Training Material List
Handles creating, reading and updating training materials.
GET /api/trainingmaterial/?format=api&offset=100&ordering=-licence
{ "count": 144, "next": "https://catalogue.france-bioinformatique.fr/api/trainingmaterial/?format=api&limit=20&offset=120&ordering=-licence", "previous": "https://catalogue.france-bioinformatique.fr/api/trainingmaterial/?format=api&limit=20&offset=80&ordering=-licence", "results": [ { "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": 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": 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/?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" ] }, { "id": 145, "name": "Introduction to Transcriptomics", "description": "This slidedecks presents the concepts behind transcriptomics", "communities": [], "elixirPlatforms": [], "doi": null, "fileLocation": "https://training.galaxyproject.org/training-material/topics/transcriptomics/tutorials/introduction/slides.html", "fileName": "transcriptomics-introduction", "topics": [ "http://edamontology.org/topic_0203", "http://edamontology.org/topic_3308", "http://edamontology.org/topic_3170" ], "keywords": [ "RNA-seq", "Transcriptomics (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" ] }, { "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": 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": 129, "name": "Bacterial Genome Annotation", "description": "This tutorial covers the questions:\r\n- Which genes are on a draft bacterial genome?\r\n- Which other genomic components can be found on a draft bacterial genome?\r\n\r\nAt the end of the tutorial, learners would be able to:\r\n- Run a series of tools to annotate a draft bacterial genome for different types of genomic components\r\n- Evaluate the annotation\r\n- Process the outputs to format them for visualization needs\r\n- Visualize a draft bacterial genome and its annotations", "communities": [], "elixirPlatforms": [], "doi": null, "fileLocation": "https://training.galaxyproject.org/training-material/topics/genome-annotation/tutorials/bacterial-genome-annotation/tutorial.html", "fileName": "bacterial-genome-annotation", "topics": [ "http://edamontology.org/topic_3301", "http://edamontology.org/topic_0219" ], "keywords": [ "Bacterial isolate", "Annotation", "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": 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/?format=api" ] }, { "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": 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": 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": 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": 78, "name": "Dr Jekyll and Mr Hyde: The dual face of metagenomics in phylogenetic analysis", "description": "The aim of this lecture is to present the impact of metagenomics and single-cell genomics on public databases. These new powerful approches allow us to have access to the diversity of life on our planet. However, care has to be taken when using these data for posterior analyses, such as phylogenetic studies, as critical errors can still be present in the databases. This course will incorporate examples taken from real studies, and we will investigate methods used for error detection.\n", "communities": [], "elixirPlatforms": [], "doi": null, "fileLocation": "http://www.france-bioinformatique.fr/sites/default/files/videos/scorms/metagenomics16/session_5/Dr_Jekyll_and_Mr_Hyde_The_dual_face_of_metagenomics_in_phylogenetic_analysis/scormcontent/index.html", "fileName": "missing.txt", "topics": [], "keywords": [ "Metagenomics" ], "audienceTypes": [], "audienceRoles": [], "difficultyLevel": "", "providedBy": [], "dateCreation": "2016-12-16", "dateUpdate": null, "licence": "CC BY-NC-ND", "maintainers": [ "https://catalogue.france-bioinformatique.fr/api/userprofile/706/?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": 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": 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": 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/?format=api" ] }, { "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" ] } ] }