Abstract

Trans‐acting small regulatory RNAs (sRNAs) are key players in the regulation of gene expression in bacteria. There are hundreds of different sRNAs in a typical bacterium, which in contrast to eukaryotic microRNAs are more heterogeneous in length, sequence composition, and secondary structure. The vast majority of sRNAs function post‐transcriptionally by binding to other RNAs (mRNAs, sRNAs) through rather short regions of imperfect sequence complementarity. Besides, every single sRNA may interact with dozens of different target RNAs and impact gene expression either negatively or positively. These facts contributed to the view that the entirety of the regulatory targets of a given sRNA, its targetome, is challenging to identify. However, recent developments show that a more comprehensive sRNAs targetome can be achieved through the combination of experimental and computational approaches. Here, we give a short introduction into these methods followed by a description of two sRNAs, RyhB, and RsaA, to illustrate the particular strengths and weaknesses of these approaches in more details. RyhB is an sRNA involved in iron homeostasis in Enterobacteriaceae, while RsaA is a modulator of virulence in Staphylococcus aureus. Using such a combined strategy, a better appreciation of the sRNA‐dependent regulatory networks is now attainable.

Highlights

  • CopraRNA was successfully tested on 18 well-known Enterobacteriaceae Small trans-acting regulatory RNAs (sRNAs) and on a small number of sRNAs from different bacteria revealing the previously identified mRNA targets as well as novel base pairing interactions (Wright et al, 2013)

  • Despite the excellent performance of the leading experimental (RILseq, MAPS) and computational (CopraRNA) methods, each individual approach suffers from false positives and struggles to recover the full targetome of a given sRNA leading to false negatives

  • In contrast to experimental methods which involve molecular cloning, bacterial growth, RNA extraction, enrichment and processing steps, library preparation, RNA-seq, and bioinformatic data analysis, CopraRNA only requires the genomic information, is free of charge and the results are available within a few hours of computation time

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Summary

Introduction

CopraRNA was successfully tested on 18 well-known Enterobacteriaceae sRNAs and on a small number of sRNAs from different bacteria revealing the previously identified mRNA targets as well as novel base pairing interactions (Wright et al, 2013). Despite the excellent performance of the leading experimental (RILseq, MAPS) and computational (CopraRNA) methods, each individual approach suffers from false positives (specificity) and struggles to recover the full targetome of a given sRNA leading to false negatives (sensitivity).

Results
Conclusion

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