An efficient deep learning based predictor for identifying miRNA-triggered phasiRNA loci in plant
Phasic small interfering RNAs are plant secondary small interference RNAs that typically generated by the convergence of miRNAs and polyadenylated mRNAs. A growing number of studies have shown that miRNA-initiated phasiRNA plays crucial roles in regulating plant growth and stress responses. Experime...
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| Format: | Article |
| Language: | English |
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AIMS Press
2023-02-01
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| Series: | Mathematical Biosciences and Engineering |
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| Online Access: | https://www.aimspress.com/article/doi/10.3934/mbe.2023295?viewType=HTML |
| _version_ | 1811162788300587008 |
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| author | Yuanyuan Bu Jia Zheng Cangzhi Jia |
| author_facet | Yuanyuan Bu Jia Zheng Cangzhi Jia |
| author_sort | Yuanyuan Bu |
| collection | DOAJ |
| description | Phasic small interfering RNAs are plant secondary small interference RNAs that typically generated by the convergence of miRNAs and polyadenylated mRNAs. A growing number of studies have shown that miRNA-initiated phasiRNA plays crucial roles in regulating plant growth and stress responses. Experimental verification of miRNA-initiated phasiRNA loci may take considerable time, energy and labor. Therefore, computational methods capable of processing high throughput data have been proposed one by one. In this work, we proposed a predictor (DIGITAL) for identifying miRNA-initiated phasiRNAs in plant, which combined a multi-scale residual network with a bi-directional long-short term memory network. The negative dataset was constructed based on positive data, through replacing 60% of nucleotides randomly in each positive sample. Our predictor achieved the accuracy of 98.48% and 94.02% respectively on two independent test datasets with different sequence length. These independent testing results indicate the effectiveness of our model. Furthermore, DIGITAL is of robustness and generalization ability, and thus can be easily extended and applied for miRNA target recognition of other species. We provide the source code of DIGITAL, which is freely available at https://github.com/yuanyuanbu/DIGITAL. |
| first_indexed | 2024-04-10T06:34:47Z |
| format | Article |
| id | doaj.art-19930bcf399344758805e4777c408df3 |
| institution | Directory Open Access Journal |
| issn | 1551-0018 |
| language | English |
| last_indexed | 2024-04-10T06:34:47Z |
| publishDate | 2023-02-01 |
| publisher | AIMS Press |
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| series | Mathematical Biosciences and Engineering |
| spelling | doaj.art-19930bcf399344758805e4777c408df32023-03-01T01:12:28ZengAIMS PressMathematical Biosciences and Engineering1551-00182023-02-012046853686510.3934/mbe.2023295An efficient deep learning based predictor for identifying miRNA-triggered phasiRNA loci in plantYuanyuan Bu0 Jia Zheng 1Cangzhi Jia2School of Science, Dalian Maritimr University, Dalian 116026, ChinaSchool of Science, Dalian Maritimr University, Dalian 116026, ChinaSchool of Science, Dalian Maritimr University, Dalian 116026, ChinaPhasic small interfering RNAs are plant secondary small interference RNAs that typically generated by the convergence of miRNAs and polyadenylated mRNAs. A growing number of studies have shown that miRNA-initiated phasiRNA plays crucial roles in regulating plant growth and stress responses. Experimental verification of miRNA-initiated phasiRNA loci may take considerable time, energy and labor. Therefore, computational methods capable of processing high throughput data have been proposed one by one. In this work, we proposed a predictor (DIGITAL) for identifying miRNA-initiated phasiRNAs in plant, which combined a multi-scale residual network with a bi-directional long-short term memory network. The negative dataset was constructed based on positive data, through replacing 60% of nucleotides randomly in each positive sample. Our predictor achieved the accuracy of 98.48% and 94.02% respectively on two independent test datasets with different sequence length. These independent testing results indicate the effectiveness of our model. Furthermore, DIGITAL is of robustness and generalization ability, and thus can be easily extended and applied for miRNA target recognition of other species. We provide the source code of DIGITAL, which is freely available at https://github.com/yuanyuanbu/DIGITAL.https://www.aimspress.com/article/doi/10.3934/mbe.2023295?viewType=HTMLdeep learningrnaiphasirnaone-hot encodinglstm |
| spellingShingle | Yuanyuan Bu Jia Zheng Cangzhi Jia An efficient deep learning based predictor for identifying miRNA-triggered phasiRNA loci in plant Mathematical Biosciences and Engineering deep learning rnai phasirna one-hot encoding lstm |
| title | An efficient deep learning based predictor for identifying miRNA-triggered phasiRNA loci in plant |
| title_full | An efficient deep learning based predictor for identifying miRNA-triggered phasiRNA loci in plant |
| title_fullStr | An efficient deep learning based predictor for identifying miRNA-triggered phasiRNA loci in plant |
| title_full_unstemmed | An efficient deep learning based predictor for identifying miRNA-triggered phasiRNA loci in plant |
| title_short | An efficient deep learning based predictor for identifying miRNA-triggered phasiRNA loci in plant |
| title_sort | efficient deep learning based predictor for identifying mirna triggered phasirna loci in plant |
| topic | deep learning rnai phasirna one-hot encoding lstm |
| url | https://www.aimspress.com/article/doi/10.3934/mbe.2023295?viewType=HTML |
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