31326 CASTANET-TOLOSAN cedex
The team develops mathematical, statistical and computational methods to address life science research problems. These methods are usually directly made available to biologists through dedicated software.
Bioinformatics problems addressed
The topics addressed in the team concern the localization and identification of functional elements in bacterial, plant and animal genomes. Three investigation levels are considered.
- Genetical level A genome is essentially seen through molecular markers whose locations on a chromosome are highly informative in genetics investigation. Localizing these markers on the chromosomes (genetic mapping and radiated hybrid mapping: Carthagène) in order to subsequently locate the regions linked to quantitative traits of interest (disease resistance, yield ...) with respect to those markers (QTL or quantitative trait loci localization by analyzing allelic transmission: MCQTL and by modelling linkage disequilibrium: HAPim). These QTLs can then be used in selecting varieties that combine several desirable traits.
- Molecular level At the molecular level, the DNA sequence of the genome is directly analyzed to decode and identify functional regions in the sequence. These may be genes coding for proteins (in bacterial genomes and EST cclusters FrameD or in eukaryotic genomes: EuGène) or non coding genes corresponding to functional RNAs (MilPat, DARN!, ApolloRNA, RNAspace). The comparison of genomes of different species and identification of key events that separate them (recombination) can enable the transfer of information between genomes.
- Gene expression level The use of DNA microarrays allows to partially observe the cellular activity at a given time. It is then possible to establish a link between the contextual conditions of the cell at observation time (disease, polluted environment) and the genes that are over (or under) expressed. This link may help trace the genes related to disease or allow for a diagnosis.
To go beyond the localization of isolated functional elements, we are are now increasingly interested in approaches aiming at the inference of gene regulatory networks. We are currently studying the simultaneous analysis of expression data and polymorphism data (such as SNP) on a collection of individuals. This allows to observe different perturbated modes of operation of the network to better infer gene network structures.