A web-based tool for the prediction of rice transcription factor function.

Area of Science:

• Plant molecular biology
• Genomics
• Bioinformatics

Background:

• Transcription factors (TFs) regulate gene expression but are understudied in rice (Oryza sativa) due to gene duplication and functional redundancy.
• Characterizing TF function is crucial for understanding plant development and stress responses.

Purpose of the Study:

• To develop a web-based tool, the Rice Transcription Factor Phylogenomics Database (RTFDB), for predicting TF function in rice.
• To leverage integrated omics data and phylogenetic analysis to elucidate TF roles in a complex genome.

Main Methods:

• Development of the RTFDB, a database integrating transcriptome and co-expression data from microarrays and RNA-Seq.
• Meta-expression analysis to identify tissue-specific, stress-responsive, pathogen-responsive, and hormone-responsive TFs.
• Phylogenetic analysis and Pearson correlation coefficient calculation to assess expression collinearity and functional redundancy of paralogous genes.

Main Results:

• RTFDB successfully identified numerous differentially expressed TFs under various conditions: 273 tissue-specific, 455 abiotic stress-responsive, 179 pathogen-responsive, and 512 hormone-responsive.
• Analysis revealed functional redundancy or dominance among paralogous genes, particularly in the highly duplicated rice genome.
• The method validated the predominant role of 83.3% of previously characterized TFs, demonstrating its predictive power.

Conclusions:

• The RTFDB is a valuable resource for functional genomics in rice, aiding in the prediction of TF function.
• Integrating expression data with evolutionary information provides insights into gene redundancy and TF roles in complex plant genomes.
• The developed methodology is transferable to other plant species with annotated genomes for TF functional studies.