Deep learning improves prediction of CRISPR-Cpf1 guide RNA activity

被引：240

作者：

Kim, Hui Kwon ^{[1
,2
]}

Min, Seonwoo ^{[3
]}

Song, Myungjae ^{[1
,4
]}

Jung, Soobin ^{[1
,2
]}

Choi, Jae Woo ^{[1
,5
]}

Kim, Younggwang ^{[1
,2
]}

Lee, Sangeun ^{[1
,2
]}

Yoon, Sungroh ^{[3
,6
]}

Kim, Hyongbum ^{[1
,2
,5
,7
,8
]}

机构：

[1] Yonsei Univ, Coll Med, Dept Pharmacol, Seoul, South Korea

[2] Yonsei Univ, Coll Med, Brain Korea Plus Project Med Sci 21, Seoul, South Korea

[3] Seoul Natl Univ, Elect & Comp Engn, Seoul, South Korea

[4] Hanyang Univ, Grad Sch Biomed Sci & Engn, Seoul, South Korea

[5] Yonsei Univ, Coll Med, Severance Biomed Sci Inst, Seoul, South Korea

[6] Seoul Natl Univ, Interdisciplinary Program Bioinformat, Seoul, South Korea

[7] Inst Basic Sci IBS, Ctr Nanomed, Seoul, South Korea

[8] Yonsei Univ, Yonsei IBS Inst, Seoul, South Korea

来源：

NATURE BIOTECHNOLOGY | 2018年 / 36卷 / 03期

基金：

新加坡国家研究基金会;

关键词：

OFF-TARGET; GENOME; CPF1; SPECIFICITIES; DESIGN; SGRNAS; MICE; DNA;

D O I：

10.1038/nbt.4061

中图分类号：

Q81 [生物工程学（生物技术）]; Q93 [微生物学];

学科分类号：

071005 ; 0836 ; 090102 ; 100705 ;

摘要：

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.

引用

页码：239 / +

页数：6

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