Training Material List
Handles creating, reading and updating training materials.
GET /api/trainingmaterial/?format=api&offset=40&ordering=-name
{ "count": 149, "next": "https://catalogue.france-bioinformatique.fr/api/trainingmaterial/?format=api&limit=20&offset=60&ordering=-name", "previous": "https://catalogue.france-bioinformatique.fr/api/trainingmaterial/?format=api&limit=20&offset=20&ordering=-name", "results": [ { "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/?format=api" ] }, { "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" ], "audienceTypes": [], "audienceRoles": [], "difficultyLevel": "", "providedBy": [], "dateCreation": "2016-11-23", "dateUpdate": null, "licence": null, "maintainers": [ "https://catalogue.france-bioinformatique.fr/api/userprofile/692/?format=api" ] }, { "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 đ§Źđ 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/?format=api" ] }, { "id": 5, "name": "Putting structured data into individual entry pages in biological database", "description": "\n \n\nPutting structured data into individual entry pages in biological database\n \n", "communities": [], "elixirPlatforms": [], "doi": null, "fileLocation": "http://ressources.france-bioinformatique.fr/sites/default/files/videos/scorms/putting-structured_bf31/scormcontent/index.html", "fileName": "missing.txt", "topics": [], "keywords": [ "biohackaton 2018" ], "audienceTypes": [], "audienceRoles": [], "difficultyLevel": "", "providedBy": [], "dateCreation": "2019-03-21", "dateUpdate": null, "licence": null, "maintainers": [ "https://catalogue.france-bioinformatique.fr/api/userprofile/669/?format=api" ] }, { "id": 38, "name": "(Proxy) Web Server Choices and Configuration", "description": "Installation and configuration of NGiNX for Galaxy\n", "communities": [], "elixirPlatforms": [], "doi": null, "fileLocation": "https://igbmc.github.io/egdw2017/day4/admin/04-web-server/index.html", "fileName": "missing.txt", "topics": [], "keywords": [ "Galaxy", "NGiNX" ], "audienceTypes": [], "audienceRoles": [], "difficultyLevel": "", "providedBy": [], "dateCreation": "2017-01-19", "dateUpdate": null, "licence": null, "maintainers": [ "https://catalogue.france-bioinformatique.fr/api/userprofile/662/?format=api" ] }, { "id": 6, "name": "ProtVista (protein annotation viewer) extension using Bioschemas data", "description": "\n \n\nProtVista (protein annotation viewer) extension using Bioschemas data\n \n", "communities": [], "elixirPlatforms": [], "doi": null, "fileLocation": "http://ressources.france-bioinformatique.fr/sites/default/files/videos/scorms/protvista-protein_2cd8/scormcontent/index.html", "fileName": "missing.txt", "topics": [], "keywords": [ "biohackaton 2018" ], "audienceTypes": [], "audienceRoles": [], "difficultyLevel": "", "providedBy": [], "dateCreation": "2019-03-21", "dateUpdate": null, "licence": null, "maintainers": [ "https://catalogue.france-bioinformatique.fr/api/userprofile/670/?format=api" ] }, { "id": 7, "name": "Prototyping the new PSICQUIC 2-0", "description": "\n \n\nPrototyping the new PSICQUIC 2.0\n \n", "communities": [], "elixirPlatforms": [], "doi": null, "fileLocation": "http://ressources.france-bioinformatique.fr/sites/default/files/videos/scorms/prototyping_353b/scormcontent/index.html", "fileName": "missing.txt", "topics": [], "keywords": [ "biohackaton 2018" ], "audienceTypes": [], "audienceRoles": [], "difficultyLevel": "", "providedBy": [], "dateCreation": "2019-03-21", "dateUpdate": null, "licence": null, "maintainers": [ "https://catalogue.france-bioinformatique.fr/api/userprofile/671/?format=api" ] }, { "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": [], "elixirPlatforms": [], "doi": null, "fileLocation": "http://www.france-bioinformatique.fr/sites/default/files/videos/scorms/metagenomics16/session_5/Procaryotic_phylogenu_on_the_fly/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/489/?format=api" ] }, { "id": 146, "name": "Processing_large_files_with_sed_awk_2024", "description": "This âSed and AWK to modify large text filesâ 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. Weâll 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": 150, "name": "Plant Data Managment for Phenotyping Experiments - MIAPPE", "description": "The Minimal Information About Plant Phenotyping Experiment, MIAPPE (www.miappe.org) has been designed by ELIXIR, EMPHASIS and Bioversity international, to guide plant scientist in the management of experimental data. Furthermore, since genetic studies relies on the integration and the linking between phenotype and genotype datasets, relevant section of MIAPPE are beginning to be used for genotyping standards. This Webinar will give an overview of the current practices and methods for plant phenotyping data standardization, and how to deal with the variability and heterogeneity inherent to research and breeding data sets. Data management approaches at some of the major research organizations will be given as examples.", "communities": [], "elixirPlatforms": [], "doi": null, "fileLocation": "https://hal.inrae.fr/hal-03102944", "fileName": "2020-12-MIAPPE-Webinar.pdf", "topics": [ "http://edamontology.org/topic_0625" ], "keywords": [ "DonnĂ©es" ], "audienceTypes": [], "audienceRoles": [], "difficultyLevel": "Novice", "providedBy": [], "dateCreation": null, "dateUpdate": null, "licence": "CC BY-SA 4.0", "maintainers": [ "https://catalogue.france-bioinformatique.fr/api/userprofile/441/?format=api" ] }, { "id": 8, "name": "Pathway effect prediction for protein targets", "description": "\n \n\nPathway effect prediction for protein targets\n \n", "communities": [], "elixirPlatforms": [], "doi": null, "fileLocation": "http://ressources.france-bioinformatique.fr/sites/default/files/videos/scorms/pathway-effect_b1e2/scormcontent/index.html", "fileName": "missing.txt", "topics": [], "keywords": [ "biohackaton 2018" ], "audienceTypes": [], "audienceRoles": [], "difficultyLevel": "", "providedBy": [], "dateCreation": "2019-03-21", "dateUpdate": null, "licence": null, "maintainers": [ "https://catalogue.france-bioinformatique.fr/api/userprofile/672/?format=api" ] }, { "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": 148, "name": "One line perl", "description": "Improve your command line skills by learning a few words of Perl (08/12/2025)\r\nThe GenoToul bioinformatics platform and Sigenae organize a series of training courses to familiarize yourself with the various resources it provides. These resources are currently: the hardware infrastructure, biological data banks and widely used bioinformatics softwares. This âPerl one-linersâ training session is organized by the Sigenae platform. Perl one-liners are small and awesome Perl programs that fit in a single line of code and perform many operations such as replacing of text, spacing, deleting, calculation, manipulation in files and many more. This training will allow you to discover the power of Perl on the command line and learn how to use it to automate your file manipulations and command line generation with classical file formats such as tabulated text, fastq, sam/bam, and vcf.\r\n\r\ncalendar\r\n \r\n\r\nThis training lasts one day and is focused on practice. It consists of 3 parts with a large variety of exercises:\r\n\r\nIntroduction to Perl and its characteristics: Perl is a widely used programming language for data processing and task automation. We will introduce the main characteristics of Perl and discuss why it is particularly suited for biologists who want to manipulate files and generate command lines.\r\nPerl on the command line: we will show how to use Perl on the command line to perform common tasks, such as searching and replacing strings, merging files, and loop over lists of files.\r\nConcrete examples: we will present several concrete examples drawn from biology, such as extracting information from genomic sequence files, converting files between different formats, and generating command lines for data biology tools.", "communities": [], "elixirPlatforms": [], "doi": null, "fileLocation": "https://web-genobioinfo.toulouse.inrae.fr/~ccabau/Perl_One-liner.pdf", "fileName": "Perl_One-liner.pdf", "topics": [], "keywords": [], "audienceTypes": [], "audienceRoles": [], "difficultyLevel": "Intermediate", "providedBy": [], "dateCreation": null, "dateUpdate": null, "licence": null, "maintainers": [ "https://catalogue.france-bioinformatique.fr/api/userprofile/300/?format=api" ] }, { "id": 9, "name": "OmicsPath: Finding Relevant omics datasets using pathway information", "description": "\n \n\nOmicsPath: Finding Relevant omics datasets using pathway information\n \n", "communities": [], "elixirPlatforms": [], "doi": null, "fileLocation": "http://ressources.france-bioinformatique.fr/sites/default/files/videos/scorms/omicspath-finding_b649/scormcontent/index.html", "fileName": "missing.txt", "topics": [], "keywords": [ "biohackaton 2018" ], "audienceTypes": [], "audienceRoles": [], "difficultyLevel": "", "providedBy": [], "dateCreation": "2019-03-21", "dateUpdate": null, "licence": null, "maintainers": [ "https://catalogue.france-bioinformatique.fr/api/userprofile/673/?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": 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 âunusualâ 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": 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/?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": 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 âmulti-omicâ 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": 151, "name": "Metagenomic training", "description": "Slides and link to practical session parts", "communities": [], "elixirPlatforms": [], "doi": null, "fileLocation": "https://bioinfo.genotoul.fr/wp-content/uploads/Formation2025.pdf", "fileName": "Formation2025.pdf", "topics": [ "http://edamontology.org/topic_3174" ], "keywords": [], "audienceTypes": [], "audienceRoles": [], "difficultyLevel": "Intermediate", "providedBy": [ { "id": 22, "name": "Genotoul-bioinfo", "url": "https://catalogue.france-bioinformatique.fr/api/team/Genotoul-bioinfo/?format=api" } ], "dateCreation": null, "dateUpdate": null, "licence": "CC BY-NC-SA", "maintainers": [ "https://catalogue.france-bioinformatique.fr/api/userprofile/300/?format=api" ] } ] }