New strategies for the identification of intronic variants related to splicing events in pancreas cancer [RNA-Seq]
Most clinical diagnostic settings and genomic research focus almost exclusively on coding regions and essential splice sites, mostly ignoring non-coding variants. Indeed, the investigation of intronic mis-splicing variants interpreting mechanisms to disease-associated splicing events requires both genomic and transcriptomic data. Unfortunately, there are not many datasets where both are available, leading to the understanding of intronic variants in diseases full of gaps. In this study, we present for the first time a full-length single nuclei RNA-sequencing (snRNA-Seq) approach improving the proper investigation of pathogenic mis-splicing intronic variants in pancreatic cancer showing its contribution to abnormal splicing changes and their transcriptional effects. Finally, we discuss the demands of further machine learning-based methods to process the growing number of single cell data and to enhance the precision and recall. Overall design: Bulk RNA Seq from PDAC compared to single nuclei RNA Seq