Network and biosignature analysis for the integration of transcriptomic and metabolomic data to characterize leaf senescence process in sunflower
In recent years, high throughput technologies have led to an increase of datasets from omics disciplines allowing the understanding of the complex regulatory networks associated with biological processes. Leaf senescence is a complex mechanism controlled by multiple genetic and environmental variab...
| Autores principales: | , , , , , |
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| Formato: | Artículo |
| Lenguaje: | Inglés |
| Publicado: |
2017
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| Materias: | |
| Acceso en línea: | http://hdl.handle.net/20.500.12123/1122 https://bmcbioinformatics.biomedcentral.com/track/pdf/10.1186/s12859-016-1045-2?site=bmcbioinformatics.biomedcentral.com https://doi.org/10.1186/s12859-016-1045-2 |
| Sumario: | In recent years, high throughput technologies have led to an increase of datasets from omics disciplines allowing the understanding of the complex regulatory networks associated with biological processes.
Leaf senescence is a complex mechanism controlled by multiple genetic and environmental variables, which has a strong impact on crop yield. Transcription factors (TFs) are key proteins in the regulation of gene expression, regulating different signaling pathways; their function is crucial for triggering and/or regulating different aspects of the leaf senescence process. The study of TF interactions and their integration with metabolic profiles under different developmental conditions, especially for a non-model organism such as sunflower, will open new insights into the details of gene regulation of leaf senescence. |
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