Submitted to: American Phytopathological Society Abstracts
Publication Type: Proceedings
Publication Acceptance Date: 4/23/2009
Publication Date: 8/3/2009
Citation: Filiatrault, M.J., Stodghill, P. 2009. Integrating molecular and computational methods to evaluate the Pseudomonas syringae transcriptome I & II. American Phytopathological Society Abstracts.
Technical Abstract: Much information can be gathered from the genomic sequence of a bacterium. However, to more fully understand the coding potential of the genome, experimental identification of the transcribed fraction is required. In particular, strand-specific information is essential to thoroughly characterize transcriptional activity. Several methods exist for capturing the complete set of transcripts in a cell, using deep-sequencing technologies, however, most of these techniques have been limited to the study of eukayotes and lack strand specific information. We will present combined computational and experimental approaches for precisely evaluating the transcriptome of the plant pathogen Pseudomonas syringae using RNA-Seq. The power of this approach is demonstrated by the fact that a single experiment has generated a number of important questions regarding gene expression in P. syringae for future investigations. The establishment of RNA-Seq for analyzing bacterial transcriptomes on a global scale significantly impacts bacterial genome annotation as well as the study of bacterial gene regulation. In Part I, we will describe the molecular methods used to prepare RNA samples and the development of a strand-specific protocol to sequence RNA using the Illumina Genome Analyzer. Next, the computational methods developed to analyze the vast amount of sequence data will be discussed. Then, we will show the application of transcriptome sequencing to the identification of polymorphisms and candidate transcriptional start sites. For Part II, we will describe a unique classification method developed to qualitatively assess transcriptional activity that combines RNA-Seq with proteomics data. Using this approach, we are able to identify transcriptional activity in areas of the genome inconsistent with the genome annotation and transcriptional activity in un-annotated areas of the genome, allowing for transcript discovery. Specific examples of areas in the genome that display unusual transcriptional activity will be highlighted.