Researchers have created a new searchable library of base editors — an especially efficient and precise kind of genetic corrector. Using experimental data from editing more than 38,000 target sites in cells with 11 of the most popular base editors (BEs), they created a machine learning model that accurately predicts base editing outcomes. Called BE-Hive, the library is free and open to the public.
Repairing thousands of disease-causing mutations
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