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

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.

journal_name

Nat Biotechnol

journal_title

Nature biotechnology

authors

Kim HK,Min S,Song M,Jung S,Choi JW,Kim Y,Lee S,Yoon S,Kim HH

doi

10.1038/nbt.4061

subject

Has Abstract

pub_date

2018-03-01 00:00:00

pages

239-241

issue

3

eissn

1087-0156

issn

1546-1696

pii

nbt.4061

journal_volume

36

pub_type

杂志文章

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