Categorizer: a tool to categorize genes into user-defined biological groups based on semantic similarity

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• Gene Ontology (GO) enrichment analysis is crucial for interpreting high-throughput experimental data.
• Current GO analysis tools often assume GO term independence and uniform semantic distances, leading to redundant or overly specific results.
• There is a need for improved gene categorization and enrichment methods that account for biological context.

Purpose of the Study:

• To develop a robust tool, Categorizer, for accurate gene categorization and enrichment analysis.
• To enhance the interpretation of genomic and proteomic data by providing biologically relevant gene groupings.
• To address limitations of existing GO analysis tools.

Main Methods:

• Developed Categorizer, a tool for classifying genes into user-defined categories.
• Implemented a specialized semantic similarity measure for Gene Ontology terms to identify the best-fit category for each gene.
• Calculated p-values for category enrichment.

Main Results:

• Categorizer demonstrated improved gene categorization and enrichment analysis compared to classical GO Slim-based approaches.
• The tool provided more accurate results for genetic modifiers of Huntington's disease.
• Categorizer outperformed other semantic similarity measures in categorization tasks.

Conclusions:

• Categorizer offers more accurate gene categorization than existing methods.
• This tool enhances the reliability of genomic and proteomic data analysis for biologists.
• Categorizer supports user-specific analysis needs for experimental and computational biologists.