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A comprehensive benchmark for multiple highly efficient base editors with maximal targeting scope

ABSTRACT: As the toolbox of base editors (BEs) expands, selecting appropriate BE and guide RNA (gRNA) to achieve optimal editing efficiency and outcome for a given target becomes challenging. Here we construct a set of 10 adenine and cytosine BEs with high activity and maximal targeting scope, and comprehensively evaluate their editing profiles and properties head-to-head with 34,040 BE-gRNA-target combinations using genomically integrated long targets and tiling gRNA strategies. A deep learning model BEEP (Base Editing Efficiency Predictor) is built for predicting the editing efficiency and outcomes. Guided by BEEP, we experimentally install 3,558 disease-associated single nucleotide variants (SNVs), including 20.1% of target sites that would not be edited due to known protospacer adjacent motif (PAM) restriction, and predicted candidate BE-gRNA-target combinations for modeling 1,752,651 ClinVar SNVs. We also identify several cancer-associated SNVs that drive the resistance to BRAF inhibitors in melanoma. These efforts benchmark the performance and illuminate the capabilities of multiple highly useful BEs for interrogating functional SNVs.

ORGANISM(S): Homo sapiens

PROVIDER: GSE284410 | GEO | 2025/02/12

REPOSITORIES: GEO

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