ArbabM, ShenMW, MokB, et al.Determinants of base editing outcomes from target library analysis and machine learning. Cell, 2020; 182:463–480.e30. DOI: 10.1016/j.cell.2020.05.037.
2.
YangB, YangL, ChenJ. Development and application of base editors. CRISPR J, 2019; 2:91–104. DOI: 10.1089/crispr.2019.0001.
3.
ZafraMP, SchatoffEM, KattiA, et al.Optimized base editors enable efficient editing in cells, organoids and mice. Nat Biotechnol, 2018; 36:888–893. DOI: 10.1038/nbt.4194.
4.
O'BrienAR, BurgioG, BauerDC. Domain-specific introduction to machine learning terminology, pitfalls and opportunities in CRISPR-based gene editing. Brief Bioinformatics, 2020 February 2 [Epub ahead of print]. DOI: 10.1093/bib/bbz145/
5.
ShenMW, ArbabM, HsuJY, et al.Predictable and precise template-free CRISPR editing of pathogenic variants. Nature, 2018; 563:646–651. DOI: 10.1038/s41586-018-0686-x.
6.
SongM, KimHK, LeeS, et al. Sequence-specific prediction of the efficiencies of adenine and cytosine base editors. Nat Biotechnol 2020 July 6 [Epub ahead of print]. DOI: 10.1038/s41587-020-0573-5.
7.
DoenchJG, HartenianE, GrahamDB, et al.Rational design of highly active sgRNAs for CRISPR-Cas9-mediated gene inactivation. Nat Biotechnol, 2014; 32:1262–1267. DOI: 10.1038/nbt.3026.
8.
WilsonLOW, HetzelS, PockrandtC, et al.VARSCOT: variant-aware detection and scoring enables sensitive and personalized off-target detection for CRISPR-Cas9. BMC Biotechnol, 2019; 19:40. DOI: 10.1186/s12896-019-0535-5.