Pathogenic gene variation or precise editing November 12, 2018 Source: Chinese Journal of Science A new paper published in Nature reports a way to achieve accurate and predictable editing of disease-causing gene mutations through machine learning. This result establishes a method for achieving accurate, template-free genome editing, offering new possibilities for genetic disease research and potential therapy. Although CRISPR-Cas9 revolutionized the genome editing technology used for research, it is important to ensure the accuracy of this technology. The CRISPR-Cas9 genome edits commonly used DNA "templates" to ensure the accuracy of DNA repair or to import specific DNA sequences into the genome. Therefore, DNA repairs that lack these templates are often considered inaccurate. Now, Richard Sherwood and colleagues at the Bregen and Women's Hospital in Cambridge, Massachusetts, and Harvard Medical School, and colleagues have invented a method for predicting the results of genome repair using machine learning, enabling accurate templateless Cas9 editing. The researchers trained a machine learning model called "inDelphi" with a database of nearly 2,000 pairs of Cas9 guide RNA (gRNA) and human DNA targets. The model identified that 5% to 11% of the Cas9 gRNA targeting the human genome produced a single and predictable repair result in more than 50% of the cases (referred to as "precision 50"). inDelphi also uses the template-free Cas9 editor to identify and predict appropriate target genes for disease-causing gene mutations, including targets that were previously considered unusable by this method. Finally, the researchers have proved that nearly 200 kinds of pathogenic mutations in human cells related to Hermansky-Pudlak syndrome, Menkes disease and familial hypercholesterolemia can achieve the accuracy of editing and repair. The standard of precision 50.
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