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나노의학 연구단
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SpCas9 activity prediction by DeepSpCas9, a deep learning–based model with high generalization performance

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Title
SpCas9 activity prediction by DeepSpCas9, a deep learning–based model with high generalization performance
Author(s)
Hui Kwon Kim; Younggwang Kim; Sungtae Lee; Seonwoo Min; Jung Yoon Bae; Jae Woo Choi; Jinman Park; Dongmin Jung; Sungroh Yoon; Hyongbum Henry Kim
Publication Date
2019-11
Journal
SCIENCE ADVANCES, v.5, no.11, pp.eaax9249 -
Publisher
AMER ASSOC ADVANCEMENT SCIENCE
Abstract
Copyright © 2019 The Authors, some rights reserved;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
URI
https://pr.ibs.re.kr/handle/8788114/6774
ISSN
2375-2548
Appears in Collections:
Center for Nanomedicine (나노의학 연구단) > Journal Papers
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