EXPath 2.0: An Updated Database for Integrating High-Throughput Gene Expression Data with Biological Pathways

Area of Science:

• Plant molecular biology
• Bioinformatics
• Systems biology

Background:

• Co-expressed genes often share regulatory relationships and biological functions.
• Gene correlation networks are valuable for studying transcriptional regulation and metabolic pathways.
• Identifying regulatory transcription factors (TFs) for co-expressed genes is crucial but often challenging.

Purpose of the Study:

• To introduce EXPath 2.0, an updated database for investigating regulatory mechanisms in plant metabolic pathways.
• To provide a comprehensive platform for analyzing gene expression profiles and identifying regulatory TFs.
• To facilitate research on plant biological processes and gene regulation.

Main Methods:

• Expanded species coverage to six plants (Arabidopsis, rice, maize, Medicago, soybean, tomato).
• Incorporated gene expression data across various developmental stages.
• Enabled construction of gene correlation networks and promoter analysis.
• Added hierarchical Gene Ontology (GO) term visualization.
• Allowed user data upload for custom analysis.

Main Results:

• EXPath 2.0 integrates 1,881 microarray and 978 RNA-seq samples.
• The database now supports a wider range of plant species and developmental stages.
• New features facilitate network construction, promoter analysis, and GO term enrichment visualization.
• Users can upload their own expression data for in-depth analysis.

Conclusions:

• EXPath 2.0 is a significantly enhanced platform for exploring plant gene expression and metabolic pathways.
• The database provides powerful tools for identifying regulatory TFs and understanding gene regulatory networks.
• It serves as a valuable resource for plant biologists studying gene regulation and biological processes.