Genome-wide Determination Of Splicing Efficiency And Dynamics From RNA-Seq Data

Abstract

Eukaryotic genes are mostly composed of a series of exons intercalated by sequences with no coding potential called introns. These sequences are generally removed from primary transcripts to form mature RNA molecules in a post-transcriptional process called splicing. An efficient splicing of primary transcripts is an essential step in gene expression and its misregulation is related to numerous human diseases. Thus, to better understand the dynamics of this process and the perturbations that might be caused by aberrant transcript processing, it is important to quantify splicing efficiency. In this thesis, I introduce SPLICE-q, a fast and user-friendly Python tool for genome-wide SPLICing Efficiency quantification. It supports studies focusing on the implications of splicing efficiency in transcript processing dynamics. SPLICE-q uses aligned reads from RNA-Seq to quantify splicing efficiency for each intron individually and allows the user to select different levels of restrictiveness concerning the introns’ overlap with other genomic elements, such as exons from other genes. I demonstrate SPLICE-q’s application using three use cases including two different species and methodologies. These analyses illustrate that SPLICE-q can detect a progressive increase of splicing efficiency throughout a time course of nascent RNA-Seq and it might be useful when it comes to understanding cancer progression beyond mere gene expression levels. Furthermore, I provide an in-depth study of time course nascent BrU-Seq data to address questions concerning differences in the speed of splicing and the underlying biological features that might be associated with it. SPLICE-q and its documentation are publicly available at: https://github.com/vrmelo/SPLICE-q.