Off-target predictions in CRISPR-Cas9 gene editing using deep learning
Open accessAbstract
Motivation: The prediction of off-target mutations in CRISPR-Cas9 is a hot topic due to its relevance to gene editing research. Existing prediction methods have been developed; however, most of them just calculated scores based on mismatches to the guide sequence in CRISPR-Cas9. Therefore, the existing prediction methods are unable to scale and improve their performance with the rapid expansion of experimental data in CRISPR-Cas9. Moreover, the existing methods still cannot satisfy enough precision in off-target predictions for gene editing at the clinical level. Results: To address it, we design and implement two algorithms using deep neural networks to predict off-target mutations in CRISPR-Cas9 gene editing (i.e. deep convolutional neural network and deep feedforward neural network). The models were trained and tested on the recently released off-target dataset, CRISPOR dataset, for performance benchmark. Another off-target dataset identified by GUIDE-seq was adopted for additional evaluation. We demonstrate that convolutional neural network achieves the best performance on CRISPOR dataset, yielding an average classification area under the ROC curve (AUC) of 97.2% under stratified 5-fold cross-validation. Interestingly, the deep feedforward neural network can also be competitive at the average AUC of 97.0% under the same setting. We compare the two deep neural network models with the state-of-the-art off-target prediction methods (i.e. CFD, MIT, CROP-IT, and CCTop) and three traditional machine learning models (i.e. random forest, gradient boosting trees, and logistic regression) on both datasets in terms of AUC values, demonstrating the competitive edges of the proposed algorithms. Additional analyses are conducted to investigate the underlying reasons from different perspectives. Availability and implementation: The example code are available at https://github.com/MichaelLinn/off_target_prediction. The related datasets are available at https://github.com/MichaelLinn/off_target_prediction/tree/master/data.
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CDRscan's identification of drugs with new potential cancer indications may result in treatments tailored for each individual patient.
“Recently, a Cancer Drug Response profile scan (CDRscan) was developed, that predicts somatic mutation profile-based drug responsiveness by linking the tumor genomic fingerprint and its sensitivity to drugs and identified 14 oncology and 23 non-oncology drugs as having new potential cancer indications which may result in treatments tailored for each individual patient78.”
From Exploiting machine learning for end-to-end drug discovery and development (2019) · cites this paper as [78]
CDRscan predicts somatic mutation profile-based drug responsiveness by linking the tumor genomic fingerprint and its sensitivity to drugs.
“Recently, a Cancer Drug Response profile scan (CDRscan) was developed, that predicts somatic mutation profile-based drug responsiveness by linking the tumor genomic fingerprint and its sensitivity to drugs and identified 14 oncology and 23 non-oncology drugs as having new potential cancer indications which may result in treatments tailored for each individual patient78.”
From Exploiting machine learning for end-to-end drug discovery and development (2019) · cites this paper as [78]
CDRscan identified 14 oncology and 23 non-oncology drugs as having new potential cancer indications.
“Recently, a Cancer Drug Response profile scan (CDRscan) was developed, that predicts somatic mutation profile-based drug responsiveness by linking the tumor genomic fingerprint and its sensitivity to drugs and identified 14 oncology and 23 non-oncology drugs as having new potential cancer indications which may result in treatments tailored for each individual patient78.”
From Exploiting machine learning for end-to-end drug discovery and development (2019) · cites this paper as [78]
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- Authors
- Lin, J., & Wong, K.-C.
- Affiliations
- City University of Hong Kong (HK)
- Corresponding author
- Wong, Ka-Chun
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2018-09-01
- Volume
- 34
- Issue
- 17
- Pages
- i656-i663
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- PMC
- PMC6129261
- OpenAlex
- W2891210592
- Cited by
- 185
- References
- 44
- Citations by year
- 2026: 12 · 2025: 36 · 2024: 24 · 2023: 23 · 2022: 21 · 2021: 25 · 2020: 20 · 2019: 22 · 2018: 1 · 2017: 1
- FWCI
- 6.05
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Bioinformatics (publishedVersion, cc-by-nc)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- CityU Scholars (submittedVersion, cc-by-nc)
- Funders
- Research Grants Council of the Hong Kong Special Administrative Region (CityU 21200816, CityU 11203217, CityU 11200218)
- 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 › Machine Learning in Bioinformatics
- Keywords
- CRISPR/Cas9, off-target prediction, genome editing, deep convolutional neural networks, deep feedforward neural network, GUIDE-seq, off-target mutations
- MeSH terms
- Gene Editing, Deep Learning, Algorithms, Logistic Models, Area Under Curve, CRISPR-Cas Systems
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