Predicting algorithm of attC site based on combination optimization strategy
Site-specific recombination systems are widely used as bioengineering tools. However, the traditional site-specific recombination system requires a consensus sequence for the specific site. Such sequence-level constraints limit effective recombination between sites. Therefore, in order to develop an...
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| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2022-12-01
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| Series: | Connection Science |
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| Online Access: | http://dx.doi.org/10.1080/09540091.2022.2086217 |
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| author | Zhendong Liu Xi Chen Dongyan Li Xinrong Lv Mengying Qin Ke Bai Zhiqiang He Yurong Yang Xiaofeng Li Qionghai Dai |
| author_facet | Zhendong Liu Xi Chen Dongyan Li Xinrong Lv Mengying Qin Ke Bai Zhiqiang He Yurong Yang Xiaofeng Li Qionghai Dai |
| author_sort | Zhendong Liu |
| collection | DOAJ |
| description | Site-specific recombination systems are widely used as bioengineering tools. However, the traditional site-specific recombination system requires a consensus sequence for the specific site. Such sequence-level constraints limit effective recombination between sites. Therefore, in order to develop an efficient site-specific recombination system, we investigated the attC site of the bacterial integration subsystem and built a predictive model to infer the important features that contribute to recombination. Here, we design an attC site prediction algorithm based on a combination optimisation strategy. Based on the structural features of attC sites, the prediction algorithm realises the high-precision prediction of the recombination frequencies between sites and the screening of the top 20 important features that play a role in recombination, which are effective for improving the design method of attC sites. The algorithm has better portability and higher prediction accuracy compared with the existing advanced algorithms, among which the Pearson correlation coefficient is 0.87, explained variance score is 0.73, root mean square error is 0.006 and mean absolute error is 0.041. This can not only provide ideas for the research of efficient recombination systems but also provide a theoretical basis for developing genetic engineering further. |
| format | Article |
| id | doaj-art-7a05d7e419a248e3885efdcd17203edd |
| institution | OA Journals |
| issn | 0954-0091 1360-0494 |
| language | English |
| publishDate | 2022-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Connection Science |
| spelling | doaj-art-7a05d7e419a248e3885efdcd17203edd2025-08-20T01:58:00ZengTaylor & Francis GroupConnection Science0954-00911360-04942022-12-013411895191210.1080/09540091.2022.20862172086217Predicting algorithm of attC site based on combination optimization strategyZhendong Liu0Xi Chen1Dongyan Li2Xinrong Lv3Mengying Qin4Ke Bai5Zhiqiang He6Yurong Yang7Xiaofeng Li8Qionghai Dai9Shandong Jianzhu UniversityShandong Jianzhu UniversityShandong Jianzhu UniversityShandong Jianzhu UniversityShandong Jianzhu UniversityShandong Jianzhu UniversityShandong Jianzhu UniversityShandong Jianzhu UniversityShandong Jianzhu UniversityTsinghua UniversitySite-specific recombination systems are widely used as bioengineering tools. However, the traditional site-specific recombination system requires a consensus sequence for the specific site. Such sequence-level constraints limit effective recombination between sites. Therefore, in order to develop an efficient site-specific recombination system, we investigated the attC site of the bacterial integration subsystem and built a predictive model to infer the important features that contribute to recombination. Here, we design an attC site prediction algorithm based on a combination optimisation strategy. Based on the structural features of attC sites, the prediction algorithm realises the high-precision prediction of the recombination frequencies between sites and the screening of the top 20 important features that play a role in recombination, which are effective for improving the design method of attC sites. The algorithm has better portability and higher prediction accuracy compared with the existing advanced algorithms, among which the Pearson correlation coefficient is 0.87, explained variance score is 0.73, root mean square error is 0.006 and mean absolute error is 0.041. This can not only provide ideas for the research of efficient recombination systems but also provide a theoretical basis for developing genetic engineering further.http://dx.doi.org/10.1080/09540091.2022.2086217attc sitesite-specific recombinationmachine learningcombination optimizationsynthetic biology |
| spellingShingle | Zhendong Liu Xi Chen Dongyan Li Xinrong Lv Mengying Qin Ke Bai Zhiqiang He Yurong Yang Xiaofeng Li Qionghai Dai Predicting algorithm of attC site based on combination optimization strategy Connection Science attc site site-specific recombination machine learning combination optimization synthetic biology |
| title | Predicting algorithm of attC site based on combination optimization strategy |
| title_full | Predicting algorithm of attC site based on combination optimization strategy |
| title_fullStr | Predicting algorithm of attC site based on combination optimization strategy |
| title_full_unstemmed | Predicting algorithm of attC site based on combination optimization strategy |
| title_short | Predicting algorithm of attC site based on combination optimization strategy |
| title_sort | predicting algorithm of attc site based on combination optimization strategy |
| topic | attc site site-specific recombination machine learning combination optimization synthetic biology |
| url | http://dx.doi.org/10.1080/09540091.2022.2086217 |
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