MicrobiomeDB: a systems biology platform for integrating, mining and analyzing microbiome experiments

Area of Science:

• Bioinformatics
• Microbiology
• Data Science

Background:

• Microbiome research generates vast datasets.
• Effective data analysis platforms are crucial for extracting meaningful insights.
• Integrating diverse data types (microbial census, sample metadata) presents a challenge.

Purpose of the Study:

• To introduce MicrobiomeDB, a data discovery and analysis platform for microbiome research.
• To enable researchers to leverage experimental variables for interrogating microbiome datasets.
• To provide tools for in silico experiments and analysis of microbial community data.

Main Methods:

• Leverages the infrastructure and user interface of the Eukaryotic Pathogens Bioinformatics Resource Center (EuPathDB).
• Employs an intuitive graphical 'strategy' approach for constructing in silico experiments.
• Integrates microbial census data with sample details for thousands of samples from various sources.

Main Results:

• The platform integrates nearly 14,000 samples from human, animal, and environmental origins.
• Includes over 9,000 samples from healthy human subjects within the Human Microbiome Project.
• Query results can be statistically analyzed and graphically visualized through interactive web applications.

Conclusions:

• MicrobiomeDB empowers researchers with a comprehensive tool for microbiome data analysis.
• Facilitates the identification of microbial taxa associated with experimental covariates.
• Enhances understanding of microbial community diversity and function through accessible data exploration.