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Related Experiment Video

Updated: May 12, 2026

Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing
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Quantifying Metagenomic Strain Associations from Microbiomes with Anpan.

Andrew R Ghazi1,2, Kelsey N Thompson1,2,3, Amrisha Bhosle1,2,3

  • 1Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.

Biorxiv : the Preprint Server for Biology
|January 20, 2025
PubMed
Summary

New methods quantify microbial strain differences, improving microbiome epidemiology. Anpan identifies genetic elements and lineages linked to health outcomes, like colorectal cancer, with higher accuracy.

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Area of Science:

  • Microbiome research
  • Genomic epidemiology
  • Computational biology

Background:

  • Microbial strain variation significantly impacts phenotypes and health.
  • Existing inferential methods struggle with complex strain-level metagenomic data.
  • Quantifying genetic and genomic differences at the strain level is crucial for understanding microbial epidemiology.

Purpose of the Study:

  • To develop and validate quantitative methods for microbiome strain epidemiology.
  • To address challenges in analyzing high-dimensional, variable, and phylogenetically related strain data.
  • To identify strain-specific genetic elements, lineages, and pathways associated with host phenotypes.

Main Methods:

  • Anpan utilizes adaptive filtering with linear models for gene carriage analysis.
  • Phylogenetic generalized linear mixed models assess sub-species lineage associations.
  • Random effects models identify phenotype-associated pathway retention or loss.

Main Results:

  • Simulations show Anpan offers improved effect size estimation and reduced false positive rates.
  • Application to colorectal cancer (CRC) data identified adaptive genes and phylogenetic effects.
  • Findings complement and extend known species-level microbiome CRC biomarkers.

Conclusions:

  • Anpan provides robust quantitative methods for strain-level microbiome epidemiology.
  • The R library facilitates the study of microbial genetic epidemiology across diverse contexts.
  • These methods enhance our understanding of microbial contributions to human health and disease.