Insights into functional classification via gene co-expression networks in sunflower using public transcriptomic datasets

RIBONE, A; GONZALEZ, S.; PANIEGO, N. B.; LIA, V. V.; RIVAROLA, M.

Buenos Aires

Congreso; CAI, Congreso Argentino de Agroinformática; 2021

Sociedad Argentina de Informatica

We present the first preliminary sunflower gene coexpression 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 severa lWRKY 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.