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Updated: Mar 8, 2026

Next-generation Sequencing of 16S Ribosomal RNA Gene Amplicons

Next-generation Sequencing of 16S Ribosomal RNA Gene Amplicons

Published on: August 29, 2014

Large-scale 16S gene assembly using metagenomics shotgun sequences.

Feng Zeng¹, Zicheng Wang², Ying Wang¹

¹Department of Automation, Xiamen University, Xiamen, Fujian, China.

Bioinformatics (Oxford, England)

|February 4, 2017

Summary

Reference-based ribosome assembly (RAMBL) reconstructs full-length 16S gene sequences from metagenomic data. This computational pipeline accurately identifies microbes and improves microbial diversity analysis.

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Last Updated: Mar 8, 2026

Next-generation Sequencing of 16S Ribosomal RNA Gene Amplicons

Next-generation Sequencing of 16S Ribosomal RNA Gene Amplicons

Published on: August 29, 2014

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

Guided Protocol for Fecal Microbial Characterization by 16S rRNA-Amplicon Sequencing

Guided Protocol for Fecal Microbial Characterization by 16S rRNA-Amplicon Sequencing

Published on: March 19, 2018

Area of Science:

Microbiology
Bioinformatics
Genomics

Background:

Combining 16S rRNA gene databases with metagenomic shotgun sequences enables unbiased microbial identification.
Accurate reconstruction of full-length 16S gene sequences is crucial for understanding microbial communities.

Purpose of the Study:

To develop a computational pipeline for reconstructing full-length 16S gene sequences from metagenomic data.
To improve the accuracy of microbial identification and diversity analysis using metagenomic sequencing.

Main Methods:

Reference-based ribosome assembly (RAMBL) integrates taxonomic tree search and Dirichlet process clustering.
Benchmarking against synthetic and real shotgun sequences validated the pipeline's accuracy.

Main Results:

RAMBL accurately reconstructs full-length 16S gene sequences, serving as a proxy for known and novel microbes.
30-40% of bacterial genera lack closely related genome sequences, highlighting the need for novel assembly methods.
RAMBL enhances microbial diversity determination and disease classification accuracy compared to marker gene sets and short reads.

Conclusions:

RAMBL provides access to full-length 16S gene sequences from large-scale metagenomic data.
This significantly improves metagenomic data analysis and interpretation, advancing microbial ecology and diagnostics.