Evaluation of off-target and on-target scoring algorithms and integration into the guide RNA selection tool CRISPOR
Open accessAbstract
BACKGROUND: The success of the CRISPR/Cas9 genome editing technique depends on the choice of the guide RNA sequence, which is facilitated by various websites. Despite the importance and popularity of these algorithms, it is unclear to which extent their predictions are in agreement with actual measurements. RESULTS: We conduct the first independent evaluation of CRISPR/Cas9 predictions. To this end, we collect data from eight SpCas9 off-target studies and compare them with the sites predicted by popular algorithms. We identify problems in one implementation but found that sequence-based off-target predictions are very reliable, identifying most off-targets with mutation rates superior to 0.1 %, while the number of false positives can be largely reduced with a cutoff on the off-target score. We also evaluate on-target efficiency prediction algorithms against available datasets. The correlation between the predictions and the guide activity varied considerably, especially for zebrafish. Together with novel data from our labs, we find that the optimal on-target efficiency prediction model strongly depends on whether the guide RNA is expressed from a U6 promoter or transcribed in vitro. We further demonstrate that the best predictions can significantly reduce the time spent on guide screening. CONCLUSIONS: To make these guidelines easily accessible to anyone planning a CRISPR genome editing experiment, we built a new website ( http://crispor.org ) that predicts off-targets and helps select and clone efficient guide sequences for more than 120 genomes using different Cas9 proteins and the eight efficiency scoring systems evaluated here.
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The MIT algorithm weights the position effect of mismatches in the sgRNA to generate a score to evaluate off-target effects.
“The algorithm of MIT weights the position effect of the mismatches in the sgRNA to generate a score to evaluate off-target effects (Hsu et al., 2013; Haeussler et al., 2016).”
From Off-target effects in CRISPR/Cas9 gene editing (2023) · cites this paper as Haeussler et al., 2016
Potential off-target sites in exon regions on chromosome 7 were predicted by CRISPOR and verified to be intact by Sanger sequencing.
“CRISPOR was used to predict all potential off-target sites in the genome72, and those sites located in exon regions on chromosome 7 were verified to be intact by Sanger sequencing.”
From The APOE-R136S mutation protects against APOE4-driven tau pathology, neurodegeneration, and neuroinflammation (2023) · cites this paper as [72]
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Lin, J., Zhang, Z., Zhang, S., Chen, J., & Wong, K. (2020) · Advanced Science · doi:10.1002/advs.201903562 · Cited by 101 · see citing works
Molecular biology at the cutting edge: A review on CRISPR/CAS9 gene editing for undergraduates
Thurtle‐Schmidt, D. M., & Lo, T. (2018) · Biochemistry and Molecular Biology Education · doi:10.1002/bmb.21108 · Cited by 103 · see citing works
Single and multiple gene knockouts by CRISPR–Cas9 in maize
Doll, N. M., Gilles, L. M., Gérentes, M.-F., Richard, C., Just, J., Fierlej, Y., Borrelli, V. M. G., Gendrot, G., Ingram, G. C., Rogowsky, P. M., et al. (2019) · Plant Cell Reports · doi:10.1007/s00299-019-02378-1 · Cited by 71 · see citing works
Ts66Yah, a mouse model of down syndrome with improved construct and face validity
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crisprSQL: a novel database platform for CRISPR/Cas off-target cleavage assays
Störtz, F., & Minary, P. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkaa885 · Cited by 37 · see citing works
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Technical considerations for the use of CRISPR/Cas9 in hematology research
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Evolutionary Biology and Gene Editing of Cat Allergen, Fel d 1
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BoostMEC: predicting CRISPR-Cas9 cleavage efficiency through boosting models
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CRISPR-Mediated Reorganization of Chromatin Loop Structure
Morgan, S. L., Chang, E. Y., Mariano, N. C., Bermudez, A., Arruda, N. L., Wu, F., Luo, Y., Shankar, G., Huynh, S. K., Huang, C.-C., et al. (2018) · Journal of Visualized Experiments · doi:10.3791/57457 · Cited by 7 · see citing works
- Authors
- Haeussler, M., Schönig, K., Eckert, H., Eschstruth, A., Mianné, J., Renaud, J.-B., Schneider-Maunoury, S., Shkumatava, A., Teboul, L., Kent, J., Joly, J.-S., & Concordet, J.-P.
- Affiliations
- University of California, Santa Cruz (US)
- Heidelberg University (DE)
- Centre National de la Recherche Scientifique (FR)
- Mary Lyon Centre at MRC Harwell (GB)
- Université Paris-Sud (FR)
- Corresponding author
- Haeussler, Maximilian
- Concordet, Jean-Paul
- ORCID
- Venue
- Genome biology
- Published
- 2016-07-05
- Volume
- 17
- Issue
- 1
- Pages
- 148-148
- ISSN
- 1465-6906 (ISSN-L)
- 1474-760X
- PMC
- PMC4934014
- OpenAlex
- W2461013690
- Cited by
- 1,756
- References
- 55
- Citations by year
- 2026: 104 · 2025: 151 · 2024: 191 · 2023: 184 · 2022: 199 · 2021: 226 · 2020: 279 · 2019: 212 · 2018: 139 · 2017: 62 · 2016: 8
- FWCI
- 72.96
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Genome biology (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- HAL (submittedVersion, cc-by)
- eScholarship (California Digital Library) (submittedVersion, cc-by)
- HAL (submittedVersion)
- Funders
- National Human Genome Research Institute (5U41HG002371-15)
- National Cancer Institute (NIH/NCI 5U54HG007990-02)
- California Institute for Regenerative Medicine (CIRM GC1R-06673C)
- Agence Nationale de la Recherche (ANR-II-INBS-0014, CILIAINTHEBRAIN)
- Fondation pour la Recherche Médicale (FRM DEQ20140329544)
- 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 prediction, SpCas9, scoring algorithm, algorithm evaluation, zebrafish, U6 promoter, in vitro transcription, genome editing
- MeSH terms
- Gene Editing, RNA, Guide, CRISPR-Cas Systems, Algorithms, Promoter Regions, Genetic, RNA, Small Nuclear, Software, Genome, RNA, Guide, Kinetoplastida, Internet, CRISPR-Cas Systems
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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