The methods developed in the lab are made available to the scientific community via web-based interfaces and as distributed software.
Non-coding sequences play an essential role in living organisms, by controlling the regulation of gene transcription. This regulation is mediated by interactions between transcription factors and short DNA motifs present at specific locations in the genome. The computational prediction of transcription factor binding sites can provide valuable hypotheses about gene regulation. The availability of an increasing number of fully sequenced genomes allows us to combine the detection of regulatory signals with comparative genomics, in order to study the conservation and divergence of cis-regulatory elements, and, thereby, decipher the evolution of genetic regulation.
Since 1997, Jacques van Helden developed methods to predict regulatory signals in non-coding sequences. These approaches are based on the detection of statistically significant motifs in genomic sequences. The approach has been thoroughly tested with microbial genomes (yeast, bacteria), and is currently extended to metacellular organisms (insects, vertebrate).
The Regulatory Sequence Analysis Tools (http://rsat.bigre.ulb.ac.be/rsat/) are available to the academic community via a Web server and as Web services.
The ACLAME project (http://aclame.ulb.ac.be) is driven by Raphaël Leplae, in collaboration with Ariane Toussaint and Gipsi Lima-Mendez. A database dedicated to the collection and classification of mobile genetic elements (MGEs) from various sources, comprising all known phage genomes, plasmids and transposons is provided to the scientific community. The project aims at building a comprehensive classification of the functional modules of MGEs at the protein, gene, and higher levels. Tools for MGEs analysis and detection in bacterial genomes are also available on the web site.
The function and evolution of living organisms is based on networks integrating various types of molecular interactions: transcriptional regulation, protein interactions, metabolic reactions, signal transduction, ...
A large bunch of information is available about the different pieces of such networks. During the second half of the 20th century, biochemists and molecular geneticists have characterize such molecular interactions on a case-by-case mode. More recently, high-throughput methods (DNA microarrays, TAP-TAG, ChIP-chip, ...) have been designed to unraveal thousands of interactions in a single experiment. These data sets can be combined to obtain a global network synthetizing our current perception of molecular interaction networks. Interpreting such networks is a challenging goal for modern biologists.
Our laboratory develops dedicated bioinformatics and statistical approaches for the interpretation of molecular interaction networks: assessing the reliability of individual interactions, extracting functional modules from the global network, deciphering the relationshp between network topology and emergent behaviour, ...
Our Network Analysis Tools (Neat, http://rsat.bigre.ulb.ac.be/neat/) are available to the academic community as a Web server and as Web services.