Ann Clin Microbiol.  2025 Mar;28(1):3. 10.5145/ACM.2025.28.1.3.

Mock communities to assess biases in nextgeneration sequencing of bacterial species representation

• Hwang Y
• Kim JY
• Kim SI
• Sung JY
• Moon HS
• Yong TS
• Hong KH
• Lee H
• Yong D

• KMID: 2566599
• DOI: http://doi.org/10.5145/ACM.2025.28.1.3

Abstract

Background
The 16S rRNA-targeted next-generation sequencing (NGS) has been widely used as the primary tool for microbiome analysis. However, whether the sequenced microbial diversity absolutely represents the original sample composition remains unclear. This study aimed to evaluate whether 16S rRNA gene-targeted NGS accurately captures bacterial community composition.
Methods
Mock communities were constructed using equal amounts of DNA from 18 bacterial strains in three formats: genomic DNA, recombinant plasmids, and polymerase chain reaction (PCR) templates. The V3V4 region of the 16S rRNA gene was amplified and sequenced using the Illumina MiSeq.
Results
Data regression analysis revealed that the recombinant plasmid produced more accurate and precise correlation curve than that by the gDNA and PCR products, with a slope closest to 1 (1.0082) and the highest R² value (0.9975). Despite the same input amount of bacterial DNA, the NGS read distribution varied across all three mock communities. Using multiple regression analysis, we found that the guanine-cytosine (GC) content of the V3V4 region, 16S rRNA gene, size of gDNA, and copy number of 16S rRNA were significantly associated with the NGS output of each bacterial species.
Conclusion
This study demonstrated that recombinant plasmids are the preferred option for quality control and that NGS output is biased owing to certain bacterial characteristics, such as %GC content, gDNA size, and 16S rRNA gene copy number. Further research is required to develop a system that compensates for NGS process biases using mock communities.

Keyword

Mock community; High-throughput nucleotide sequencing; Illumina MiSeq; GC contents; 16S rRNA gene copy number

Figure

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  Fig. 1. Effect of quantitative change in input to NGS output. All three types of mock communities were prepared with different input ratios of A and B (1:1, 1:2, 1:4, 1:10, and 1:100) to systematically assess the quantitative reflection of bacterial input in NGS output. Group A is presented in orange and group B is presented in blue. The regression equation for gDNA is represented as y = 1.2259x - 26.705 with a coefficient of determination (R²) of 0.9854. The regression equations for plasmid DNA and PCR product are ‘y = 1.0082x - 1.6091 with an R² value of 0.9975’ and ‘y = 1.054x - 7.0206 with an R² value of 0.9939’, respectively.

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