ArticlePloS one2015
Clustering and Differential Alignment Algorithm: Identification of Early Stage Regulators in the Arabidopsis thaliana Iron Deficiency Response.
Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 16 citations in OpenAlex.
- Cellular clarity: a logistic regression approach to identify root epidermal regulators of iron deficiency response.BMC genomics · 2023Article
- Integrative omics approaches for biosynthetic pathway discovery in plants.Natural product reports · 2022Review
- A reevaluation of the role of thePlant direct · 2021Article
- Time-Based Systems Biology Approaches to Capture and Model Dynamic Gene Regulatory Networks.Annual review of plant biology · 2021Review
- A temporal hierarchy underpins the transcription factor-DNA interactome of the maize UPR.The Plant journal : for cell and molecular biology · 2021Article
- Putative cis-Regulatory Elements Predict Iron Deficiency Responses in Arabidopsis Roots.Plant physiology · 2020Article
- Iron homeostasis in plants - a brief overview.Metallomics : integrated biometal science · 2017Review
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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.
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