BEdeepoff: an in silico tool for off-target prediction of ABE and CBE base editors
Zhang, C.; Wang, D.; Qi, T.; Zhang, Y.; Hou, L.; Lan, F.; Yang, J.; Ong, S.-G.; Wang, H.; Shi, L.; Wang, Y.
Show abstract
Base 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.
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