RT Journal Article SR Electronic T1 SpCas9 activity prediction by DeepSpCas9, a deep learning–based model with high generalization performance JF Science Advances JO Sci Adv FD American Association for the Advancement of Science SP eaax9249 DO 10.1126/sciadv.aax9249 VO 5 IS 11 A1 Kim, Hui Kwon A1 Kim, Younggwang A1 Lee, Sungtae A1 Min, Seonwoo A1 Bae, Jung Yoon A1 Choi, Jae Woo A1 Park, Jinman A1 Jung, Dongmin A1 Yoon, Sungroh A1 Kim, Hyongbum Henry YR 2019 UL http://advances.sciencemag.org/content/5/11/eaax9249.abstract AB 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.