Synergizing CRISPR/Cas9 off-target predictions for ensemble insights and practical applications

Bioinformatics
Shixiong ZhangKa-Chun Wong

Abstract

The RNA-guided CRISPR/Cas9 system has been widely applied to genome editing. CRISPR/Cas9 system can effectively edit the on-target genes. Nonetheless, it has recently been demonstrated that many homologous off-target genomic sequences could be mutated, leading to unexpected gene-editing outcomes. Therefore, a plethora of tools were proposed for the prediction of off-target activities of CRISPR/Cas9. Nonetheless, each computational tool has its own advantages and drawbacks under diverse conditions. It is hardly believed that a single tool is optimal for all conditions. Hence, we would like to explore the ensemble learning potential on synergizing multiple tools with genomic annotations together to enhance its predictive abilities. We proposed an ensemble learning framework which synergizes multiple tools together to predict the off-target activities of CRISPR/Cas9 in different combinations. Interestingly, the ensemble learning using AdaBoost outperformed other individual off-target predictive tools. We also investigated the effect of evolutionary conservation (PhyloP and PhastCons) and chromatin annotations (ChromHMM and Segway) and found that only PhyloP can enhance the predictive capabilities further. Case studies are conducte...Continue Reading

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Citations

May 23, 2019·Human Genetics·Minyoung Lee, Hyongbum Kim
Mar 4, 2020·Computational and Structural Biotechnology Journal·Guishan ZhangXianhua Dai
May 1, 2021·Molecules : a Journal of Synthetic Chemistry and Natural Product Chemistry·Mohsen HesamiAndrew Maxwell Phineas Jones

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