This proposal aims to develop a novel method for identifying RNA endonucleolytic cleavage sites and using this information to predict RNA half-life and cell fate decisions. By integrating high-throughput sequencing data (e.g., RNA-Seq, Degradome-Seq) with computational modeling and machine learning, we will map cleavage sites, analyze their association with RNA stability, and correlate these patterns with cellular outcomes such as differentiation, proliferation, or apoptosis. The approach includes refining cleavage site predictions using sequence motifs, RNA structures, and protein interactions, followed by training and validating predictive models. Expected outcomes include a comprehensive map of cleavage sites, predictive tools for RNA half-life and cell fate, and insights into RNA regulatory networks. This work has significant implications for understanding RNA metabolism, with potential applications in developmental biology, cancer research, and therapeutic development.