Multicenter assessment of microbial community profiling using 16S rRNA gene sequencing and shotgun metagenomic sequencing
Open accessAbstract
INTRODUCTION: Microbiome research based on high-throughput sequencing has grown exponentially in recent years, but methodological variations can easily undermine the reproducibility across studies. OBJECTIVES: To systematically evaluate the comparability of sequencing results of 16S rRNA gene sequencing (16Ss)- and shotgun metagenomic sequencing (SMs)-based microbial community profiling in laboratories under routine conditions. METHODS: We designed a multicenter study across 35 participating laboratories in China using designed mock communities and homogenized fecal samples. RESULTS: <0.05). The results from laboratories with near-identical protocols showed slight interlaboratory deviations. However, a high degree of interlaboratory deviation was found in the observed abundances of specific taxa, such as Bacteroides spp. (range: 0.3%-53.5%), Enterococci spp. (range: 0.8%-43.9%) and Fusobacterium spp. (range: 0.1%-39.8%). SMs performed better than 16Ss in detecting low-abundance bacteria (B. bifidum). The differences in DNA extraction methods, amplified regions and bioinformatics analysis tools (taxonomic classifiers and database) were important factors causing interlaboratory deviations. Addressing laboratory contamination is an urgent task because various sources of unexpected microbes were found in negative control samples. CONCLUSIONS: Well-defined control samples, such as the mock communities in this study, should be routinely used in microbiome research for monitoring potential biases. The findings in this study will provide guidance in the choice of more reasonable operating procedures to minimize potential methodological biases in revealing human microbiota composition.
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Unlike 16S ribosomal RNA sequencing, shotgun metagenomics sequences permit a parallel assessment of all microbial kingdoms (bacterial, fungal, viral) in a given sample.
“Unlike 16S ribosomal RNA sequencing, shotgun metagenomics sequences permit a parallel assessment of all microbial kingdoms (bacterial, fungal, viral) in a given sample.77, 78”
From The oral microbiome: Role of key organisms and complex networks in oral health and disease (2021) · cites this paper as [77]
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“Well-defined reference materials are the premise for performing multicenter quality assessment evaluations [19,38,39].”
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Marine bacteria and omic approaches: A novel and potential repository for bioremediation assessment
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“It can be used to generate gene sequence for ultimate microbial community analysis besides being an efficient, easily accessible and cost- effective technology for elaborating microbial populations (Han et al., 2020).”
BackgroundMethodXie, J., Wang, Y., Zhong, Q., Bai, S., Zhou, C., Tian, T., & Chen, J. (2022) · Frontiers in Cellular and Infection Microbiology · doi:10.3389/fcimb.2022.906303 · Cited by 33 · see citing works
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“Previous inter-laboratories studies reported biases in bacterial taxonomic profiling, following the transfer of different raw biospecimens 77 , human stool samples 36 , to raw 16S rRNA sequences obtained from mock microbial community samples 78 .”
Future potential of metagenomics in microbiology laboratories
Schuele, L., Cassidy, H., Peker, N., Rossen, J. W. A., & Couto, N. (2021) · Expert Review of Molecular Diagnostics · doi:10.1080/14737159.2021.2001329 · Cited by 35 · see citing works
“Possible negative controls can consist of a sampling blank, nucleic acid extraction blank and/or no-template control [107,109].”
BackgroundPu, G., Hou, L., Du, T., Zhou, W., Liu, C., Niu, P., Wu, C., Bao, W., Huang, R., & Li, P. (2022) · mSystems · doi:10.1128/msystems.00937-22 · Cited by 29 · see citing works
“Shotgun metagenomic sequencing sequences the genomes of all the microbes isolated from the entire microbial community (16).”
BackgroundKey Technologies for Progressing Discovery of Microbiome-Based Medicines
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“0 (14) to quantify the relative abundances of the genes and obtained a nonredundant gene catalog comprising 4045593 genes.”
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“There are field and laboratory issues that can severely affect: downstream sample processing, bioinformatic data generation, statistical data analysis, and result interpretation; thus affecting reproducibility [11,12].”
Impact of DNA Extraction Methods on Gut Microbiome Profiles: A Comparative Metagenomic Study
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“3d), supporting results of prior studies (Ducarmon et al. 2020; Han et al. 2020; Li et al. 2020).”
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“haemolyticus strains was done by 16S rRNA gene sequencing, which is known to be a reliable molecular technique for microbial identification (Johnson et al., 2019; Han et al., 2020).”
MethodHigh-throughput DNA extraction strategy for fecal microbiome studies
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Microbiologic surveys for Baijiu fermentation are affected by experimental design
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“…sequencing technology has enabled the identification of clusters known as Operational Taxonomic Units or Amplicon Sequence Variants, whose analysis provides information on the community diversity, richness, and evenness, as well as the degree of divergence between different sample types [44,45].”
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The impact of cefuroxime prophylaxis on human intestinal microbiota in surgical oncological patients
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“The use of in house bacterial mock communities, such as we have done in our study, is often encouraged because it can reflect the variability of interesting or relevant taxa more accurately than commercially available communities (Han et al., 2020).”
BackgroundManter, D. K., Reardon, C. L., Ashworth, A. J., Ibekwe, A. M., Lehman, R. M., Maul, J. E., Miller, D. N., Creed, T., Ewing, P. M., Park, S., et al. (2024) · Communications Biology · doi:10.1038/s42003-024-06594-8 · Cited by 8 · see citing works
“Guidelinesontheminimumreportinginformation and quality for amplicon 19 and marker-gene sequencing 20 have been developed to support reproducibility, though lab-to-lab variability still remains a challenge to inter-experimental comparisons 12,13,21 .”
BackgroundMethodInfluentialTaxonomic diversity and functional potential of microbial communities in oyster calcifying fluid
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Comparative 16S Metabarcoding of Nile Tilapia Gut Microbiota from the Northern Lakes of Egypt
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“The generally agreed criteria for identifying the members of mixed microbial communities and defining the overall microorganisms in the environment are ribosomal RNA (rRNA) sequences [10, 32, 37].”
BackgroundThu, M. S., Sawaswong, V., Chanchaem, P., Klomkliew, P., Campbell, B. J., Hirankarn, N., Fothergill, J. L., & Payungporn, S. (2024) · Access Microbiology · doi:10.1099/acmi.0.000754.v3 · Cited by 7 · see citing works
“Methodological biases can cause significant variances in the observed microbial profiles, resulting in considerable variation between studies [24, 25].”
BackgroundA Comprehensive Assessment of Metagenomic cfDNA Sequencing for Microbe Detection
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“NGS provides comprehensive, culture-independent profiling of dominant and rare taxa with functional insights [ 69 ], though it requires strict contamination control and advanced bioinformatics [ 65, 69 ].”
Mycoremediation: A Step towards Sustainability
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Bioinformatics Toward Improving Bioremediation
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“However, the effect of the removal of host cell-derived nucleic acids is still controversial, especially for samples with low microbial content, the removal of human host DNA background can significantly reduce the detection rate of target microorganisms (30).”
BackgroundNovel potential biomarkers for predicting childhood caries via metagenomic analysis
Zhang, H., Zheng, X., Huang, Y., Zou, Y., Zhang, T., Repo, M. A., Yin, M., You, Y., Jie, Z., & Xu, W.-A. (2025) · Frontiers in Cellular and Infection Microbiology · doi:10.3389/fcimb.2025.1522970 · Cited by 4 · see citing works
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“A study combining EQUC and 16S rRNA sequencing revealed that men with more severe LUTS, indicated by higher IPSS scores, were signi fi cantly more likely to test positive for bacteria in catheterized urine.”
InfluentialMetagenomics for antimicrobial resistance: from resistome surveillance to mechanistic inference
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“Multicenter evaluations and assay validation studies highlight that analytical variability can be large enough to mask biological signals if controls, thresholds, and reporting standards are not explicit (31, 33).”
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“9,19,20 At the phylum level, PV patients are characterized by a significant decrease in Firmicutes and an increase in Proteobacteria.”
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Environmental clean-up using metagenomics: From genomics to microbial bioremediation of contaminants
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Gut microbiota and kidney diseases. Literature review
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BackgroundKlebsiella , Enterobacter , Citrobacter , and Selected Other Enterobacteriaceae
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Klebsiella , Enterobacter , Citrobacter , and Selected Other Enterobacteriaceae
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Ecological Importance of Bacteria in Bioremediation of Contaminated Sites
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Oral Microbiome in Periodontal Disease: Where Are We Now?
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“Short-read sequencing is associated with lower error rates, whereas long-read sequencing allows for the analysis of longer sequences [ 42 , 43].”
Identification of Plant‐derived Microbial Consortium Resistant to Biotic Stress
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Characterization of novel proteins and enzymes in stress environments from microbial communities
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“Two studies opted for shotgun metagenomics, a more comprehensive technique that, in addition to taxonomic profiling, enables the identification of functional genes and metabolic pathways, thereby offering a more detailed view of the microbial alterations induced by cholestasis (22,23) .”
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“Even workflows designed to account for contamination can generate divergent results across sites, reinforcing the need for harmonized reference materials and shared mock communities (Sinha et al., 2017; Han et al., 2020; Forry et al., 2024).”
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The Role of Artificial Intelligence and Machine Learning in Revolutionizing Probiotic Research
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- Authors
- Han, D., Gao, P., Li, R., Tan, P., Xie, J., Zhang, R., & Li, J.
- Affiliations
- Chinese Academy of Medical Sciences & Peking Union Medical College (CN)
- Corresponding author
- Zhang, Rui
- Li, Jinming
- Venue
- Journal of Advanced Research
- Publisher
- Elsevier BV
- Published
- 2020-11
- Volume
- 26
- Pages
- 111-121
- ISSN
- 2090-1224 (ISSN-L)
- 2090-1232
- PMC
- PMC7584675
- OpenAlex
- W3044724507
- Cited by
- 93
- References
- 72
- Citations by year
- 2026: 22 · 2025: 13 · 2024: 14 · 2023: 11 · 2022: 16 · 2021: 15 · 2020: 2
- FWCI
- 3.55
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Journal of Advanced Research (publishedVersion, cc-by-nc-nd)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Natural Science Foundation of China (81703276)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gut microbiota and health
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Physical Sciences › Environmental Science › Ecology › Microbial Community Ecology and Physiology
- Keywords
- human gut microbiome, 16S rRNA gene sequencing, shotgun metagenomics, inter-laboratory variability, methodological bias, mock microbial community, fecal samples, laboratory contamination, DNA extraction methods, taxonomic classification
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