BEdeepoff: an in silico tool for off-target prediction of ABE and CBE base editors

Abstract

AbstractBase editors, including adenine base editors (ABEs) and cytosine base editors (CBEs), are valuable tools for introducing point mutations, but they frequently induce unwanted off-target mutations. Here, we performed a high-throughput gRNA-target library screening to measure editing efficiencies at integrated genomic off-targets and obtained datasets of 48,632 and 52,429 off-targets for ABE and CBE, respectively. We used the datasets to train deep learning models, resulting in ABEdeepoff and CBEdeepoff which can predict editing efficiencies at off-targets. These tools are freely accessible via online web server http://www.deephf.com/#/bedeep.