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

Abstract

BackgroundBacterial 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.ResultTo 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.ConclusionThe standalone application implements a set of known algorithms and interaction techniques, and applies them to the new problem of identifying reasonable sRNA target candidates.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-017-1598-8) contains supplementary material, which is available to authorized users.

Secretory and circulating bacterial small RNAs: a mini-review of the literature

“…Since then, the abundance of bacterial sRNAs and their significance in physiological responses have been much better appreciated, due to the application of a combination of cloning-based techniques and computational methods [3,4]. Integrated data concerning bacterial-specific sRNAs have contributed greatly to unveiling the regulatory networks of major bacterial pathogens [1,5]. However, a main question that remains to be addressed is how study results should be translated into clinical benefits.…”

Posttranscriptional Regulation in Response to Different Environmental Stresses in Campylobacter jejuni

“…In this study, 513 sRNA-target interactions were extracted by combining WGCNA, DESeq, and IntaRNA. Key sRNA-target interactions were selected by similarly searching for KEGG pathways enrichment as rNAV 2.0 ( 54 ). Notably, all four CjSA21 targets are Tlps that transduce external stimuli that lead to a chemotactic response.…”

Synthetic Biology of Small RNAs and Riboswitches

“…Additionally, DsrA prevents Rho-dependent transcription termination of rpoS by sharing the same binding site with the Rho terminator (25). Several new sequencing-based methods have been developed to aid in the rapid, high-throughput identification of sRNA targets (34, 53–55) to complement computational tools (56, 57) and validation of hypothesized sRNA-mRNA interactions by additional fluorescence (58), genetics (deletion and overexpression), and biochemical assays (59). Determination of the target network aids in engineering sRNAs to regulate multiple targets under specific conditions without off-target effects.…”