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

Journal: Science advances
Published Date:

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.

Authors

  • Hui Kwon Kim
    Department of Pharmacology, Yonsei University College of Medicine, Seoul, Republic of Korea.
  • Younggwang Kim
    Department of Pharmacology, Yonsei University College of Medicine, Seoul, Republic of Korea.
  • Sungtae Lee
    Department of Pharmacology, Yonsei University College of Medicine, Seoul, Republic of Korea.
  • Seonwoo Min
  • Jung Yoon Bae
    Department of Pharmacology, Yonsei University College of Medicine, Seoul, Republic of Korea.
  • Jae Woo Choi
    Department of Pharmacology, Yonsei University College of Medicine, Seoul, Republic of Korea.
  • Jinman Park
    Department of Pharmacology, Yonsei University College of Medicine, Seoul, Republic of Korea.
  • Dongmin Jung
    Department of Pharmacology, Yonsei University College of Medicine, Seoul, Republic of Korea.
  • Sungroh Yoon
    4 Department of Electrical and Computer Engineering and Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Korea.
  • Hyongbum Henry Kim
    Department of Pharmacology, Yonsei University College of Medicine, Seoul, Republic of Korea.