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Updated: Nov 8, 2025

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Microbiome Sample Comparison and Search: From Pair-Wise Calculations to Model-Based Matching.

Yuguo Zha1, Hui Chong1, Kang Ning1

  • 1Key Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Key Laboratory of Bioinformatics and Molecular-Imaging, Department of Bioinformatics and Systems Biology, Center of AI Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, China.

Frontiers in Microbiology
|April 26, 2021
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Summary

Comparing microbiome samples is computationally challenging. This review systematically compares distance-based, unsupervised, and supervised methods for accurate and efficient microbiome analysis and origin tracking.

Keywords:
comparisondistance-basedmicrobiomesearchsupervisedunsupervised

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Area of Science:

  • Microbiome research
  • Computational biology
  • Bioinformatics

Background:

  • Vast amounts of microbiome data are accumulating globally.
  • Efficient comparison and search of these samples are computationally challenging.
  • Existing methods for microbiome sample analysis have limitations.

Purpose of the Study:

  • To systematically compare existing methods for microbiome sample comparison and search.
  • To assess the accuracy and efficiency of different analytical approaches.
  • To provide guidance on method selection for microbiome data analysis.

Main Methods:

  • Systematic comparison of distance-based, unsupervised, and supervised algorithms.
  • Theoretical and practical assessment of accuracy and efficiency.
  • Description of applicable scenarios and diverse applications.

Main Results:

  • Different methods yield varying results for microbiome sample comparison and search.
  • Accuracy and efficiency differ across methods based on specific settings.
  • Guidance is provided on selecting appropriate methods for various applications.

Conclusions:

  • Method selection is crucial for accurate microbiome sample comparison and origin identification.
  • Future directions include leveraging deep learning for enhanced source tracking.
  • Standardized comparison frameworks are needed for advancing microbiome research.