rNAV 2.0: a visualization tool for bacterial sRNA-mediated regulatory networks mining

Bacterial sRNA-mediated regulatory networks has been introduced as a powerful way to analyze the fast rewiring capabilities of a bacteria in response to changing environmental conditions. The identification of mRNA targets of bacterial sRNAs is essential to investigate their functional activities. However, this step remains challenging with the lack of knowledge of the topological and biological constraints behind the formation of sRNA-mRNA duplexes. Even with the most sophisticated bioinformatics target prediction tools, the large proportion of false predictions may be prohibitive for further analyses. To deal with this issue, sRNA target analyses can be carried out from the resulting gene lists given by RNA-SEQ experiments when available. However, the number of resulting target candidates may be still huge and cannot be easily interpreted by domain experts who need to confront various biological features to prioritize the target candidates. Therefore, novel strategies have to be carried out to improve the specificity of computational prediction results, before proposing new candidates for an expensive experimental validation stage.

To address this issue, we propose a new visualization tool rNAV 2.0, for detecting and filtering bacterial sRNA targets for regulatory networks. rNAV is designed to cope with a variety of biological constraints, including the gene annotations, the conserved regions of interaction or specific patterns of regulation. Depending on the application, these constraints can be variously combined to analyze the target candidates, prioritized for instance by a known conserved interaction region, or because of a common function.

The standalone application implements a set of known algorithms and interaction techniques, and applies them to the new problem of identifying reasonable sRNA target candidates.