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

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.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.Transcriptional regulation is essential for all eukaryotes and is central to the complex development and environmental responses of higher organisms. The identification of transcription factors (TFs), TF-target genes and transcriptional regulatory networks is therefore of fundamental importance for biology. The ability of TFs to modify the expression of many physiologically important target genes has made them attractive targets for biotechnology [1,2]. Traditionally, experimental approaches have sought to identify TF-targets by measuring gene expression in loss- or gain-of-function mutants, whilst TF binding to their target promoters has been measured using gel-shift assays, co-transfection assays or chromatin-immunoprecipitation (ChIP). With the arrival of genome-scale technologies, approaches have been scaled up to allow for the unbiased identification of either genes with altered expression in TF mutants using expression profiling, or promoters and other genomic sequences that ar