Predicting the efficiency of prime editing guide RNAs in human cells
- Authors
- HuiKwon Kim; GOO SANG YU; JINMAN PARK; Seonwoo Min; Sungtae Lee; Sungroh Yoon; HYONGBUM HENRY KIM
- Issue Date
- Feb-2021
- Publisher
- NATURE PUBLISHING GROUP
- Keywords
- RNAs (pegRNAs); prime editing
- Citation
- NATURE BIOTECHNOLOGY, v.39, no.2, pp 198 - 206
- Pages
- 9
- Journal Title
- NATURE BIOTECHNOLOGY
- Volume
- 39
- Number
- 2
- Start Page
- 198
- End Page
- 206
- URI
- https://yscholarhub.yonsei.ac.kr/handle/2021.sw.yonsei/5257
- DOI
- 10.1038/s41587-020-0677-y
- ISSN
- 1087-0156
- 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.
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