Small, noncoding RNA genes pervade bacteria. Our understanding of these noncoding genes has increased dramatically in recent years, thanks, in part, to advances in high-throughput sequencing technology. High-throughput sequencing technology enables, among other things, experiments that produce massive amounts of data about RNA transcripts in 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 a computational system for managing and analyzing large sets of data from bacterial high-throughput sequencing experiments. As part of this computational system, new algorithms will be developed for determining a map of all RNA transcripts evinced by high-throughput sequencing data for a bacterial species. Further, a public database will be created to store and manage information about the growing number of small RNA genes characterized in bacteria. Finally, since many small RNA genes in bacteria act as regulators of other RNAs, computational methods will be developed to identify the interactions between these noncoding genes and their RNA targets. The methods developed will be applied and evaluated in several different bacterial systems.
High-throughput sequencing experiments can provide information about gene expression in human pathogens during infection, but existing computational methods for processing the information is insufficient. In this project, computational tools and methods will be developed for analyzing high-throughput sequencing data, and these new methods will be evaluated on data collected from a model bacterial organism, Escherichia coli, and from the human pathogens Neisseria gonorrhoeae and Streptococcus pyogenes. More broadly, the database and computational infrastructure developed in this project will serve as resources to biological and medical researchers studying myriad bacterial pathogens of humans.
|Calfee, Gregory; Danger, Jessica L; Jain, Ira et al. (2018) Identification and Characterization of Serotype-Specific Variation in Group A Streptococcus Pilus Expression. Infect Immun 86:|
|Gerrick, Elias R; Barbier, Thibault; Chase, Michael R et al. (2018) Small RNA profiling in Mycobacterium tuberculosis identifies MrsI as necessary for an anticipatory iron sparing response. Proc Natl Acad Sci U S A 115:6464-6469|
|Zhang, Yi-Fan; Han, Kook; Chandler, Courtney E et al. (2017) Probing the sRNA regulatory landscape of P. aeruginosa: post-transcriptional control of determinants of pathogenicity and antibiotic susceptibility. Mol Microbiol 106:919-937|
|Han, Kook; Tjaden, Brian; Lory, Stephen (2016) GRIL-seq provides a method for identifying direct targets of bacterial small regulatory RNA by in vivo proximity ligation. Nat Microbiol 2:16239|
|Tjaden, Brian (2015) De novo assembly of bacterial transcriptomes from RNA-seq data. Genome Biol 16:1|
|Kery, Mary Beth; Feldman, Monica; Livny, Jonathan et al. (2014) TargetRNA2: identifying targets of small regulatory RNAs in bacteria. Nucleic Acids Res 42:W124-9|
|McClure, Ryan; Tjaden, Brian; Genco, Caroline (2014) Identification of sRNAs expressed by the human pathogen Neisseria gonorrhoeae under disparate growth conditions. Front Microbiol 5:456|
|Zhang, Aixia; Schu, Daniel J; Tjaden, Brian C et al. (2013) Mutations in interaction surfaces differentially impact E. coli Hfq association with small RNAs and their mRNA targets. J Mol Biol 425:3678-97|
|McClure, Ryan; Balasubramanian, Divya; Sun, Yan et al. (2013) Computational analysis of bacterial RNA-Seq data. Nucleic Acids Res 41:e140|