MicrobiomeAnalyst 2.0: comprehensive statistical, functional and integrative analysis of microbiome data.

Area of Science:

• Microbiome research across biomedical, agricultural, and environmental sciences.

Background:

• Complex microbiome datasets require advanced bioinformatics tools for pattern discovery, biomarker identification, and functional analysis.
• Existing tools may lack comprehensive features for integrated multi-omics data interpretation.

Purpose of the Study:

• Introduce MicrobiomeAnalyst 2.0, an updated platform for microbiome data analysis.
• Enhance statistical, visualization, functional, and integrative analysis capabilities for microbiome studies.

Main Methods:

• Development of three new modules: Raw Data Processing, Microbiome Metabolomics Profiling, and Statistical Meta-Analysis.
• Integration of multi-factor differential analysis, interactive visualizations, updated functional prediction, and correlation analysis methods.
• Expansion of taxon set libraries and demonstration with a type 1 diabetes multi-omics dataset.

Main Results:

• MicrobiomeAnalyst 2.0 provides a user-friendly interface for processing amplicon data, performing taxonomic annotation, and conducting downstream statistical analysis.
• The platform enables joint analysis of microbiome and metabolomics data to explore associations between community composition and metabolic activities.
• New modules facilitate the identification of consistent microbial signatures through meta-analysis of multiple studies.

Conclusions:

• MicrobiomeAnalyst 2.0 significantly advances microbiome data analysis by offering expanded functionalities for processing, interpretation, and integration.
• The platform supports comprehensive multi-omics analysis, aiding researchers in uncovering complex biological patterns and translational insights.