VirHostNet 2.0: surfing on the web of virus/host molecular interactions data
VirHostNet release 2.0 (http://virhostnet.prabi.fr) is a knowledgebase dedicated to the network-based exploration of virus–host protein–protein interactions. Since the
previous VirhostNet release (2009), a second run of manual curation was performed to annotate the new torrent of high-throughput protein–protein interactions data from the literature. This resource is shared publicly, in
PSI-MI TAB 2.5 format, using a PSICQUIC web service. The new interface of VirHostNet 2.0 is based on Cytoscape web library and provides a user-friendly access to the most complete and accurate resource of virus–virus and
virus–host protein–protein interactions as well as their projection onto their corresponding host cell protein interaction networks. We hope that the VirHostNet 2.0 system will facilitate systems biology and gene-centered
analysis of infectious diseases and will help to identify new molecular targets for antiviral drugs design. This resource will
also continue to help worldwide scientists to improve our knowledge on molecular mechanisms involved in the antiviral response mediated by the cell and in the viral strategies selected by viruses to hijack the host immune
Cheese ecosystems insights with shotgun metagenomics and a metadata extended genomics database.
The manufacturing process of cheeses, as for most fermented food, involves a complex flora, which is composed of bacteria, yeast and filamentous fungi. They can be directly inoculated as starter culture or develop from the
food-chain environment (raw milk, cheese factory...). Therefore the exact composition of most cheeses is not completely known.
Further understandings of cheeses ecosystems and control of cheese quality both need a better characterization of the cheese flora with a precise taxonomic identification. The FoodMicrobiome-Transfert project aims to address
In the framework of this project, we are developing a tool to facilitate metagenomics analysis. This tool is composed of read alignment wrapper tool, a database and a web interface to run analysis.
GeDI : an in-house metagenomics analysis tool
Shotgun metagenomics sequencing data brings information about the studied ecosystem, but also yields noise signal. Hence retrieving the link between sequence and organism is not trivial and require different strategies.
Several current metagenomics tools are based on a set of gene markers, or on the k-mer composition of the reads, but few are able to identify species up to the strain level. We are developing an in-house software to wrap read
alignments on reference genomes and extract information from these processes. It relies on the intersection between features (CDS) and alignments data (BAM) to infer species presence or absence.
A web application and a database to exploit GeDI possibilities
The application will allow the users to submit metagenomes and personal genomes. They will be able to choose a list of genomes from our public database and from their personal genome library. They will finally be able to
execute GeDI to analyze their metagenome data. The database, currently in development, will store (i) genomics data from food-related microorganisms that will be used for metagenomics data analysis, (ii) metadata associated
with the ecology of these microorganisms and (iii) metagenomics analysis results. The database genomics part will be enriched with expert annotations, with a focus on genes of technological interest. The application will
permit to visualize and compare analysis results and cheese environments metadata.
The tools will be hosted on the Migale platform. The GeDI software will be run transparently on the Migale Galaxy portal using our specific web interface. The Python 3 Django web communicates with Galaxy using the bioblend
library and allow us to easily manage datasets and outputs. Information are exchanged through bioinformatics standard files (GFF, BAM, etc.), thus easing the use or the export to others tools.
On Migale platform
Food Microbiome-Transfert, a database to characterize cheese ecosystems.
INRA Bioinformatics days 2016
On Migale platform