Computational prediction of regulatory, premature transcription termination in bacteria.

A common strategy for regulation of gene expression in bacteria is conditional transcription termination. This strategy is frequently employed by 5'UTR cis-acting RNA elements (riboregulators), including riboswitches and attenuators. Such riboregulators can assume two mutually exclusive RNA structures, one of which forms a transcriptional terminator and results in premature termination, and the other forms an antiterminator that allows read-through into the coding sequence to produce a full-length mRNA. We developed a machine-learning based approach, which, given a 5'UTR of a gene, predicts whether it can form the two alternative structures typical to riboregulators employing conditional termination. Using a large positive training set of riboregulators derived from 89 human microbiome bacteria, we show high specificity and sensitivity for our classifier. We further show that our approach allows the discovery of previously unidentified riboregulators, as exemplified by the detection of new LeuA leaders and T-boxes in Streptococci Finally, we developed PASIFIC (www.weizmann.ac.il/molgen/Sorek/PASIFIC/), an online web-server that, given a user-provided 5'UTR sequence, predicts whether this sequence can adopt two alternative structures conforming with the conditional termination paradigm. This webserver is expected to assist in the identification of new riboswitches and attenuators in the bacterial pan-genome.