Effective use of sequence information to predict CRISPR-Cas9 off-target
The CRISPR/Cas9 gene-editing system is the third-generation gene-editing technology that has been widely used in biomedical applications. However, off-target effects occurring CRISPR/Cas9 system has been a challenging problem it faces in practical applications. Although many predictive models have b...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2022-01-01
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| Series: | Computational and Structural Biotechnology Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2001037022000137 |
| _version_ | 1797978282631626752 |
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| author | Zhong-Rui Zhang Zhen-Ran Jiang |
| author_facet | Zhong-Rui Zhang Zhen-Ran Jiang |
| author_sort | Zhong-Rui Zhang |
| collection | DOAJ |
| description | The CRISPR/Cas9 gene-editing system is the third-generation gene-editing technology that has been widely used in biomedical applications. However, off-target effects occurring CRISPR/Cas9 system has been a challenging problem it faces in practical applications. Although many predictive models have been developed to predict off-target activities, current models do not effectively use sequence pair information. There is still room for improved accuracy. This study aims to effectively use sequence pair information to improve the model's performance for predicting off-target activities. We propose a new coding scheme for coding sequence pairs and design a new model called CRISPR-IP for predicting off-target activity. Our coding scheme distinguishes regions with different functions in the sequence pairs through the function channel. Moreover, it distinguishes between bases and base pairs using type channels, effectively representing the sequence pair information. The CRISPR-IP model is based on CNN, BiLSTM, and the attention layer to learn features of sequence pairs. We performed performance verification on two data sets and found that our coding scheme can represent sequence pair information effectively, and the CRISPR-IP model performance is better than others. Data and source codes are available at https://github.com/BioinfoVirgo/CRISPR-IP. |
| first_indexed | 2024-04-11T05:20:21Z |
| format | Article |
| id | doaj.art-5517dab8aa0f4058b9af53373179956f |
| institution | Directory Open Access Journal |
| issn | 2001-0370 |
| language | English |
| last_indexed | 2024-04-11T05:20:21Z |
| publishDate | 2022-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Computational and Structural Biotechnology Journal |
| spelling | doaj.art-5517dab8aa0f4058b9af53373179956f2022-12-24T04:51:13ZengElsevierComputational and Structural Biotechnology Journal2001-03702022-01-0120650661Effective use of sequence information to predict CRISPR-Cas9 off-targetZhong-Rui Zhang0Zhen-Ran Jiang1School of Computer Science and Technology, East China Normal University, Shanghai 200062, ChinaCorresponding author.; School of Computer Science and Technology, East China Normal University, Shanghai 200062, ChinaThe CRISPR/Cas9 gene-editing system is the third-generation gene-editing technology that has been widely used in biomedical applications. However, off-target effects occurring CRISPR/Cas9 system has been a challenging problem it faces in practical applications. Although many predictive models have been developed to predict off-target activities, current models do not effectively use sequence pair information. There is still room for improved accuracy. This study aims to effectively use sequence pair information to improve the model's performance for predicting off-target activities. We propose a new coding scheme for coding sequence pairs and design a new model called CRISPR-IP for predicting off-target activity. Our coding scheme distinguishes regions with different functions in the sequence pairs through the function channel. Moreover, it distinguishes between bases and base pairs using type channels, effectively representing the sequence pair information. The CRISPR-IP model is based on CNN, BiLSTM, and the attention layer to learn features of sequence pairs. We performed performance verification on two data sets and found that our coding scheme can represent sequence pair information effectively, and the CRISPR-IP model performance is better than others. Data and source codes are available at https://github.com/BioinfoVirgo/CRISPR-IP.http://www.sciencedirect.com/science/article/pii/S2001037022000137CRISPR-Cas9Off-target predictionDeep learningEncoding scheme |
| spellingShingle | Zhong-Rui Zhang Zhen-Ran Jiang Effective use of sequence information to predict CRISPR-Cas9 off-target Computational and Structural Biotechnology Journal CRISPR-Cas9 Off-target prediction Deep learning Encoding scheme |
| title | Effective use of sequence information to predict CRISPR-Cas9 off-target |
| title_full | Effective use of sequence information to predict CRISPR-Cas9 off-target |
| title_fullStr | Effective use of sequence information to predict CRISPR-Cas9 off-target |
| title_full_unstemmed | Effective use of sequence information to predict CRISPR-Cas9 off-target |
| title_short | Effective use of sequence information to predict CRISPR-Cas9 off-target |
| title_sort | effective use of sequence information to predict crispr cas9 off target |
| topic | CRISPR-Cas9 Off-target prediction Deep learning Encoding scheme |
| url | http://www.sciencedirect.com/science/article/pii/S2001037022000137 |
| work_keys_str_mv | AT zhongruizhang effectiveuseofsequenceinformationtopredictcrisprcas9offtarget AT zhenranjiang effectiveuseofsequenceinformationtopredictcrisprcas9offtarget |