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Unveiling the hidden window of prime editing

Chen, P.-R.; Wei, Y.; Yuan, X.-Z.; Wang, S.-G.; Xia, P.-F.

2025-05-21 synthetic biology
10.1101/2025.05.20.655067 bioRxiv
Show abstract

Prime editing enables versatile and precise genomic modifications, following a fundamental principle that the editing extends from the nick site towards the 3 direction of the cleaved DNA strand. Here, we report the unknown competency of prime editing on the opposite 5 direction of the nicked DNA strand through a hidden reverse transcription pathway, where a designed edit can be integrated upstream of the nick.

Matching journals

The top 1 journal accounts for 50% of the predicted probability mass.

1
Nature Communications
○ 5641 papers in training set
Top 0.1%
50.9%
Similar papers in this journal
50% of probability mass above
2
Nature
○ 645 papers in training set
Top 2%
7.5%
Similar papers in this journal
3
Nature Chemical Biology
○ 119 papers in training set
Top 0.3%
6.4%
Similar papers in this journal
4
Nature Structural & Molecular Biology
236 papers in training set
Top 0.9%
5.2%
5
Science
● 477 papers in training set
Top 2%
4.6%
Similar papers in this journal
6
Nature Biotechnology
○ 172 papers in training set
Top 2%
3.0%
7
Science Advances
● 1243 papers in training set
Top 14%
2.5%
8
Nucleic Acids Research
◐ 1281 papers in training set
Top 7%
2.3%
9
Proceedings of the National Academy of Sciences
● 2444 papers in training set
Top 31%
1.4%
10
Cell
○ 431 papers in training set
Top 7%
1.3%
11
eLife
● 5828 papers in training set
Top 61%
1.1%
12
Nature Nanotechnology
○ 32 papers in training set
Top 0.6%
1.0%
13
Nature Plants
○ 94 papers in training set
Top 2%
1.0%
14
Nature Methods
○ 385 papers in training set
Top 6%
0.9%
15
Angewandte Chemie International Edition
○ 93 papers in training set
Top 2%
0.8%
16
Nature Genetics
○ 286 papers in training set
Top 5%
0.8%
17
Cell Chemical Biology
○ 94 papers in training set
Top 2%
0.6%
18
National Science Review
◐ 21 papers in training set
Top 0.4%
0.6%

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.