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

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 re...

• Type: Journal
• Year: 2007
• Where: BMCBI
• Authors: Henning Redestig, Daniel Weicht, Joachim Selbig, Matthew A. Hannah
• Keywords: BMCBI 2007, Target Genes, TF Target Genes, Transcription Factors