Prediction of Base Editing Efficiencies and Outcomes Using DeepABE and DeepCBE.

Adenine base editors (ABEs) and cytosine base editors (CBEs) have been widely used to introduce disease-relevant point mutations at target DNA sites of interest. However, the introduction of point mutations using base editors can be difficult due to low editing efficiencies and/or the existence of multiple target nucleotides within the base editing window at the target site. Thus, previous works have relied heavily on experimentally evaluating the base editing efficiencies and outcomes using time-consuming and labor-intensive multi-step experimental processes. DeepABE and DeepCBE are deep learning-based computational models to predict the efficiencies and outcome frequencies of ABE and CBE at given target DNA sites, in silico. Here, we describe the step-by-step procedure for the accurate determination of specific target nucleotides for ABE or CBE editing on the online available web tool, (DeepBaseEditor, https://deepcrispr.info/DeepBaseEditor ).