A quantitative computational framework for allopolyploid single-cell data integration and core gene ranking in development
Polyploidization drives regulatory and phenotypic innovation. How the merger of different genomes contributes to polyploid development is a fundamental issue in evolutionary developmental biology and breeding research. Clarifying this issue is challenging because of genome complexity and the difficu...
| Autores principales: | , , , , , , , , , , , , , , , , , , , , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
Oxford University Press
2024
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/162570 |
| _version_ | 1855538404905713664 |
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| author | Meiyue Wang Zijuan Li Haoyu Wang Junwei Zhao Yuyun Zhang Kande Lin Shusong Zheng Yilong Feng Yu'e Zhang Wan Teng Yiping Tong Wenli Zhang Yongbiao Xue Hude Mao Hao Li Bo Zhang Awais Rasheed Bhavani, Sridhar Chenghong Liu Hong-Qing Ling Yue-Qing Hu Yijing Zhang |
| author_browse | Awais Rasheed Bhavani, Sridhar Bo Zhang Chenghong Liu Hao Li Haoyu Wang Hong-Qing Ling Hude Mao Junwei Zhao Kande Lin Meiyue Wang Shusong Zheng Wan Teng Wenli Zhang Yijing Zhang Yilong Feng Yiping Tong Yongbiao Xue Yu'e Zhang Yue-Qing Hu Yuyun Zhang Zijuan Li |
| author_facet | Meiyue Wang Zijuan Li Haoyu Wang Junwei Zhao Yuyun Zhang Kande Lin Shusong Zheng Yilong Feng Yu'e Zhang Wan Teng Yiping Tong Wenli Zhang Yongbiao Xue Hude Mao Hao Li Bo Zhang Awais Rasheed Bhavani, Sridhar Chenghong Liu Hong-Qing Ling Yue-Qing Hu Yijing Zhang |
| author_sort | Meiyue Wang |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Polyploidization drives regulatory and phenotypic innovation. How the merger of different genomes contributes to polyploid development is a fundamental issue in evolutionary developmental biology and breeding research. Clarifying this issue is challenging because of genome complexity and the difficulty in tracking stochastic subgenome divergence during development. Recent single-cell sequencing techniques enabled probing subgenome-divergent regulation in the context of cellular differentiation. However, analyzing single-cell data suffers from high error rates due to high dimensionality, noise, and sparsity, and the errors stack up in polyploid analysis due to the increased dimensionality of comparisons between subgenomes of each cell, hindering deeper mechanistic understandings. In this study, we develop a quantitative computational framework, called "pseudo-genome divergence quantification" (pgDQ), for quantifying and tracking subgenome divergence directly at the cellular level. Further comparing with cellular differentiation trajectories derived from single-cell RNA sequencing data allows for an examination of the relationship between subgenome divergence and the progression of development. pgDQ produces robust results and is insensitive to data dropout and noise, avoiding high error rates due to multiple comparisons of genes, cells, and subgenomes. A statistical diagnostic approach is proposed to identify genes that are central to subgenome divergence during development, which facilitates the integration of different data modalities, enabling the identification of factors and pathways that mediate subgenome-divergent activity during development. Case studies have demonstrated that applying pgDQ to single-cell and bulk tissue transcriptomic data promotes a systematic and deeper understanding of how dynamic subgenome divergence contributes to developmental trajectories in polyploid evolution. |
| format | Journal Article |
| id | CGSpace162570 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | Oxford University Press |
| publisherStr | Oxford University Press |
| record_format | dspace |
| spelling | CGSpace1625702025-10-26T12:55:42Z A quantitative computational framework for allopolyploid single-cell data integration and core gene ranking in development Meiyue Wang Zijuan Li Haoyu Wang Junwei Zhao Yuyun Zhang Kande Lin Shusong Zheng Yilong Feng Yu'e Zhang Wan Teng Yiping Tong Wenli Zhang Yongbiao Xue Hude Mao Hao Li Bo Zhang Awais Rasheed Bhavani, Sridhar Chenghong Liu Hong-Qing Ling Yue-Qing Hu Yijing Zhang RNA sequence genetic variation evolution Polyploidization drives regulatory and phenotypic innovation. How the merger of different genomes contributes to polyploid development is a fundamental issue in evolutionary developmental biology and breeding research. Clarifying this issue is challenging because of genome complexity and the difficulty in tracking stochastic subgenome divergence during development. Recent single-cell sequencing techniques enabled probing subgenome-divergent regulation in the context of cellular differentiation. However, analyzing single-cell data suffers from high error rates due to high dimensionality, noise, and sparsity, and the errors stack up in polyploid analysis due to the increased dimensionality of comparisons between subgenomes of each cell, hindering deeper mechanistic understandings. In this study, we develop a quantitative computational framework, called "pseudo-genome divergence quantification" (pgDQ), for quantifying and tracking subgenome divergence directly at the cellular level. Further comparing with cellular differentiation trajectories derived from single-cell RNA sequencing data allows for an examination of the relationship between subgenome divergence and the progression of development. pgDQ produces robust results and is insensitive to data dropout and noise, avoiding high error rates due to multiple comparisons of genes, cells, and subgenomes. A statistical diagnostic approach is proposed to identify genes that are central to subgenome divergence during development, which facilitates the integration of different data modalities, enabling the identification of factors and pathways that mediate subgenome-divergent activity during development. Case studies have demonstrated that applying pgDQ to single-cell and bulk tissue transcriptomic data promotes a systematic and deeper understanding of how dynamic subgenome divergence contributes to developmental trajectories in polyploid evolution. 2024-09-04 2024-11-21T20:50:09Z 2024-11-21T20:50:09Z Journal Article https://hdl.handle.net/10568/162570 en Open Access application/pdf Oxford University Press Wang, M., Li, Z., Wang, H., Lin, K., Zheng, S., Feng, Y., Teng, W., Tong, Y., Zhang, W., Liu, C., Ling, H., Hu, Y., & Zhang, Y. (2024). A quantitative computational framework for allopolyploid single-cell data integration and core gene ranking in development. Molecular Biology And Evolution, 41(9), msae178. https://doi.org/10.1093/molbev/msae178 |
| spellingShingle | RNA sequence genetic variation evolution Meiyue Wang Zijuan Li Haoyu Wang Junwei Zhao Yuyun Zhang Kande Lin Shusong Zheng Yilong Feng Yu'e Zhang Wan Teng Yiping Tong Wenli Zhang Yongbiao Xue Hude Mao Hao Li Bo Zhang Awais Rasheed Bhavani, Sridhar Chenghong Liu Hong-Qing Ling Yue-Qing Hu Yijing Zhang A quantitative computational framework for allopolyploid single-cell data integration and core gene ranking in development |
| title | A quantitative computational framework for allopolyploid single-cell data integration and core gene ranking in development |
| title_full | A quantitative computational framework for allopolyploid single-cell data integration and core gene ranking in development |
| title_fullStr | A quantitative computational framework for allopolyploid single-cell data integration and core gene ranking in development |
| title_full_unstemmed | A quantitative computational framework for allopolyploid single-cell data integration and core gene ranking in development |
| title_short | A quantitative computational framework for allopolyploid single-cell data integration and core gene ranking in development |
| title_sort | quantitative computational framework for allopolyploid single cell data integration and core gene ranking in development |
| topic | RNA sequence genetic variation evolution |
| url | https://hdl.handle.net/10568/162570 |
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