Computational studies on RNA processing in higher eukaryotes

Most cellular processes are regulated by RNA-binding proteins (RBPs). These RBPs usually use defined binding sites to recognize and directly interact with their target RNA molecule. Individual-nucleotide resolution UV crosslinking and immunoprecipitation (iCLIP) experiments are an important tool to de- scribe such interactions in cell cultures in-vivo. This experimental protocol yields millions of individual sequencing reads from which the binding spec- trum of the RBP under study can be deduced. In this PhD thesis I studied how RNA processing is driven from RBP binding by analyzing iCLIP-derived sequencing datasets. First, I described a complete data analysis pipeline to detect RBP binding sites from iCLIP sequencing reads. This workflow covers all essential process- ing steps, from the first quality control to the final annotation of binding sites. I described the accurate integration of biological iCLIP replicates to boost the initial peak calling step while ensuring high specificity through replicate re- producibility analysis. Further I proposed a routine to level binding site width to streamline downstream analysis processes. This was exemplified in the re- analysis of the binding spectrum of the U2 small nuclear RNA auxiliary factor 2 (U2AF2, U2AF65). I recaptured the known dominance of U2AF65 to bind to intronic sequences of protein-coding genes, where it likely recognizes the polypyrimidine tract as part of the core spliceosome machinery. In the second part of my thesis, I analyzed the binding spectrum of the serine and arginine rich splicing factor 6 (SRSF6) in the context of diabetes. In pancreatic beta-cells, the expression of SRSF6 is regulated by the transcription factor GLIS3, which encodes for a diabetes susceptibility gene. It is known that SRSF6 promotes beta-cell death through the splicing dysregulation of genes essential to beta-cell function and survival. However, the exact mechanism of how these RNAs are targeted by SRSF6 remains poorly understood. Here, I applied the defined iCLIP processing pipeline to describe the binding landscape of the splicing factor SRSF6 in the human pancreatic beta-cell line EndoC-H1. The initial binding sites definition revealed a predominant binding to coding sequences (CDS) of protein-coding genes. This was followed up by extensive motif analysis which revealed a so far, in human, unknown purine-rich binding motif. SRSF6 seemed to specifically recognize repetitions of the triplet GAA. I also showed that the number of contiguous triplets correlated with increasing binding site strength. I further integrated RNA-sequencing data from the same cell type, with SRSF6 in KD and in basal conditions, to analyze SRSF6- related splicing changes. I showed that the exact positioning of SRSF6 on alternatively spliced exons regulates the produced transcript isoforms. This mechanism seemed to control exons in several known susceptibility genes for diabetes. In summary, in my PhD thesis, I presented a comprehensive workflow for the processing of iCLIP-derived sequencing data. I applied this pipeline on a dataset from pancreatic beta-cells to unveil the impact of SRSF6-mediated splicing changes. Thus, my analysis provides novel insights into the regulation of diabetes susceptibility genes.