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.