PROJECT SUMMARY High-throughput sequencing technology enables, among other things, experiments that produce massive amounts of data about RNA transcripts in bacteria. These high-throughput sequencing experiments continue to advance our understanding of bacterial transcriptomes, including regulatory RNA genes, which pervade bacteria. However, processing the large resulting data sets from high-throughput sequencing experiments can be a bottleneck in biological and medical research studies, partly because existing methods are insufficient for analyzing these data sets from bacteria. This project aims to develop new methods for processing high-throughput bacterial sequencing data. A robust and user-friendly computational system will be implemented for end- to-end analysis of high-throughput bacterial sequencing data. As part of this system, a novel algorithm will be implemented to significantly reduce the time it takes for the most computationally expensive stage of processing bacterial sequencing data, which is aligning reads from sequencing experiments to a bacterial genome. While high-throughput bacterial sequencing experiments commonly are performed on large sets of cells in bulk, recent advances have made possible the effective use of these experiments on individual cells, enabling greater resolution of bacterial transcriptome studies. New methods will be developed to process high-throughput bacterial sequencing data from single-cell experiments. Further, a computational system will be designed for systematic annotation of transcripts evinced from bacterial sequencing data to aid biological and medical researchers in efficient and reliable interpretation of the massive data sets. Finally, since many RNA genes in bacteria act as regulators of other transcripts, novel approaches will be developed to identify the interactions between these noncoding RNAs and their regulatory targets. The methods developed will be applied and evaluated in different bacterial systems.