Investigating the effect of paralogs on microarray gene-set analysis.

Area of Science:

• Genomics
• Bioinformatics
• Systems Biology

Background:

• Microarray experiments generate large datasets of gene expression.
• Gene-set analysis (GSA) methods interpret these datasets by grouping genes into functional sets.
• The impact of paralogs on GSA accuracy is not well understood.

Purpose of the Study:

• To investigate the influence of paralogs on gene-set analysis (GSA) outcomes.
• To evaluate the utility of accounting for paralogs in GSA.
• To assess if removing paralogs can improve the identification of biologically relevant gene sets.

Main Methods:

• Utilized the Indygene web tool to identify and remove paralogous genes from datasets based on sequence similarity.
• Reanalyzed previously published microarray datasets using Indygene.
• Applied three different GSA approaches to the processed datasets.

Main Results:

• Paralogs exhibit highly correlated expression patterns, potentially confounding GSA.
• The Indygene tool effectively reduced paralogy relationships in gene lists.
• Removing paralogs prior to GSA generated significantly different results compared to standard GSA.

Conclusions:

• Accounting for paralogs is crucial for accurate GSA.
• Pre-processing data to remove paralogs can lead to novel and plausible biological discoveries.
• The redundancy and non-independence of paralogs necessitate careful consideration in GSA methodologies.