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Obtaining High-Quality Transcriptome Data from Cereal Seeds by a Modified Method for Gene Expression Profiling

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The Rice Online Expression Profiles Array Database Version 2 (ROADv2): An Interactive Atlas for Rice Functional

Wonjae Hwang¹, Woo-Jong Hong^1,2, Eui-Jung Kim^1,3

¹Graduate School of Green-Bio Science and Crop Biotech Institute, Kyung Hee University, Yongin, 17104, Republic of Korea.

Rice (New York, N.Y.)

|December 26, 2024

Summary

This summary is machine-generated.

The updated Rice Online expression profiles Array Database version 2 (ROADv2) now uses RNA-Seq data for more accurate rice gene expression analysis. This enhanced resource offers new protein-protein interaction network analysis and improved visualization for rice functional genomics.

Keywords:

Gene Ontology (GO) enrichment Kyoto Encyclopedia of Genes and Genomes (KEGG)Meta-expression analysis Predicted protein–protein interaction Rice

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Published on: December 22, 2017

Area of Science:

Plant Biology
Genomics
Bioinformatics

Background:

Rice (Oryza sativa) is a vital global crop, necessitating advanced tools for understanding its complex gene expression patterns.
Previous versions of the Rice Online expression profiles Array Database (ROAD) relied on microarray data, limiting accuracy.
The need for a comprehensive, up-to-date resource for rice functional genomics research is critical.

Purpose of the Study:

To introduce ROADv2, an upgraded database for rice gene expression analysis utilizing RNA-Sequencing (RNA-Seq) data.
To enhance data accuracy, annotation, and functional enrichment information for rice genes.
To incorporate novel features like protein-protein interaction (PPI) network analysis and improved interactive visualizations.

Main Methods:

Transitioned from microarray to RNA-Seq data for 328 diverse rice gene expression datasets.
Updated gene annotation to RGAP version 7 and integrated recent Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) versions.
Implemented user-interactive heatmaps, coexpression correlation analysis, network graphs, and PPI network analysis.

Main Results:

ROADv2 provides accurate gene expression profiles across various experimental series (anatomy, stress, hormone, nutrients).
New features include interactive heatmaps, coexpression analysis with network visualization, and functional enrichment tools (GO, KEGG).
Protein-protein interaction network analysis integrated with coexpression data enhances prediction accuracy.

Conclusions:

ROADv2 serves as a comprehensive and reliable resource for rice functional genomics.
The database facilitates deeper insights into gene function, regulation, and interactions in rice.
Enhanced accuracy and novel analytical tools in ROADv2 support advanced rice research.