Sequence determinants of improved CRISPR sgRNA design
Name
Xu-2015-Sequqnce determinants.pdf
Size
1.4 MB
Format
Adobe PDF
Checksum (MD5)
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Author(s)
Xu, Han
Xiao, Tengfei
Chen, Chen-Hao
Li, Wei
Meyer, Clifford A.
Wu, Qiu
Wu, Di
Cong, Le
Zhang, Feng
Liu, Jun S.
Date Issued
August 2015
Journal
Genome Research
Publisher
Cold Spring Harbor Laboratory Press
Citation
Xu, Han, Tengfei Xiao, Chen-Hao Chen, Wei Li, Clifford A. Meyer, Qiu Wu, Di Wu, et al. “Sequence Determinants of Improved CRISPR sgRNA Design.” Genome Res. 25, no. 8 (June 10, 2015): 1147–1157.
Version
Final published version
Abstract
The CRISPR/Cas9 system has revolutionized mammalian somatic cell genetics. Genome-wide functional screens using CRISPR/Cas9-mediated knockout or dCas9 fusion-mediated inhibition/activation (CRISPRi/a) are powerful techniques for discovering phenotype-associated gene function. We systematically assessed the DNA sequence features that contribute to single guide RNA (sgRNA) efficiency in CRISPR-based screens. Leveraging the information from multiple designs, we derived a new sequence model for predicting sgRNA efficiency in CRISPR/Cas9 knockout experiments. Our model confirmed known features and suggested new features including a preference for cytosine at the cleavage site. The model was experimentally validated for sgRNA-mediated mutation rate and protein knockout efficiency. Tested on independent data sets, the model achieved significant results in both positive and negative selection conditions and outperformed existing models. We also found that the sequence preference for CRISPRi/a is substantially different from that for CRISPR/Cas9 knockout and propose a new model for predicting sgRNA efficiency in CRISPRi/a experiments. These results facilitate the genome-wide design of improved sgRNA for both knockout and CRISPRi/a studies.
MIT Department
Massachusetts Institute of Technology. Department of Biological Engineering
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
McGovern Institute for Brain Research at MIT
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Creative Commons Attribution
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DOI of Published Version
https://doi.org/10.1101/gr.191452.115
