Title |
A systematic evaluation of nucleotide properties for CRISPR sgRNA design
|
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Published in |
BMC Bioinformatics, June 2017
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DOI | 10.1186/s12859-017-1697-6 |
Pubmed ID | |
Authors |
Pei Fen Kuan, Scott Powers, Shuyao He, Kaiqiao Li, Xiaoyu Zhao, Bo Huang |
Abstract |
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. 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 (∼2% increase in AUC). 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/~pfkuan/softwares.html#predictsgrna . |
X Demographics
Geographical breakdown
Country | Count | As % |
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Unknown | 2 | 100% |
Demographic breakdown
Type | Count | As % |
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Members of the public | 2 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 81 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 11 | 14% |
Researcher | 11 | 14% |
Student > Master | 9 | 11% |
Student > Bachelor | 8 | 10% |
Other | 4 | 5% |
Other | 11 | 14% |
Unknown | 27 | 33% |
Readers by discipline | Count | As % |
---|---|---|
Biochemistry, Genetics and Molecular Biology | 19 | 23% |
Agricultural and Biological Sciences | 14 | 17% |
Medicine and Dentistry | 5 | 6% |
Nursing and Health Professions | 3 | 4% |
Computer Science | 3 | 4% |
Other | 9 | 11% |
Unknown | 28 | 35% |