Methylation in the CHH context allows to predict recombination in rice
DNA methylation is the most studied epigenetic trait. It is considered a key factor in regulating plant development and physiology, and has been associated with the regulation of several genomic features, including transposon silencing, regulation of gene expression, and recombination rates. Nonethe...
| Main Authors: | , , , , , , |
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| Format: | Journal Article |
| Language: | Inglés |
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MDPI
2022
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10568/128722 |
| _version_ | 1855534583337975808 |
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| author | Peñuela, Mauricio Gallo-Franco, Jenny Johana Finke, Jorge Rocha, Camilo Gkanogiannis, Anestis Ghneim-Herrera, Thaura Lorieux, Mathias |
| author_browse | Finke, Jorge Gallo-Franco, Jenny Johana Ghneim-Herrera, Thaura Gkanogiannis, Anestis Lorieux, Mathias Peñuela, Mauricio Rocha, Camilo |
| author_facet | Peñuela, Mauricio Gallo-Franco, Jenny Johana Finke, Jorge Rocha, Camilo Gkanogiannis, Anestis Ghneim-Herrera, Thaura Lorieux, Mathias |
| author_sort | Peñuela, Mauricio |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | DNA methylation is the most studied epigenetic trait. It is considered a key factor in regulating plant development and physiology, and has been associated with the regulation of several genomic features, including transposon silencing, regulation of gene expression, and recombination rates. Nonetheless, understanding the relation between DNA methylation and recombination rates remains a challenge. This work explores the association between recombination rates and DNA methylation for two commercial rice varieties. The results show negative correlations between recombination rates and methylated cytosine counts for all contexts tested at the same time, and for CG and CHG contexts independently. In contrast, a positive correlation between recombination rates and methylated cytosine count is reported in CHH contexts. Similar behavior is observed when considering only methylated cytosines within genes, transposons, and retrotransposons. Moreover, it is shown that the centromere region strongly affects the relationship between recombination rates and methylation. Finally, machine learning regression models are applied to predict recombination using the count of methylated cytosines in the CHH context as the entrance feature. These findings shed light on the understanding of the recombination landscape of rice and represent a reference framework for future studies in rice breeding, genetics, and epigenetics. |
| format | Journal Article |
| id | CGSpace128722 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | MDPI |
| publisherStr | MDPI |
| record_format | dspace |
| spelling | CGSpace1287222025-12-08T10:29:22Z Methylation in the CHH context allows to predict recombination in rice Peñuela, Mauricio Gallo-Franco, Jenny Johana Finke, Jorge Rocha, Camilo Gkanogiannis, Anestis Ghneim-Herrera, Thaura Lorieux, Mathias methylation epigenetics machine learning modelling metilación epigenético aprendizaje automático DNA methylation is the most studied epigenetic trait. It is considered a key factor in regulating plant development and physiology, and has been associated with the regulation of several genomic features, including transposon silencing, regulation of gene expression, and recombination rates. Nonetheless, understanding the relation between DNA methylation and recombination rates remains a challenge. This work explores the association between recombination rates and DNA methylation for two commercial rice varieties. The results show negative correlations between recombination rates and methylated cytosine counts for all contexts tested at the same time, and for CG and CHG contexts independently. In contrast, a positive correlation between recombination rates and methylated cytosine count is reported in CHH contexts. Similar behavior is observed when considering only methylated cytosines within genes, transposons, and retrotransposons. Moreover, it is shown that the centromere region strongly affects the relationship between recombination rates and methylation. Finally, machine learning regression models are applied to predict recombination using the count of methylated cytosines in the CHH context as the entrance feature. These findings shed light on the understanding of the recombination landscape of rice and represent a reference framework for future studies in rice breeding, genetics, and epigenetics. 2022-10-19 2023-02-16T11:18:12Z 2023-02-16T11:18:12Z Journal Article https://hdl.handle.net/10568/128722 en Open Access application/pdf MDPI Peñuela, M.; Gallo-Franco, J.J.; Finke, J.; Rocha, C.; Gkanogiannis, A.; Ghneim-Herrera, T.; Lorieux, M. (2022) Methylation in the CHH context allows to predict recombination in rice. International Journal of Molecular Sciences 23(20):12505 14 p. ISSN: 1661-6596 |
| spellingShingle | methylation epigenetics machine learning modelling metilación epigenético aprendizaje automático Peñuela, Mauricio Gallo-Franco, Jenny Johana Finke, Jorge Rocha, Camilo Gkanogiannis, Anestis Ghneim-Herrera, Thaura Lorieux, Mathias Methylation in the CHH context allows to predict recombination in rice |
| title | Methylation in the CHH context allows to predict recombination in rice |
| title_full | Methylation in the CHH context allows to predict recombination in rice |
| title_fullStr | Methylation in the CHH context allows to predict recombination in rice |
| title_full_unstemmed | Methylation in the CHH context allows to predict recombination in rice |
| title_short | Methylation in the CHH context allows to predict recombination in rice |
| title_sort | methylation in the chh context allows to predict recombination in rice |
| topic | methylation epigenetics machine learning modelling metilación epigenético aprendizaje automático |
| url | https://hdl.handle.net/10568/128722 |
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