<p>Previous high-throughput evaluations of CRISPR activities for a large number of target and guide RNA sequences were based on measuring insertion–deletion frequencies rather than cleavage efficiencies. Here we develop two high-throughput in vitro methods, Cut-seq1 and Cut-seq2, to evaluate Cas9 cleavage efficiency for tens of thousands, or even hundreds of thousands, of guide RNA–target pairs. These methods reveal low correlations between in vitro cleavage efficiencies and insertion–deletion frequencies in cells, yet high concordances in protospacer adjacent motif compatibility. Using the resulting large datasets of in vitro cleavage efficiencies, we develop DeepCut, a set of deep learning models that can identify optimized single-guide RNAs that can selectively cleave specific sequences, even in the presence of similar noise sequences. Using these optimized single-guide RNAs, we develop a method, CLOVE-seq (which stands for cleavage for large-scale optimized variant enrichment sequencing), to enrich rare variants in a multiplexed manner by Cas9-mediated specific cleavage of noise or rare variant sequences. Our methods can enhance the understanding of CRISPR nuclease activities and could be used to detect a large number of rare variants in various biomedical contexts.</p>