Anticipating On‐Target and Off‐Target Effects of CRISPR/Cas9 Genome Editing Via a Feedforward Neural Network Model
ABSTRACT Background Clustered regularly interspaced short palindromic repeats —CRISPR‐associated protein 9 (CRISPR/Cas9) is a gene editing technology that can deliver highly precise genome editing. However, it is difficult to predict both on‐ and off‐target effects of CRISPR/Cas9, which is essential...
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| Format: | Article |
| Language: | English |
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Wiley
2025-06-01
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| Series: | Medicine Advances |
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| Online Access: | https://doi.org/10.1002/med4.70016 |
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| author | Pavithra Nagendran Gowtham Murugesan Jeyakumar Natarajan |
| author_facet | Pavithra Nagendran Gowtham Murugesan Jeyakumar Natarajan |
| author_sort | Pavithra Nagendran |
| collection | DOAJ |
| description | ABSTRACT Background Clustered regularly interspaced short palindromic repeats —CRISPR‐associated protein 9 (CRISPR/Cas9) is a gene editing technology that can deliver highly precise genome editing. However, it is difficult to predict both on‐ and off‐target effects of CRISPR/Cas9, which is essential for ensuring the safety and efficiency of genetic modifications made using this technology. Methods In this study, we used the SITE‐Seq dataset, which comprises CRISPR targets, to classify sequences for both on‐ and off‐target effects. To evaluate sequence pairs, we built a feedforward neural network (FNN) with 10 fully connected layers and compared its performance with that of other state‐of‐the‐art models. Results We showed that our FNN model attained an accuracy rate of 0.95, greatly improving prediction reliability for both on‐ and off‐target effects compared with other methods. Conclusion This work contributes a valuable predictive modeling framework to the field of CRISPR research, addressing both on‐ and off‐target effects in a unified manner, which is an essential requirement for the safe and effective application of genomic editing technologies. |
| format | Article |
| id | doaj-art-0b5fd46db6d240dcb4d7b1afe7c7ab5c |
| institution | DOAJ |
| issn | 2834-4391 2834-4405 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Wiley |
| record_format | Article |
| series | Medicine Advances |
| spelling | doaj-art-0b5fd46db6d240dcb4d7b1afe7c7ab5c2025-08-20T03:15:46ZengWileyMedicine Advances2834-43912834-44052025-06-0132889610.1002/med4.70016Anticipating On‐Target and Off‐Target Effects of CRISPR/Cas9 Genome Editing Via a Feedforward Neural Network ModelPavithra Nagendran0Gowtham Murugesan1Jeyakumar Natarajan2Data Mining and Text Mining Laboratory Department of Bioinformatics Bharathiar University Coimbatore IndiaData Mining and Text Mining Laboratory Department of Bioinformatics Bharathiar University Coimbatore IndiaData Mining and Text Mining Laboratory Department of Bioinformatics Bharathiar University Coimbatore IndiaABSTRACT Background Clustered regularly interspaced short palindromic repeats —CRISPR‐associated protein 9 (CRISPR/Cas9) is a gene editing technology that can deliver highly precise genome editing. However, it is difficult to predict both on‐ and off‐target effects of CRISPR/Cas9, which is essential for ensuring the safety and efficiency of genetic modifications made using this technology. Methods In this study, we used the SITE‐Seq dataset, which comprises CRISPR targets, to classify sequences for both on‐ and off‐target effects. To evaluate sequence pairs, we built a feedforward neural network (FNN) with 10 fully connected layers and compared its performance with that of other state‐of‐the‐art models. Results We showed that our FNN model attained an accuracy rate of 0.95, greatly improving prediction reliability for both on‐ and off‐target effects compared with other methods. Conclusion This work contributes a valuable predictive modeling framework to the field of CRISPR research, addressing both on‐ and off‐target effects in a unified manner, which is an essential requirement for the safe and effective application of genomic editing technologies.https://doi.org/10.1002/med4.70016clustered regularly interspaced palindromic repeatsdeep learningFeedforward Neural Networkoff‐targeton‐target |
| spellingShingle | Pavithra Nagendran Gowtham Murugesan Jeyakumar Natarajan Anticipating On‐Target and Off‐Target Effects of CRISPR/Cas9 Genome Editing Via a Feedforward Neural Network Model Medicine Advances clustered regularly interspaced palindromic repeats deep learning Feedforward Neural Network off‐target on‐target |
| title | Anticipating On‐Target and Off‐Target Effects of CRISPR/Cas9 Genome Editing Via a Feedforward Neural Network Model |
| title_full | Anticipating On‐Target and Off‐Target Effects of CRISPR/Cas9 Genome Editing Via a Feedforward Neural Network Model |
| title_fullStr | Anticipating On‐Target and Off‐Target Effects of CRISPR/Cas9 Genome Editing Via a Feedforward Neural Network Model |
| title_full_unstemmed | Anticipating On‐Target and Off‐Target Effects of CRISPR/Cas9 Genome Editing Via a Feedforward Neural Network Model |
| title_short | Anticipating On‐Target and Off‐Target Effects of CRISPR/Cas9 Genome Editing Via a Feedforward Neural Network Model |
| title_sort | anticipating on target and off target effects of crispr cas9 genome editing via a feedforward neural network model |
| topic | clustered regularly interspaced palindromic repeats deep learning Feedforward Neural Network off‐target on‐target |
| url | https://doi.org/10.1002/med4.70016 |
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