Transcription factor target prediction using multiple short expression time series from Arabidopsis thalianaReport as inadecuate




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BMC Bioinformatics

, 8:454

First Online: 18 November 2007Received: 11 June 2007Accepted: 18 November 2007

Abstract

BackgroundThe 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.

ResultsIn 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.

ConclusionCERMT 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.

Electronic supplementary materialThe online version of this article doi:10.1186-1471-2105-8-454 contains supplementary material, which is available to authorized users.

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Author: Henning Redestig - Daniel Weicht - Joachim Selbig - Matthew A Hannah

Source: https://link.springer.com/







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