Plant Regulomics: a data‐driven interface for retrieving upstream regulators from plant multi‐omics data

Abstract

SummaryHigh‐throughput technology has become a powerful approach for routine plant research. Interpreting the biological significance of high‐throughput data has largely focused on the functional characterization of a large gene list or genomic loci that involves the following two aspects: the functions of the genes or loci and how they are regulated as a whole, i.e. searching for the upstream regulators. Traditional platforms for functional annotation largely help resolving the first issue. Addressing the second issue is essential for a global understanding of the regulatory mechanism, but is more challenging, and requires additional high‐throughput experimental evidence and a unified statistical framework for data‐mining. The rapid accumulation of ’omics data provides a large amount of experimental data. We here present Plant Regulomics, an interface that integrates 19 925 transcriptomic and epigenomic data sets and diverse sources of functional evidence (58 112 terms and 695 414 protein−protein interactions) from six plant species along with the orthologous genes from 56 whole‐genome sequenced plant species. All pair‐wise transcriptomic comparisons with biological significance within the same study were performed, and all epigenomic data were processed to genomic loci targeted by various factors. These data were well organized to gene modules and loci lists, which were further implemented into the same statistical framework. For any input gene list or genomic loci, Plant Regulomics retrieves the upstream factors, treatments, and experimental/environmental conditions regulating the input from the integrated ’omics data. Additionally, multiple tools and an interactive visualization are available through a user‐friendly web interface. Plant Regulomics is available at http://bioinfo.sibs.ac.cn/plant-regulomics.

EAT-UpTF: Enrichment Analysis Tool for Upstream Transcription Factors of a Group of Plant Genes

“…Compared to the previous webtools, such as TF2Network (Kulkarni et al, 2018) and AthaMap (Steffens et al, 2004), which conduct cis-element-based construction of TF regulatory networks, EAT-UpTF involves simple and rapid processing of data without cis-element identification, and thereby promptly visualizes gene regulatory networks showing TF-target gene interactions. While processing our study, a remarkable webtool 'Plant Regulomics' 2 has been released (Ran et al, 2020), which might implement a similar logic and code of EAT-UpTF, supporting the relevance of this analysis.…”

Perspectives of CRISPR/Cas-mediated cis-engineering in horticulture: unlocking the neglected potential for crop improvement

“…De novo CRE discovery Prior knowledge of CREs in cis-regulatory region is helpful to apply cis-engineering in crop improvement. Many previously described CREs, especially transcription factor-binding sites (TFBSs), in plant promoters can be identified by submitting sequences to various databases, including TRANSFAC 84 , PLACE 85 , PlantCARE 86 , JAS-PAR Core PLANTAE 87 , PlantTFDB 88 , and Plant Regulomics 89 . After the TFBSs have been predicted, the regions can be validated by either in vitro methods based on DNA-protein interaction, such as DNA electrophoretic mobility shift assay, DNA pull-down and yeast one-hybrid assays, or in vivo CHIP-based methods, for example, CHIP with DNA microarray (CHIP-chip) and CHIP-seq.…”

Toward the Genetic Basis and Multiple QTLs of Kernel Hardness in Wheat

“…Fourth, transcriptomic resources of wheat are increasingly accumulating [154][155][156]. Other multi-omic resources are emerging, including epigenomics [157][158][159].…”