PT - JOURNAL ARTICLE AU - Kim, Hui Kwon AU - Kim, Younggwang AU - Lee, Sungtae AU - Min, Seonwoo AU - Bae, Jung Yoon AU - Choi, Jae Woo AU - Park, Jinman AU - Jung, Dongmin AU - Yoon, Sungroh AU - Kim, Hyongbum Henry TI - SpCas9 activity prediction by DeepSpCas9, a deep learning–based model with high generalization performance AID - 10.1126/sciadv.aax9249 DP - 2019 Nov 01 TA - Science Advances PG - eaax9249 VI - 5 IP - 11 4099 - http://advances.sciencemag.org/content/5/11/eaax9249.short 4100 - http://advances.sciencemag.org/content/5/11/eaax9249.full SO - Sci Adv2019 Nov 01; 5 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.