a b s t r a c tInsight 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.

Time course transcriptome datasets are commonly used to predict key gene regulators associated with stress responses and to explore gene functionality. Techniques developed to extract causal relationships between genes from high throughput time course expression data are limited by low signal levels coupled with noise and sparseness in time points. We deal with these limitations by proposing the Cluster and Differential Alignment Algorithm (CDAA). This algorithm was designed to process transcriptome data by first grouping genes based on stages of activity and then using similarities in gene expression to predict influential connections between individual genes. Regulatory relationships are assigned based on pairwise alignment scores generated using the expression patterns of two genes and some inferred delay between the regulator and the observed activity of the target. We applied the CDAA to an iron deficiency time course microarray dataset to identify regulators that influence 7 target transcription factors known to participate in the Arabidopsis thaliana iron deficiency response. The algorithm predicted that 7 regulators previously unlinked to iron homeostasis influence the expression of these known transcription factors. We validated over half of predicted influential relationships using qRT-PCR expression analysis in mutant backgrounds. One predicted regulator-target relationship was shown to be a direct binding interaction according to yeast one-hybrid (Y1H) analysis. These results serve as a proof of concept emphasizing the utility of the CDAA for identifying unknown or missing nodes in regulatory cascades, providing the fundamental knowledge needed for constructing predictive gene regulatory networks. We propose that this tool can be used successfully for similar time course datasets to extract additional information and infer reliable regulatory connections for individual genes.

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