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Updated: May 21, 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

Unlocking the Metagenome: Pipeline for Microbiome Data Analysis.

Aarthi Ravikrishnan^1,2

¹Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), Singapore, Republic of Singapore. aarthi_ravikrishnan@a-star.edu.sg.

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

|May 19, 2026

Summary

Shotgun metagenomics reveals microbial diversity and function. This chapter details a bioinformatics workflow for analyzing metagenomic data, offering insights into microbial communities and experimental considerations for reproducible research.

Keywords:

Microbial communities Shotgun metagenomics Statistical analyses Taxonomic profiles

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Last Updated: May 21, 2026

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Area of Science:

Microbiology
Bioinformatics
Genomics

Background:

Metagenomic technologies have transformed the study of microbial communities.
Shotgun metagenomics offers a comprehensive approach to analyzing microbial diversity and function, surpassing traditional culture-based methods.

Purpose of the Study:

To outline a standard bioinformatics workflow for generating taxonomic profiles from shotgun metagenomic data.
To demonstrate basic statistical analyses for deriving insights from metagenomic datasets.
To discuss critical experimental and analytical considerations for robust metagenomic research.

Main Methods:

Sequencing of all DNA within a sample (shotgun metagenomics).
Bioinformatics pipeline for taxonomic profiling.
Statistical analysis of metagenomic data.

Main Results:

A detailed bioinformatics workflow for metagenomic data analysis.
Demonstration of statistical approaches for microbial community insights.
Discussion of key factors for accurate metagenomic data interpretation.

Conclusions:

Shotgun metagenomics provides a powerful, culture-independent method for exploring microbial ecosystems.
Standardized bioinformatics workflows and careful consideration of experimental factors are essential for reliable and reproducible metagenomic research.
Automation of workflows enhances consistency in large-scale metagenomic analyses.