Cited 13 times in

Deep learning models to predict the editing efficiencies and outcomes of diverse base editors

DC Field Value Language
dc.contributor.author김형범-
dc.contributor.author조성래-
dc.contributor.author송명재-
dc.date.accessioned2023-07-12T03:12:28Z-
dc.date.available2023-07-12T03:12:28Z-
dc.date.issued2024-03-
dc.identifier.issn1087-0156-
dc.identifier.urihttps://ir.ymlib.yonsei.ac.kr/handle/22282913/195537-
dc.description.abstractApplications of base editing are frequently restricted by the requirement for a protospacer adjacent motif (PAM), and selecting the optimal base editor (BE) and single-guide RNA pair (sgRNA) for a given target can be difficult. To select for BEs and sgRNAs without extensive experimental work, we systematically compared the editing windows, outcomes and preferred motifs for seven BEs, including two cytosine BEs, two adenine BEs and three C•G to G•C BEs at thousands of target sequences. We also evaluated nine Cas9 variants that recognize different PAM sequences and developed a deep learning model, DeepCas9variants, for predicting which variants function most efficiently at sites with a given target sequence. We then develop a computational model, DeepBE, that predicts editing efficiencies and outcomes of 63 BEs that were generated by incorporating nine Cas9 variants as nickase domains into the seven BE variants. The predicted median efficiencies of BEs with DeepBE-based design were 2.9- to 20-fold higher than those of rationally designed SpCas9-containing BEs.-
dc.description.statementOfResponsibilityrestriction-
dc.languageEnglish-
dc.publisherNature America Publishing-
dc.relation.isPartOfNATURE BIOTECHNOLOGY-
dc.rightsCC BY-NC-ND 2.0 KR-
dc.titleDeep learning models to predict the editing efficiencies and outcomes of diverse base editors-
dc.typeArticle-
dc.contributor.collegeCollege of Medicine (의과대학)-
dc.contributor.departmentDept. of Pharmacology (약리학교실)-
dc.contributor.googleauthorNahye Kim-
dc.contributor.googleauthorSungchul Choi-
dc.contributor.googleauthorSungjae Kim-
dc.contributor.googleauthorMyungjae Song-
dc.contributor.googleauthorJung Hwa Seo-
dc.contributor.googleauthorSeonwoo Min-
dc.contributor.googleauthorJinman Park-
dc.contributor.googleauthorSung-Rae Cho-
dc.contributor.googleauthorHyongbum Henry Kim-
dc.identifier.doi10.1038/s41587-023-01792-x-
dc.contributor.localIdA01148-
dc.contributor.localIdA03831-
dc.relation.journalcodeJ02290-
dc.identifier.eissn1546-1696-
dc.identifier.pmid37188916-
dc.identifier.urlhttps://www.nature.com/articles/s41587-023-01792-x-
dc.contributor.alternativeNameKim, Hyongbum-
dc.contributor.affiliatedAuthor김형범-
dc.contributor.affiliatedAuthor조성래-
dc.citation.volume42-
dc.citation.number3-
dc.citation.startPage484-
dc.citation.endPage497-
dc.identifier.bibliographicCitationNATURE BIOTECHNOLOGY, Vol.42(3) : 484-497, 2024-03-
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Pharmacology (약리학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Rehabilitation Medicine (재활의학교실) > 1. Journal Papers

qrcode

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.