DeepCRISPR: Optimized CRISPR guide RNA design by deep learning
Open accessAbstract
A major challenge for effective application of CRISPR systems is to accurately predict the single guide RNA (sgRNA) on-target knockout efficacy and off-target profile, which would facilitate the optimized design of sgRNAs with high sensitivity and specificity. Here we present DeepCRISPR, a comprehensive computational platform to unify sgRNA on-target and off-target site prediction into one framework with deep learning, surpassing available state-of-the-art in silico tools. In addition, DeepCRISPR fully automates the identification of sequence and epigenetic features that may affect sgRNA knockout efficacy in a data-driven manner. DeepCRISPR is available at http://www.deepcrispr.net/ .
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DeepCRISPR utilizes deep learning to predict off-target cleavage sites and considers epigenetic features such as chromatin opening and DNA methylation to figure out genome-wide off-target profiles.
“DeepCRISPR is a comprehensive computational platform which utilizes deep learning to predict off-target cleavage sites. It considers epigenetic features such as chromatin opening and DNA methylation to figure out genome-wide off-target profiles (Chuai et al., 2018).”
From Off-target effects in CRISPR/Cas9 gene editing (2023) · cites this paper as Chuai et al., 2018
DeepCRISPR uses a deep convolutional denoising neural network (DCD-NN) and CNN to predict both on- and off-target activities of sgRNAs with improved accuracy.
“DeepCRISPR uses a deep convolutional denoising neural network (DCD-NN) and CNN to predict both on-and off-target activities of sgRNAs with improved accuracy.”
From Hybrid Neural Networks for Off-Target Prediction in Gene Editing: Integrating Sequence and Biophysical Features (2025) · cites this paper as [7]
Key sequence information previously obtained in SpCas9-WT was further verified in several variants in this study, such as the preference of G base in the gRNA spacer, including the preference of G bases in the PAM-distal region.
“Some of the key sequence information previously obtained in SpCas9-WT was further verified in several variants in our study, such as the preference of G base in the gRNA spacer, including the preference of G bases in the PAM-distal region.”
From Machine learning-based prediction models to guide the selection of Cas9 variants for efficient gene editing (2024) · cites this paper as [38]
The 'Automating feature identification in a learning schema' section (page unavailable) discusses nucleotide preferences, but its on-target findings favor cytosine at particular positions and disfavor thymine; it does not report a preference for G in the gRNA spacer. Searches for guanine preference and G at PAM-distal spacer positions found passages about mismatches and substitutions, not the claimed G-base preference. The citing authors’ verification in variants is described as their own study, not attributed to DeepCRISPR. (LLM, conf=0.72)
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“It considers epigenetic features such as chromatin opening and DNA methylation to figure out genomewide off-target profiles (Chuai et al., 2018).”
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- Authors
- Chuai, G., Ma, H., Yan, J., Chen, M., Hong, N., Xue, D., Zhou, C., Zhu, C., Chen, K., Duan, B., Gu, F., Qu, S., Huang, D., Wei, J., & Liu, Q.
- Affiliations
- Tongji University (CN)
- ShanghaiTech University (CN)
- Wenzhou Medical University (CN)
- Corresponding author
- Wei, Jia
- Liu, Qi
- ORCID
- Venue
- Genome biology
- Published
- 2018-06-26
- Volume
- 19
- Issue
- 1
- Pages
- 80-80
- ISSN
- 1465-6906 (ISSN-L)
- 1474-760X
- PMC
- PMC6020378
- OpenAlex
- W2810756255
- Cited by
- 641
- References
- 56
- Citations by year
- 2026: 140 · 2025: 129 · 2024: 83 · 2023: 47 · 2022: 69 · 2021: 55 · 2020: 63 · 2019: 47 · 2018: 8
- FWCI
- 15.38
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Genome biology (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, other-oa)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Natural Science Foundation of Shanghai (17ZR1449400)
- Harvard University
- National Natural Science Foundation of China (61520106006, 61572361, 61732012)
- Shanghai Rising-Star Program (16QA1403900)
- 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 guide RNA design, single guide RNA, off-target prediction, deep learning, epigenetic features, sequence features
- MeSH terms
- Machine Learning, RNA, Guide, CRISPR-Cas Systems, Cell Line, Computer Simulation, HeLa Cells, Humans, RNA Editing, HL-60 Cells, Computational Biology, HCT116 Cells, Cell Line, Tumor, HEK293 Cells, Clustered Regularly Interspaced Short Palindromic Repeats, CRISPR-Cas Systems
About these checks
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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