한빛사 논문
Hui Kwon Kim1,2,*, Younggwang Kim1,2,*, Sungtae Lee1, Seonwoo Min3, Jung Yoon Bae1, Jae Woo Choi1,4, Jinman Park1,2, Dongmin Jung1,4, Sungroh Yoon3,5 and Hyongbum Henry Kim1,2,4,6,7,†
1 Department of Pharmacology, Yonsei University College of Medicine, Seoul, Republic of Korea.
2 Brain Korea 21 Plus Project for Medical Sciences, Yonsei University College of Medicine, Seoul, Republic of Korea.
3 Electrical and Computer Engineering, Seoul National University, Seoul, Republic of Korea.
4 Severance Biomedical Science Institute, Yonsei University College of Medicine, Seoul, Republic of Korea.
5 Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Republic of Korea.
6 Center for Nanomedicine, Institute for Basic Science (IBS), Seoul, Republic of Korea.
7 Yonsei-IBS Institute, Yonsei University, Seoul, Republic of Korea.
* These authors contributed equally to this work.
† Corresponding author : Hyongbum Henry Kim
Abstract
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.
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