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Related Experiment Video

Updated: Jun 7, 2025

Tick Microbiome Characterization by Next-Generation 16S rRNA Amplicon Sequencing
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mbDriver: identifying driver microbes in microbial communities based on time-series microbiome data.

Xiaoxiu Tan1, Feng Xue1, Chenhong Zhang2

  • 1Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, 800 Dongchuan Road, Minhang District, Shanghai 200240, China.

Briefings in Bioinformatics
|November 11, 2024
PubMed
Summary

Identifying driver microbes in human microbial communities is key for disease biomarkers. The novel mbDriver framework accurately identifies these key microbes and their interactions using time-series data.

Keywords:
community dynamicsdenoisingecological networktime series abundance data

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Area of Science:

  • Microbiome research
  • Systems biology
  • Computational biology

Background:

  • Alterations in human microbial communities are linked to disease onset and progression.
  • Identifying key driver microbes is crucial for developing biomarkers for disease prevention, diagnosis, and treatment.
  • Current methods need improvement to account for individual microbial contributions and their interactions.

Purpose of the Study:

  • To introduce mbDriver, a novel framework for identifying driver microbes from time-series microbiome abundance data.
  • To provide a robust method that considers both individual microbial effects and their interactions within a community.
  • To enhance the accuracy and applicability of driver microbe identification.

Main Methods:

  • Data preprocessing of time-series abundance data using smoothing splines based on the negative binomial distribution.
  • Parameter estimation for the generalized Lotka-Volterra (gLV) model using regularized least squares.
  • Quantification of each microbe's contribution to community steady state via causal graph manipulation.

Main Results:

  • mbDriver demonstrated superior performance over existing methods on simulated datasets.
  • Nonparametric spline-based denoising and regularized least squares estimation were validated.
  • Practical application showcased effectiveness on dietary fiber intervention and ulcerative colitis datasets.

Conclusions:

  • mbDriver accurately identifies driver microbes by analyzing their interactions and individual contributions.
  • Identified driver microbes in a dietary intervention study impacted short-chain fatty acid abundances.
  • Driver microbes from an ulcerative colitis study correlated with metabolism-related pathways, highlighting their clinical relevance.