期刊
BMC BIOINFORMATICS
卷 18, 期 -, 页码 -出版社
BMC
DOI: 10.1186/s12859-017-1697-6
关键词
CRISPR; Machine learning; Predictive modeling; Thermodynamics
类别
资金
- NIH [U01CA168409]
Background: CRISPR is a versatile gene editing tool which has revolutionized genetic research in the past few years. Optimizing sgRNA design to improve the efficiency of target/DNA cleavage is critical to ensure the success of CRISPR screens. Results: By borrowing knowledge from oligonucleotide design and nucleosome occupancy models, we systematically evaluated candidate features computed from a number of nucleic acid, thermodynamic and secondary structure models on real CRISPR datasets. Our results showed that taking into account position-dependent dinucleotide features improved the design of effective sgRNAs with area under the receiver operating characteristic curve (AUC) > 0.8, and the inclusion of additional features offered marginal improvement (similar to 2% increase in AUC). Conclusion: Using a machine-learning approach, we proposed an accurate prediction model for sgRNA design efficiency. An R package predictSGRNA implementing the predictive model is available at http://www.ams.sunysb.edu/similar to pfkuan/softwares.html#predictsgrna.
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