Database Construction and Computational Analysis of Bacterial Small Regulatory RNAs

Small regulatory RNAs (sRNAs) are the most abundant post-transcriptional regulators in bacteria. They serve ubiquitous roles that control nearly every aspects of bacterial physiology. Despite the crucial regulatory roles that sRNAs played and the emergence of numerous sRNAs, effective sRNA database is not available. There is none for integrating and analyzing high throughput deep sequencing data, In addition, we are still unclear about the number of sRNAs even in a typical bacterial genome. Construction of a comprehensive database and further discovery of important features from sRNAs sequences will guide the computational prediction of new sRNA sequences for a better understanding of the pervasive sRNA-mediated regulation in bacteria. With these objectives, I have accomplished the following in this research. First, I designed and constructed BSRD (kwanlab.bio.cuhk.edu.hk/BSRD), a comprehensive bacterial small regulatory RNA database, aims to serve as the central repository for bacterial sRNAs sequences with extensive annotations and expression profiles. Second, I performed systematic analyses of many sequence and structural features that are possibly related to sRNA properties and identified a subset of significant features that effectively discriminate sRNAs sequences from random sequences. I then used a neural network model that integrated these subfeatures on unlabeled testing datasets, and it have achieved a 92.2% recall and 89.8% specificity. Finally, I applied this prediction model for genome-wide identification of sRNAs-encoded genes using a sliding-window approach. I recovered multiple known sRNAs and hundreds of predicted new sRNAs. These candidate novel sRNAs deserve extensive study to better understand the sRNA-mediated regulatory network in bacteria.