The DOI matches its Crossref record; 48 of the 48 references it cites were found in a registry or an index.
Refined sgRNA efficacy prediction improves large- and small-scale CRISPR–Cas9 applications
Open accessAbstract
Genome editing with the CRISPR-Cas9 system has enabled unprecedented efficacy for reverse genetics and gene correction approaches. While off-target effects have been successfully tackled, the effort to eliminate variability in sgRNA efficacies-which affect experimental sensitivity-is in its infancy. To address this issue, studies have analyzed the molecular features of highly active sgRNAs, but independent cross-validation is lacking. Utilizing fluorescent reporter knock-out assays with verification at selected endogenous loci, we experimentally quantified the target efficacies of 430 sgRNAs. Based on this dataset we tested the predictive value of five recently-established prediction algorithms. Our analysis revealed a moderate correlation (r = 0.04 to r = 0.20) between the predicted and measured activity of the sgRNAs, and modest concordance between the different algorithms. We uncovered a strong PAM-distal GC-content-dependent activity, which enabled the exclusion of inactive sgRNAs. By deriving nine additional predictive features we generated a linear model-based discrete system for the efficient selection (r = 0.4) of effective sgRNAs (CRISPRater). We proved our algorithms' efficacy on small and large external datasets, and provide a versatile combined on- and off-target sgRNA scanning platform. Altogether, our study highlights current issues and efforts in sgRNA efficacy prediction, and provides an easily-applicable discrete system for selecting efficient sgRNAs.
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Cites
A Programmable Dual-RNA–Guided DNA Endonuclease in Adaptive Bacterial Immunity
Jinek, M., Chylinski, K., Fonfara, I., Hauer, M., Doudna, J. A., & Charpentier, E. (2012) · Science · doi:10.1126/science.1225829 · Cited by 17,116
Self-citationMultiplex Genome Engineering Using CRISPR/Cas Systems
Cong, L., Ran, F. A., Cox, D., Lin, S., Barretto, R., Habib, N., Hsu, P. D., Wu, X., Jiang, W., Marraffini, L. A., et al. (2013) · Science · doi:10.1126/science.1231143 · Cited by 15,339
Efron, B., Hastie, T., Johnstone, I., & Tibshirani, R. (2004) · The Annals of Statistics · doi:10.1214/009053604000000067 · Cited by 9,357
RNA-Guided Human Genome Engineering via Cas9
Mali, P., Yang, L., Esvelt, K. M., Aach, J., Guell, M., DiCarlo, J. E., Norville, J. E., & Church, G. M. (2013) · Science · doi:10.1126/science.1232033 · Cited by 9,210
Genome-scale CRISPR-Cas9 knockout screening in human cells
Shalem, O., Sanjana, N. E., Hartenian, E., Shi, X., Scott, D. A., Mikkelsen, T. S., Heckl, D., Ebert, B. L., Root, D. E., Doench, J. G., et al. (2014) · Science · doi:10.1126/science.1247005 · Cited by 5,234
Self-citationDNA targeting specificity of RNA-guided Cas9 nucleases
Hsu, P. D., Scott, D. A., Weinstein, J. A., Ran, F. A., Konermann, S., Agarwala, V., Li, Y., Fine, E. J., Wu, X., Shalem, O., et al. (2013) · Nature Biotechnology · doi:10.1038/nbt.2647 · Cited by 4,741
Optimized sgRNA design to maximize activity and minimize off-target effects of CRISPR-Cas9
Doench, J. G., Fusi, N., Sullender, M., Hegde, M., Vaimberg, E. W., Donovan, K. F., Smith, I., Tothova, Z., Wilen, C., Orchard, R., et al. (2016) · Nature Biotechnology · doi:10.1038/nbt.3437 · Cited by 4,637
Double nicking by RNA-guided CRISPR Cas9 for enhanced genome editing specificity
Ran, F. A., Hsu, P. D., Lin, C.-Y., Gootenberg, J. S., Konermann, S., Trevino, A. E., Scott, D. A., Inoue, A., Matoba, S., Zhang, Y., et al. (2013) · Cell · doi:10.1016/j.cell.2013.08.021 · Cited by 3,299
High-Frequency off-Target Mutagenesis Induced by CRISPR-Cas Nucleases in Human Cells
Fu, Y., Foden, J. A., Khayter, C., Maeder, M. L., Reyon, D., Joung, J. K., & Sander, J. D. (2013) · Nature Biotechnology · doi:10.1038/nbt.2623 · Cited by 3,292
Genetic screens in human cells using the CRISPR-Cas9 system
Wang, T., Wei, J. J., Sabatini, D. M., & Lander, E. S. (2014) · Science · doi:10.1126/science.1246981 · Cited by 2,856
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DeepCRISPR: Optimized CRISPR guide RNA design by deep learning
Chuai, G., Ma, H., Yan, J., Chen, M., Hong, N., Xue, D., Zhou, C., Zhu, C., Chen, K., Duan, B., et al. (2018) · Genome biology · doi:10.1186/s13059-018-1459-4 · Cited by 641
Base-resolution models of transcription-factor binding reveal soft motif syntax
Avsec, Ž., Weilert, M., Shrikumar, A., Krueger, S., Alexandari, A., Dalal, K., Fropf, R., McAnany, C., Gagneur, J., Kundaje, A., et al. (2021) · Nature Genetics · doi:10.1038/s41588-021-00782-6 · Cited by 610
CRISPR technologies and the search for the PAM-free nuclease
Collias, D., & Beisel, C. L. (2021) · Nature Communications · doi:10.1038/s41467-020-20633-y · Cited by 304
Enhancing CRISPR-Cas9 gRNA efficiency prediction by data integration and deep learning
Xiang, X., Corsi, G. I., Anthon, C., Qu, K., Pan, X., Liang, X., Han, P., Dong, Z., Liu, L., Zhong, J., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-23576-0 · Cited by 209
Metadata
- Authors
- Labuhn, M., Adams, F. F., Ng, M., Knoess, S., Schambach, A., Charpentier, E. M., Schwarzer, A., Mateo, J. L., Klusmann, J.-H., & Heckl, D.
- Venue
- Nucleic Acids Research
- Publisher
- Oxford University Press (OUP)
- Published
- 2017-12-18
- Volume
- 46
- Issue
- 3
- Pages
- 1375-1385
- ISSN
- 0305-1048 (ISSN-L)
- 1362-4962
- OpenAlex
- W2778131996
- Cited by
- 293
- References
- 48
- FWCI
- 6.79
- Type
- journal article
- Language
- en
- Open access
- gold
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- Labuhn, M., Adams, F. F., Ng, M., Knoess, S., Schambach, A., Charpentier, E. M., Schwarzer, A., Mateo, J. L., Klusmann, J.-H., & Heckl, D. (2018). Refined sgRNA efficacy prediction improves large- and small-scale CRISPR–Cas9 applications. Nucleic Acids Research. https://doi.org/10.1093/nar/gkx1268
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