Challenges, Strategies, and Perspectives for Reference-Independent Longitudinal Multi-Omic Microbiome Studies

Area of Science:

• Microbiome research
• Systems biology
• Computational biology

Background:

• Multi-omic studies (metagenomics, metatranscriptomics, metaproteomics, metabolomics) are advancing microbial community analysis.
• Current large-scale datasets primarily come from spatial or case-control sampling.
• Reference-independent approaches aid in discovering novel microbial organisms and functions.

Purpose of the Study:

• To highlight the significance of longitudinal multi-omic microbiome datasets.
• To discuss the advantages of longitudinal data for understanding community dynamics, causality, and prediction.
• To emphasize the value of integrating host-derived omics, environmental data, and metadata.

Main Methods:

• Discussing challenges and strategies for longitudinal microbiome studies.
• Covering sampling, biomolecular extraction, and multi-omic measurements.
• Focusing on reference-independent data integration, modeling, and validation.

Main Results:

• Longitudinal data enables better understanding of community dynamics, trends, and causality.
• Integration of host-derived omics and environmental data enhances insights into community drivers.
• Future experiments can uncover ecological mechanisms, microbe-microbe, and microbe-host interactions.

Conclusions:

• Longitudinal multi-omic microbiome studies are the next frontier for in-depth microbiome research.
• These studies are crucial for resolving community structure, function, and predicting behavior.
• Carefully designed longitudinal studies will advance our understanding of microbial ecology and interactions.