Deep learning improves prediction of CRISPR-Cpf1 guide RNA activity

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

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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • CRISPR-Cas Systems / genetics*
  • Cell Line
  • Deep Learning
  • Endonucleases / genetics*
  • Neural Networks, Computer
  • RNA, Guide / genetics*

Substances

  • RNA, Guide
  • Endonucleases