| Download | - View final version: Predicting CRISPR-Cas9 off-target effects in human primary cells using bidirectional LSTM with BERT embedding (PDF, 1.5 MiB)
- View supplementary information: Predicting CRISPR-Cas9 off-target effects in human primary cells using bidirectional LSTM with BERT embedding (DOCX, 289 KiB)
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| DOI | Resolve DOI: https://doi.org/10.1093/bioadv/vbae184 |
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| Author | Search for: Sari, Orhan; Search for: Liu, Ziying1ORCID identifier: https://orcid.org/0000-0001-7821-6723; Search for: Pan, Youlian1ORCID identifier: https://orcid.org/0000-0002-0158-0081; Search for: Shao, Xiaojian1ORCID identifier: https://orcid.org/0000-0002-3044-621X |
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| Editor | Search for: Lengauer, Thomas |
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| Affiliation | - National Research Council Canada. Digital Technologies
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| Funder | Search for: National Research Council Canada |
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| Format | Text, Article |
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| Abstract | Motivation:
Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)-Cas9 system is a ground-breaking genome editing tool, which has revolutionized cell and gene therapies. One of the essential components involved in this system that ensures its success is the design of an optimal single-guide RNA (sgRNA) with high on-target cleavage efficiency and low off-target effects. This is challenging as many conditions need to be considered, and empirically testing every design is time-consuming and costly. In silico prediction using machine learning models provides high-performance alternatives.
Results:
We present CrisprBERT, a deep learning model incorporating a Bidirectional Encoder Representations from Transformers (BERT) architecture to provide a high-dimensional embedding for paired sgRNA and DNA sequences and Bidirectional Long Short-term Memory networks for learning, to predict the off-target effects of sgRNAs utilizing only the sgRNAs and their paired DNA sequences. We proposed doublet stack encoding to capture the local energy configuration of the Cas9 binding and applied the BERT model to learn the contextual embedding of the doublet pairs. Our results showed that the new model achieved better performance than state-of-the-art deep learning models regarding single split and leave-one-sgRNA-out cross-validations as well as independent testing.
Availability and implementation:
The CrisprBERT is available at GitHub: https://github.com/OSsari/CrisprBERT. |
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| Publication date | 2024-12-30 |
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| Publisher | Oxford University Press |
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| Licence | |
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| In | |
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| Language | English |
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| Peer reviewed | Yes |
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| Export citation | Export as RIS |
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| Report a correction | Report a correction (opens in a new tab) |
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| Record identifier | 3d9ab978-c101-4d38-a75e-984f04eb3430 |
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| Record created | 2025-02-26 |
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| Record modified | 2025-11-03 |
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