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Methods to Assess Microbial Populations01:30

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A Method for Targeted 16S Sequencing of Human Milk Samples
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Microbial network inference for longitudinal microbiome studies with LUPINE.

Saritha Kodikara1, Kim-Anh Lê Cao2

  • 1Melbourne Integrative Genomics, School of Mathematics and Statistics, The University of Melbourne, Royal Parade, 3052, Parkville, Victoria, Australia.

Microbiome
|March 3, 2025
PubMed
Summary
This summary is machine-generated.

Researchers developed LUPINE, a new method for analyzing longitudinal microbiome data to infer microbial interactions over time. This approach effectively captures dynamic relationships in complex microbial ecosystems, even with limited data.

Keywords:
16SLongitudinalNetworkPartial correlation

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Area of Science:

  • Microbiome research
  • Network inference
  • Bioinformatics

Background:

  • Microbiome studies traditionally use cross-sectional data, limiting understanding of dynamic microbial interactions.
  • Longitudinal microbiome studies are gaining traction for inferring temporal associations between taxa.
  • Existing network inference methods are underexplored for longitudinal microbiome data.

Purpose of the Study:

  • To introduce LUPINE (LongitUdinal modelling with Partial least squares regression for NEtwork inference), a novel method for longitudinal microbiome network inference.
  • To address limitations of traditional methods in handling sparse, compositional, and multivariate microbiome data.
  • To capture dynamic microbial interactions by considering all past time points.

Main Methods:

  • LUPINE leverages conditional independence and low-dimensional data representation.
  • The method is designed for scenarios with small sample sizes and few time points.
  • LUPINE incorporates information from all past time points to infer networks.

Main Results:

  • LUPINE successfully infers microbial networks across time, capturing dynamic interactions.
  • Validation in simulated data and four case studies demonstrated LUPINE's ability to identify relevant taxa.
  • The method proved effective across diverse experimental designs, including human and mouse studies.
  • Metrics were used to compare inferred networks and detect changes over time or due to external factors.

Conclusions:

  • LUPINE is an innovative methodology for longitudinal microbiome data analysis.
  • The approach is suitable for various biological contexts beyond microbiome research.
  • Publicly available R code and data facilitate application and further research.