Machine Learning Can Improve The Success Rate of Genome Editing
Researchers at the Wellcome Sanger Institute have created a new tool that predicts the likelihood of inserting a gene-edited DNA sequence into a cell’s genome. This is known as prime editing.
Prime editing is an evolution of CRISPR/Cas9 gene-editing technology. It has tremendous potential to treat any genetic disease, including cancer and cystic fibrosis. However, it is not clear what factors are responsible for the edits’ success.
The study was published in Nature Biotechnology on 16 February. It evaluated thousands of DNA sequences that were introduced to the genome by prime editors. These data were used to train an algorithm that can learn from machine learning to find the best solution for any given genetic flaw. This will help speed up efforts to introduce prime editing to the clinic.
Senior author Dr. Leopold Parts of the Wellcome Sanger Institute study said that prime editing has the potential to improve human health. However, we must first understand how to do it safely and efficiently. Understanding the rules of the game is key, and this study will provide valuable data and tools to help with that.
CRISPR–Cas9, which was developed in 2012, is the first easily-programmable gene editing technology. More information about CRISPR–Cas9 can be found on the YourGenome website.
