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Related Experiment Video

Updated: Apr 12, 2026

Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing
11:22

Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing

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Identifying personal microbiomes using metagenomic codes.

Eric A Franzosa1, Katherine Huang2, James F Meadow3

  • 1Biostatistics Department, Harvard School of Public Health, Boston, MA 02115; Microbial Systems and Communities, Genome Sequencing and Analysis Program, The Broad Institute, Cambridge, MA 02142;

Proceedings of the National Academy of Sciences of the United States of America
|May 13, 2015
PubMed
Summary
This summary is machine-generated.

Human microbiome composition can uniquely identify individuals. Gut microbiome codes were highly stable, identifying over 80% of individuals over time, demonstrating microbiome identifiability.

Keywords:
forensic geneticshuman microbiomemetagenomicsmicrobial ecologystrain variation

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Area of Science:

  • Microbiome research
  • Genomic analysis
  • Human identifiability

Background:

  • Human microbiome composition varies significantly between individuals.
  • The stability and uniqueness of microbiome data for individual identification over time are not well understood.

Purpose of the Study:

  • To develop and apply a computational algorithm for creating unique and stable metagenomic codes.
  • To assess the feasibility of using microbiome data for individual identification across different body sites and time intervals.

Main Methods:

  • Developed a hitting set-based algorithm to generate body site-specific metagenomic codes.
  • Applied the algorithm to data from the Human Microbiome Project.
  • Analyzed strain variation using clade-specific marker genes.
  • Compared identification accuracy between initial and follow-up samples (30-300 days apart).

Main Results:

  • Metagenomic codes, particularly those capturing strain variation, could distinguish between hundreds of individuals.
  • Approximately 30% of individuals were uniquely identifiable using codes from typical body sites in follow-up samples.
  • Gut microbiome codes demonstrated exceptional stability, identifying over 80% of individuals.
  • Code mismatch at later time points was primarily due to the loss of specific microbial strains.

Conclusions:

  • Metagenomic codes can uniquely and stably identify individuals, especially from gut microbiome data.
  • Microbiome-based identifiability is feasible, with implications for study design and ethical considerations.
  • Temporal variations in the human microbiome ecology influence identifiability.