Insights into functional classification via gene co-expression networks in sunflower using public transcriptomic datasets
We present the first preliminary sunflower gene co-expression network using public transcriptome data in Helianthus annuus and show its utility in identifying and classifying uncharacterized genes involved in stress response. The locus HanXRQChr09g0248321 was identified and linked to several WRKY tr...
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| Autores principales: | , , , , |
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| Formato: | Objeto de conferencia |
| Lenguaje: | Inglés |
| Publicado: |
2021
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| Materias: | |
| Acceso en línea: | http://sedici.unlp.edu.ar/handle/10915/140689 http://50jaiio.sadio.org.ar/pdfs/cai/CAI-15.pdf |
| Aporte de: |
| Sumario: | We present the first preliminary sunflower gene co-expression network using public transcriptome data in Helianthus annuus and show its utility in identifying and classifying uncharacterized genes involved in stress response. The locus HanXRQChr09g0248321 was identified and linked to several WRKY transcription factors in an enriched “stressed-response” module.
Moreover, the homologue in Arabidopsis thaliana was shown to be differentially expressed in multiple “stress” conditions. We present our work and validate our methodology to existing knowledge and show its capability to identify/rank new candidates for crop breeding programs. Our future goal is to link genetic variation with gene networks to understand phenotypic variability in sunflower stress responses. |
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