Plant Regulomics

Plant Regulomics retrieves upstream regulators from integrated plant transcriptomic and epigenomic datasets to identify regulatory networks and dissect regulation of gene lists or genomic loci across six model plant species.

Key Features:

• Integrated datasets: Aggregates 19,925 transcriptomic and epigenomic datasets for analysis.
• Functional annotation: Includes 58,112 functional terms for mapping gene functions.
• Protein-protein interactions: Incorporates 695,414 protein-protein interactions to support regulatory network inference.
• Model species coverage: Contains data for Arabidopsis thaliana, Oryza sativa, Zea mays, Glycine max, Solanum lycopersicum, and Triticum aestivum.
• Orthology: Provides orthologous genes from 56 whole-genome sequenced plant species for comparative analyses.
• Transcriptomic framework: Uses a unified statistical framework to organize pairwise transcriptomic comparisons with biological significance within individual studies.
• Epigenomic processing: Processes epigenomic data to identify genomic loci targeted by various factors and map regulatory elements.
• Module and loci organization: Organizes datasets into gene modules and loci lists to enable integrated analysis across data types.
• Upstream regulator retrieval: Accepts gene lists or genomic loci as input to retrieve upstream factors, treatments, and experimental or environmental conditions that regulate the input.

Scientific Applications:

• Regulatory network discovery: Identification of upstream regulators and reconstruction of regulatory networks governing plant genes or loci.
• Functional characterization: Functional interpretation of gene lists using integrated functional terms, transcriptomic, and epigenomic evidence.
• Comparative genomics: Cross-species comparison of regulatory relationships using orthologs from 56 whole-genome sequenced plant species.
• Treatment and environment inference: Detection of treatments and experimental or environmental conditions associated with regulation of input genes or loci.
• Integration of multi-omics evidence: Joint analysis of transcriptomic, epigenomic, PPI, and functional term data to map regulatory elements and interactions.

Methodology:

Implements a unified statistical framework for organizing pairwise transcriptomic comparisons, processes epigenomic data to identify factor-targeted genomic loci, organizes datasets into gene modules and loci lists, and integrates transcriptomic, epigenomic, functional term, protein-protein interaction, and orthology data.