SpCas9 activity prediction by DeepSpCas9, a deep learning-based model with high generalization performance

Hui Kwon Kim; Younggwang Kim; Sungtae Lee; Seonwoo Min; Jung Yoon Bae; Jae Woo Choi; Jinman Park; Dongmin Jung; Sungroh Yoon; Hyongbum Henry Kim

doi:10.1126/sciadv.aax9249

SpCas9 activity prediction by DeepSpCas9, a deep learning-based model with high generalization performance

Sci Adv. 2019 Nov 6;5(11):eaax9249. doi: 10.1126/sciadv.aax9249. eCollection 2019 Nov.

Authors

Hui Kwon Kim^{1

2}, Younggwang Kim^{1

2}, Sungtae Lee¹, Seonwoo Min³, Jung Yoon Bae¹, Jae Woo Choi^{1

4}, Jinman Park^{1

2}, Dongmin Jung^{1

4}, Sungroh Yoon^{3

5}, Hyongbum Henry Kim^{1

2

4

6

7}

Affiliations

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.

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.

Copyright © 2019 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

CRISPR-Associated Protein 9 / metabolism*
CRISPR-Cas Systems*
Deep Learning*
Gene Editing / methods
Humans
Internet
Mutation
RNA, Guide, CRISPR-Cas Systems
Reproducibility of Results

Substances

CRISPR-Associated Protein 9
Cas9 endonuclease Streptococcus pyogenes