MetaMIS: a metagenomic microbial interaction simulator based on microbial community profiles

Area of Science:

• Ecology
• Microbiology
• Bioinformatics

Background:

• Microbial community complexity and dynamics are crucial ecological factors.
• Next-generation sequencing (NGS) and metagenomics enable exploration of microbial interactions.
• Lotka-Volterra models, traditionally used for animal interactions, are now applied to metagenomic data.

Purpose of the Study:

• To present the Metagenomic Microbial Interacticon Simulator (MetaMIS), a tool for analyzing microbial community time-series data.
• To infer microbial interactions and interpret interaction networks using Lotka-Volterra models.
• To evaluate biological reality through Bray-Curtis dissimilarity and refine microbial biotic roles.

Main Methods:

• Utilizes Lotka-Volterra models to infer microbial interactions from operational taxonomic unit (OTU) abundance tables.
• Employs Bray-Curtis dissimilarity for assessing biological relevance.
• Designed to handle high levels of missing data and minimize the influence of rare microbes.

Main Results:

• MetaMIS infers microbial interaction networks and identifies interaction patterns (e.g., mutualism, competition).
• A case study on the human male fecal microbiome identified Micrococcaceae as a highly connected OTU playing a critical role.
• Comparative analysis revealed a consensus interaction network between female and male fecal microbiomes.

Conclusions:

• MetaMIS offers an efficient, user-friendly platform for gaining insights from metagenomics data.
• The tool facilitates the organization of multiple interaction networks for comparative studies.
• MetaMIS is freely available for research use.