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.