| Téléchargement | - Voir la version finale : Predicting CRISPR-Cas9 off-target effects in human primary cells using bidirectional LSTM with BERT embedding (PDF, 1.5 Mio)
- Voir les données supplémentaires : Predicting CRISPR-Cas9 off-target effects in human primary cells using bidirectional LSTM with BERT embedding (DOCX, 289 Kio)
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| DOI | Trouver le DOI : https://doi.org/10.1093/bioadv/vbae184 |
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| Auteur | Rechercher : Sari, Orhan; Rechercher : Liu, Ziying1Identifiant ORCID : https://orcid.org/0000-0001-7821-6723; Rechercher : Pan, Youlian1Identifiant ORCID : https://orcid.org/0000-0002-0158-0081; Rechercher : Shao, Xiaojian1Identifiant ORCID : https://orcid.org/0000-0002-3044-621X |
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| Éditeur | Rechercher : Lengauer, Thomas |
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| Affiliation | - Conseil national de recherches Canada. Technologies numériques
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| Bailleur de fonds | Rechercher : National Research Council Canada |
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| Format | Texte, Article |
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| Résumé | 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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| Date de publication | 2024-12-30 |
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| Maison d’édition | Oxford University Press |
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| Licence | |
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| Dans | |
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| Langue | anglais |
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| Publications évaluées par des pairs | Oui |
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| Exporter la notice | Exporter en format RIS |
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| Signaler une correction | Signaler une correction (s'ouvre dans un nouvel onglet) |
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| Identificateur de l’enregistrement | 3d9ab978-c101-4d38-a75e-984f04eb3430 |
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| Enregistrement créé | 2025-02-26 |
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| Enregistrement modifié | 2025-11-03 |
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