Matériel de formation des plateformes IFB

Support tools for rapid adoption of compact identifiers in the publishing process

ProtVista (protein annotation viewer) extension using Bioschemas data

Pathway effect prediction for protein targets

JSON schema validation with ontologies

Improve Orphanet disease description knowledge by phenotypic automated recognition using scrapping toolkits

Galaxy training material improvement and extension

Exploring Pharmacogenomic LOD for Molecular Explanations of Gene-Drug Relationships

Development of BioJS components

Development of a catalog of federated SPARQL queries in the field of Rare Diseases

CWL support in Galaxy

bio.tools, EDAM drop-in hackathon and discussions

Assessing the FAIRness of Training Materials

Alternative episodes for the 4 Open Source Software (4OSS) lesson focused on different Open Source technologies: Github, Docker, Jupyter Notebook and so on

Presentation of the workshop (Chairman: Victoria Dominguez Del Angel)

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

How to handle Users, Groups, and Quotas in Galaxy

How to use the administation panel of Galaxy

Installation and configuration of NGiNX for Galaxy

How to choose a database for Galaxy and configure it

How to install a local instance of Galaxy

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.

BioBlend module, a python library to use Galaxy API

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

Introduction message of the EGDW 2017

Introduction to statistics with R

Not available

Introduction to RADSeq through STACKS on Galaxy

Not available

Design, describe, explore and model

Variant calling practical session

Quality, normalisation and peak calling

Galaxy II: common tools, quality control; alignment; data managment

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.

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.

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.

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.

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

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.

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.

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.

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.

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.

Presentation of the workshop (Chairman: Claudine Médigue)

• 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)

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.

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.

R and RStudio overview.

TEannot is able to annote a genome using DNA sequences library. This library can be a predicted TE library built by TEdenovo

Prediction of Region of Genomic Plasticity (RGPs) and CoDing Sequences (CDSs) and visualization

This module is separated in different courses:

• MicroScope: General overview, Keyword search and gene cart functionalities

• 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

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

• 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.

Global Objective

Given a set of ChIP-seq peaks annotate them in order to find associated genes, genomic categories and functional terms.

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

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

Visualisation of next-gen sequencing data with Integrative Genomics Viewer

1. 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)

• 1. Using the Gene-regulation appliance
  • 1.1 Requirements
  • 1.2 Virtual disk creation
  • 1.3 Creation of an instance
  • 1.4 Connection to the device
  • 1.5 Download source data
  • 1.6 Execute workflow
• 2. Visualizing results
  • 2.1 Install and run the X2Go client on your host computer
  • 2.2 Visualize results
    • FastQC
    • IGV
• 3. Create your own Gene-regulation appliance
  • Creation of an instance
  • Installing programs and dependencies
  • Get the gene-regulation repository
  • Run makefile to install the dependencies