A guide to network analysis, multi-omics integration, and applications in livestock microbiome research

Area of Science:

• Microbiome research
• Network science
• Animal science

Background:

• Livestock gut microbiome interactions influence animal growth, health, and methane emissions.
• Understanding these microbial networks is crucial for functional insights beyond species lists.

Purpose of the Study:

• To review how network analysis and multi-omics data can elucidate livestock gut microbiome functions.
• To focus on identifying keystone species and their ecological roles.

Main Methods:

• Synthesizing network analysis approaches combined with multi-omics data (genomic, metabolic, host).
• Evaluating methods from species correlation to integrated modeling.
• Highlighting computational techniques like metabolic modeling and machine learning.

Main Results:

• Network analysis integrated with multi-omics data can identify influential keystone species.
• Computational methods transform network data into predictive tools for microbiome function.
• Integration of diverse data types is essential to distinguish ecological relationships from noise.

Conclusions:

• Network science offers a framework for actively managing livestock microbiomes.
• Future research requires longitudinal studies, livestock-specific models, and analytical standards.
• This approach aims to enhance sustainable livestock production through microbiome management.