Prediction of efficiencies for diverse prime editing systems in multiple cell types.
Cell
; 186(10): 2256-2272.e23, 2023 05 11.
Article
en En
| MEDLINE
| ID: mdl-37119812
ABSTRACT
Applications of prime editing are often limited due to insufficient efficiencies, and it can require substantial time and resources to determine the most efficient pegRNAs and prime editors (PEs) to generate a desired edit under various experimental conditions. Here, we evaluated prime editing efficiencies for a total of 338,996 pairs of pegRNAs including 3,979 epegRNAs and target sequences in an error-free manner. These datasets enabled a systematic determination of factors affecting prime editing efficiencies. Then, we developed computational models, named DeepPrime and DeepPrime-FT, that can predict prime editing efficiencies for eight prime editing systems in seven cell types for all possible types of editing of up to 3 base pairs. We also extensively profiled the prime editing efficiencies at mismatched targets and developed a computational model predicting editing efficiencies at such targets. These computational models, together with our improved knowledge about prime editing efficiency determinants, will greatly facilitate prime editing applications.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Simulación por Computador
/
Edición Génica
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ARN Guía de Sistemas CRISPR-Cas
Tipo de estudio:
Prognostic_studies
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Risk_factors_studies
Idioma:
En
Revista:
Cell
Año:
2023
Tipo del documento:
Article