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Updated: Jan 2, 2026
10:24
Next-generation Sequencing of 16S Ribosomal RNA Gene Amplicons
Published on: August 29, 2014
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Systematic processing of ribosomal RNA gene amplicon sequencing data
Julien Tremblay1, Etienne Yergeau2
1Energy Mining and Environment, National Research Council Canada, Montreal, QC H4P-2R2, Canada.
Gigascience
|December 10, 2019
Summary
AmpliconTagger is a flexible Python-based pipeline for analyzing ribosomal RNA (rRNA) gene amplicon sequencing data. It empowers specialized users to integrate diverse bioinformatic tools and leverage high-performance computing (HPC) for advanced microbial community analysis.
Area of Science:
- Microbiology
- Bioinformatics
- Computational Biology
Background:
- High-throughput sequencing has made microbiology data-intensive.
- 16S/18S/ITS ribosomal RNA (rRNA) gene amplicon sequencing is widely used for phylogenetic studies.
- Existing bioinformatic pipelines often lack flexibility for specialized users on high-performance computing (HPC) environments.
Purpose of the Study:
- To develop a flexible and customizable bioinformatic pipeline for rRNA gene amplicon sequencing data analysis.
- To enable integration of various bioinformatic tools and leverage HPC capabilities.
- To provide a solution for specialized users needing advanced control over their analyses.
Main Methods:
- Developed AmpliconTagger, a Python-based pipeline for rRNA gene amplicon analysis.
- Designed for HPC environments with support for job dependencies and smart-restart functionality.
- Integrated and tested with established algorithms for operational taxonomic unit (OTU) and amplicon sequence variant (ASV) generation.
Main Results:
- AmpliconTagger demonstrates fine-tuning and integration of diverse bioinformatic procedures.
- The pipeline supports short rRNA gene amplicons (16S, 18S, ITS) and long-read data.
- Successful application for generating taxonomic summaries and diversity metrics was shown.
Conclusions:
- AmpliconTagger offers a versatile platform for systematic analysis of amplicon sequence data.
- The pipeline enhances the ability of specialized users to perform advanced microbial community analyses.
- It facilitates the integration of stand-alone software for evolving bioinformatic methodologies.
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