Department of Biosciences, Biotechnology and Environment, University of Bari Aldo Moro, 70126 Bari, Italy.
Institute of Biomembranes, Bioenergetics and Molecular Biotechnologies, Consiglio Nazionale delle Ricerche, 70126 Bari, Italy.
Genes (Basel). 2023 Jul 31;14(8):1567. doi: 10.3390/genes14081567.
The 16S rRNA amplicon-based sequencing approach represents the most common and cost-effective strategy with great potential for microbiome profiling. The use of second-generation sequencing (NGS) technologies has led to protocols based on the amplification of one or a few hypervariable regions, impacting the outcome of the analysis. Nowadays, comparative studies are necessary to assess different amplicon-based approaches, including the full-locus sequencing currently feasible thanks to third-generation sequencing (TGS) technologies. This study compared three different methods to achieve the deepest microbiome taxonomic characterization: (a) the single-region approach, (b) the multiplex approach, covering several regions of the target gene/region, both based on NGS short reads, and (c) the full-length approach, which analyzes the whole length of the target gene thanks to TGS long reads. Analyses carried out on benchmark microbiome samples, with a known taxonomic composition, highlighted a different classification performance, strongly associated with the type of hypervariable regions and the coverage of the target gene. Indeed, the full-length approach showed the greatest discriminating power, up to species level, also on complex real samples. This study supports the transition from NGS to TGS for the study of the microbiome, even if experimental and bioinformatic improvements are still necessary.
基于 16S rRNA 扩增子的测序方法是最常见和最具成本效益的策略,具有很大的微生物组分析潜力。第二代测序(NGS)技术的应用导致了基于一个或几个高变区扩增的方案,影响了分析结果。如今,有必要进行比较研究来评估不同的基于扩增子的方法,包括由于第三代测序(TGS)技术而成为可能的全基因序列测序。本研究比较了三种不同的方法来实现最深入的微生物组分类特征化:(a)单区方法,(b)基于 NGS 短读的多区方法,覆盖目标基因/区域的多个区域,以及(c)全长方法,该方法通过 TGS 长读来分析目标基因的全长。在具有已知分类组成的基准微生物组样本上进行的分析突出了不同的分类性能,这与高变区的类型和目标基因的覆盖度密切相关。事实上,全长方法显示了最大的区分能力,甚至在复杂的实际样本中也可以达到种水平。本研究支持从 NGS 向 TGS 的转变,以研究微生物组,即使仍需要实验和生物信息学的改进。