Prediction of plant-derived xenomiRs from plant miRNA sequences using random forest and one-dimensional convolutional neural network models
Abstract Background An increasing number of studies reported that exogenous miRNAs (xenomiRs) can be detected in animal bodies, however, some others reported negative results. Some attributed this divergence to the selective absorption of plant-derived xenomiRs by animals. Results Here, we analyzed...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2018-11-01
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| Series: | BMC Genomics |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s12864-018-5227-3 |
| _version_ | 1811264116304642048 |
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| author | Qi Zhao Qian Mao Zheng Zhao Tongyi Dou Zhiguo Wang Xiaoyu Cui Yuanning Liu Xiaoya Fan |
| author_facet | Qi Zhao Qian Mao Zheng Zhao Tongyi Dou Zhiguo Wang Xiaoyu Cui Yuanning Liu Xiaoya Fan |
| author_sort | Qi Zhao |
| collection | DOAJ |
| description | Abstract Background An increasing number of studies reported that exogenous miRNAs (xenomiRs) can be detected in animal bodies, however, some others reported negative results. Some attributed this divergence to the selective absorption of plant-derived xenomiRs by animals. Results Here, we analyzed 166 plant-derived xenomiRs reported in our previous study and 942 non-xenomiRs extracted from miRNA expression profiles of four species of commonly consumed plants. Employing statistics analysis and cluster analysis, our study revealed the potential sequence specificity of plant-derived xenomiRs. Furthermore, a random forest model and a one-dimensional convolutional neural network model were trained using miRNA sequence features and raw miRNA sequences respectively and then employed to predict unlabeled plant miRNAs in miRBase. A total of 241 possible plant-derived xenomiRs were predicted by both models. Finally, the potential functions of these possible plant-derived xenomiRs along with our previously reported ones in human body were analyzed. Conclusions Our study, for the first time, presents the systematic plant-derived xenomiR sequences analysis and provides evidence for selective absorption of plant miRNA by human body, which could facilitate the future investigation about the mechanisms underlying the transference of plant-derived xenomiR. |
| first_indexed | 2024-04-12T19:57:18Z |
| format | Article |
| id | doaj.art-ffd77ae5442647ffaffb3600ac5f3f76 |
| institution | Directory Open Access Journal |
| issn | 1471-2164 |
| language | English |
| last_indexed | 2024-04-12T19:57:18Z |
| publishDate | 2018-11-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Genomics |
| spelling | doaj.art-ffd77ae5442647ffaffb3600ac5f3f762022-12-22T03:18:37ZengBMCBMC Genomics1471-21642018-11-0119111310.1186/s12864-018-5227-3Prediction of plant-derived xenomiRs from plant miRNA sequences using random forest and one-dimensional convolutional neural network modelsQi Zhao0Qian Mao1Zheng Zhao2Tongyi Dou3Zhiguo Wang4Xiaoyu Cui5Yuanning Liu6Xiaoya Fan7Sino-Dutch Biomedical and Information Engineering School, Northeastern UniversityLight Industry College, Liaoning UniversityDepartment of Network Engineering, Zhengzhou Science and Technology InstituteSchool of Life Science and Medicine, Dalian University of TechnologySino-Dutch Biomedical and Information Engineering School, Northeastern UniversitySino-Dutch Biomedical and Information Engineering School, Northeastern UniversityComputer Science and Technology College, Jilin UniversityBio-, Electro- And Mechanical Systems, Université Libre de BruxellesAbstract Background An increasing number of studies reported that exogenous miRNAs (xenomiRs) can be detected in animal bodies, however, some others reported negative results. Some attributed this divergence to the selective absorption of plant-derived xenomiRs by animals. Results Here, we analyzed 166 plant-derived xenomiRs reported in our previous study and 942 non-xenomiRs extracted from miRNA expression profiles of four species of commonly consumed plants. Employing statistics analysis and cluster analysis, our study revealed the potential sequence specificity of plant-derived xenomiRs. Furthermore, a random forest model and a one-dimensional convolutional neural network model were trained using miRNA sequence features and raw miRNA sequences respectively and then employed to predict unlabeled plant miRNAs in miRBase. A total of 241 possible plant-derived xenomiRs were predicted by both models. Finally, the potential functions of these possible plant-derived xenomiRs along with our previously reported ones in human body were analyzed. Conclusions Our study, for the first time, presents the systematic plant-derived xenomiR sequences analysis and provides evidence for selective absorption of plant miRNA by human body, which could facilitate the future investigation about the mechanisms underlying the transference of plant-derived xenomiR.http://link.springer.com/article/10.1186/s12864-018-5227-3miRNAPlant-derived xenomiRCross-kingdom regulationSelective absorptionStatistics analysisMachine learning |
| spellingShingle | Qi Zhao Qian Mao Zheng Zhao Tongyi Dou Zhiguo Wang Xiaoyu Cui Yuanning Liu Xiaoya Fan Prediction of plant-derived xenomiRs from plant miRNA sequences using random forest and one-dimensional convolutional neural network models BMC Genomics miRNA Plant-derived xenomiR Cross-kingdom regulation Selective absorption Statistics analysis Machine learning |
| title | Prediction of plant-derived xenomiRs from plant miRNA sequences using random forest and one-dimensional convolutional neural network models |
| title_full | Prediction of plant-derived xenomiRs from plant miRNA sequences using random forest and one-dimensional convolutional neural network models |
| title_fullStr | Prediction of plant-derived xenomiRs from plant miRNA sequences using random forest and one-dimensional convolutional neural network models |
| title_full_unstemmed | Prediction of plant-derived xenomiRs from plant miRNA sequences using random forest and one-dimensional convolutional neural network models |
| title_short | Prediction of plant-derived xenomiRs from plant miRNA sequences using random forest and one-dimensional convolutional neural network models |
| title_sort | prediction of plant derived xenomirs from plant mirna sequences using random forest and one dimensional convolutional neural network models |
| topic | miRNA Plant-derived xenomiR Cross-kingdom regulation Selective absorption Statistics analysis Machine learning |
| url | http://link.springer.com/article/10.1186/s12864-018-5227-3 |
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