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Alain Viari

Publications and source records attributed to Alain Viari.

8 recordsLinked to original sources

Genepi: a blackboard framework for genome annotation.

BACKGROUND: Genome annotation can be viewed as an incremental, cooperative, data-driven, knowledge-based process that involves multiple methods to predict gene locations and structures. This process might have to be executed more than once and might be subjected to several revisions as the biological (new data) or methodological (new methods) knowledge evolves. In this context, although a lot of annotation platforms already exist, there is still a strong need for computer systems which take in charge, not only the primary annotation, but also the update and advance of the associated knowledge. In this paper, we propose to adopt a blackboard architecture for designing such a system RESULTS: We have implemented a blackboard framework (called Genepi) for developing automatic annotation systems. The system is not bound to any specific annotation strategy. Instead, the user will specify a blackboard structure in a configuration file and the system will instantiate and run this particular annotation strategy. The characteristics of this framework are presented and discussed. Specific adaptations to the classical blackboard architecture have been required, such as the description of the activation patterns of the knowledge sources by using an extended set of Allen's temporal relations. Although the system is robust enough to be used on real-size applications, it is of primary use to bioinformatics researchers who want to experiment with blackboard architectures. CONCLUSION: In the context of genome annotation, blackboards have several interesting features related to the way methodological and biological knowledge can be updated. They can readily handle the cooperative (several methods are implied) and opportunistic (the flow of execution depends on the state of our knowledge) aspects of the annotation process.

Algorithms↗

Repseek, a tool to retrieve approximate repeats from large DNA sequences.

UNLABELLED: Chromosomes or other long DNA sequences contain many highly similar repeated sub-sequences. While there are efficient methods for detecting strict repeats or detecting already characterized repeats, there is no software available for detecting approximate repeats in large DNA sequences allowing for weighted substitutions and indels in a coherent statistical framework. Here, we present an implementation of a two-steps method (seed detection followed by their extension) that detects those approximate repeats. Our method is computationally efficient enough to handle large sequences and is flexible enough to account for influencing factors, such as sequence-composition biases both at the seed detection and alignment levels. AVAILABILITY: http://wwwabi.snv.jussieu.fr/public/RepSeek/

Algorithms↗

GenoLink: a graph-based querying and browsing system for investigating the function of genes and proteins.

BACKGROUND: A large variety of biological data can be represented by graphs. These graphs can be constructed from heterogeneous data coming from genomic and post-genomic technologies, but there is still need for tools aiming at exploring and analysing such graphs. This paper describes GenoLink, a software platform for the graphical querying and exploration of graphs. RESULTS: GenoLink provides a generic framework for representing and querying data graphs. This framework provides a graph data structure, a graph query engine, allowing to retrieve sub-graphs from the entire data graph, and several graphical interfaces to express such queries and to further explore their results. A query consists in a graph pattern with constraints attached to the vertices and edges. A query result is the set of all sub-graphs of the entire data graph that are isomorphic to the pattern and satisfy the constraints. The graph data structure does not rely upon any particular data model but can dynamically accommodate for any user-supplied data model. However, for genomic and post-genomic applications, we provide a default data model and several parsers for the most popular data sources. GenoLink does not require any programming skill since all operations on graphs and the analysis of the results can be carried out graphically through several dedicated graphical interfaces. CONCLUSION: GenoLink is a generic and interactive tool allowing biologists to graphically explore various sources of information. GenoLink is distributed either as a standalone application or as a component of the Genostar/Iogma platform. Both distributions are free for academic research and teaching purposes and can be requested at academy@genostar.com. A commercial licence form can be obtained for profit company at info@genostar.com. See also http://www.genostar.org.

Computer Graphics↗

Comparative genomic analysis of three strains of Ehrlichia ruminantium reveals an active process of genome size plasticity.

Ehrlichia ruminantium is the causative agent of heartwater, a major tick-borne disease of livestock in Africa that has been introduced in the Caribbean and is threatening to emerge and spread on the American mainland. We sequenced the complete genomes of two strains of E. ruminantium of differing phenotypes, strains Gardel (Erga; 1,499,920 bp), from the island of Guadeloupe, and Welgevonden (Erwe; 1,512,977 bp), originating in South Africa and maintained in Guadeloupe in a different cell environment. Comparative genomic analysis of these two strains was performed with the recently published parent strain of Erwe (Erwo) and other Rickettsiales (Anaplasma, Wolbachia, and Rickettsia spp.). Gene order is highly conserved between the E. ruminantium strains and with A. marginale. In contrast, there is very little conservation of gene order with members of the Rickettsiaceae. However, gene order may be locally conserved, as illustrated by the tuf operons. Eighteen truncated protein-encoding sequences (CDSs) differentiate Erga from Erwe/Erwo, whereas four other truncated CDSs differentiate Erwe from Erwo. Moreover, E. ruminantium displays the lowest coding ratio observed among bacteria due to unusually long intergenic regions. This is related to an active process of genome expansion/contraction targeted at tandem repeats in noncoding regions and based on the addition or removal of ca. 150-bp tandem units. This process seems to be specific to E. ruminantium and is not observed in the other Rickettsiales.

Conserved Sequence↗

Comparative genomics of three strains of Ehrlichia ruminantium: a review.

The tick-borne Rickettsiale Ehrlichia ruminantium (E. ruminantium) is the causative agent of heartwater in Africa and the Caribbean. Heartwater, responsible for major losses on livestock in Africa represents also a threat for the American mainland. Three complete genomes corresponding to two different groups of differing phenotypes, Gardel and Welgevonden, have been recently described. One genome (Erga) represents the Gardel group from Guadeloupe Island and two genomes (Erwo and Erwe) belong to the Welgevonden group. Erwo, isolated in South Africa, is the parental strain of Erwe, which was maintained for 18 years in Guadeloupe under different culture conditions than Erwo. The three strains display genomes of differing sizes with 1,499,920 bp, 1,512,977 bp, and 1,516,355 bp for Erga, Erwe, and Erwo, respectively. Gene sequences and order are highly conserved between the three strains, although several gene truncations could be pinpointed, most of them occurring within three regions of accumulated differences (RAD). E. ruminantium displays a strong leading/lagging compositional bias inducing a strand-specific codon usage. Finally, a striking feature of E. ruminantium is the presence of long intergenic regions containing tandem repeats. These repeats are at the origin of an active process, specific to E. ruminantium, of genome expansion/contraction based on the addition or removal of tandem units.

Animals↗

Syntons, metabolons and interactons: an exact graph-theoretical approach for exploring neighbourhood between genomic and functional data.

MOTIVATION: Modern comparative genomics does not restrict to sequence but involves the comparison of metabolic pathways or protein-protein interactions as well. Central in this approach is the concept of neighbourhood between entities (genes, proteins, chemical compounds). Therefore there is a growing need for new methods aiming at merging the connectivity information from different biological sources in order to infer functional coupling. RESULTS: We present a generic approach to merge the information from two or more graphs representing biological data. The method is based on two concepts. The first one, the correspondence multigraph, precisely defines how correspondence is performed between the primary data-graphs. The second one, the common connected components, defines which property of the multigraph is searched for. Although this problem has already been informally stated in the past few years, we give here a formal and general statement together with an exact algorithm to solve it. AVAILABILITY: The algorithm presented in this paper has been implemented in C. Source code is freely available for download at: http://www.inrialpes.fr/helix/people/viari/cccpart.

Algorithms↗

Ab initio reconstruction of metabolic pathways.

We propose a new formulation for the problem of ab initio metabolic pathway reconstruction. Given a set of biochemical reactions together with their substrates and products, we consider the reactions as transfers of atoms between the chemical compounds and we look for successions of reactions transferring a maximal (or preset) number of atoms between a given source and sink compound. We state this problem as the one of finding a composition of partial injections that maximizes the image size. First, we study the theoretical complexity of this problem, state some related problems and then give a practical algorithm to solve them. Finally, we present two applications of this approach to the reconstruction of the tryptophan biosynthesis pathway and to the glycolysis.

Algorithms↗

Integration of data and methods for genome analysis.

The development of genomic and post-genomic technologies has created an explosion in the quantity, diversity and availability of both biological data and methods of analysis. Biologists are currently facing the problem of using all these resources to convert raw data into new valuable knowledge. This review presents software platforms designed to handle data and/or methods in the context of genome analysis.

Animals↗