PlantRGS

PlantRGS identifies optimal reference genes for quantitative gene expression normalization across plant species by mining whole-genome microarray datasets to find genes with minimal expression variance across experimental conditions.

Key Features:

• Whole-Genome Analysis: Performs analysis at the whole-genome level to survey all annotated genes as potential reference candidates.
• Extensive Microarray Data Integration: Leverages over 11,000 tissue samples from microarray datasets to support cross-condition assessment.
• Multi-Species Coverage: Incorporates data from nine plant species to enable cross-species reference gene identification.
• Customizable Analysis Parameters and Outputs: Supports specification of experimental conditions and selection of the number of reference genes, with multiple output formats for downstream use.
• Validation of Stability: Identifies novel reference genes that have been validated to exhibit superior expression stability compared to traditional reference genes.

Scientific Applications:

• Gene Expression Profiling: Provides stable reference genes for accurate normalization across diverse experimental conditions in quantitative expression studies.
• Comparative Genomics: Supplies consistent reference points to facilitate cross-species expression comparisons among the nine included plant species.
• Functional Genomics: Enhances reliability of gene function studies by supplying stable benchmarks for normalization in functional assays.

Methodology:

Performs whole-genome analysis and meta-analysis of integrated microarray datasets (over 11,000 tissue samples across nine plant species) to identify candidate reference genes based on minimal expression variance across experimental conditions.