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  The complexity of gene expression dynamics revealed by permutation entropy

Sun, X., Zou, Y., Nikiforova, V., Kurths, J., & Walther, D. (2010). The complexity of gene expression dynamics revealed by permutation entropy. BMC Bioinformatics, 11, 607. doi:10.1186/1471-2105-11-607.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0014-22F0-1 Version Permalink: http://hdl.handle.net/11858/00-001M-0000-0014-22F1-0
Genre: Journal Article

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 Creators:
Sun, X.1, Author
Zou, Y.1, Author
Nikiforova, V.2, Author              
Kurths, J.1, Author
Walther, D.3, Author              
Affiliations:
1External Organizations, escidoc:persistent22              
2System Integration, Department Willmitzer, Max Planck Institute of Molecular Plant Physiology, Max Planck Society, escidoc:1753349              
3BioinformaticsCIG, Infrastructure Groups and Service Units, Max Planck Institute of Molecular Plant Physiology, Max Planck Society, escidoc:1753303              

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Free keywords: Arabidopsis/genetics Computational Biology/*methods *Entropy Gene Expression Profiling/*methods Genes, Plant Signal Transduction Stress, Physiological
 Abstract: ABSTRACT: BACKGROUND: High complexity is considered a hallmark of living systems. Here we investigate the complexity of temporal gene expression patterns using the concept of Permutation Entropy (PE) first introduced in dynamical systems theory. The analysis of gene expression data has so far focused primarily on the identification of differentially expressed genes, or on the elucidation of pathway and regulatory relationships. We aim to study gene expression time series data from the viewpoint of complexity. RESULTS: Applying the PE complexity metric to abiotic stress response time series data in Arabidopsis thaliana, genes involved in stress response and signaling were found to be associated with the highest complexity not only under stress, but surprisingly, also under reference, non-stress conditions. Genes with house-keeping functions exhibited lower PE complexity. Compared to reference conditions, the PE of temporal gene expression patterns generally increased upon stress exposure. High-complexity genes were found to have longer upstream intergenic regions and more cis-regulatory motifs in their promoter regions indicative of a more complex regulatory apparatus needed to orchestrate their expression, and to be associated with higher correlation network connectivity degree. Arabidopsis genes also present in other plant species were observed to exhibit decreased PE complexity compared to Arabidopsis specific genes. CONCLUSIONS: We show that Permutation Entropy is a simple yet robust and powerful approach to identify temporal gene expression profiles of varying complexity that is equally applicable to other types of molecular profile data.

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Language(s): eng - English
 Dates: 2010-12-222010
 Publication Status: Published in print
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Title: BMC Bioinformatics
Source Genre: Journal
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Pages: - Volume / Issue: 11 Sequence Number: - Start / End Page: 607 Identifier: -