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Deep learning improves prediction of CRISPR-Cpf1 guide RNA activity

Authors
 Hui Kwon Kim  ;  Seonwoo Min  ;  Myungjae Song  ;  Soobin Jung  ;  Jae Woo Choi  ;  Younggwang Kim  ;  Sangeun Lee  ;  Sungroh Yoon  ;  Hyongbum Henry Kim 
Citation
 NATURE BIOTECHNOLOGY, Vol.36(3) : 239-241, 2018 
Journal Title
 NATURE BIOTECHNOLOGY 
ISSN
 1087-0156 
Issue Date
2018
Abstract
We present two algorithms to predict the activity of AsCpf1 guide RNAs. Indel frequencies for 15,000 target sequences were used in a deep-learning framework based on a convolutional neural network to train Seq-deepCpf1. We then incorporated chromatin accessibility information to create the better-performing DeepCpf1 algorithm for cell lines for which such information is available and show that both algorithms outperform previous machine learning algorithms on our own and published data sets.
Full Text
http://www.nature.com/articles/nbt.4061
DOI
10.1038/nbt.4061
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Pharmacology (약리학교실) > 1. Journal Papers
Yonsei Authors
Kim, Hyongbum(김형범) ORCID logo https://orcid.org/0000-0002-4693-738X
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/161917
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