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Updated: Aug 6, 2025

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

Bioinformatic and Statistical Analysis of Microbiome Data.

Youngchul Kim¹

¹Department of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA. Youngchul.kim@moffitt.org.

Methods in Molecular Biology (Clifton, N.J.)

|March 17, 2023

Summary

This summary is machine-generated.

Next-generation sequencing (NGS) enables microbiome research. This chapter details bioinformatic and statistical methods for analyzing microbiome data to understand host-microbiome associations and microbial community functions.

Keywords:

16s rRNA sequencing Alpha diversity Beta diversity Metagenomics Microbiome Microbiome-wide association Phylogeny tree

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Published on: January 10, 2025

Area of Science:

Microbiology
Bioinformatics
Computational Biology

Background:

Next-generation sequencing (NGS) has revolutionized microbiome research, enabling the study of uncultured microbes and their genomes.
Strong evidence links the microbiome to human health and disease, necessitating advanced analytical approaches.
Bioinformatic and statistical analyses are crucial for interpreting complex microbiome data.

Purpose of the Study:

To introduce bioinformatic methods for processing raw NGS microbiome data into taxonomic and functional profiles, including phylogenetic trees.
To present statistical and machine learning approaches for analyzing these processed data.
To facilitate the inference of microbial community biodiversity and host-microbiome associations.

Main Methods:

Bioinformatic pipelines for generating taxonomic and functional count tables from raw NGS data.
Construction of phylogenetic trees from microbiome sequencing data.
Application of statistical and machine learning methods for analyzing microbiome data outputs.

Main Results:

The chapter provides a comprehensive overview of analytical techniques for microbiome research.
It outlines methods to generate essential data structures like count tables and phylogenetic trees.
It discusses approaches to infer biodiversity and host-microbiome interactions.

Conclusions:

Understanding the strengths and weaknesses of various analytical methods is key for correct application in microbiome research.
This knowledge can guide researchers in analyzing microbiome data effectively.
It may also spur the development of novel analytical techniques for future microbiome studies.