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Updated: Oct 20, 2025

Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing
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Microbial trend analysis for common dynamic trend, group comparison, and classification in longitudinal microbiome

Chan Wang1, Jiyuan Hu1, Martin J Blaser2

  • 1Division of Biostatistics, Department of Population Health, New York University School of Medicine, New York, 10016, NY, USA.

BMC Genomics
|September 16, 2021
PubMed
Summary

We developed a microbial trend analysis (MTA) framework to analyze dynamic microbiome data over time. MTA effectively characterizes microbial trends, identifies key taxa, and classifies subjects, aiding health and disease research.

Keywords:
ClassificationCompositionDynamicHigh dimensionalityHypothesis testingLongitudinal microbiomePhylogenetic treeVariable selection

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Area of Science:

  • Microbiome research
  • Computational biology
  • Statistical analysis

Background:

  • The human microbiome's dynamic nature is crucial for health and disease.
  • Longitudinal microbiome studies are increasing, yet analytical methods for temporal data are limited.
  • Characterizing microbial dynamics over time is essential for understanding health and disease phenotypes.

Purpose of the Study:

  • To develop a novel analytical framework for high-dimensional, phylogenetically-based longitudinal microbiome data.
  • To enable comprehensive characterization of microbial dynamics and their association with phenotypes.
  • To address the limitations of existing methods in analyzing temporal microbiome data.

Main Methods:

  • Introduced the microbial trend analysis (MTA) framework.
  • MTA captures community-level microbial dynamic trends and identifies dominant taxa.
  • MTA facilitates group comparisons of microbial trends and individual subject classification based on microbial profiles.

Main Results:

  • Simulations confirmed MTA's robustness and power in hypothesis testing, taxon identification, and subject classification.
  • Real-data analysis using MTA demonstrated its utility in a mouse longitudinal study.
  • MTA effectively characterizes common microbial trends and differentiates between groups.

Conclusions:

  • The microbial trend analysis (MTA) framework is a valuable tool for analyzing longitudinal microbiome data.
  • MTA provides an effective approach to investigate dynamic microbial patterns.
  • This framework enhances the investigation of microbiome temporal dynamics in health and disease research.