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CRISPR gene editing was launched into the spotlight this week when Chinese scientist, He Jiankui, claimed to have made the world’s first genome-edited babies using the technology. The resulting ethical debate about manipulating the human germline was important, to be sure, but it overshadowed a more immediate concern: Before CRISPR research can be safely translated into therapies, scientists will need better methods for avoiding potential damaging off-target effects of the technology.
The problem, in a nutshell, is that after the CRISPR-Cas9 editing tool cuts double-stranded DNA, the DNA repairs itself but sometimes introduces mutations during the process. Scientists believe the errors depend on several factors, including the targeted sequence and the guide RNA (gRNA), but they also seem to follow a reproducible pattern.
Now, researchers at the Wellcome Sanger Institute say they have used machine learning to develop a tool that can predict which mutations CRISPR will introduce into a cell. They believe the technology could boost the efficiency of CRISPR research and ease the process of translating it into safe and effective treatments.
