Sequence determinants of improved CRISPR sgRNA design
- Han Xu1,2,10,
- Tengfei Xiao1,2,3,10,
- Chen-Hao Chen4,
- Wei Li1,2,
- Clifford A. Meyer1,2,
- Qiu Wu1,2,5,
- Di Wu6,
- Le Cong7,8,
- Feng Zhang7,8,
- Jun S. Liu6,
- Myles Brown2,3,9 and
- X. Shirley Liu1,2
- 1Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard School of Public Health, Boston, Massachusetts 02115, USA;
- 2Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, Massachusetts 02215, USA;
- 3Department of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, Massachusetts 02115, USA;
- 4Program in Biological and Biomedical Sciences, Harvard Medical School, Boston, Massachusetts 02215, USA;
- 5Department of Bioinformatics, School of Life Science and Technology, Tongji University, Shanghai, 200092 China;
- 6Department of Statistics, Harvard University, Cambridge, Massachusetts 02138, USA;
- 7Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA;
- 8McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA;
- 9Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts 02215, USA
- Corresponding authors: myles_brown{at}dfci.harvard.edu, xsliu{at}jimmy.harvard.edu
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↵10 These authors contributed equally to this work.
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.
Footnotes
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[Supplemental material is available for this article.]
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Article published online before print. Article, supplemental material, and publication date are at http://www.genome.org/cgi/doi/10.1101/gr.191452.115.
- Received February 23, 2015.
- Accepted June 10, 2015.
This article is distributed exclusively by Cold Spring Harbor Laboratory Press for the first six months after the full-issue publication date (see http://genome.cshlp.org/site/misc/terms.xhtml). After six months, it is available under a Creative Commons License (Attribution-NonCommercial 4.0 International), as described at http://creativecommons.org/licenses/by-nc/4.0/.
