Machine learning prediction of prime editing efficiency across diverse chromatin contexts.
Nat Biotechnol
; 2024 Jun 21.
Article
em En
| MEDLINE
| ID: mdl-38907037
ABSTRACT
The success of prime editing depends on the prime editing guide RNA (pegRNA) design and target locus. Here, we developed machine learning models that reliably predict prime editing efficiency. PRIDICT2.0 assesses the performance of pegRNAs for all edit types up to 15 bp in length in mismatch repair-deficient and mismatch repair-proficient cell lines and in vivo in primary cells. With ePRIDICT, we further developed a model that quantifies how local chromatin environments impact prime editing rates.
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1
Coleções:
01-internacional
Base de dados:
MEDLINE
Idioma:
En
Revista:
Nat Biotechnol
Assunto da revista:
BIOTECNOLOGIA
Ano de publicação:
2024
Tipo de documento:
Article
País de afiliação:
Suíça