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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 http://deepcrispr.info/DeepSpCas9.

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

2019-Nov