CRISPR, which stands for Clustered Regularly Interspaced Short Palindromic Repeats, is a gene-editing technology that enables scientists to edit an organism’s DNA. Many scientists consider the CRISPR-Cas9 system — which creates modified RNA segments that bind to the CRISPR-associated protein 9 enzyme — to be one of the most precise and least expensive gene-editing techniques currently in use.
However, off-target CRISPR mutations that lead to suboptimal outcomes have hindered the development of effective gene-editing therapies, according to the researchers. In a study published in Nature Biomedical Engineering Jan. 10, the researchers laid out two separate machine-learning models that predict the off-target effects of CRISPR-Cas9.
The predictive approach employed by the two models, which the researchers dubbed “Elevation,” “consistently outperforms competing approaches,” according to the study authors. The research team hopes the predictive approach is able to help researchers inform and guide RNA modifications.
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