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Updated: May 16, 2026

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

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

Published on: October 15, 2019

Statistical object data analysis of taxonomic trees from human microbiome data.

Patricio S La Rosa¹, Berkley Shands, Elena Deych

¹Division of General Medical Sciences, Washington University in St. Louis, St. Louis, Missouri, United States of America.

|November 16, 2012

Summary

This study introduces a new statistical method using object-oriented data analysis (OODA) to analyze human microbiome data. The method models taxonomic trees, offering novel insights into host-microbe interactions and human health.

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Area of Science:

Microbiology
Statistical Inference
Bioinformatics

Background:

Human microbiome research investigates microbial communities in the human body and their impact on health.
Analyzing complex microbial data, such as taxonomic trees derived from 16S rRNA gene sequences, presents significant statistical challenges.

Purpose of the Study:

To propose a novel parametric statistical inference method for analyzing human microbiome data.
To develop a robust framework for modeling and comparing sets of taxonomic trees derived from microbial samples.

Main Methods:

Object-Oriented Data Analysis (OODA) applied to taxonomic trees.
Introduction of a weighted tree structure for analyzing Ribosomal Database Project (RDP) data.
Development of an approximate Maximum Likelihood Estimation (MLE) procedure and Likelihood Ratio Test (LRT) statistics for comparing metagenomic populations.

Main Results:

A novel weighted tree structure for RDP data analysis was introduced.
An approximate MLE procedure and LRT statistics were derived for comparing microbial population distributions.
The Jumpstart Human Microbiome Project (HMP) dataset was analyzed, yielding new insights.

Conclusions:

The proposed OODA-based method provides a powerful tool for analyzing complex human microbiome data.
This approach facilitates formal comparison of microbial community structures and offers avenues for future research in host-microbe interactions and health.