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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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Autores principales: Kao, Pei-Hsiu, Baiya, Supaporn, Lai, Zheng-Yuan, Huang, Chih-Min, Jhan, Li-Hsin, Lin, Chian-Jiun, Lai, Ya-Syuan, Kao, Chung-Feng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9562094/
https://www.ncbi.nlm.nih.gov/pubmed/36247545
http://dx.doi.org/10.3389/fpls.2022.1019709
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author Kao, Pei-Hsiu
Baiya, Supaporn
Lai, Zheng-Yuan
Huang, Chih-Min
Jhan, Li-Hsin
Lin, Chian-Jiun
Lai, Ya-Syuan
Kao, Chung-Feng
author_facet Kao, Pei-Hsiu
Baiya, Supaporn
Lai, Zheng-Yuan
Huang, Chih-Min
Jhan, Li-Hsin
Lin, Chian-Jiun
Lai, Ya-Syuan
Kao, Chung-Feng
author_sort Kao, Pei-Hsiu
collection PubMed
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.
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spelling pubmed-95620942022-10-15 An advanced systems biology framework of feature engineering for cold tolerance genes discovery from integrated omics and non-omics data in soybean Kao, Pei-Hsiu Baiya, Supaporn Lai, Zheng-Yuan Huang, Chih-Min Jhan, Li-Hsin Lin, Chian-Jiun Lai, Ya-Syuan Kao, Chung-Feng Front Plant Sci Plant Science 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. Frontiers Media S.A. 2022-09-30 /pmc/articles/PMC9562094/ /pubmed/36247545 http://dx.doi.org/10.3389/fpls.2022.1019709 Text en Copyright © 2022 Kao, Baiya, Lai, Huang, Jhan, Lin, Lai and Kao https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Plant Science
Kao, Pei-Hsiu
Baiya, Supaporn
Lai, Zheng-Yuan
Huang, Chih-Min
Jhan, Li-Hsin
Lin, Chian-Jiun
Lai, Ya-Syuan
Kao, Chung-Feng
An advanced systems biology framework of feature engineering for cold tolerance genes discovery from integrated omics and non-omics data in soybean
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 Plant Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9562094/
https://www.ncbi.nlm.nih.gov/pubmed/36247545
http://dx.doi.org/10.3389/fpls.2022.1019709
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