTALw, SeaView, Phylo_win and Njplot)\n", "communities": [], "elixirPlatforms": [], "doi": null, "fileLocation": "http://www.prabi.fr/spip.php?article60", "fileName": "missing.txt", "topics": [], "keywords": [ "Phylogenetics" ], "audienceTypes": [], "audienceRoles": [], "difficultyLevel": "", "providedBy": [ { "id": 19, "name": "PRABI-AMSB", "url": "https://catalogue.france-bioinformatique.fr/api/team/PRABI-AMSB/?format=api" } ], "dateCreation": null, "dateUpdate": null, "licence": null, "maintainers": [] }, { "id": 59, "name": "Variant annotation", "description": "Add meta-information on variant to facilitate interpretation\n", "communities": [], "elixirPlatforms": [], "doi": null, "fileLocation": "http://www.france-bioinformatique.fr/sites/default/files/V06_variants_annotation.pdf", "fileName": "missing.txt", "topics": [], "keywords": [ "Variant analysis" ], "audienceTypes": [], "audienceRoles": [], "difficultyLevel": "", "providedBy": [], "dateCreation": "2016-11-23", "dateUpdate": null, "licence": null, "maintainers": [ "https://catalogue.france-bioinformatique.fr/api/userprofile/693/?format=api" ] }, { "id": 116, "name": "Chip-seq: Functional Annotation tutorial", "description": "Global Objective\nGiven a set of ChIP-seq peaks annotate them in order to find associated genes, genomic categories and functional terms.\n", "communities": [], "elixirPlatforms": [], "doi": null, "fileLocation": "http://dputhier.github.io/EBA_2015_ChIP-Seq/tutorial/02_annotation/annotation.html", "fileName": "missing.txt", "topics": [], "keywords": [ "Chip-Seq", "Functional Annotation", "NGS" ], "audienceTypes": [], "audienceRoles": [], "difficultyLevel": "", "providedBy": [], "dateCreation": null, "dateUpdate": null, "licence": null, "maintainers": [] }, { "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/?format=api" ] }, { "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", "communities": [], "elixirPlatforms": [], "doi": null, "fileLocation": "http://www.france-bioinformatique.fr/sites/default/files/videos/scorms/metagenomics16/session_6/MG_RSAT/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/705/?format=api" ] }, { "id": 146, "name": "Processing_large_files_with_sed_awk_2024", "description": "This “Sed and AWK to modify large text files” training session is organized by the Genotoul bioinfo platform.\r\n\r\nThe Linux sed command is a powerful and very fast text editor without an interface. Sed can select, substitute, add, delete, and modify text in files and streams. Sed relies heavily on regular expressions for pattern matching and text selection. We’ll manipulate regexes and the sed command to modify and filter several type of file often used in bioinformatics.\r\n\r\nAWK enables to easily process columns in large text files but is also a quite powerfull programming language. This training session aims at introducing you AWK principles. You will learn about variables, operators and functions useful to manipulate very large files. \r\n\r\nFor example you can use AWK to generate your unix command lines to be launched on the cluster. AWK enables to process millions of lines in text files. The course includes short feature presentations between long hands-on sessions in which you will be able to understand the global ideas as well as details.", "communities": [], "elixirPlatforms": [], "doi": null, "fileLocation": "https://web-genobioinfo.toulouse.inrae.fr/~klopp/SedAwk2024/Processing_large_files_with_sed_awk_2024.pdf", "fileName": "Processing_large_files_with_sed_awk_2024.pdf", "topics": [], "keywords": [], "audienceTypes": [ "Professional (continued)" ], "audienceRoles": [ "Biologists", "Bioinformaticians" ], "difficultyLevel": "Intermediate", "providedBy": [ { "id": 22, "name": "Genotoul-bioinfo", "url": "https://catalogue.france-bioinformatique.fr/api/team/Genotoul-bioinfo/?format=api" } ], "dateCreation": null, "dateUpdate": "2024-03-01", "licence": null, "maintainers": [ "https://catalogue.france-bioinformatique.fr/api/userprofile/300/?format=api" ] }, { "id": 125, "name": "ETBII 2023", "description": "All the training materials dedicated to the IFB's Integrative Bioinformatics Thematic School, which took place in January 2023.", "communities": [], "elixirPlatforms": [], "doi": null, "fileLocation": "https://moodle.france-bioinformatique.fr/course/view.php?id=13", "fileName": "ETBII 2023 Training materials", "topics": [ "http://edamontology.org/topic_3474", "http://edamontology.org/topic_3316", "http://edamontology.org/topic_3391", "http://edamontology.org/topic_3365", "http://edamontology.org/topic_2269", "http://edamontology.org/topic_0092", "http://edamontology.org/topic_0091", "http://edamontology.org/topic_3307" ], "keywords": [ "Biological network inference and analysis", "Multivariate analyses", "Semantic web", "Data Integration" ], "audienceTypes": [ "Professional (initial)" ], "audienceRoles": [ "Life scientists", "Computer scientists", "Bioinformaticians" ], "difficultyLevel": "Novice", "providedBy": [ { "id": 29, "name": "IFB Core", "url": "https://catalogue.france-bioinformatique.fr/api/team/IFB%20Core/?format=api" } ], "dateCreation": null, "dateUpdate": null, "licence": null, "maintainers": [ "https://catalogue.france-bioinformatique.fr/api/userprofile/762/?format=api" ] }, { "id": 134, "name": "Nucleoli segmentation and feature extraction using CellProfiler", "description": "This tutorial covers the following questions:\r\n- How do I run an image analysis pipeline on public data using CellProfiler?\r\n- How do I analyse the DNA channel of fluorescence siRNA screens?\r\n- How do I download public image data into my history?\r\n- How do I segment and label cell nuclei?\r\n- How do I segment nucleoli (as the absence of DNA)?\r\n- How do I combine nuclei and nucleoli into one segmentation mask?\r\n- How do I extract the background of an image?\r\n- How do I relate the nucleoli to their parent nucleus?\r\n- How do I measure the image and object features?\r\n- How do I measure the image quality?\r\n\r\nAt the end of the tutorial, learners would be able to:\r\n- How to download images from a public image repository.\r\n- How to segment cell nuclei using CellProfiler in Galaxy.\r\n- How to segment cell nucleoli using CellProfiler in Galaxy.\r\n- How to extract features for images, nuclei and nucleoli.", "communities": [], "elixirPlatforms": [], "doi": null, "fileLocation": "https://training.galaxyproject.org/training-material/topics/imaging/tutorials/tutorial-CP/tutorial.html", "fileName": "tutorial-CP", "topics": [ "http://edamontology.org/topic_3383", "http://edamontology.org/topic_3382" ], "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/?format=api" ] }, { "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_1775", "http://edamontology.org/topic_3941" ], "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/?format=api" ] }, { "id": 133, "name": "Introduction to image analysis using Galaxy", "description": "This tutorial covers the questions:\r\n- How do I use Galaxy with imaging data?\r\n- How do I convert images using Galaxy?\r\n- How do I display images in Galaxy?\r\n- How do I filter images in Galaxy?\r\n- How do I segment simple images in Galaxy?\r\n\r\nAt the end of the tutorial, learners would be able to:\r\n- How to handle images in Galaxy.\r\n- How to perform basic image processing in Galaxy.", "communities": [], "elixirPlatforms": [], "doi": null, "fileLocation": "https://training.galaxyproject.org/training-material/topics/imaging/tutorials/imaging-introduction/tutorial.html", "fileName": "imaging-introduction", "topics": [ "http://edamontology.org/topic_3383", "http://edamontology.org/topic_3382" ], "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/?format=api" ] }, { "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/?format=api" ] }, { "id": 146, "name": "Processing_large_files_with_sed_awk_2024", "description": "This “Sed and AWK to modify large text files” training session is organized by the Genotoul bioinfo platform.\r\n\r\nThe Linux sed command is a powerful and very fast text editor without an interface. Sed can select, substitute, add, delete, and modify text in files and streams. Sed relies heavily on regular expressions for pattern matching and text selection. We’ll manipulate regexes and the sed command to modify and filter several type of file often used in bioinformatics.\r\n\r\nAWK enables to easily process columns in large text files but is also a quite powerfull programming language. This training session aims at introducing you AWK principles. You will learn about variables, operators and functions useful to manipulate very large files. \r\n\r\nFor example you can use AWK to generate your unix command lines to be launched on the cluster. AWK enables to process millions of lines in text files. The course includes short feature presentations between long hands-on sessions in which you will be able to understand the global ideas as well as details.", "communities": [], "elixirPlatforms": [], "doi": null, "fileLocation": "https://web-genobioinfo.toulouse.inrae.fr/~klopp/SedAwk2024/Processing_large_files_with_sed_awk_2024.pdf", "fileName": "Processing_large_files_with_sed_awk_2024.pdf", "topics": [], "keywords": [], "audienceTypes": [ "Professional (continued)" ], "audienceRoles": [ "Biologists", "Bioinformaticians" ], "difficultyLevel": "Intermediate", "providedBy": [ { "id": 22, "name": "Genotoul-bioinfo", "url": "https://catalogue.france-bioinformatique.fr/api/team/Genotoul-bioinfo/?format=api" } ], "dateCreation": null, "dateUpdate": "2024-03-01", "licence": null, "maintainers": [ "https://catalogue.france-bioinformatique.fr/api/userprofile/300/?format=api" ] }, { "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/?format=api" ] }, { "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/?format=api" ] }, { "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", "communities": [], "elixirPlatforms": [], "doi": null, "fileLocation": "https://training.galaxyproject.org/training-material/topics/transcriptomics/tutorials/ref-based/tutorial.html", "fileName": "rna-seq", "topics": [ "http://edamontology.org/topic_0102", "http://edamontology.org/topic_1775", "http://edamontology.org/topic_0203", "http://edamontology.org/topic_3308", "http://edamontology.org/topic_3170" ], "keywords": [ "Galaxy", "RNA-seq" ], "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/?format=api" ] } ] }