In addition to these innovations, the development of predictive models for prime editing efficiency has emerged as a critical area of research. A high-throughput screen analyzed over 92,000 prime editing guide RNAs (pegRNAs) to identify sequence context features that influence editing outcomes, leading to the creation of PRIDICT, a deep learning model for predicting pegRNA performance (ref: Mathis doi.org/10.1038/s41587-022-01613-7/). This predictive capability is vital for optimizing prime editing applications across various genetic contexts. Moreover, advancements in base editing techniques have been reported, including the creation of an adenine transversion base editor that achieves high editing activity at specific genomic loci (ref: Tong doi.org/10.1038/s41587-022-01595-6/) and improved cytosine base editors that reduce off-target effects while maintaining on-target efficiency (ref: Lam doi.org/10.1038/s41587-022-01611-9/). These developments underscore the importance of refining CRISPR tools to enhance their precision and applicability in therapeutic settings.