title: Clustering and Differential Alignment Algorithm: Identification of Early Stage Regulators in the Arabidopsis thaliana Iron Deficiency Response.
abstract: 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.
authors: Electrical and Computer Engineering, North Carolina State University, Raleigh, North Carolina, United States of America.
authors: Alexandr
authors: A
authors: Koryachko
authors: Plant and Microbial Biology, North Carolina State University, Raleigh, North Carolina, United States of America.
authors: Anna
authors: A
authors: Matthiadis
authors: Plant and Microbial Biology, North Carolina State University, Raleigh, North Carolina, United States of America.
authors: Durreshahwar
authors: D
authors: Muhammad
authors: Plant Biology, University of California Davis, Davis, California, United States of America.
authors: Jessica
authors: J
authors: Foret
authors: Plant Biology, University of California Davis, Davis, California, United States of America.
authors: Siobhan M
authors: SM
authors: Brady
authors: Civil, Construction, and Environmental Engineering, North Carolina State University, Raleigh, North Carolina, United States of America.
authors: Joel J
authors: JJ
authors: Ducoste
authors: Electrical and Computer Engineering, North Carolina State University, Raleigh, North Carolina, United States of America.
authors: James
authors: J
authors: Tuck
authors: Plant and Microbial Biology, North Carolina State University, Raleigh, North Carolina, United States of America.
authors: Terri A
authors: TA
authors: Long
authors: Electrical and Computer Engineering, North Carolina State University, Raleigh, North Carolina, United States of America.
authors: Cranos
authors: C
authors: Williams
mesh_terms: D000465:Algorithms; D017360:Arabidopsis / Q000235:genetics / Q000378:metabolism; D030541:Databases, Genetic; D018506:Gene Expression Regulation, Plant; D000090463:Iron Deficiencies; D016415:Sequence Alignment; D012984:Software; D059467:Transcriptome