AnzaloneAV, RandolphPB, DavisJR, et al.Search-and-replace genome editing without double-strand breaks or donor DNA. Nature, 2019; 576:149.
2.
MikuniT, NishiyamaJ, SunY, et al.High-throughput, high-resolution mapping of protein localization in mammalian brain by in vivo genome editing. Cell, 2016; 165:1803–1817.
3.
LandrumMJ, LeeJM, BensonM, et al.ClinVar: Improving access to variant interpretations and supporting evidence. Nucleic Acids Res, 2018; 46:D1062–D1067.
4.
KoeppelJ, WellerJ, PeetsEM, et al.Prediction of prime editing insertion efficiencies using sequence features and DNA repair determinants. Nat Biotechnol, 2023; doi: 10.1038/s41587-023-01678-y
5.
DrummML, ZiadyAG, DavisPB. Genetic variation and clinical heterogeneity in cystic fibrosis. Annu Rev Pathol, 2012; 7:267–282; doi: 10.1146/annurev-pathol-011811-120900
6.
GeurtsMH, de PoelE, Pleguezuelos-ManzanoC, et al.Evaluating CRISPR-based prime editing for cancer modeling and CFTR repair in organoids. Life Sci Alliance, 2021; 4:e202000940.
7.
AnzaloneAV, KoblanLW, LiuDR. Genome editing with CRISPR–Cas nucleases, base editors, transposases and prime editors. Nat Biotechnol, 2020; 38(7):824–844; doi:10.1038/s41587-020-0561-9
8.
NelsonJW, RandolphPB, ShenSP, et al.Engineered pegRNAs improve prime editing efficiency. Nat Biotechnol, 2022; 40:402–410.
9.
VelimirovicM, ZanettiLC, ShenMW, et al.Peptide fusion improves prime editing efficiency. Nat Commun, 2022; 13:3512.
10.
ChenPJ, HussmannJA, YanJ, et al.Enhanced prime editing systems by manipulating cellular determinants of editing outcomes. Cell, 2021; 184:5635–5652.e29.
11.
Ferreira da SilvaJ, OliveiraGP, Arasa-VergeEA, et al.Prime editing efficiency and fidelity are enhanced in the absence of mismatch repair. Nat Commun, 2022; 13:760.
12.
ArbabM, ShenMW, MokB, et al. Determinants of base editing outcomes from target library analysis and machine learning. Cell 2020:182;463–480.e30.
13.
ShenM, ArbabM, HsuJY, et al.Predictable and precise template-free CRISPR editing of pathogenic variants. Nature, 2018; 563:646–651.
14.
ArbabM, MatuszekZ, KrayKM, et al.Base editing rescue of spinal muscular atrophy in cells and in mice. Science, 2023; 380:eadg6518.
15.
SchepersM, PaesD, TianeA, et al.Selective PDE4 subtype inhibition provides new opportunities to intervene in neuroinflammatory versus myelin damaging hallmarks of multiple sclerosis. Brain Behav Immun, 2023; 109:1–22.
16.
KimY, LeeS, ChoS, et al.High-throughput functional evaluation of human cancer-associated mutations using base editors. Nat Biotechnol, 2022; 40:874–884.
17.
KimHK, YuG, ParkJ, et al.Predicting the efficiency of prime editing guide RNAs in human cells. Nat Biotechnol, 2020; 39:198–206.
18.
LiY, ChenJ, TsaiSQ, et al.Easy-Prime: A machine learning–based prime editor design tool. Genome Biol, 2021; 22:235.
19.
MathisN, AllamA, KisslingL, et al.Predicting prime editing efficiency and product purity by deep learning. Nat Biotechnol, 2023; doi: 10.1038/s41587-022-01613-7
20.
LiX, ZhouL, GaoBQ, et al.Highly efficient prime editing by introducing same-sense mutations in pegRNA or stabilizing its structure. Nat Commun, 2022; 13:1–9.
21.
YarnallMTN, IoannidiEI, Schmitt-UlmsC, et al.Drag-and-drop genome insertion of large sequences without double-strand DNA cleavage using CRISPR-directed integrases. Nat Biotechnol, 2022; 41:500–512. doi:10.1038/s41587-022-01527-4
22.
AnzaloneAV, GaoXD, PodrackyCJ, et al.Programmable deletion, replacement, integration and inversion of large DNA sequences with twin prime editing. Nat Biotechnol, 2022; 40:731–740.
