Reference Gene Identification via Expression Stability Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional reference genes used in gene expression analysis, such as GAPDH and β-actin, are not consistently expressed across different tissues and experimental conditions, leading to biased expression profiles due to their variable expression levels, which complicates accurate normalization in quantitative PCR methods.
Innovation Solution
A method involving the analysis of gene expression data from EST, SAGE, and microarray datasets to identify novel endogenous reference genes with stable expression across a wide range of samples, using statistical concepts like zero's proportion and coefficient of variation, and employing specific primers and probes for amplification and normalization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional reference genes (GAPDH, β-actin) are used for normalization, then gene expression analysis can be performed, but expression stability and accuracy of normalization deteriorate due to variable expression levels across different tissues and conditions
Solution Approach 1:
The patent applies parameter changes by systematically evaluating reference genes across multiple datasets (EST, SAGE, microarray) using statistical parameters (coefficient of variation, zero's proportion) to identify genes with optimal expression stability. This transforms the selection from using fixed traditional genes to dynamically selecting genes based on measured expression parameters across diverse conditions
Solution Approach 2:
The patent achieves universality by identifying reference genes that function across multiple tissue types and experimental conditions simultaneously. The selected genes (e.g., RPLP0, GAPDH in specific contexts) demonstrate multi-functional applicability as normalization references in diverse gene expression analysis scenarios, replacing tissue-specific traditional references
2Ease of operation
If traditional reference genes are assumed to be constitutively expressed, then analysis simplicity is improved, but measurement accuracy deteriorates due to lack of proper validation across conditions
Solution Approach 1:
The patent implements preliminary action by pre-evaluating and validating reference gene expression stability across multiple datasets and conditions before actual gene expression analysis. This advance validation creates a reliable foundation that simplifies subsequent operations, as researchers can directly use pre-validated genes without performing their own extensive validation experiments
Solution Approach 2:
The patent enables self-service by providing a systematic framework that allows researchers to independently identify and validate suitable reference genes for their specific experimental conditions. The methodology empowers users to select appropriate normalization genes based on their own data characteristics rather than relying on potentially inappropriate traditional references
Data Source
AI summary
Disclosed are data processing and analysis methods for gene expression data for identifying endogenous reference genes and a composition for the quantitative analysis of gene expression, comprising a pair of primers and/or probes useful in amplifying the identified endogenous reference genes. Introduced with the concepts of “Zero's proportion” and CV, the method allows different datasets to be integrally analyzed, thereby searching for novel reference genes. By the method, 2,087 genes are first found as housekeeping genes which are expressed in most tissues, and the usefulness thereof in the relative quantification of different target genes is determined by analyzing their expression stability. Of the 2,087 genes, 13 genes show higher expression stability with lower expression levels across a wide range of samples than traditional reference genes such as GAPDH and ACTS, and therefore are suitable for the normalization of universal genes having relatively low expression levels.