Abstract
Developing seeds undergo coordinated physiological and morphological changes crucial for development of the embryo, dormancy and germination. The metabolic changes that occur during seed development are regulated by interconnected network of Transcription Factors (TFs) that regulate gene expression in a spatiotemporal manner. The complexity of these networks is such that the TFs that play key regulatory roles during seed development are largely unknown. In this study, we created a genome-scale regulatory network dedicated to describing regulation of biological processes within various compartments and developmental stages of Arabidopsis seeds. Differential network analysis revealed key TFs that rewire their targeting patterns specifically during seed development, many of which were already known, and a few novel ones that we verified experimentally. Our method shows that a high-resolution tissue-specific transcriptome dataset can be accurately modeled as a functional regulatory network predictive of related TFs. We provide an easy to use webtool using which researchers can upload a newly generated transcriptome and identify key TFs important to their dataset as well as gauge their regulatory effect on phenotypes observed in the experiment. We refer to this network as Seed Active Network (SANe) and made it accessible at https://plantstress-pereira.uark.edu/SANe/. We anticipate SANe will facilitate the discovery of TFs yet unknown for their involvement in seed related metabolic pathways and provide an interface to generate new hypothesis for experimentation.
