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Prediction of Off-Target Effects in CRISPR/Cas9 System by Ensemble Learning

[ Vol. 16 , Issue. 9 ]

Author(s):

Yongxian Fan* and Haibo Xu   Pages 1169 - 1178 ( 10 )

Abstract:


Background: CRISPR/Cas9, a new generation of targeted gene editing technology with low cost and simple operation has been widely employed in the field of gene editing. The erroneous cutting of off-target sites in CRISPR/Cas9 is called off-target effect, which is also the biggest complication that CRISPR/Cas9 confronts in practical application. To be specific, the off-target effects could lead to unexpected gene editing results. Therefore, accurately predicting CRISPR/Cas9 off-target effect is a very important task. Predicting off-target effects of CRISPR/Cas9 by machine learning method is feasible, but most existing off-target tools did not pay close attention to the effects of gene encoding on prediction.

Methods: We compared three encoding methods based on One-Hot and combined the gene sequence with four CRISPR/Cas9 off-target prediction tools to build an ensemble model with XGBoost, designated as XGBCRISPR. The grid search is employed to find the optimal parameters to achieve the best performance.

Results: The performance is compared with existing tools based on the ROC value and PRC value. The experimental results show that the XGBCRISPR model is superior to the existing tools.

Conclusion: The new model could achieve better prediction result than existing tools, but the accuracy of model can be improved further as many off-target scores appear.

Keywords:

CRISPR/Cas9, off-target effects, machine learning, ensemblelearning, XGBoost, XGBCRISPR.

Affiliation:

School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, Guangxi, School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, Guangxi

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