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Huang M, Zhang L, Yung WS, Hu Y, Wang Z, Li MW, Lam HM. Molecular evidence for enhancer-promoter interactions in light responses of soybean seedlings. PLANT PHYSIOLOGY 2023; 193:2287-2291. [PMID: 37668345 DOI: 10.1093/plphys/kiad487] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 08/18/2023] [Accepted: 08/18/2023] [Indexed: 09/06/2023]
Abstract
Interactions of enhancers with promoters and transcription factors mediate chromatin loop formation to regulate downstream gene expression in response to environmental stimuli such as light.
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Affiliation(s)
- Mingkun Huang
- Plant Functional Genomics and Bioinformatics Centre, Lushan Botanical Garden Jiangxi Province and Chinese Academy of Sciences, 332900 Jiujiang, Jiangxi, P.R. China
- School of Life Sciences and Centre for Soybean Research of the State Key Laboratory of Agrobiotechnology, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, P.R. China
| | - Ling Zhang
- Plant Functional Genomics and Bioinformatics Centre, Lushan Botanical Garden Jiangxi Province and Chinese Academy of Sciences, 332900 Jiujiang, Jiangxi, P.R. China
| | - Wai-Shing Yung
- School of Life Sciences and Centre for Soybean Research of the State Key Laboratory of Agrobiotechnology, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, P.R. China
| | - Yufang Hu
- Plant Functional Genomics and Bioinformatics Centre, Lushan Botanical Garden Jiangxi Province and Chinese Academy of Sciences, 332900 Jiujiang, Jiangxi, P.R. China
| | - Zhili Wang
- School of Life Sciences and Centre for Soybean Research of the State Key Laboratory of Agrobiotechnology, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, P.R. China
| | - Man-Wah Li
- School of Life Sciences and Centre for Soybean Research of the State Key Laboratory of Agrobiotechnology, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, P.R. China
| | - Hon-Ming Lam
- School of Life Sciences and Centre for Soybean Research of the State Key Laboratory of Agrobiotechnology, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, P.R. China
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Li H, Chen T, Jia L, Wang Z, Li J, Wang Y, Fu M, Chen M, Wang Y, Huang F, Jiang Y, Li T, Zhou Z, Li Y, Yao W, Wang Y. SoybeanGDB: A comprehensive genomic and bioinformatic platform for soybean genetics and genomics. Comput Struct Biotechnol J 2023; 21:3327-3338. [PMID: 38213885 PMCID: PMC10781885 DOI: 10.1016/j.csbj.2023.06.012] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2023] [Revised: 06/09/2023] [Accepted: 06/10/2023] [Indexed: 01/13/2024] Open
Abstract
Soybean (Glycine max (L.) Merr.) is a globally significant crop, widely cultivated for oilseed production and animal feeds. In recent years, the rapid growth of multi-omics data from thousands of soybean accessions has provided unprecedented opportunities for researchers to explore genomes, genetic variations, and gene functions. To facilitate the utilization of these abundant data for soybean breeding and genetic improvement, the SoybeanGDB database (https://venyao.xyz/SoybeanGDB/) was developed as a comprehensive platform. SoybeanGDB integrates high-quality de novo assemblies of 39 soybean genomes and genomic variations among thousands of soybean accessions. Genomic information and variations in user-specified genomic regions can be searched and downloaded from SoybeanGDB, in a user-friendly manner. To facilitate research on genetic resources and elucidate the biological significance of genes, SoybeanGDB also incorporates a variety of bioinformatics analysis modules with graphical interfaces, such as linkage disequilibrium analysis, nucleotide diversity analysis, allele frequency analysis, gene expression analysis, primer design, gene set enrichment analysis, etc. In summary, SoybeanGDB is an essential and valuable resource that provides an open and free platform to accelerate global soybean research.
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Affiliation(s)
- Haoran Li
- National Key Laboratory of Wheat and Maize Crop Science, College of Life Sciences, Henan Agricultural University, Zhengzhou 450002, China
| | - Tiantian Chen
- National Key Laboratory of Wheat and Maize Crop Science, College of Life Sciences, Henan Agricultural University, Zhengzhou 450002, China
| | - Lihua Jia
- National Key Laboratory of Wheat and Maize Crop Science, College of Life Sciences, Henan Agricultural University, Zhengzhou 450002, China
| | - Zhizhan Wang
- National Key Laboratory of Wheat and Maize Crop Science, College of Life Sciences, Henan Agricultural University, Zhengzhou 450002, China
| | - Jiaming Li
- National Key Laboratory of Wheat and Maize Crop Science, College of Life Sciences, Henan Agricultural University, Zhengzhou 450002, China
| | - Yazhou Wang
- National Key Laboratory of Wheat and Maize Crop Science, College of Life Sciences, Henan Agricultural University, Zhengzhou 450002, China
| | - Mengjia Fu
- National Key Laboratory of Wheat and Maize Crop Science, College of Life Sciences, Henan Agricultural University, Zhengzhou 450002, China
| | - Mingming Chen
- National Key Laboratory of Wheat and Maize Crop Science, College of Life Sciences, Henan Agricultural University, Zhengzhou 450002, China
| | - Yuping Wang
- National Key Laboratory of Wheat and Maize Crop Science, College of Life Sciences, Henan Agricultural University, Zhengzhou 450002, China
| | - Fangfang Huang
- National Key Laboratory of Wheat and Maize Crop Science, College of Life Sciences, Henan Agricultural University, Zhengzhou 450002, China
| | - Yingru Jiang
- National Key Laboratory of Wheat and Maize Crop Science, College of Life Sciences, Henan Agricultural University, Zhengzhou 450002, China
| | - Tao Li
- National Key Laboratory of Wheat and Maize Crop Science, College of Life Sciences, Henan Agricultural University, Zhengzhou 450002, China
| | - Zhengfu Zhou
- Henan Academy of Crop Molecular Breeding, Henan Academy of Agricultural Sciences, Zhengzhou 450002, China
| | - Yang Li
- National Key Laboratory of Wheat and Maize Crop Science, College of Life Sciences, Henan Agricultural University, Zhengzhou 450002, China
| | - Wen Yao
- National Key Laboratory of Wheat and Maize Crop Science, College of Life Sciences, Henan Agricultural University, Zhengzhou 450002, China
| | - Yihan Wang
- National Key Laboratory of Wheat and Maize Crop Science, College of Life Sciences, Henan Agricultural University, Zhengzhou 450002, China
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Wang Z, Zhu Y, Liu Z, Li H, Tang X, Jiang Y. Comparative analysis of tissue-specific genes in maize based on machine learning models: CNN performs technically best, LightGBM performs biologically soundest. Front Genet 2023; 14:1190887. [PMID: 37229198 PMCID: PMC10203421 DOI: 10.3389/fgene.2023.1190887] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Accepted: 04/17/2023] [Indexed: 05/27/2023] Open
Abstract
Introduction: With the advancement of RNA-seq technology and machine learning, training large-scale RNA-seq data from databases with machine learning models can generally identify genes with important regulatory roles that were previously missed by standard linear analytic methodologies. Finding tissue-specific genes could improve our comprehension of the relationship between tissues and genes. However, few machine learning models for transcriptome data have been deployed and compared to identify tissue-specific genes, particularly for plants. Methods: In this study, an expression matrix was processed with linear models (Limma), machine learning models (LightGBM), and deep learning models (CNN) with information gain and the SHAP strategy based on 1,548 maize multi-tissue RNA-seq data obtained from a public database to identify tissue-specific genes. In terms of validation, V-measure values were computed based on k-means clustering of the gene sets to evaluate their technical complementarity. Furthermore, GO analysis and literature retrieval were used to validate the functions and research status of these genes. Results: Based on clustering validation, the convolutional neural network outperformed others with higher V-measure values as 0.647, indicating that its gene set could cover as many specific properties of various tissues as possible, whereas LightGBM discovered key transcription factors. The combination of three gene sets produced 78 core tissue-specific genes that had previously been shown in the literature to be biologically significant. Discussion: Different tissue-specific gene sets were identified due to the distinct interpretation strategy for machine learning models and researchers may use multiple methodologies and strategies for tissue-specific gene sets based on their goals, types of data, and computational resources. This study provided comparative insight for large-scale data mining of transcriptome datasets, shedding light on resolving high dimensions and bias difficulties in bioinformatics data processing.
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Affiliation(s)
- Zijie Wang
- School of Agriculture, Sun Yat-sen University, Shenzhen, China
| | - Yuzhi Zhu
- School of Agriculture, Sun Yat-sen University, Shenzhen, China
| | - Zhule Liu
- School of Agriculture, Sun Yat-sen University, Shenzhen, China
| | - Hongfu Li
- School of Agriculture, Sun Yat-sen University, Shenzhen, China
| | - Xinqiang Tang
- School of Intelligent Systems Engineering, Sun Yat-sen University, Shenzhen, China
| | - Yi Jiang
- School of Agriculture, Sun Yat-sen University, Shenzhen, China
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