Computational approaches to identify regulators of plant stress response using high-throughput gene expression data

Abstract

Insight into biological stress regulatory pathways can be derived from high-throughput transcriptomic data using computational algorithms. These algorithms can be integrated into a computational approach to provide specific testable predictions that answer biological questions of interest. This review conceptually organizes a wide variety of developed algorithms into a classification system based on desired type of output predictions. This classification is then used as a structure to describe completed approaches in the literature, with a focus on project goals, overall path of implemented algorithms, and biological insight gained. These algorithms and approaches are introduced mainly in the context of research on the model plant species Arabidopsis thaliana under stress conditions, though the nature of computational techniques makes these approaches easily applicable to a wide range of species, data types, and conditions.