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Optimization of C-to-G base editors with sequence context preference predictable by machine learning methods

Crossref DOI link: https://doi.org/10.1038/s41467-021-25217-y

Published Online: 2021-08-12

Update policy: https://doi.org/10.1007/springer_crossmark_policy

Authors

Yuan, Tanglong https://orcid.org/0000-0003-4670-2625
Yan, Nana https://orcid.org/0000-0002-6418-3139
Fei, Tianyi https://orcid.org/0000-0001-8093-6391
Zheng, Jitan https://orcid.org/0000-0003-4432-3306
Meng, Juan

Li, Nana https://orcid.org/0000-0001-7082-0379
Liu, Jing https://orcid.org/0000-0002-4036-789X
Zhang, Haihang https://orcid.org/0000-0002-4979-9963
Xie, Long https://orcid.org/0000-0003-2854-9065
Ying, Wenqin https://orcid.org/0000-0001-5711-5923
Li, Di https://orcid.org/0000-0003-0262-6827
Shi, Lei https://orcid.org/0000-0002-4976-2159
Sun, Yongsen

Li, Yongyao https://orcid.org/0000-0003-3092-5613
Li, Yixue https://orcid.org/0000-0002-1198-7176
Sun, Yidi https://orcid.org/0000-0002-4191-2917
Zuo, Erwei https://orcid.org/0000-0001-8259-0275
Funding

Funding for this research was provided by:

National Natural Science Foundation of China (31922048)
License Information

Text and Data Mining valid from 2021-08-12

Version of Record valid from 2021-08-12
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Article History

Received: 13 June 2021

Accepted: 29 July 2021

First Online: 12 August 2021

Competing interests

: The engineered CGBE editors are covered in a pending patent application (E.Z., Y.S. and T.Y.). The other authors declare no competing interests.