Microbial characterization based on multifractal analysis of metagenomes

Area of Science:

• Microbiology
• Computational Biology
• Ecology

Background:

• Species diversity in microbiomes is a key area in metagenomic research.
• Understanding microbiome complexity is crucial for various biological and ecological studies.

Purpose of the Study:

• To propose and validate a novel multifractal analysis for metagenomic data.
• To explore the relationship between multifractal dimensions and traditional species diversity indices.
• To assess the applicability of multifractal analysis in distinguishing age-related changes in infant gut microbiomes.

Main Methods:

• Utilized chaotic game representation (CGR) to visualize metagenomic data.
• Defined and calculated multifractal dimensions for both simulated and real metagenomes.
• Analyzed Pearson correlation coefficients between multifractal dimensions and species richness, Shannon, and Simpson diversity indices.
• Applied the multifractal analysis to gut microbiome data from infants of different age groups.

Main Results:

• Metagenomes visualized using CGR exhibit self-similarity.
• Multifractal dimensions show strong correlations with traditional diversity indices (species richness, Shannon, Simpson) at specific q values.
• The multifractal spectrum of infant gut microbiomes demonstrates age-related differences, indicating developmental changes.

Conclusions:

• Multifractal analysis provides a unified framework for understanding metagenomic diversity.
• The multifractal spectrum is a significant characteristic of metagenomes, mirroring findings in macrobial ecology.
• This approach effectively differentiates age-related variations in infant gut microbial communities.