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Related Experiment Video

Updated: May 24, 2026

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A cross-omics data analysis strategy for metabolite-microbe pair identification.

Tao Sun1, Dongnan Sun1, Junliang Kuang1

  • 1Center for Translational Medicine, The Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.

Proteomics
|July 12, 2024
PubMed
Summary
This summary is machine-generated.

BiOFI is a new framework to identify reliable metabolome-microbiome correlations. It uses a scoring system and pathway database to validate biological links, improving data mining for microbiome and metabolome research.

Keywords:
correlation pairscross‐omicsmetabolomicsmicrobiomicspathway linking relationships

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Area of Science:

  • Microbiology
  • Metabolomics
  • Bioinformatics

Background:

  • Metabolomics and microbiomics are crucial for understanding biological systems.
  • Existing data mining methods struggle with complex many-to-many associations between metabolome and microbiome data, leading to many statistically significant but biologically unvalidated candidates.
  • There is a need for robust frameworks to identify and validate biologically relevant links between these two omics layers.

Purpose of the Study:

  • To introduce BiOFI, a novel strategic framework designed to identify and validate statistically significant and biologically relevant metabolome-microbiome correlation pairs.
  • To develop a comprehensive scoring system and integrate functional pathway information for enhanced biological interpretation of omics data.
  • To provide a freely accessible R package for the research community to facilitate metabolome-microbiome association studies.

Main Methods:

  • BiOFI employs a multi-faceted scoring system that considers intergroup differences, impact on feature correlation networks, and organism abundance.
  • The framework integrates a built-in database linking metabolites, microbes, and KEGG functional pathways.
  • Feature pairs are ranked by combining importance scores derived from the scoring system and correlation strength.

Main Results:

  • Validation using a dataset from cesarean-section infants demonstrated the effectiveness and interpretability of the BiOFI framework.
  • BiOFI successfully identified and ranked biologically plausible metabolome-microbiome correlations.
  • The framework provides a systematic approach to move beyond simple statistical significance towards biological validation.

Conclusions:

  • BiOFI offers a validated and interpretable strategy for identifying robust metabolome-microbiome correlations.
  • The framework addresses the challenge of numerous unvalidated candidates in omics data mining.
  • The accessible BiOFI R package supports researchers in exploring complex interactions between host metabolism and the microbiome.