Abstract
A regulon refers to a group of genes regulated by a transcription factor binding to regulatory motifs to achieve specific biological functions. To infer tissue-specific gene regulons in Arabidopsis, we developed a novel pipeline named InferReg. InferReg utilizes a gene expression matrix that includes 3400 Arabidopsis transcriptomes to make initial predictions about the regulatory relationships between transcription factors (TFs) and target genes (TGs) using co-expression patterns. It further improves these anticipated interactions by integrating TF binding site enrichment analysis to eliminate false positives that are only supported by expression data. InferReg further trained a graph convolutional network with 133 transcription factors, supported by ChIP-seq, as positive samples, to learn the regulatory logic between TFs and TGs to improve the accuracy of the regulatory network. To evaluate the functionality of InferReg, we utilized it to discover tissue-specific regulons in 5 Arabidopsis tissues: flower, leaf, root, seed, and seedling. We ranked the activities of regulons for each tissue based on reliability using Borda ranking and compared them with existing databases. The results demonstrated that InferReg not only identified known tissue-specific regulons but also discovered new ones. By applying InferReg to rice expression data, we were able to identify rice tissue-specific regulons, showing that our approach can be applied more broadly. We used InferReg to successfully identify important regulons in various tissues of Arabidopsis and Oryza, which has improved our understanding of tissue-specific regulations and the roles of regulons in tissue differentiation and development. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s42994-024-00176-2.