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

Cell64.50
Volume: 182, Issue: 2, Pages: 463
Published: Jul 23, 2020
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...
Paper Details
Title
Determinants of Base Editing Outcomes from Target Library Analysis and Machine Learning
DOI
Published Date
Jul 23, 2020
Journal
Volume
182
Issue
2
Pages
463
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