crisprSQL: a novel database platform for CRISPR/Cas off-target cleavage assays
Open accessAbstract
With ongoing development of the CRISPR/Cas programmable nuclease system, applications in the area of in vivo therapeutic gene editing are increasingly within reach. However, non-negligible off-target effects remain a major concern for clinical applications. Even though a multitude of off-target cleavage datasets have been published, a comprehensive, transparent overview tool has not yet been established. Here, we present crisprSQL (http://www.crisprsql.com), an interactive and bioinformatically enhanced collection of CRISPR/Cas9 off-target cleavage studies aimed at enriching the fields of cleavage profiling, gene editing safety analysis and transcriptomics. The current version of crisprSQL contains cleavage data from 144 guide RNAs on 25,632 guide-target pairs from human and rodent cell lines, with interaction-specific references to epigenetic markers and gene names. The first curated database of this standard, it promises to enhance safety quantification research, inform experiment design and fuel development of computational off-target prediction algorithms.
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Cites
An integrated encyclopedia of DNA elements in the human genome
The ENCODE Project Consortium (2012) · Nature · doi:10.1038/nature11247 · Cited by 18,072 · see citing works
NCBI GEO: archive for functional genomics data sets—update
Barrett, T., Wilhite, S. E., Ledoux, P., Evangelista, C., Kim, I. F., Tomashevsky, M., Marshall, K. A., Phillippy, K. H., Sherman, P. M., Holko, M., et al. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks1193 · Cited by 10,418 · see citing works
DNA 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 · see citing works
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 · see citing works
GENCODE reference annotation for the human and mouse genomes
Frankish, A., Diekhans, M., Ferreira, A.-M., Johnson, R., Jungreis, I., Loveland, J. E., Mudge, J. M., Sisu, C., Wright, J. C., Armstrong, J. C., et al. (2018) · Nucleic Acids Research · doi:10.1093/nar/gky955 · Cited by 2,929 · see citing works
Gruber, A. R., Lorenz, R., Bernhart, S. H., Neuböck, R., & Hofacker, I. L. (2008) · Nucleic Acids Research · doi:10.1093/nar/gkn188 · Cited by 2,717 · see citing works
In vivo genome editing using Staphylococcus aureus Cas9
Ran, F. A., Cong, L., Yan, W. X., Scott, D. A., Gootenberg, J. S., Kriz, A. J., Zetsche, B., Shalem, O., Wu, X., Makarova, K. S., et al. (2015) · Nature · doi:10.1038/nature14299 · Cited by 2,687 · see citing works
High-fidelity CRISPR-Cas9 nucleases with no detectable genome-wide off-target effects
Kleinstiver, B. P., Pattanayak, V., Prew, M. S., Tsai, S. Q., Nguyen, N. T., Zheng, Z., & Joung, J. K. (2016) · Nature · doi:10.1038/nature16526 · Cited by 2,673 · see citing works
Rationally engineered Cas9 nucleases with improved specificity
Slaymaker, I. M., Gao, L., Zetsche, B., Scott, D. A., Yan, W. X., & Zhang, F. (2016) · Science · doi:10.1126/science.aad5227 · Cited by 2,414 · see citing works
Expanded encyclopaedias of DNA elements in the human and mouse genomes
The ENCODE Project Consortium, Abascal, F., Acosta, R., Addleman, N. J., Adrian, J., Afzal, V., Ai, R., Aken, B., Akiyama, J. A., Jammal, O. A., et al. (2020) · Nature · doi:10.1038/s41586-020-2493-4 · Cited by 2,378 · see citing works
GUIDE-seq enables genome-wide profiling of off-target cleavage by CRISPR-Cas nucleases
Tsai, S. Q., Zheng, Z., Nguyen, N. T., Liebers, M., Topkar, V. V., Thapar, V., Wyvekens, N., Khayter, C., Iafrate, A. J., Le, L. P., et al. (2015) · Nature Biotechnology · doi:10.1038/nbt.3117 · Cited by 2,268 · see citing works
Haeussler, M., Schönig, K., Eckert, H., Eschstruth, A., Mianné, J., Renaud, J.-B., Schneider-Maunoury, S., Shkumatava, A., Teboul, L., Kent, J., et al. (2016) · Genome biology · doi:10.1186/s13059-016-1012-2 · Cited by 1,756 · see citing works
Analysis of off-target effects of CRISPR/Cas-derived RNA-guided endonucleases and nickases
Cho, S. W., Kim, S., Kim, Y., Kweon, J., Kim, H. S., Bae, S., & Kim, J.-S. (2014) · Genome Research · doi:10.1101/gr.162339.113 · Cited by 1,356 · see citing works
Enhanced proofreading governs CRISPR-Cas9 targeting accuracy
Chen, J. S., Dagdas, Y. S., Kleinstiver, B. P., Welch, M. M., Sousa, A. A., Harrington, L. B., Sternberg, S. H., Joung, J. K., Yildiz, A., & Doudna, J. A. (2017) · Nature · doi:10.1038/nature24268 · Cited by 1,168 · see citing works
Genome-Scale Identification of Nucleosome Positions in S. cerevisiae
Yuan, G., Liu, Y.-J., Dion, M. F., Slack, M. D., Wu, L. F., Altschuler, S. J., & Rando, O. J. (2005) · Science · doi:10.1126/science.1112178 · Cited by 1,139 · see citing works
BamTools: a C++ API and toolkit for analyzing and managing BAM files
Barnett, D. W., Garrison, E. K., Quinlan, A. R., Strömberg, M. P., & Marth, G. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr174 · Cited by 997 · see citing works
Digenome-seq: Genome-wide profiling of CRISPR-cas9 off-target effects in human cells
Kim, D., Bae, S., Park, J., Kim, E., Kim, S., Yu, H. R., Hwang, J., Kim, J.-I., & Kim, J.-S. (2015) · Nature Methods · doi:10.1038/nmeth.3284 · Cited by 992 · see citing works
BEDOPS: high-performance genomic feature operations
Neph, S. J., Kuehn, M. S., Reynolds, A. P., Haugen, E., Thurman, R. E., Johnson, A., Rynes, E., Maurano, M. T., Vierstra, J. D., Thomas, S., et al. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts277 · Cited by 971 · see citing works
CIRCLE-Seq: A highly sensitive in vitro screen for genome-wide CRISPR-cas9 nuclease off-targets
Tsai, S. Q., Nguyen, N. T., Malagon-Lopez, J., Topkar, V. V., Aryee, M. J., & Joung, J. K. (2017) · Nature Methods · doi:10.1038/nmeth.4278 · Cited by 814 · see citing works
Histone lysine methylation: a signature for chromatin function
Sims, R. J., Nishioka, K., & Reinberg, D. (2003) · Trends in Genetics · doi:10.1016/j.tig.2003.09.007 · Cited by 684 · see citing works
Genome-wide detection of DNA double-stranded breaks induced by engineered nucleases
Frock, R. L., Hu, J., Meyers, R. M., Ho, Y.-J., Kii, E., & Alt, F. W. (2015) · Nature Biotechnology · doi:10.1038/nbt.3101 · Cited by 673 · see citing works
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 · see citing works
Sanz, L. A., Hartono, S. R., Lim, Y. W., Steyaert, S., Rajpurkar, A. R., Ginno, P. A., Xu, X., & Chedin, F. L. (2016) · Molecular Cell · doi:10.1016/j.molcel.2016.05.032 · Cited by 560 · see citing works
Mapping the genomic landscape of CRISPR-Cas9 cleavage
Cameron, P., Fuller, C. K., Donohoue, P. D., Jones, B. N., Thompson, M. S., Carter, M. M., Gradia, S., Vidal, B., Garner, E., Slorach, E. M., et al. (2017) · Nature Methods · doi:10.1038/nmeth.4284 · Cited by 443 · see citing works
Wang, X., Wang, Y., Wu, X., Wang, J., Wang, Y., Qiu, Z., Chang, T., Huang, H., Lin, R.-J., & Yee, J.-K. (2015) · Nature Biotechnology · doi:10.1038/nbt.3127 · Cited by 442 · see citing works
Cornerstones of CRISPR–Cas in drug discovery and therapy
Fellmann, C., Gowen, B. G., Lin, P., Doudna, J. A., & Corn, J. E. (2016) · Nature Reviews Drug Discovery · doi:10.1038/nrd.2016.238 · Cited by 442 · see citing works
Prediction of off-target activities for the end-to-end design of CRISPR guide RNAs
Listgarten, J., Weinstein, M., Kleinstiver, B. P., Sousa, A. A., Joung, J. K., Crawford, J., Gao, K., Hoang, L., Elibol, M., Doench, J. G., et al. (2018) · Nature Biomedical Engineering · doi:10.1038/s41551-017-0178-6 · Cited by 336 · see citing works
EWS–FLI1 increases transcription to cause R-loops and block BRCA1 repair in Ewing sarcoma
Gorthi, A., Romero, J. C., Loranc, E., Cao, L., Lawrence, L. A., Goodale, E., Iniguez, A. B., Bernard, X., Masamsetti, V. P., Roston, S. R., et al. (2018) · Nature · doi:10.1038/nature25748 · Cited by 333 · see citing works
De Magis, A., Manzo, S. G., Russo, M., Marinello, J., Morigi, R., Sordet, O., & Capranico, G. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1810409116 · Cited by 332 · see citing works
Genome-wide target specificities of CRISPR-Cas9 nucleases revealed by multiplex Digenome-seq
Kim, D., Kim, S., Kim, S., Park, J., & Kim, J.-S. (2016) · Genome Research · doi:10.1101/gr.199588.115 · Cited by 208 · see citing works
CRISPR off-target analysis in genetically engineered rats and mice
Anderson, K. R., Haeussler, M., Watanabe, C. K., Janakiraman, V., Lund, J. M., Modrusan, Z. D., Stinson, J., Bei, Q., Buechler, A., Yu, C., et al. (2018) · Nature Methods · doi:10.1038/s41592-018-0011-5 · Cited by 207 · see citing works
Nucleosomes inhibit target cleavage by CRISPR-Cas9 in vivo
Yarrington, R. M., Verma, S., Schwartz, S., Trautman, J. K., & Carroll, D. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1810062115 · Cited by 195 · see citing works
Nadel, J., Athanasiadou, R., Lemetre, C., Wijetunga, N. A., Broin, P. Ó., Sato, H., Zhang, Z., Jeddeloh, J. A., Montagna, C., Golden, A., et al. (2015) · Epigenetics & Chromatin · doi:10.1186/s13072-015-0040-6 · Cited by 175 · see citing works
CRISPR-Cas9 off-targeting assessment with nucleic acid duplex energy parameters
Alkan, F., Wenzel, A., Anthon, C., Havgaard, J. H., & Gorodkin, J. (2018) · Genome biology · doi:10.1186/s13059-018-1534-x · Cited by 168 · see citing works
CRISPR-Cas9 for in vivo Gene Therapy: Promise and Hurdles
Dai, W.-J., Zhu, L., Yan, Z., Xu, Y., Wang, Q., & Lu, X. (2016) · Molecular Therapy — Nucleic Acids · doi:10.1038/mtna.2016.58 · Cited by 144 · see citing works
Mitigating off-target effects in CRISPR/Cas9-mediated in vivo gene editing
Han, H. A., Pang, J. K. S., & Soh, B.-S. (2020) · Journal of Molecular Medicine · doi:10.1007/s00109-020-01893-z · Cited by 143 · see citing works
Massively parallel kinetic profiling of natural and engineered CRISPR nucleases
Jones, S. K., Hawkins, J. A., Johnson, N. V., Jung, C., Hu, K., Rybarski, J. R., Chen, J. S., Doudna, J. A., Press, W. H., & Finkelstein, I. J. (2020) · Nature Biotechnology · doi:10.1038/s41587-020-0646-5 · Cited by 126 · see citing works
Evaluation and Reduction of CRISPR Off-Target Cleavage Events
Vakulskas, C. A., & Behlke, M. A. (2019) · Nucleic Acid Therapeutics · doi:10.1089/nat.2019.0790 · Cited by 121 · see citing works
DIG-Seq: A genome-wide CRISPR off-target profiling method using chromatin DNA
Kim, D., & Kim, J.-S. (2018) · Genome Research · doi:10.1101/gr.236620.118 · Cited by 108 · see citing works
How specific is CRISPR/Cas9 really?
O’Geen, H., Yu, A. S., & Segal, D. J. (2015) · Current Opinion in Chemical Biology · doi:10.1016/j.cbpa.2015.10.001 · Cited by 108 · see citing works
Deep learning improves the ability of sgRNA off-target propensity prediction
Liu, Q., Cheng, X., Liu, G., Li, B., & Liu, X. (2020) · BMC Bioinformatics · doi:10.1186/s12859-020-3395-z · Cited by 88 · see citing works
Liu, Q., He, D., & Xie, L. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007480 · Cited by 85 · see citing works
GenomeCRISPR - a database for high-throughput CRISPR/Cas9 screens
Rauscher, B., Heigwer, F., Breinig, M., Winter, J., & Boutros, M. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkw997 · Cited by 84 · see citing works
Zhang, G., Dai, Z., & Dai, X. (2020) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2020.01.013 · Cited by 79 · see citing works
The Role of CCCTC-Binding Factor (CTCF) in Genomic Imprinting, Development, and Reproduction1
Franco, M. M., Prickett, A. R., & Oakey, R. J. (2014) · Biology of Reproduction · doi:10.1095/biolreprod.114.122945 · Cited by 49 · see citing works
Data imbalance in CRISPR off-target prediction
Gao, Y., Chuai, G., Yu, W., Qu, S., & Liu, Q. (2019) · Briefings in Bioinformatics · doi:10.1093/bib/bbz069 · Cited by 31 · see citing works
Advances in therapeutic application of CRISPR-Cas9
Sun, J., Wang, J., Zheng, D., & Hu, X. (2019) · Briefings in Functional Genomics · doi:10.1093/bfgp/elz031 · Cited by 25 · see citing works
Newman, A., Starrs, L. M., & Burgio, G. (2020) · BioEssays · doi:10.1002/bies.202000047 · Cited by 23 · see citing works
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Multiplex genome-editing technologies for revolutionizing plant biology and crop improvement
Abdelrahman, M., Wei, Z., Rohila, J. S., & Zhao, K. (2021) · Frontiers in Plant Science · doi:10.3389/fpls.2021.721203 · Cited by 125 · see citing works
Yaish, O., Asif, M., & Orenstein, Y. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac157 · Cited by 19 · see citing works
Verma, P. (2025) · International Journal of High School Research · doi:10.36838/v7i2.113 · Cited by 1 · see citing works
- Authors
- Störtz, F., & Minary, P.
- Affiliations
- University of Oxford (GB)
- Department of Computer Science, University of Oxford, Parks Road, Oxford OX1 3QD, UK
- Corresponding author
- Störtz, Florian
- ORCID
- Störtz, Florian
- Venue
- Nucleic Acids Research
- Publisher
- Oxford University Press (OUP)
- Published
- 2020-10-21
- Volume
- 49
- Issue
- D1
- Pages
- D855-D861
- ISSN
- 0305-1048 (ISSN-L)
- 1362-4962
- PMC
- PMC7778913
- OpenAlex
- W3093632917
- Cited by
- 37
- References
- 48
- Citations by year
- 2026: 3 · 2025: 12 · 2024: 8 · 2023: 3 · 2022: 7 · 2021: 4
- FWCI
- 1.14
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Nucleic Acids Research (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- Biotechnology and Biological Sciences Research Council (BB/M011224/1, BB/S507593/1)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › CRISPR and Genetic Engineering
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA regulation and disease
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA Interference and Gene Delivery
- Keywords
- CRISPR/Cas9, off-target cleavage, database platform, guide RNA, gene editing safety, transcriptomics, epigenetic modifications, off-target prediction, therapeutic gene editing
- MeSH terms
- Biological Assay, Streptococcus pyogenes, User-Computer Interface, Databases, Genetic, Epigenesis, Genetic, DNA Cleavage, CRISPR-Cas Systems
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