Abstract
The transcription factors GATA4, GATA5 and GATA6 play important roles in heart muscle differentiation. The data presented in this article are related to the research article entitled “Genome-wide transcriptomics analysis identifies sox7 and sox18 as specifically regulated by gata4 in cardiomyogenesis” (Afouda et al., 2017) [1]. The present study identifies genes regulated by these individual cardiogenic GATA factors using genome-wide transcriptomics analysis. We have presented genes that are specifically regulated by each of them, as well those regulated by either of them. The gene ontology terms (GO) associated with the genes differentially affected are also presented. The data set will allow further investigations on the gene regulatory network downstream of individual cardiogenic GATA factors during cardiac muscle formation.
Highlights
The transcription factors GATA4, GATA5 and GATA6 play important roles in heart muscle differentiation
The data presented in this article are related to the research article entitled “Genome-wide transcriptomics analysis identifies sox7 and sox18 as regulated by gata4 in cardiomyogenesis” (Afouda et al, 2017) [1]
The present study identifies genes regulated by these individual cardiogenic GATA factors using genome-wide transcriptomics analysis
Summary
At least 30 explants were used for RNA preparation with a previously described protocol [3,4]. RNA quantity and quality were checked on electrophoretic agarose gel, a fraction of which was used for validation with gene expression analysis by quantitative RT-PCR [2] to confirm expected increase or decrease of known control gene expression before RNA-seq sequencing. Differential expression analysis was performed using DESeq. 2 [11] with an adjusted p value o0.05. Expressed genes were identified using a threshold of log-2 fold change 4 1 (for at least two times increased) or o-1 (for at least two times reduced) in comparison to Activin-induced Xenopus animal cap cardiac explant controls. Analyses of differentially expressed genes were performed using Partek genomics. For gene ontology (GO) analyses, GO classes containing at least six genes were taken into consideration
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