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Combining Massive Oligo Pools and AI to Predict Guide RNA Efficiency for Prime Editing Screens
A key challenge exists whenever a new CRISPR application is developed: How do you predict which guide RNA sequence will maximize the chance of an edit? Prime editing is one of the newest CRISPR technologies. It is highly promising as it can install small and precise mutations with a unique reverse transcriptase dependent mechanism that eliminates the need for donor DNA and double-strand breaks. Due to its precise editing capabilities, it has potential applications in CRISPR therapeutics for treating genetic disease.
Covered in this Application Note
Combining Massive Oligo Pools and AI to Predict Guide RNA Efficiency for Prime Editing Screens
Prediction models versus conventional machine learning when using data-sets for model training
Designing highly efficient pegRNAs that can be used with PE2
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