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Cell DOI:10.1016/j.cell.2020.05.037

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

Publication TypeJournal Article
Year of Publication2020
AuthorsArbab, M, Shen, MW, Mok, B, Wilson, C, Matuszek, Ż, Cassa, CA, Liu, DR
JournalCell
Date Published2020 Jun 09
ISSN1097-4172
Abstract

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 mammalian cells and used the resulting outcomes to train BE-Hive, a machine learning model that accurately predicts base editing genotypic outcomes (R ≈ 0.9) and efficiency (R ≈ 0.7). We corrected 3,388 disease-associated SNVs with ≥90% precision, including 675 alleles with bystander nucleotides that BE-Hive correctly predicted would not be edited. We discovered determinants of previously unpredictable C-to-G, or C-to-A editing and used these discoveries to correct coding sequences of 174 pathogenic transversion SNVs with ≥90% precision. Finally, we used insights from BE-Hive to engineer novel CBE variants that modulate editing outcomes. These discoveries illuminate base editing, enable editing at previously intractable targets, and provide new base editors with improved editing capabilities.

DOI10.1016/j.cell.2020.05.037
Pubmed

http://www.ncbi.nlm.nih.gov/pubmed/32533916?dopt=Abstract

Alternate JournalCell
PubMed ID32533916