Project description:Common wheat is one of the most widely cultivated staple crops worldwide. Elucidating the gene regulatory network will provide essential information for mechanism studies and targeted manipulation of gene activity for breeding. However, detecting cis-regulatory elements and transcription factor (TF) bindings in the extremely large intergenic regions of the wheat genome is challenging. Linking cis-regulatory elements and TF binding to target genes is even more difficult given that enhancers can function irrespective of the strand and distance from target genes. Combining genome-wide TF binding profiles, epigenomic patterns, and transcriptome analysis is a compelling approach to detect the hierarchical regulatory network. We generated and collected 189 TF binding profiles, 90 epigenomic datasets, and 2,356 transcriptomic datasets in common wheat, which were further integrated using machine learning approach to infer direct target genes and the hierarchical regulatory network. We developed a web-based platform, Wheat-RegNet, that provides four major functions: (i) to identify regulatory elements regulating input gene(s), and to infer the tissue and environmental response specificity; (ii) to identify the TFs responsible for regulating input gene(s) or locus/loci; (iii) to construct the hierarchical regulatory network regulating input gene(s); and (iv) to browse hundreds of TF binding, epigenomic, and transcriptomic profiles of an input region or gene(s). Well-organized results and multiple tools for interactive visualization are available through a user-friendly web interface, making Wheat-RegNet a highly useful resource for exploring gene regulatory information for hypothesis-driven studies and for targeted manipulation for breeding research in common wheat. Wheat-RegNet is freely available at http://bioinfo.sibs.ac.cn/Wheat-RegNet