An advanced systems biology framework of feature engineering for cold tolerance genes discovery from integrated omics and non-omics data in soybean
Soybean is sensitive to low temperatures during the crop growing season. An urgent demand for breeding cold-tolerant cultivars to alleviate the production loss is apparent to cope with this scenario. Cold-tolerant trait is a complex and quantitative trait controlled by multiple genes, environmental...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-09-01
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| Series: | Frontiers in Plant Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2022.1019709/full |
| _version_ | 1818030751287869440 |
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| author | Pei-Hsiu Kao Supaporn Baiya Zheng-Yuan Lai Chih-Min Huang Li-Hsin Jhan Chian-Jiun Lin Ya-Syuan Lai Chung-Feng Kao Chung-Feng Kao |
| author_facet | Pei-Hsiu Kao Supaporn Baiya Zheng-Yuan Lai Chih-Min Huang Li-Hsin Jhan Chian-Jiun Lin Ya-Syuan Lai Chung-Feng Kao Chung-Feng Kao |
| author_sort | Pei-Hsiu Kao |
| collection | DOAJ |
| description | Soybean is sensitive to low temperatures during the crop growing season. An urgent demand for breeding cold-tolerant cultivars to alleviate the production loss is apparent to cope with this scenario. Cold-tolerant trait is a complex and quantitative trait controlled by multiple genes, environmental factors, and their interaction. In this study, we proposed an advanced systems biology framework of feature engineering for the discovery of cold tolerance genes (CTgenes) from integrated omics and non-omics (OnO) data in soybean. An integrative pipeline was introduced for feature selection and feature extraction from different layers in the integrated OnO data using data ensemble methods and the non-parameter random forest prioritization to minimize uncertainties and false positives for accuracy improvement of results. In total, 44, 143, and 45 CTgenes were identified in short-, mid-, and long-term cold treatment, respectively, from the corresponding gene-pool. These CTgenes outperformed the remaining genes, the random genes, and the other candidate genes identified by other approaches in an independent RNA-seq database. Furthermore, we applied pathway enrichment and crosstalk network analyses to uncover relevant physiological pathways with the discovery of underlying cold tolerance in hormone- and defense-related modules. Our CTgenes were validated by using 55 SNP genotype data of 56 soybean samples in cold tolerance experiments. This suggests that the CTgenes identified from our proposed systematic framework can effectively distinguish cold-resistant and cold-sensitive lines. It is an important advancement in the soybean cold-stress response. The proposed pipelines provide an alternative solution to biomarker discovery, module discovery, and sample classification underlying a particular trait in plants in a robust and efficient way. |
| first_indexed | 2024-12-10T05:40:33Z |
| format | Article |
| id | doaj.art-dddcd3908b074ea2833254ccd5dfff77 |
| institution | Directory Open Access Journal |
| issn | 1664-462X |
| language | English |
| last_indexed | 2024-12-10T05:40:33Z |
| publishDate | 2022-09-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Plant Science |
| spelling | doaj.art-dddcd3908b074ea2833254ccd5dfff772022-12-22T02:00:19ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2022-09-011310.3389/fpls.2022.10197091019709An advanced systems biology framework of feature engineering for cold tolerance genes discovery from integrated omics and non-omics data in soybeanPei-Hsiu Kao0Supaporn Baiya1Zheng-Yuan Lai2Chih-Min Huang3Li-Hsin Jhan4Chian-Jiun Lin5Ya-Syuan Lai6Chung-Feng Kao7Chung-Feng Kao8Department of Agronomy, College of Agriculture and Natural Resources, National Chung Hsing University, Taichung, TaiwanDepartment of Resource and Environment, Faculty of Science at Sriracha, Kasetsart University, Sriracha, ThailandDepartment of Agronomy, College of Agriculture and Natural Resources, National Chung Hsing University, Taichung, TaiwanDepartment of Agronomy, College of Agriculture and Natural Resources, National Chung Hsing University, Taichung, TaiwanDepartment of Agronomy, College of Agriculture and Natural Resources, National Chung Hsing University, Taichung, TaiwanDepartment of Agronomy, College of Agriculture and Natural Resources, National Chung Hsing University, Taichung, TaiwanDepartment of Agronomy, College of Agriculture and Natural Resources, National Chung Hsing University, Taichung, TaiwanDepartment of Agronomy, College of Agriculture and Natural Resources, National Chung Hsing University, Taichung, TaiwanAdvanced Plant Biotechnology Center, National Chung Hsing University, Taichung, TaiwanSoybean is sensitive to low temperatures during the crop growing season. An urgent demand for breeding cold-tolerant cultivars to alleviate the production loss is apparent to cope with this scenario. Cold-tolerant trait is a complex and quantitative trait controlled by multiple genes, environmental factors, and their interaction. In this study, we proposed an advanced systems biology framework of feature engineering for the discovery of cold tolerance genes (CTgenes) from integrated omics and non-omics (OnO) data in soybean. An integrative pipeline was introduced for feature selection and feature extraction from different layers in the integrated OnO data using data ensemble methods and the non-parameter random forest prioritization to minimize uncertainties and false positives for accuracy improvement of results. In total, 44, 143, and 45 CTgenes were identified in short-, mid-, and long-term cold treatment, respectively, from the corresponding gene-pool. These CTgenes outperformed the remaining genes, the random genes, and the other candidate genes identified by other approaches in an independent RNA-seq database. Furthermore, we applied pathway enrichment and crosstalk network analyses to uncover relevant physiological pathways with the discovery of underlying cold tolerance in hormone- and defense-related modules. Our CTgenes were validated by using 55 SNP genotype data of 56 soybean samples in cold tolerance experiments. This suggests that the CTgenes identified from our proposed systematic framework can effectively distinguish cold-resistant and cold-sensitive lines. It is an important advancement in the soybean cold-stress response. The proposed pipelines provide an alternative solution to biomarker discovery, module discovery, and sample classification underlying a particular trait in plants in a robust and efficient way.https://www.frontiersin.org/articles/10.3389/fpls.2022.1019709/fullsoybeancold tolerancefeature engineeringomics and non-omics data integrationsystems biologynon-parameter random forest prioritization |
| spellingShingle | Pei-Hsiu Kao Supaporn Baiya Zheng-Yuan Lai Chih-Min Huang Li-Hsin Jhan Chian-Jiun Lin Ya-Syuan Lai Chung-Feng Kao Chung-Feng Kao An advanced systems biology framework of feature engineering for cold tolerance genes discovery from integrated omics and non-omics data in soybean Frontiers in Plant Science soybean cold tolerance feature engineering omics and non-omics data integration systems biology non-parameter random forest prioritization |
| title | An advanced systems biology framework of feature engineering for cold tolerance genes discovery from integrated omics and non-omics data in soybean |
| title_full | An advanced systems biology framework of feature engineering for cold tolerance genes discovery from integrated omics and non-omics data in soybean |
| title_fullStr | An advanced systems biology framework of feature engineering for cold tolerance genes discovery from integrated omics and non-omics data in soybean |
| title_full_unstemmed | An advanced systems biology framework of feature engineering for cold tolerance genes discovery from integrated omics and non-omics data in soybean |
| title_short | An advanced systems biology framework of feature engineering for cold tolerance genes discovery from integrated omics and non-omics data in soybean |
| title_sort | advanced systems biology framework of feature engineering for cold tolerance genes discovery from integrated omics and non omics data in soybean |
| topic | soybean cold tolerance feature engineering omics and non-omics data integration systems biology non-parameter random forest prioritization |
| url | https://www.frontiersin.org/articles/10.3389/fpls.2022.1019709/full |
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