Prioritization of differentially expressed genes through integrating public expression data

Area of Science:

• Genomics
• Bioinformatics
• Molecular Biology

Background:

• Differentially expressed gene (DEG) analysis is vital for understanding phenotype variations, but often yields numerous candidate genes.
• Validating a large number of candidate genes through traditional laboratory methods is time-consuming and resource-intensive.
• There is a significant need for efficient methods to prioritize significant DEGs.

Purpose of the Study:

• To develop and evaluate a novel computational method for prioritizing bona fide differentially expressed genes (DEGs).
• To reduce the number of candidate genes requiring experimental validation in gene expression studies.
• To facilitate the identification of novel causal genes for functional research.

Main Methods:

• Constructed the normal range of gene expression by integrating public expression data.
• Developed a prioritization strategy based on ranking cumulative probability differences between case and control groups.
• Validated the method using DEGs from a pig muscle tissue study.

Main Results:

• The proposed prioritization method achieved an area under the receiver operating characteristic curve (AUC) of 96.42%.
• Demonstrated effective reduction of candidate gene lists from differential expression experiments.
• Successfully identified key genes with high accuracy, as validated in the pig muscle tissue dataset.

Conclusions:

• The novel method provides an effective approach for prioritizing differentially expressed genes (DEGs).
• This computational tool can significantly shorten the list of candidate genes, aiding in the discovery of novel causal genes.
• The method is broadly applicable and can be extended to various tissues and species to advance functional genomics research.