USDA-NIFA Postdoctoral Research Fellow Iowa State University
Large-scale omics data, such as transcriptomics, proteomics, and phosphoproteomics, can give us insights into genes involved in certain biological processes. Gene Regulatory Network (GRN) inference methods allow us to predict relationships between genes from these integrative omics data and can help us identify candidate genes for experimental validation, eliminating future large-scale experiments. To this end, I developed a computational pipeline named Spatiotemporal Clustering and Inference of Omics Networks (SCION: https://github.com/nmclark2/SCION) that infers GRNs from integrative omics data. SCION is available as an R Shiny app and is designed to be used by those with little to no coding experience. This pipeline can predict what regulation occurs at each molecular scale, such as using protein abundance of transcription factors to predict transcript levels of their targets. I also introduced an importance score based on network motifs, such as feedback and feed-forward loops, that can be used to predict the most functionally important regulators in the network. These regulators can then become the focus of future validation experiments. I will detail how SCION has been used to address many different biological questions, such as those involving cell division, hormone signaling, and disease resistance, in both Arabidopsis and maize. Overall, this systems biology approach allows us to identify new regulators controlling these biological processes and could be used across plant biology to answer a breadth of biological questions.