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Predicting the efficiency of prime editing guide RNAs in human cells

Authors
 Hui Kwon Kim  ;  Goosang Yu  ;  Jinman Park  ;  Seonwoo Min  ;  Sungtae Lee  ;  Sungroh Yoon  ;  Hyongbum Henry Kim 
Citation
 NATURE BIOTECHNOLOGY, Vol.39(2) : 198-206, 2021-02 
Journal Title
 NATURE BIOTECHNOLOGY 
ISSN
 1087-0156 
Issue Date
2021-02
MeSH
Algorithms ; CRISPR-Associated Protein 9 / metabolism ; Cell Line, Tumor ; Computer Simulation ; Gene Editing* ; HEK293 Cells ; Humans ; Machine Learning ; RNA, Guide / genetics*
Abstract
Prime editing enables the introduction of virtually any small-sized genetic change without requiring donor DNA or double-strand breaks. However, evaluation of prime editing efficiency requires time-consuming experiments, and the factors that affect efficiency have not been extensively investigated. In this study, we performed high-throughput evaluation of prime editor 2 (PE2) activities in human cells using 54,836 pairs of prime editing guide RNAs (pegRNAs) and their target sequences. The resulting data sets allowed us to identify factors affecting PE2 efficiency and to develop three computational models to predict pegRNA efficiency. For a given target sequence, the computational models predict efficiencies of pegRNAs with different lengths of primer binding sites and reverse transcriptase templates for edits of various types and positions. Testing the accuracy of the predictions using test data sets that were not used for training, we found Spearman's correlations between 0.47 and 0.81. Our computational models and information about factors affecting PE2 efficiency will facilitate practical application of prime editing.
Full Text
https://www.nature.com/articles/s41587-020-0677-y
DOI
10.1038/s41587-020-0677-y
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Pharmacology (약리학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Others (기타) > 1. Journal Papers
Yonsei Authors
Kim, Hyongbum(김형범) ORCID logo https://orcid.org/0000-0002-4693-738X
Kim, Hui Kwon(김희권)
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/182172
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