Deep learning improves the ability of sgRNA off-target propensity prediction.

ABSTRACT: BACKGROUND:CRISPR/Cas9 system, as the third-generation genome editing technology, has been widely applied in target gene repair and gene expression regulation. Selection of appropriate sgRNA can improve the on-target knockout efficacy of CRISPR/Cas9 system with high sensitivity and specificity. However, when CRISPR/Cas9 system is operating, unexpected cleavage may occur at some sites, known as off-target. Presently, a number of prediction methods have been developed to predict the off-target propensity of sgRNA at specific DNA fragments. Most of them use artificial feature extraction operations and machine learning techniques to obtain off-target scores. With the rapid expansion of off-target data and the rapid development of deep learning theory, the existing prediction methods can no lon

SUBMITTER: Liu Q 

PROVIDER: S-EPMC7011380 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature