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In Science advances ; h5-index 0.0

We evaluated SpCas9 activities at 12,832 target sequences using a high-throughput approach based on a human cell library containing single-guide RNA-encoding and target sequence pairs. Deep learning-based training on this large dataset of SpCas9-induced indel frequencies led to the development of a SpCas9 activity-predicting model named DeepSpCas9. When tested against independently generated datasets (our own and those published by other groups), DeepSpCas9 showed high generalization performance. DeepSpCas9 is available at

Kim Hui Kwon, Kim Younggwang, Lee Sungtae, Min Seonwoo, Bae Jung Yoon, Choi Jae Woo, Park Jinman, Jung Dongmin, Yoon Sungroh, Kim Hyongbum Henry