Accelerating Prime Editing: Machine Learning Helps Design the Best Fix for a Given Genetic Flaw

A new study published in the journal Nature Biotechnology has used machine learning to accelerate the development of prime editing, a promising gene-editing technology. The study analyzed thousands of DNA sequences introduced into the genome using prime editors, and used the data to train a machine learning algorithm to design the best fix for a given genetic flaw. By using machine learning to streamline the process of designing genetic fixes, this research could help speed up efforts to bring prime editing into clinical use. Researchers at the Wellcome Sanger Institute have developed a new tool to predict the chances of successfully inserting a gene-edited sequence of DNA into the genome of a cell, using a technique known as prime editing. An evolution of CRISPR-Cas9 gene editing technology, prime editing has huge potential to treat genetic diseases in humans, from cancer to cystic fibrosis.