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Nat Biotechnol. 2018 Mar;36(3):239-241. doi: 10.1038/nbt.4061. Epub 2018 Jan 29.

Deep learning improves prediction of CRISPR-Cpf1 guide RNA activity.

Kim HK1,2, Min S3, Song M1,4, Jung S1,2, Choi JW1,5, Kim Y1,2, Lee S1,2, Yoon S3,6, Kim HH1,2,5,7,8.

Author information

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
Graduate School of Biomedical Science and Engineering, Hanyang University, Seoul, Republic of Korea.
5
Severance Biomedical Science Institute, Yonsei University College of Medicine, Seoul, Republic of Korea.
6
Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Republic of Korea.
7
Center for Nanomedicine, Institute for Basic Science (IBS), Seoul, Republic of Korea.
8
Yonsei-IBS Institute, Yonsei University, Seoul, Republic of Korea.

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.

PMID:
29431740
DOI:
10.1038/nbt.4061
[Indexed for MEDLINE]

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