Determinants of Base Editing Outcomes from Target Library Analysis and Machine Learning

• Published in: Cell Vol. 182; no. 2; pp. 463 - 480.e30
• Main Authors: Arbab, Mandana, Shen, Max W., Mok, Beverly, Wilson, Christopher, Matuszek, Żaneta, Cassa, Christopher A., Liu, David R.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 23.07.2020
• Subjects: adenine; alleles; Animals; artificial intelligence; base editing; cytosine; disease correction; Gene Editing - methods; Gene Library; Humans; Machine Learning; mammals; Mice; Mouse Embryonic Stem Cells - cytology; Mouse Embryonic Stem Cells - metabolism; nucleotides; Point Mutation; precision genome editing; RNA, Guide, CRISPR-Cas Systems; transversion base editing; transversion base editing; machine learning; base editing; disease correction; precision genome editing

Although base editors are widely used to install targeted point mutations, the factors that determine base editing outcomes are not well understood. We characterized sequence-activity relationships of 11 cytosine and adenine base editors (CBEs and ABEs) on 38,538 genomically integrated targets in ma...