Magus: Collaborative Genome Annotation
As part of our contribution the Génolevures Consortium, we have developed over the past few years an efficient set of tools for web-based collaborative annotation of eukaryote genomes. The Magus genome annotation system integrates genome sequences and sequences features, in silico analyses, and views of external data resources into a familiar user interface requiring only a Web navigator. Magus implements the annotation workflows and enforces curation standards to guarantee consistency and integrity. As a novel feature the system provides a workflow for simultaneous annotation of related genomes through the use of protein families identified by in silico analyses; this has resulted in a three-fold increase in curation speed, compared to one-at-a-time curation of individual genes. This allows us to maintain Génolevures standards of high-quality manual annotation while efficiently using the time of our volunteer curators.
Magus is built on: a standard sequence feature database, the Stein lab generic genome browser  , various biomedical ontologies (http://obo.sf.net ), and a web interface implementing a representational state transfer (REST) architecture  .
For more information see magus.gforge.inria.fr , the Magus Gforge web site. Magus is developed in an Inria Technology Development Action (ADT).
Génolevures On Line: Comparative Genomics of Yeasts
The Génolevures online database provides tools and data for exploring the annotated genome sequences of more than 20 genomes, determined and manually annotated by the Génolevures Consortium to facilitate comparative genomic studies of hemiascomycetous yeasts. Data are presented with a focus on relations between genes and genomes: conservation of genes and gene families, speciation, chromosomal reorganization and synteny. The Génolevures site includes an area for specific studies by members of its international community.
Génolevures online uses the Magus system for genome navigation, with project-specific extensions developed by David Sherman, Pascal Durrens, and Tiphaine Martin. An advanced query system for data mining in Génolevures is being developed by Natalia Golenetskaya. The contents of the knowledge base are expanded and maintained by the CNRS through GDR 2354 Génolevures. Technical support for Génolevures On Line is provided the CNRS through UMR 5800 LaBRI.
For more information see genolevures.org , the Génolevures web site.
BioRica: Multi-scale Stochastic Modeling
BioRica is a high-level modeling framework integrating discrete and continuous multi-scale dynamics within the same semantics field. A model in BioRica node is hierarchically composed of nodes, which may be existing models. Individual nodes can be of two types:
- Discrete nodes are composed of states, and transitions described by constrained events, which can be non deterministic. This captures a range of existing discrete formalisms (Petri nets, finite automata, etc.). Stochastic behavior can be added by associating the likelihood that an event fires when activated. Markov chains or Markov decision processes can be concisely described. Timed behavior is added by defining the delay between an event’s activation and the moment that its transition occurs.
- Continuous nodes are described by ODE systems, potentially a hybrid system whose internal state flows continuously while having discrete jumps.
The system has been implemented as a distributable software package
The BioRica compiler reads a specification for hierarchical model and compiles it into an executable simulator. The modeling language is a stochastic extension to the AltaRica Dataflow language, inspired by work of Antoine Rauzy. Input parsers for SBML 2 version 4 are curently being validated. The compiled code uses the Python runtime environment and can be run stand-alone on most systems  .
For more information see biorica.gforge.inria.fr , the BioRica Gforge web site. BioRica was developed as an Inria Technology Development Action (ADT).
Pathtastic: Inference of whole-genome metabolic models
Pathtastic (now Pantograph)is a software tool for inferring whole-genome metabolic models for eukaryote cell factories. It is based on metabolic scaffolds, abstract descriptions of reactions and pathways on which inferred reactions are hung and are eventually connected by an interative mapping and specialization process. Scaffold fragments can be repeatedly used to build specialized subnetworks of the complete model.
Pathtastic uses a consensus procedure to infer reactions from complementary genome comparisons, and an algebra for assisted manual editing of pathways.
For more information see pathtastic.gforge.inria.fr , the Pathtastic Gforge web site.
YAGA: Yeast Genome Annotation
With the arrival of new generations of sequencers, laboratories, at a lower cost, can be sequenced groups of genomes. You can no longer manually annotate these genomes. The YAGA software’s objective is to syntactically annotate a raw sequence (genetic element: gene, CDS, tRNA, centromere, gap, …) and functionally as well as generate EMBL files for publication. The annotation takes into account data from comparative genomics, such as protein family profiles.
After determining the constraints of the annotation, the YAGA software can automatically annotate de novo all genomes from their raw sequences.The predictors used by the YAGA software can also take into account the data RNAseq to reinforce the prediction of genes.The current settings of the software are intended for annotation of the genomes of yeast, but the software is adaptable for all types of species.
Inria Bioscience Resources
Inria Bioscience Resources is a portal designed to improve the visibility of bioinformatics tools and resources developed by Inria teams. This portal will help the community of biologists and bioinformatians understand the variety of bioinformatics projects in Inria, test the different applications, and contact project-teams. Eight project-teams participate in the development of this portal. Inria Bioscience Resources is developed in an Inria Technology Development Action (ADT).