RNA molecules carry more than 150 distinct chemical modifications that drive RNA metabolism in cells. The conversion of adenosine to inosine (A-to-I) in RNA molecules, known as RNA editing, can produce non-synonymous protein-coding changes or modify mRNA regions that drive different mRNA-protein interactions, thereby expanding transcriptomic variability in both composition and regulation within RNA metabolism. In the proposed project, we will study how RNA editing regulates RNA metabolism, including RNA localization and stability, and its role in processes such as local protein translation in neuronal models and cancer, to define how this widespread RNA modification drives brain development and cancer progression. Two novel NGS methods based on high-throughput sequencing and intracellular barcoding will be used to determine localized RNAs and to establish microRNA-mRNA interactions as protein-mRNA interactions, coupled with other RNA genomics approaches (e.g., ribosome profiling, SLAM-Seq, isoform sequencing…); AI (i.e ADAR-GPT) prediction tools will be used to model and infer RNA editing to increase our understanding of how local subcellular gene expression regulates cellular fate. The NAISS small allocation will support advanced modeling of RNA modifications and define changes in interactions between RNA editing sites and RNA-binding proteins, as well as secondary structure changes produced by RNA editing and their interactome changes, using machine learning and other AI tools that require GPU computing power. Deliverables include open pipelines, reference datasets linking localization signatures to cellular fitness, and benchmarks across a wide range of biological functions, from embryonic development to aging and cancer.