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Multi-omics time-series analysis in microbiome research: a systematic review.

Moiz Khan Sherwani1, Matti O Ruuskanen2, Dylan Feldner-Busztin3

  • 1Center for Evolutionary Hologenomics, GLOBE Institute, University of Copenhagen, Øster Farimagsgade 5, 1353 Copenhagen K, Denmark.

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This review systematically evaluates computational methods for longitudinal multi-omics integration. It identifies trends and high-performing frameworks, offering a roadmap for advancing integrative data science in biology.

Keywords:
host-associated microbiomesmachine learningmulti-omicsstatistical modelingtime-series

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Area of Science:

  • Integrative biology and data science
  • Computational biology
  • Systems biology

Background:

  • Advances in data generation offer new insights into living systems.
  • Integrating temporal variation across scales is key for understanding biological mechanisms and phenotypes.
  • Longitudinal multi-omics studies are increasing, but analytical methods are still developing.

Purpose of the Study:

  • To systematically review and evaluate computational methods for longitudinal multi-omics integration.
  • To categorize and compare existing methods, focusing on four study types: host/microbiome, host-only, microbiome-only, and methodological frameworks.
  • To provide a roadmap for future research and application in this field.

Main Methods:

  • Systematic literature review of computational methods for longitudinal multi-omics integration.
  • Categorization of studies into host/microbiome, host-only, microbiome-only, and methodological frameworks.
  • Evaluation of methods based on performance, interpretability, and ease of use.

Main Results:

  • Identified current methodological trends in longitudinal multi-omics integration.
  • Highlighted widely used and high-performing computational frameworks.
  • Organized methods into thematic groups: statistical modeling, machine learning, dimensionality reduction, and latent factor approaches.

Conclusions:

  • There is a need for standardized and evaluated methods in longitudinal multi-omics data analysis.
  • This review provides a comprehensive overview and critical foundation for advancing integrative longitudinal data science.
  • The findings support reproducible and scalable analysis in this rapidly evolving field.