Clustering and Differential Alignment Algorithm: Identification of Early Stage Regulators in the Arabidopsis thaliana Iron Deficiency Response

PloS One
Alexandr KoryachkoCranos Williams

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...Continue Reading

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Citations

Jul 8, 2017·Metallomics : Integrated Biometal Science·James M ConnortonJorge Rodríguez-Celma
Oct 25, 2020·The Plant Journal : for Cell and Molecular Biology·Dae Kwan Ko, Federica Brandizzi
Nov 18, 2018·Current Opinion in Plant Biology·Samiul HaqueRosangela Sozzani
Mar 6, 2021·Annual Review of Plant Biology·Jose M AlvarezGloria M Coruzzi

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Methods Mentioned

BETA
RNA-Seq
PCR
one-hybrid
chip
immunoprecipitation

Software Mentioned

MatLab
Cluster Differential Alignment Algorithm ( CDAA

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