Evaluating differential gene expression using RNA-sequencing data
Unlike the genome, the cell transcriptome is dynamic and specific for a given cell developmental stage or physiological condition. Understanding the transcriptome is essential for interpreting the functional elements of the genome and revealing the molecular constituents of cells. Recently, developments of high-throughput DNA sequencing methodologies have provided a new method to sequence RNA at unprecedented high resolutions. This method is termed RNA-Seq and has been emerging as the preferred technology for both characterization and quantification of the cell transcripts.
Bearing this in mind, in this thesis I propose a bioinformatics pipeline to compare two RNA-Seq samples. This pipeline permits biological insight into the analysed samples, by extracting the main biological processes that are differentially active among the samples in analysis. Subsequent to this pipeline, I developed a novel methodology to inspect the activation of a given cellular pathway in a time-course RNA-Seq dataset.
The evaluation of a Listeria monocytogenes RNA-Seq dataset with the developed tools testified its proper functioning. It was possible to identify global changes in the human host transcriptome and associate these changes to different stages of the Listeria monocytogenes infection lifecycle.