UC San Diego

RNA-binding proteins (RBPs) regulate post-transcriptional gene expression and are implicated in diseases such as cancer, neurodegeneration, and muscular dystrophy. However, pinpointing actionable RBP-binding sites remains challenging due to limited functional data and analytical tools.This thesis advances computational and technological methods for RBP profiling. I developed software to analyze eCLIP data, uncovering functional RBP patterns. Using deep learning and data integration, I prioritized disease-relevant RBP-RNA interactions and variants from 411 datasets. Additionally, I introduced a multiplex CLIP pipeline, enabling high-throughput profiling of multiple RBPs. These contributions improve RBP-binding site detection and facilitate insights for therapeutic development.