RNApredator: fast accessibility-based prediction of sRNA targets

Area of Science:

• Bacterial genomics
• RNA biology
• Bioinformatics

Background:

• Bacterial genomes contain numerous small RNAs (sRNAs) that regulate gene expression post-transcriptionally.
• sRNAs function by base-pairing with mRNA 5'-untranslated regions, affecting transcript stability and translation.
• Accurate prediction of sRNA-mRNA interactions is crucial for understanding bacterial regulatory networks.

Purpose of the Study:

• To develop and present RNApredator, a novel web server for predicting bacterial sRNA targets.
• To enhance the specificity of sRNA target prediction by incorporating target accessibility.
• To provide a user-friendly platform with automated post-processing for analyzing predicted interactions.

Main Methods:

• Utilized the RNAplex dynamic programming algorithm for computing putative sRNA-mRNA targets.
• Integrated target accessibility assessment into the prediction pipeline.
• Developed a web server supporting over 2155 bacterial genomes and plasmids.
• Implemented automated post-processing for Gene Ontology and pathway enrichment analysis.

Main Results:

• RNApredator demonstrates improved specificity in sRNA target predictions compared to existing web servers.
• The RNAplex algorithm achieves high predictive performance comparable to complex methods.
• RNApredator offers significant computational efficiency, requiring orders of magnitude less time.
• Automated analysis provides insights into functional enrichment and accessibility profiles.

Conclusions:

• RNApredator is an efficient and specific tool for predicting bacterial sRNA targets.
• The consideration of target accessibility is key to improving prediction accuracy.
• This web server facilitates deeper understanding of sRNA-mediated gene regulation in bacteria.