Comparison of functional classification systems

Area of Science:

• Microbiology
• Bioinformatics
• Computational Biology

Background:

• Functional profiling in microbiome analysis assigns sequence data to specific biological functions using classification systems.
• Several large (eggNOG, KEGG, InterPro, SEED) and small (CARD, EC, MetaCyc, VFDB) functional classifications are currently employed.
• Choosing the right classification system is crucial for accurate and meaningful microbiome data interpretation.

Purpose of the Study:

• To systematically compare major functional classification systems used in microbiome research.
• To evaluate these systems based on overlap, redundancy, structural hierarchy, and assignment rates.
• To provide cross-classification mappings to aid researchers in data integration.

Main Methods:

• Comparative analysis of multiple functional gene annotation databases (eggNOG, KEGG, InterPro, SEED, CARD, EC, MetaCyc, VFDB).
• Assessment of classification systems using metrics such as sequence redundancy, hierarchical structure, and read/contig assignment efficiency.
• Development of mappings between key functional concepts across different classification ontologies.

Main Results:

• For large classifications, eggNOG demonstrated superior performance in terms of sequence redundancy and structural organization.
• SEED was identified as having the most coherent and cleanest hierarchical structure.
• KEGG and InterPro:BP were found to be potentially more valuable for applications in medical research and disease-associated microbiome studies.
• Practical assignment rates varied significantly across different classification systems when applied to real-world metagenomic datasets.

Conclusions:

• No single functional classification system is universally optimal for all microbiome research applications.
• The choice of classification system should be guided by the specific research question, data type, and intended application (e.g., general biology vs. medical insights).
• The provided comparative analysis and cross-classification mappings can assist researchers in selecting the most appropriate functional annotation tools and interpreting results.