SpCas9 activity prediction by DeepSpCas9, a deep learning–based model with high generalization performance

  • Hui Kwon Kim
  • , Younggwang Kim
  • , Sungtae Lee
  • , Seonwoo Min
  • , Jung Yoon Bae
  • , Jae Woo Choi
  • , Jinman Park
  • , Dongmin Jung
  • , Sungroh Yoon
  • , Hyongbum Henry Kim

Research output: Contribution to journalArticlepeer-review

163 Scopus citations

Abstract

We evaluated SpCas9 activities at 12,832 target sequences using a high-throughput approach based on a human cell library containing single-guide RNA–encoding and target sequence pairs. Deep learning–based training on this large dataset of SpCas9-induced indel frequencies led to the development of a SpCas9 activity–predicting model named DeepSpCas9. When tested against independently generated datasets (our own and those published by other groups), DeepSpCas9 showed high generalization performance. DeepSpCas9 is available at http://deepcrispr.info/DeepSpCas9.

Original languageEnglish
Article numberaax9249
JournalScience Advances
Volume5
Issue number11
DOIs
StatePublished - 6 Nov 2019
Externally publishedYes

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