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Updated: Oct 10, 2025

A Clinical Metaproteomics Workflow Implemented within Galaxy Bioinformatics Platform to Analyze Host-Microbiome Interactions Underlying Human Disease
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Mathematical-based microbiome analytics for clinical translation.

Jayanth Kumar Narayana1, Micheál Mac Aogáin2,3, Wilson Wen Bin Goh1,4

  • 1Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore.

Computational and Structural Biotechnology Journal
|December 13, 2021
PubMed
Summary
This summary is machine-generated.

Human microbiome research is evolving beyond single-species studies. New computational tools are enabling deeper analysis of microbial communities for improved clinical applications in various diseases.

Keywords:
IntegrationMachine learningMathematical modellingMicrobial association analysisMicrobiomeTopological data analysis

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Area of Science:

  • Microbiology
  • Bioinformatics
  • Computational Biology

Background:

  • Traditional microbiology focused on culturing single species from infections, overlooking in vivo microbial diversity.
  • Next-generation sequencing and bioinformatics now allow comprehensive human microbiome characterization (taxonomy, function, resistance, phages).
  • Current microbiome analysis is limited by a lack of advanced computational tools.

Purpose of the Study:

  • To review challenges in microbiome data analysis, such as compositionality and batch effects.
  • To describe emerging analytical techniques for microbiome research.
  • To explore the clinical translation potential of these advanced analytical methods.

Main Methods:

  • Evaluation of microbiome dataset characteristics (compositionality, batch effects).
  • Description of analytical techniques: integrative analysis, machine learning, microbial association networks, topological data analysis (TDA), mathematical modeling.
  • Presentation of clinical translation pathways and implementation tools.

Main Results:

  • Microbiome datasets present unique analytical challenges due to their compositional nature and batch effects.
  • A range of advanced computational and mathematical methods are emerging for microbiome analysis.
  • These methods offer promising avenues for clinical applications in acute and chronic diseases.

Conclusions:

  • Advanced analytical techniques are crucial for unlocking the full potential of human microbiome research.
  • Mathematical modeling and machine learning show significant promise for clinical translation.
  • Addressing data challenges and developing new tools will drive microbiome-based diagnostics and therapeutics.