Regulatory networks based on regulatory RNAs or small RNAs (sRNAs) are prominent in all bacteria. Often induced by metabolic or environmental stress, sRNAs act by base-pairing with their mRNA targets to modulate their stability or translation. Over the years, research on sRNAs has evolved from studying single, hand-picked examples to holistic approaches. Through these, complete RNA-RNA interactomes could be unraveled and their regulatory networks deciphered, including transcription factors, two-component systems and RNA binding proteins. In parallel, bioinformatics approaches for the identification of regulatory RNAs and the prediction of candidate targets now achieve acceptable quality.
As a result, the number of proven and predicted regulatory networks involving sRNAs is increasing at an accelerated level. This immediately raises the question of validity in a technical, and also biological interpretation, and how to discriminate real (functional) against false (incidental) interactions.
The application of both bioinformatics and experimental high-throughput methods to analyse RNA-based regulatory networks in different bacterial models immediately raised the question of how to validate the results. Actually, there are at least two aspects for validation: first, artifacts of the complex lab protocols and, second, functional vs. non-functional interactions. While the first can be addressed by filtering and statistics, the second are much more difficult to distinguish. The exemplary validation of individual hand-picked candidates does not really allow for any generalization, especially not, if the top-candidates are chosen.
Greater confidence in the results can be achieved through a deeper understanding of the methods (be they lab protocols or algorithms) and cleverly designed controls, both experimental and statistical. It is even more important to increase the confidence with real-world benchmarks. Here combinations of existing methods (experimental as well as computational), the compilation of gold-standard networks, and the use of thorough statistics may be helpful tools.
Among others, we would be pleased to receive contributions on the following topics:
• experimental high-throughput methods for the identification of RNA-RNA interactions
• computational approaches (including machine learning and artificial intelligence) for data-based reconstruction or
de novo prediction of regulatory RNA networks
• data collections for bacterial RNA regulatory networks, e.g., for the purpose of benchmarking
• strategies for large-scale validation of RNA networks, e.g., through the combination of different experimental, computational and statistical methods
• submissions for definitions of nomenclatures and standard data formats to ease the interaction between scientists of different backgrounds
We accept review, opinion, original research and all other types of papers supported by the publisher (please find a full list incl. descriptions
here).
