
Matériel de formation des plateformes IFB
Using blockchain in biomedical provenance, the identifiers use case
Using blockchain in biomedical provenance, the identifiers use case
- Carlos Castro Iragorri
- biohackaton 2018
Transfer of Research Assets between FAIRDOM SEEKs
Transfer of Research Assets between FAIRDOM SEEKs
- Stuart Owen
- biohackaton 2018
Support tools for rapid adoption of compact identifiers in the publishing process
Support tools for rapid adoption of compact identifiers in the publishing process
- Manuel Bernal Llinares
- biohackaton 2018
Putting structured data into individual entry pages in biological database
Putting structured data into individual entry pages in biological database
- Jun-ichi Onami
- biohackaton 2018
ProtVista (protein annotation viewer) extension using Bioschemas data
ProtVista (protein annotation viewer) extension using Bioschemas data
- Gustavo Salaza
- biohackaton 2018
Prototyping the new PSICQUIC 2.0
Prototyping the new PSICQUIC 2.0
- Noemi del Toro
- biohackaton 2018
Pathway effect prediction for protein targets
Pathway effect prediction for protein targets
- Rabie Saidi
- biohackaton 2018
OmicsPath: Finding Relevant omics datasets using pathway information
OmicsPath: Finding Relevant omics datasets using pathway information
- Yasset Perez-Riverol
- biohackaton 2018
JSON schema validation with ontologies
JSON schema validation with ontologies
- Simon Jupp
- biohackaton 2018
Improve Shiny and RStudio integration within Galaxy using Galaxy Interactive Environment
Improve Shiny and RStudio integration within Galaxy using Galaxy Interactive Environment
- Gildas Le Corguillé
- biohackaton 2018
Improve Orphanet disease description knowledge by phenotypic automated recognition using scrapping toolkits
Improve Orphanet disease description knowledge by phenotypic automated recognition using scrapping toolkits
- David Lagorce
- biohackaton 2018
Import workflows into TeSS Concept Maps
Import workflows into TeSS Concept Maps
- Niall Beard
- biohackaton 2018
Galaxy training material improvement and extension
Galaxy training material improvement and extension
- Bérénice Batut
- biohackaton 2018
From Biotea to Bioschemas: definition of profiles required to represent scholarly publications
From Biotea to Bioschemas: definition of profiles required to represent scholarly publications
- Alexander Garcia
- biohackaton 2018
Exploring Pharmacogenomic LOD for Molecular Explanations of Gene-Drug Relationships
Exploring Pharmacogenomic LOD for Molecular Explanations of Gene-Drug Relationships
- Adrien Coulet
- biohackaton 2018
Enrichment and propagation of metagenomic experimental metadata
Enrichment and propagation of metagenomic experimental metadata
- Ola Tarkowska
- biohackaton 2018
Development of BioJS components
Development of BioJS components
- Yo Yehudi
- biohackaton 2018
Development of a GA4GH-compliant, language-agnostic workflow execution service
Development of a GA4GH-compliant, language-agnostic workflow execution service
- Alexander Kanitz
- biohackaton 2018
Development of a catalog of federated SPARQL queries in the field of Rare Diseases
Development of a catalog of federated SPARQL queries in the field of Rare Diseases
- Marc Hanauer
- biohackaton 2018
Data clearinghouse, validation and curation of BioSamples/ENA/Breeding API endpoints/MAR databases
Data clearinghouse, validation and curation of BioSamples/ENA/Breeding API endpoints/MAR databases
- Luca Cherubin
- biohackaton 2018
CWL support in Galaxy
CWL support in Galaxy
- Hervé Ménager
- biohackaton 2018
Building a semantic search engine for biology publications using event stream processing
Building a semantic search engine for biology publications using event stream processing
- Mustafa Anil Tuncel
- biohackaton 2018
bio.tools & EDAM drop-in hackathon & discussions
bio.tools, EDAM drop-in hackathon and discussions
- Jon Ison
- biohackaton 2018
Bioconda packaging of the Regulatory Sequence Analysis Tools (RSAT)
Bioconda packaging of the Regulatory Sequence Analysis Tools (RSAT)
- Jacques van Helden
- biohackaton 2018
Assessing the FAIRness of Training Materials
Assessing the FAIRness of Training Materials
- Leyla Garcia
- biohackaton 2018
Application of RDF-based models and tools for enhancing interoperable
Application of RDF-based models and tools for enhancing interoperable use of biomedical resources
- Toshiaki Katayama
- biohackaton 2018
Alternative episodes for the 4 Open Source Software
Alternative episodes for the 4 Open Source Software (4OSS) lesson focused on different Open Source technologies: Github, Docker, Jupyter Notebook and so on
- Mateusz Kuzak
- biohackaton 2018
Adding bioschemas markup to data repository
Adding bioschemas markup to data repository
- Alasdair Gray
- biohackaton
Welcome message
Presentation of the workshop (Chairman: Victoria Dominguez Del Angel)
- Victoria Dominguez Del Angel
- Rafa C Jimenez
- biohackaton
Galaxy Docker Training Tutorial
- Galaxy docker integration
- Enable Galaxy to use BioContainers (Docker)
- Galaxy with Docker swarm
- Abdulrahman Azab
- Galaxy
- Docker
Docker and Galaxy
Questions
- Why Docker? What is it?
- How to use Docker?
- How to integrate Galaxy in Docker to facilitate its deployment?
Objectives
- Docker basics
- Galaxy Docker image (usage)
- Galaxy Docker (internals)
- Galaxy flavours
- Abdulrahman Azab
- Victoria Dominguez
- Galaxy
- Docker
Docker for Beginners
- What is Docker?
- Building an image
- BioShadock Orchestration
- Victoria Dominguez
- Docker
Users, Groups, and Quotas in Galaxy
How to handle Users, Groups, and Quotas in Galaxy
- Stéphanie Le Gras
- Galaxy
Defining and importing genomes, Data Managers into Galaxy
- Intro to built in datasets
- Built in data hierarchy
- Some problems
- Data Managers
- Stéphanie Le Gras
- Galaxy
Galaxy Administration
How to use the administation panel of Galaxy
- Stéphanie Le Gras
- Galaxy
Connecting Galaxy to a compute cluster
Problems
- Running jobs on the Galaxy server negatively impacts Galaxy UI performance
- Even adding one other host helps
- Can restart Galaxy without interrupting jobs
Solution:
- Connecter Galaxy to a computing cluster
- Stéphanie Le Gras
- Galaxy
- Cluster
(Proxy) Web Server Choices and Configuration
Installation and configuration of NGiNX for Galaxy
- Stéphanie Le Gras
- Galaxy
- NGiNX
Galaxy Handlers
Galaxy is a web application that uses handlers to perform actions.
There are two main types of actions that are carried out by handlers:
- Respond to user requests; These actions are carried out by web handlers
- Manage the execution of tools; These actions are performed by job handlers.
By default, Galaxy is configured to run a single handler that handles both user queries and jobs.
Depending on the number of users accessing your Galaxy instance or the number of jobs you need to manage you may need to start web handlers or additional job handlers.
- Julien Seiler
- Galaxy
Galactic Database
How to choose a database for Galaxy and configure it
- Julien Seiler
- Galaxy
Galaxy Configuration Hierarchy
How to configure your local instance of Galaxy
- Julien Seiler
- Galaxy
Galaxy Installation
How to install a local instance of Galaxy
- Julien Seiler
- Galaxy
Galaxy Interactive Tour
Questions
- What is a Galaxy Interactive Tour?
- How to create a Galaxy Interactive Tour?
Objectives
- Discover what is a Galaxy Interactive Tour
- Be able to create a Galaxy Interactive Tour
- Be able to add a Galaxy Interactive Tour in a Galaxy instance
- Bérénice Batut
- Björn Grüning
- Stéphanie Le Gras
- Galaxy
Galaxy Visualisation - Tutorial
Visualizations may be very helpful in understanding data better. There is a whole range of visualizations, from rather simple scatter and barplots up to projections of high dimensional data or even entire genomes. Many of these visualizations often require a lot of tweaking and changes in settings like zooming in and assigning colors, etc. Therefore, visualizations are ideally interactive, and changing settings is often an initial step in exploring data. For this reason it may be inconvenient to make use of static galaxy tools because it lacks these interactive features. For these situations Galaxy offers the option to create visualizations plugins, file format specific javascripts that integrate with the history menu, without making redundant copies of data.
In this tutorial we shall go through how this system works and create a simple visualization plugin. The tool will create a visualization of the number of aligned reads per chromosome of a BAM file, and we will discuss possible optimizations and advantages and disadvantages of the proposed implementation.
- Saskia Hiltermann
- Youri Hoogstrate
- Galaxy
Galaxy Visualisation - Slides
Questions
- How can visualization plugins benefit science?
Objectives
- Implement a first Galaxy visualization
- Understand the client side vs. server side principle
- Saskia Hiltermann
- Youri Hoogstrate
- Galaxy
BioBlend API
BioBlend module, a python library to use Galaxy API
- Olivia Doppelt
- Fabien Mareuil
- Julien Seiler
- Galaxy
- Python
- API
ToolShed upload and tool-iuc PR
Questions
- What is a Tool Shed?
- How to install tools and workflows from a Tool Shed into a Galaxy instance?
- What are the Tool Shed repository types?
- How to publish with Planemo?
Objectives
- Discover what is a Tool Shed
- Be able to install tools and workflows from a Tool Shed into a Galaxy instance
- Be able to publish tools with Planemo
- Bérénice Batut
- Björn Grüning
- Gildas Le Corguillé
- Galaxy
Tool development and integration into Galaxy
Questions:
- What is a tool for Galaxy?
- How to build a tool/wrapper with the good practices?
- How to deal with the tool environment?
Objectives:
- Discover what is a wrapper and its structure
- Use the Planemo utilities to develop a good wrapper
- Deal with the dependencies
- Write functional tests
- Make a tool ready for publishing in a ToolShed
- Abdulrahman Azab
- Bérénice Batut
- Björn Grüning
- Gildas Le Corguillé
- Galaxy
Development in Galaxy
Galaxy is an open-source project. Everyone can contribute to its development with core Galaxy development, integration of softwares in Galaxy environment, ... Here, you will find some materials to learn how to contribute to Galaxy project.
- Anthony Bretaudeau
- Bérénice Batut
- Björn Grüning
- Gildas Le Corguillé
- Galaxy
Welcome and Introduction
Introduction message of the EGDW 2017
Eukaryotic small RNA
Small RNAseq data analysis for miRNA identification
- Matthias Zytnicki
- RNA-seq
Statistics with RStudio
Introduction to statistics with R
- Jaces Van Helden
- R
RNA - Seq de novo
Practical session on transciptome de novo assembly
- Xi Liu
- Erwan Corre
- RNA-seq
Transcriptome de novo assembly
Not available
- Erwan Corre
- Xi Liu
- Transcriptomics
x2Go
- Denis Puthier
- Cloud
RADSeq Data Analysis
Introduction to RADSeq through STACKS on Galaxy
- Yvan Le Bras
- NGS
DNA - seq Bioinformatics Analysis
Detection of Copy Number Variations
- Elodie Girard
- DNA-seq
Isoform discovery and quanti cation from RNA-Seq data
Not available
- Claire Toffano-Nioche
- Thibault Dayris
- RNA-seq
Variant annotation
- Viven Deshaies
- Variant analysis
Differential analysis of RNA-Seq data
Design, describe, explore and model
- Rachel Legendre
- Hugo Varet
- RNA-seq
- Genomics
Differential gene expression analysis : Practical part
RNA-seq: Differential gene expression analysis practical session
- Rachel Legendre
- RNA-seq
- Genomics
RNA-seq: Differential gene expression analysis
- Rachel Legendre
- RNA-seq
- Genomics
Variant Filtering
...
- N.Lapalu
- Genomics
Variants: alignment and pre-treatment; GATK
Variant calling practical session
- Elodie Girard
- Genomics
Variants: alignment and pre-treatment; GATK
DNA-seq Bioinformatics Analysis: from raw sequences to processed alignments
- Elodie Girard
- Genomics
- DNA-seq
Chip-seq Analysis
Quality, normalisation and peak calling
- Stéphanie Legras
- Denis Puthier
- Tao Ye
- Céline Hernandez
- Matthieu Defrance
- Chip-seq
- Genomics
Galaxy III: Visualization
Visualization of Next Generation Sequencing Data using the Integrative Genomics Viewer (IGV)
- Elodie Girard
- Genomics
Galaxy: Initiation II
Galaxy II: common tools, quality control; alignment; data managment
- Gildas Le Corguillé
- Genomics
Third generation sequencing : the revolution of long reads
- Claude Thermes
- Genomics
New perspectives on nitrite-oxidizing bacteria - linking genomes to physiology
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.
The 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.
- Sebastian Lücker
- metagenomics
Revealing and analyzing microbial networks: from topology to functional behaviors
Understanding the interactions between microbial communities and their environment well enough to be able to predict diversity on the basis of physicochemical parameters is a fundamental pursuit of microbial ecology that still eludes us. However, modeling microbial communities is a complicated task, because (i) communities are complex, (ii) most are described qualitatively, and (iii) quantitative understanding of the way communities interacts with their surroundings remains incomplete. Within this seminar, we will illustrate two complementary approaches that aim to overcome these points in different manners.
- Damien Eveillard
- metagenomics
Holistic metagenomics in marine communities
Complex microscopic communities are composed of species belonging to all life realms, from single-cell prokaryotes to multicellular eukaryotes of small size. Each component of a community needs to be studied for a full understanding of the functions performed by the whole assemblage, however methods to investigate microbiomes are generally restricted to a single kingdom. Using examples from the Tara Oceans project, we will show how size fractionation and use of varied metabarcoding, metagenomics and metatranscriptomics approaches can help studying the marine plankton community as a whole, in a wide geographic space.
- Patrick Wincker
- metagenomics
Hidden in the permafrost
The last decade witnessed the discovery of four families of giant viruses infecting Acanthamoeba. They have genome encoding from 500 to 2000 genes, a large fraction of which encoding proteins of unknown origin. These unique proteins meant to recognize and manipulate the same building blocks as cells raise the question on their origin as well as the role viruses played in the cellular word evolution. The Mimiviridae and the Pandoraviridae are increasingly populated by members from very diverse habitats and are ubiquitous on the planet. After prospecting the space, we went back in the past and isolated two other giant virus families from a 30,000 years old permafrost sample, Pithovirus and Mollivirus sibericum. A metagenomics study of the sample was performed to inventory its biodiversity and assess to what extend the host and the viruses were dominant. I will describe the two sequencing approaches which have been used and compare the results.
1: Raoult D, Audic S, Robert C, Abergel C, Renesto P, Ogata H, La Scola B, Suzan M, Claverie JM. The 1.2-megabase genome sequence of Mimivirus. Science. 2004 Nov 19;306(5700):1344-50.
2: Philippe N, Legendre M, Doutre G, Couté Y, Poirot O, Lescot M, Arslan D, Seltzer V, Bertaux L, Bruley C, Garin J, Claverie JM, Abergel C. Pandoraviruses:amoeba viruses with genomes up to 2.5 Mb reaching that of parasitic eukaryotes. Science. 2013 Jul 19;341(6143):281-6.
3: Legendre M, Bartoli J, Shmakova L, Jeudy S, Labadie K, Adrait A, Lescot M, Poirot O, Bertaux L, Bruley C, Couté Y, Rivkina E, Abergel C, Claverie JM. Thirty-thousand-year-old distant relative of giant icosahedral DNA viruses with a pandoravirus morphology. Proc Natl Acad Sci U S A. 2014 Mar 18;111(11):4274-9.
4: Legendre M, Lartigue A, Bertaux L, Jeudy S, Bartoli J, Lescot M, Alempic JM, Ramus C, Bruley C, Labadie K, Shmakova L, Rivkina E, Couté Y, Abergel C, Claverie JM. In-depth study of Mollivirus sibericum, a new 30,000-y-old giant virus infecting Acanthamoeba. Proc Natl Acad Sci U S A. 2015 Sep 22;112(38):E5327-35.
- Chantal Abergel
- Jean-Michel Claverie
- metagenomics
200 billion sequences and counting: analysis, discovery and exploration of datasets with EBI Metagenomics
EBI metagenomics (EMG, https://www.ebi.ac.uk/metagenomics/) is a freely available hub for the analysis and exploration of metagenomic, metatranscriptomic, amplicon and assembly data. The resource provides rich functional and taxonomic analyses of user-submitted sequences, as well as analysis of publicly available metagenomic datasets held within the European Nucleotide Archive (ENA). EMG has recently undergone rapid expansion, with an over 10-fold increase in data volumes in the first 5 months of 2016. It now houses ~ 50k publicly available data sets, and represents one of the largest collections of analysed metagenomic data. As its data content has grown, EMG has increasingly become a platform for data discovery. To support this process, we have made a series of user-interface improvements, including the classification of projects by biome, presentation of results data for better visualisation and more convenient download, and provision of project level summary files. More recently, we have indexed project metadata for use with the EBI search engine, enabling exploration across different datasets. For example, users are able to search with a particular taxonomic lineage or protein function and discover the projects, samples and sequencing runs in which that lineage or function is found. This functionality allows users to explore associations between biomes, environmental conditions and organisms and functions (e.g., discovering protein coding sequences that correspond to certain enzyme families found in aquatic environments at a given temperature range). Here, we give an overview of the EMG data analysis pipeline and web site, and illustrate the use of the new search facility for data discovery.
- Alex Mitchell
- metagenomics
MG-RAST — experiences from processing a quarter million metagenomic data sets
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
- Folker Meyer
- metagenomics
Fast filtering, mapping and assembly of 16S ribosomal RNA
The application of next-generation sequencing technologies to RNA orDNA directly extracted from a community of organisms yields a mixtureof nucleotide fragments. The task to distinguish amongst these and tofurther categorize the families of ribosomal RNAs (or any other givenmarker) is an important step for examining the phylogeneticclassification of the constituting species. In thisperspective, we have developed a complete bioinformatics suite, called MATAM, capable of handling large sets of reads in a fast and accurate way. MATAM covers all steps of the analysis, from the identificationof reads of interest in the raw sequencing data to the reconstructionof the full-length sequences of the marker and alignment to areference database for taxonomic assignment. Part of MATAM is basedon the SortMeRNA software, also developed by the team.
- Hélène Touzet
- metagenomics
Reconstructing genomes from metagenomes: The holy grail of microbiology
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.
- A. Murat Eren
- metagenomics
Prokaryotic Phylogeny on the Fly: databases and tools for online taxonomic identification
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
- Guy Perrière
- metagenomics
Dr Jekyll and Mr Hyde: The dual face of metagenomics in phylogenetic analysis
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.
- Violette Da Cunha
- metagenomics
Soil metagenomics, potential and pitfalls
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.
Metagenomics 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.
- Pascal Simonet
- metagenomics
Multiple Comparative Metagenomics using Multiset k-mer Counting
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.
These 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.
Experiments 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.
- Pierre Peterlongo
- metagenomics
Assessing microbial biogeography by using a metagenomic approach
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.
This 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.
This 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.
- Sébastien Terrat
- metagenomics
Sequencing 6000 chloroplast genomes : the PhyloAlps project
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 ?
- Eric Coissac
- metagenomics
Rationale and Tools to look for the unknown in (metagenomic) sequence data
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.
One 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.
- Jean-Michel Claverie
- metagenomics
Who is doing what on the cheese surface? Overview of the cheese microbial ecosystem functioning by metatranscriptomic analyses
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.
- Eric Dugat-Bony
- metagenomics
Exploiting collisions between DNA molecules to characterize the genomic structures of complex communities
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 quantitativeinformation 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.
- Romain Koszul
- metagenomics
Gut metagenomics in cardiometabolic diseases
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.
- Karine Clement
- metagenomics
Deciphering the human intestinal tract microbiome using metagenomic computational methods
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].
However 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.
Since 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.
In 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.
- Matthieu Almeida
- metagenomics
From Samples to Data : Assuring Downstream Analysis with Upstream Planning
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.
- Kennedy Sean
- metagenomics
Welcome message
Presentation of the workshop (Chairman: Claudine Médigue)
- Medigue Claudine
- metagenomics
Docker Tutorial
Docker is free software that automates the deployment of applications in software containers executant in isolation. A Docker container, away from traditional virtual machines, requires no separate operating system and not providing any but relies instead on the core functionality and uses the isolation of resources and namespaces separated to isolate the operating system as seen by the application.
- Rey Julien
- Docker
A Simple Phylogenetic Tree Construction (part 2)
- Understand the method used in identifying an unknown sequence.
- Understand the limitations of this method
- Get to grips with various software (CLUSTALw, SeaView, Phylo_win and Njplot)
- PRABI
- Phylogenetics
A Simple Phylogenetic Tree Construction (part 1)
- Understand the method behind constructing a phylogenetic tree from the search for sequences to the analysis of the tree.
- Get to grips with various bio-informatic software (BLAST, CLUSTALw, SeaView and Phylo_win).
- PRAVI
- Phylogenetics
HOVERGEN tutorial
HOVERGEN is a database containing homologous vertebrate protein and nucleotide sequences. It allows to easily select similar gene sequences from a wide range of vertebrates. Hence it becomes particularly useful in comparative genomics, phylogeny and evolutionary studies on a molecular level. HOVERGEN Clean contains only complete sequences which reattach to their family. Hence its library is smaller, but more reliable.
- PRABI
- proteomics
- genomics
Cross Taxa Tutorial
How query databases according to complex taxonomic critera
Cross-Taxa allows to retrieve gene families that are shared by a given set of taxa, or which are specific to a set of taxa. It is also possible to select genes families which are associated to a certain set of taxa but which are not found in a second set of taxa. Any taxonomic level can be used.
- PRABI: LBBE
- genomics
Searching for sequence: Tutorial
Quick Search is dedicated to a quick search for sequences or sequence families in the databases available on the PBIL server. It is an alternative to WWW Query which allows more complex queries. Quick Search allows you to retrieve sequences or sequence families associated to a single word without specifying what is this word. You can enter indifferently a keyword, a sequence name or accession number, or a taxa name.
- PRABI: LBBE
- genomics
- pattern recognition
Exercices on Galaxy: metagenomics
Find Rapidly OTU with Galaxy Solution
- Frédéric Escudié
- Lucas Auer
- Metagenomics
- Galaxy
Training on Galaxy : Metagenomics
- FRÉDÉRIC ESCUDIÉ
- LUCAS AUER
- Metagenomics
- Galaxy
Analysis of community composition data using phyloseq
- Formation 16S
- Microbiomes
- R
A Quick and focused overview of R data types and ggplot2 syntax
R and RStudio overview.
- M. Mariadassou
- R
- Statistics
- Graphical analysis
PASTEClassifier Tutorial
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 )
- URGI
- Transposons
- Genomics
REPET: TEdannot Tutorial
TEannot is able to annote a genome using DNA sequences library. This library can be a predicted TE library built by TEdenovo
- URGI
- Annotation
- Genomics
REPET: TEdenovo tutorial
The TEdenovo pipeline follows a philosophy in three first steps:
- Detection of repeated sequences (potential TE)
- Clustering of these sequences
- Generation of consensus sequences for each cluster, representing the ancestral TE
- URGI
- Annotation
- Genomics
RGP finder: prediction of Genomic Islands
Prediction of Region of Genomic Plasticity (RGPs) and CoDing Sequences (CDSs) and visualization
- LABGeM CEA
- Data visualization
- Genomics
- CDS
- RGP
NGS data exploration with the MicroScope Platform
Exploring data annotation on the genomics and transcriptomics levels with the MicroScope Platform and its tools
- LABGeM CEA
- NGS
- SNP
- RNA-seq
- Genomics
- Transcriptomics
Exploring microbiomes with the MicroScope Platform
This module is separated in different courses:
- MicroScope: General overview, Keyword search and gene cart functionalities
- Functional annotation of microbial genomes
- Functional annotation of microbial genomes: Prediction of enzymatic functions
- Relational annotation of bacterial genomes: synteny
- Automatic functional assignation and expert annotation of genes
- Relational annotation of bacterial genomes: phylogenetic profiles
- Relational annotation of bacterial genomes: pan-genome analysis
- Relational annotation of bacterial genomes: metabolic pathways
- Syntactic re-annotation of public microbial genomes
- Syntactic annotation of microbial genomes
- LABGeM CEA
- Annotation
- Genomics
- Transcriptomics
- Metabolomics
- Microbial evolution
Exploring Microscope Platform
How to use the Microscope Platform to annotate and analyze microbial genomes.
- LABGeM CEA
- Annotation
- Genomics
- Sequence analysis
- Microbial evolution
- Metabolomics
- Transcriptomics
Genomic copy number Tutorial
- Unknown
- Structural genomics
- Copy number
Genomic copy number Analysis
No description available
- Bastien Job
- Structural genomics
- Copy number
RNA-Seq: isoform detection and quantification
- transcriptome from new condition
- tissue-speci c transcriptome
- different development stages
- transcriptome from non model organism
- cancer cell
- RNA maturation mutant
- Is it possible to discover new isoforms?
- Is it possible to quantify abundance of each isoform
- Thibault Dayris
- Claire Toffano-Nioche
- RNA-seq
- Transcriptomics
- Isoforms
RNA-Seq: Differential Expression Analysis
- Be careful about experimental design : avoid putting all the
- replicates in the same lane, using the same barcode for the
- replicates, putting different number of samples in lanes etc...
- Non- uniformity of the per base read distribution (Illumina Random
- Hexamer Priming bias visible on the 13 first bases)
- Bias hierarchy : biological condition concentration run/flowcell lane
- At equivalent expression level, a long gene will have more reads than a short one.
- Non random coverage along the transcript.
- Multiple hit for some reads alignments.
- Coline Billerey
- RNA-seq
- transcriptomics
- Differential Expression
Variant Filtering
Use cases:
- Extact a subset of variants
- Combine variants from several analysis
-
Compare obtained variants from several data types
-
Identify new variants compare to a reference list
-
Apply specific filters for Chip Design
- Nicolas Lapalu
- Variant calling
- NGS
DNA-seq analysis: From raw reads to processed alignments
Objectives:
- Mapping the DNA-seq data to the reference genome
- Process the alignments for the variant calling
- Elodie Girard
- DNA-seq
- Genomics
- Alignment
- Variant calling
Chip-seq: Discovering motifs in peaks with RSAT
- Read mapping: from raw reads to aligned reads.
- Peak calling: from aligned reads to regions/peaks of high read density.
- ChIP-seq annotation
- Identification of genes related to the peaks.
- Profiles of ChIP-seq reads around reference points (TSS, histone marks,).
- Functional enrichment of the genes related to the peaks.
- J. Van Helden
- Chip Seq
- NGS
- Motif Analysis
Chip-seq: Motif Analysis Tutorial
Introduction
Goal
The aim is to :
- Get familiar with motif analysis of ChIP-seq data.
- Learn de novo motif discovery methods.
In practice :
- Motif discovery with peak-motifs
- Differential analysis
- Random controls
- Unknown
- NGS
- Chip-seq
- Pattern recognition
- Motif analysis
Chip Seq: Annotation and visualization Tutorial
Global Objective
Given a set of ChIP-seq peaks annotate them in order to find associated genes, genomic categories and functional terms.
- M. Defrance
- C. Herrmann
- D. Puthier
- M. Thomas-Chollier
- S. Le Gras
- J. van Helden
- NGS
- Chip-seq
- Annotation
- Data Visualization
Chip Seq: Annotation and visualization Lesson
How to add biological meaning to peaks
- M. Defrance
- C. Herrmann
- D. Puthier
- M. Thomas-Chollier
- S Le Gras
- J van Helden
- Chip-seq
- NGS
- Annotation
- Data Visualization
Chip-seq: Pattern Analysis tutorial
Goal
The aim is to :
- Get familiar with motif analysis of ChIP-seq data.
- Learn de novo motif discovery methods.
In practice :
- Motif discovery with peak-motifs
- Differential analysis
- Random controls
- Carl Herrmann
- Matthieu Defrance
- Denis Puthier
- Morgane Thomas-Chollier
- Stéphanie Le Gras
- Jacques van Helden
- Chip-seq
- NGS
- Pattern recognition
Chip-seq: Functional Annotation tutorial
Global Objective
Given a set of ChIP-seq peaks annotate them in order to find associated genes, genomic categories and functional terms.
- Unknown
- Chip-seq
- NGS
- Functional Annotation
Chip-seq: Peak calling tutorial
The aim is to :
- Understand how to process reads to obtain peaks (peak-calling).
- Become familiar with differential analysis of peaks
In practice :
- Obtain dataset from GEO
- Analyze mapped reads
- Obtain set(s) of peaks, handle replicates
- Differential analysis of peak
- Carl Herrmann
- Matthieu Defrance
- Denis Puthier
- Morgane Thomas-Chollier
- Peak calling
- NGS
- Chip-seq
Chip-seq: Introduction to the Workshop
- Data visualization, quality control, normalization peak calling
- Peak annotation
- From peaks to motifs
- Carl Hermann
- Chip-Seq
Visualization of NGS data with IGV
Visualisation of next-gen sequencing data with Integrative Genomics Viewer
- A. Lermine
- NGS
- Genomics
- Data visualization
Initiation to Galaxy
DNA-sequence analysis: from raw reads to variants calling within the galaxy environement.
- A. Lermine
- DNA Analysis
- Galaxy
- Variant calling
The IFB cloud for bioinformatics
-
Practical work to introduce basic and advanced usage of the IFB cloud
- Howto launch virtual machines
- Managing your data in the cloud ;
- Howto to connect to your VMS (SSH, web, remote desktop)
- Personalizing your VMs (approver, galaxy, docker)
- Blanchet Christophe
- Dominguez del Angel Victoria
- Virtual machine
- Cloud computing
Docker tutorial: Gene regulation
- Claire Rioualen
- Jacques Van Helden
- Docker
- Gene regulation
IFB Cloud tutorial: Gene regulation
- Claire Rioualen
- Jacques Van Helden
- Gene Regulation
- Cloud Computing
Snakemake tutorial: Gene regulation
Workflow 1: Rules and targets
Workflow 2: Introducing wildcards
Workflow 3: Keywords
Workflow 4: Combining rules
Workflow 5: Configuration file
Workflow 6: Separated files
- Claire Rioualen
- Jacques Van Helden
- Gene regulation
- Snakemake