CopraRNA (Comparative prediction algorithm for small RNA targets) is the most recent asset to the Freiburg RNA Tools webserver. It incorporates and extends the functionality of the existing tool IntaRNA (Interacting RNAs) in order to predict targets, interaction domains and consequently the regulatory networks of bacterial small RNA molecules. The CopraRNA prediction results are accompanied by extensive postprocessing methods such as functional enrichment analysis and visualization of interacting regions. Here, we introduce the functionality of the CopraRNA and IntaRNA webservers and give detailed explanations on their postprocessing functionalities. Both tools are freely accessible at http://rna.informatik.uni-freiburg.de.

The Freiburg RNA tools webserver is a well established online resource for RNA-focused research. It provides a unified user interface and comprehensive result visualization for efficient command line tools. The webserver includes RNA-RNA interaction prediction (IntaRNA, CopraRNA, metaMIR), sRNA homology search (GLASSgo), sequence-structure alignments (LocARNA, MARNA, CARNA, ExpaRNA), CRISPR repeat classification (CRISPRmap), sequence design (antaRNA, INFO-RNA, SECISDesign), structure aberration evaluation of point mutations (RaSE), and RNA/protein-family models visualization (CMV), and other methods. Open education resources offer interactive visualizations of RNA structure and RNA-RNA interaction prediction as well as basic and advanced sequence alignment algorithms. The services are freely available at http://rna.informatik.uni-freiburg.de.

Bacterial small RNAs (sRNAs) are important post-transcriptional regulators of gene expression. The functional and evolutionary characterization of sRNAs requires the identification of homologs, which is frequently challenging due to their heterogeneity, short length and partly, little sequence conservation. We developed the GLobal Automatic Small RNA Search go (GLASSgo) algorithm to identify sRNA homologs in complex genomic databases starting from a single sequence. GLASSgo combines an iterative BLAST strategy with pairwise identity filtering and a graph-based clustering method that utilizes RNA secondary structure information. We tested the specificity, sensitivity and runtime of GLASSgo, BLAST and the combination RNAlien/cmsearch in a typical use case scenario on 40 bacterial sRNA families. The sensitivity of the tested methods was similar, while the specificity of GLASSgo and RNAlien/cmsearch was significantly higher than that of BLAST. GLASSgo was on average ∼87 times faster than RNAlien/cmsearch, and only ∼7.5 times slower than BLAST, which shows that GLASSgo optimizes the trade-off between speed and accuracy in the task of finding sRNA homologs. GLASSgo is fully automated, whereas BLAST often recovers only parts of homologs and RNAlien/cmsearch requires extensive additional bioinformatic work to get a comprehensive set of homologs. GLASSgo is available as an easy-to-use web server to find homologous sRNAs in large databases.

DNA methylation in bacteria is important for defense against foreign DNA, but is also involved in DNA repair, replication, chromosome partitioning, and regulatory processes. Thus, characterization of the underlying DNA methyltransferases in genetically tractable bacteria is of paramount importance. Here, we characterized the methylome and orphan methyltransferases in the model cyanobacterium Synechocystis sp. PCC 6803. Single molecule real-time (SMRT) sequencing revealed four DNA methylation recognition sequences in addition to the previously known motif m5CGATCG, which is recognized by M.Ssp6803I. For three of the new recognition sequences, we identified the responsible methyltransferases. M.Ssp6803II, encoded by the sll0729 gene, modifies GGm4CC, M.Ssp6803III, encoded by slr1803, represents the cyanobacterial dam-like methyltransferase modifying Gm6ATC, and M.Ssp6803V, encoded by slr6095 on plasmid pSYSX, transfers methyl groups to the bipartite motif GGm6AN7TTGG/CCAm6AN7TCC. The remaining methylation recognition sequence GAm6AGGC is probably recognized by methyltransferase M.Ssp6803IV encoded by slr6050. M.Ssp6803III and M.Ssp6803IV were essential for the viability of Synechocystis, while the strains lacking M.Ssp6803I and M.Ssp6803V showed growth similar to the wild type. In contrast, growth was strongly diminished of the Δsll0729 mutant lacking M.Ssp6803II. These data provide the basis for systematic studies on the molecular mechanisms impacted by these methyltransferases.

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.