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Updated: Jul 28, 2025
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Next-generation Sequencing of 16S Ribosomal RNA Gene Amplicons
Published on: August 29, 2014
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Amplicon Sequencing Pipelines in Metagenomics
Dapeng Wang1,2,3
1National Heart and Lung Institute, Imperial College London, London, UK. dapeng.wang@imperial.ac.uk.
Methods in Molecular Biology (Clifton, N.J.)
|May 31, 2023
Summary
This study presents best practices for amplicon sequencing data generation and processing. It offers parallel pipelines using mothur and DADA2 for accurate taxonomic profiling in metagenomics.
Area of Science:
- Metagenomics and Bioinformatics
- Microbial Ecology
- Computational Biology
Background:
- Taxonomic profiling in amplicon sequencing is crucial but challenged by sample heterogeneity and technical noise.
- Existing tools address specific data analysis steps, while integrated pipelines offer comprehensive solutions.
- Metagenomics data analysis complexity necessitates standardized and accurate approaches.
Purpose of the Study:
- To discuss best practices for amplicon sequencing data generation.
- To describe bioinformatics approaches for enhancing data processing accuracy.
- To enable straightforward comparisons between different taxonomic profiling pipelines.
Main Methods:
- Implementation of two independent, stepwise bioinformatics pipelines for taxonomic profiling.
- Utilized mothur and DADA2 software packages in parallel for comparative analysis.
- Detailed description of basic principles for key steps in amplicon sequence data processing.
Main Results:
- Provided a framework for generating high-quality amplicon sequencing data.
- Demonstrated bioinformatics approaches to improve accuracy in taxonomic profiling.
- Facilitated direct comparison of results obtained from mothur and DADA2 pipelines.
Conclusions:
- Adherence to best practices in data generation is essential for reliable metagenomic analysis.
- Parallel pipelines using mothur and DADA2 offer robust methods for accurate taxonomic profiling.
- The presented approaches mitigate challenges in complex metagenomics data analysis.
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