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  Transcription factor target prediction using multiple short expression time series from Arabidopsis thaliana

Redestig, H., Weicht, D., Selbig, J., & Hannah, M. A. (2007). Transcription factor target prediction using multiple short expression time series from Arabidopsis thaliana. BMC Bioinformatics, 8, 454. doi:10.1186/1471-2105-8-454.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0014-2893-1 Version Permalink: http://hdl.handle.net/11858/00-001M-0000-0014-6A47-A
Genre: Journal Article

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 Creators:
Redestig, H.1, Author              
Weicht, D.1, Author              
Selbig, J.1, Author              
Hannah, M. A.2, Author              
Affiliations:
1BioinformaticsCRG, Cooperative Research Groups, Max Planck Institute of Molecular Plant Physiology, Max Planck Society, ou_1753315              
2Small Molecules, Department Willmitzer, Max Planck Institute of Molecular Plant Physiology, Max Planck Society, ou_1753340              

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Free keywords: Arabidopsis Proteins/*genetics Base Sequence Binding Sites DNA, Plant/*genetics Gene Expression Profiling/*methods Gene Targeting/*methods Molecular Sequence Data Protein Binding Sequence Analysis, DNA/*methods Time Factors Transcription Factors/*genetics
 Abstract: BACKGROUND: The central role of transcription factors (TFs) in higher eukaryotes has led to much interest in deciphering transcriptional regulatory interactions. Even in the best case, experimental identification of TF target genes is error prone, and has been shown to be improved by considering additional forms of evidence such as expression data. Previous expression based methods have not explicitly tried to associate TFs with their targets and therefore largely ignored the treatment specific and time dependent nature of transcription regulation. RESULTS: In this study we introduce CERMT, Covariance based Extraction of Regulatory targets using Multiple Time series. Using simulated and real data we show that using multiple expression time series, selecting treatments in which the TF responds, allowing time shifts between TFs and their targets and using covariance to identify highly responding genes appear to be a good strategy. We applied our method to published TF - target gene relationships determined using expression profiling on TF mutants and show that in most cases we obtain significant target gene enrichment and in half of the cases this is sufficient to deliver a usable list of high-confidence target genes. CONCLUSION: CERMT could be immediately useful in refining possible target genes of candidate TFs using publicly available data, particularly for organisms lacking comprehensive TF binding data. In the future, we believe its incorporation with other forms of evidence may improve integrative genome-wide predictions of transcriptional networks.

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Language(s): eng - English
 Dates: 2007-11-182007
 Publication Status: Published in print
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Title: BMC Bioinformatics
Source Genre: Journal
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Pages: - Volume / Issue: 8 Sequence Number: - Start / End Page: 454 Identifier: -