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Related Experiment Video

Updated: Jan 18, 2026

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EasyMultiProfiler: an efficient multi-omics data integration and analysis workflow for microbiome research.

Bingdong Liu1,2, Yaxi Liu3, Shuangbin Xu4

  • 1Department of Endocrinology and Metabolism, Zhujiang Hospital, Southern Medical University, Guangzhou, 510280, China.

Science China. Life Sciences
|September 11, 2025
PubMed
Summary
This summary is machine-generated.

EasyMultiProfiler (EMP) streamlines multi-omics data analysis for host-microbiome research. This workflow enhances data integration, standardization, and reproducibility, enabling deeper biological insights.

Keywords:
integrative analysismetabolomemicrobiomemulti-omicstranscriptome

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Area of Science:

  • Microbiology
  • Bioinformatics
  • Genomics

Background:

  • Host-microbiome interactions are vital for health and disease.
  • Multi-omics approaches offer comprehensive insights but face integration challenges.
  • Current methods struggle with data consistency, standardization, and reproducibility.

Purpose of the Study:

  • To develop a streamlined analytical workflow for multi-omics microbiome data.
  • To address challenges in data integration, standardization, and reproducibility.
  • To provide a robust platform for extracting biological insights from complex datasets.

Main Methods:

  • Developed EasyMultiProfiler (EMP), a unified data analysis framework.
  • Utilized SummarizedExperiment and MultiAssayExperiment classes for data storage.
  • Integrated five modules: data extraction, preparation, support, analysis, and visualization.

Main Results:

  • EMP offers a user-friendly, natural language-style workflow.
  • Successfully addressed data integration and standardization issues.
  • Enhanced reproducibility and reliability in multi-omics analysis.

Conclusions:

  • EasyMultiProfiler (EMP) provides an efficient and standardized solution for microbiome multi-omics research.
  • Empowers researchers and clinicians to gain deeper biological understanding.
  • Overcomes key barriers in contemporary microbiome data analysis.