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Prediction of efficiencies for diverse prime editing systems in multiple cell types

Authors
 Goosang Yu  ;  Hui Kwon Kim  ;  Jinman Park  ;  Hyunjong Kwak  ;  Yumin Cheong  ;  Dongyoung Kim  ;  Jiyun Kim  ;  Jisung Kim  ;  Hyongbum Henry Kim 
Citation
 CELL, Vol.186(10) : 2256-2272.e23, 2023-05 
Journal Title
CELL
ISSN
 0092-8674 
Issue Date
2023-05
MeSH
CRISPR-Cas Systems ; Computer Simulation* ; Datasets as Topic ; Gene Editing* / methods ; Knowledge ; Organ Specificity ; RNA, Guide, CRISPR-Cas Systems* / chemistry
Keywords
deep learning ; efficiency ; features ; high-throughput evaluations ; off-target effects ; prediction ; prime editing ; prime editors ; sequence
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. © 2023 Elsevier Inc.
Full Text
https://www.sciencedirect.com/science/article/pii/S0092867423003318
DOI
10.1016/j.cell.2023.03.034
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Pharmacology (약리학교실) > 1. Journal Papers
Yonsei Authors
Kim, Hyongbum(김형범) ORCID logo https://orcid.org/0000-0002-4693-738X
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/195549
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