An Arabidopsis expression predictor enables inference of transcriptional regulators for gene modules.

The regulation of gene expression by transcription factors (TFs) has been studied for a long time, but no model that can accurately predict transcriptome profiles based on TF activities currently exists. Here, we developed a computational approach, named EXPLICIT (Expression Prediction via Log-linear Combination of Transcription Factors), to construct a universal predictor for Arabidopsis to predict the expression of 29 182 non-TF genes using 1678 TFs. When applied to RNA-Seq samples from diverse tissues, EXPLICIT generated accurate predicted transcriptomes correlating well with actual expression, with an average correlation coefficient of 0.986. After recapitulating the quantitative relationships between TFs and their target genes, EXPLICIT enabled downstream inference of TF regulators for genes and gene modules functioning in diverse plant pathways, including those involved in suberin, flavonoid, glucosinolate metabolism, lateral root, xylem, secondary cell wall development or endoplasmic reticulum stress response. Our approach showed a better ability to recover the correct TF regulators when compared with existing plant tools, and provides an innovative way to study transcriptional regulation.