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Published Online: 11 July 2022

Comparison Between Full-Length 16S rRNA Metabarcoding and Whole Metagenome Sequencing Suggests the Use of Either Is Suitable for Large-Scale Microbiome Studies

Publication: Foodborne Pathogens and Disease
Volume 19, Issue Number 7


Since the number of studies of the microbial communities related to food and food-associated matrices almost completely reliant on next-generation sequencing techniques is rising, evaluations of these high-throughput methods are critical. Currently, the two most used sequencing methods to profile the microbiota of complex samples, including food and food-related matrices, are the 16S ribosomal RNA (rRNA) metabarcoding and the whole metagenome sequencing (WMS), both of which are powerful tools for the monitoring of foodborne pathogens and the investigation of the microbiome. Herein, the microbial profiles of 20 bulk tank milk filters from different dairy farms were investigated using both the full-length 16S (FL-16S) rRNA metabarcoding, a third-generation sequencing method whose application in food and food-related matrices is yet in its infancy, and the WMS, to evaluate the correlation and the reliability of these two methods to explore the microbiome of food-related matrices. Metabarcoding and metagenomic data were generated on a MinION platform (Oxford Nanopore Technologies) and on a Illumina NovaSeq 6000 platform, respectively. Our findings support the greater resolution of WMS in terms of both increased detection of bacterial taxa and enhanced detection of diversity; in contrast, FL-16S rRNA metabarcoding has proven to be a promising, less expensive, and more practical tool to profile most abundant taxa. The significant correlation of the two technologies both in terms of taxa diversity and richness, together with the similar profiles defined for both highly abundant taxa and core microbiomes, including Acinetobacter, Bacillus, and Escherichia genera, highlights the possible application of both methods for different purposes. This study allowed the first comparison of FL-16S rRNA sequencing and WMS to investigate the microbial composition of a food-related matrix, pointing out the advantageous use of FL-16S rRNA to identify dominant microorganisms and the superior power of WMS for the taxonomic detection of low abundant microorganisms and to perform functional analysis of the microbial communities.

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Abellan-Schneyder I, Matchado MS, Reitmeier S, et al. Primer, pipelines, parameters: Issues in 16S rRNA gene sequencing. mSphere 2021;6(1):e01202-20;
Addis MF, Tanca A, Uzzau S, et al. The bovine milk microbiota: Insights and perspectives from -omics studies. Mol Biosyst 2016;12(8):2359–2372;
Biegert G, El Alam MB, Karpinets T, et al. Diversity and composition of gut microbiome of cervical cancer patients: Do results of 16S rRNA sequencing and whole genome sequencing approaches align? J Microbiol Methods 2021;185:106213;
Bolyen E, Rideout JR, Dillon MR, et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat Biotechnol 2019;37(8):852–857;
Brumfield KD, Huq A, Colwell RR, et al. Microbial resolution of whole genome shotgun and 16S amplicon metagenomic sequencing using publicly available NEON data. PLoS One 2020;15(2):e0228899;
Bukin YS, Galachyants YP, Morozov IV, et al. The effect of 16S rRNA region choice on bacterial community metabarcoding results. Sci Data 2019;6(1):190007;
Catozzi C, Ceciliani F, Lecchi C, et al. Short communication: Milk microbiota profiling on water buffalo with full-length 16S rRNA using nanopore sequencing. J Dairy Sci 2020;103(3):2693–2700;
Chen S, Zhou Y, Chen Y, et al. Fastp: An ultra-fast all-in-one FASTQ preprocessor. 2018;34(17):i884–i890;
Choi J, In Lee S, Rackerby B, et al. Assessment of overall microbial community shift during cheddar cheese production from raw milk to aging. Appl Microbiol Biotechnol 2020;104(14):6249–6260;
Chong J, Liu P, Zhou G, et al. Using MicrobiomeAnalyst for comprehensive statistical, functional, and meta-analysis of microbiome data. Nat Protoc 2020;15(3):799–821;
Cole JR, Wang Q, Fish JA, et al. Ribosomal database project: Data and tools for high throughput rRNA analysis. Nucleic Acids Res 2014;42(D1):D633–D642;
Cuccato M, Rubiola S, Giannuzzi D, et al. 16S rRNA sequencing analysis of the gut microbiota in broiler chickens prophylactically administered with antimicrobial agents. Antibiotics 2021;10(2):146;
DeSantis TZ, Hugenholtz P, Larsen N, et al. Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl Environ Microbiol 2006;72(7):5069–5072;
Durazzi F, Sala C, Castellani G, et al. Comparison between 16S rRNA and shotgun sequencing data for the taxonomic characterization of the gut microbiota. Sci Rep 2021;11(1):3030;
Ewels P, Magnusson M, Lundin S, et al. MultiQC: Summarize analysis results for multiple tools and samples in a single report. Bioinformatics 2016;32(19):3047–3048;
Ferrocino I, Bellio A, Romano A, et al. RNA-based amplicon sequencing reveals microbiota development during ripening of artisanal versus industrial Lard d'Arnad. Appl Environ Microbiol 2017;83(16):e00983-17;
Jeong J, Yun K, Mun S, et al. The effect of taxonomic classification by full-length 16S rRNA sequencing with a synthetic long-read technology. Sci Rep 2021;11(1):1727;
Jovel J, Patterson J, Wang W, et al. Characterization of the gut microbiome using 16S or shotgun metagenomics. Front Microbiol 2016;7(459);
Kable ME, Srisengfa Y, Laird M, et al. The core and seasonal microbiota of raw bovine milk in tanker trucks and the impact of transfer to a milk processing facility. mBio 2016;7(4):e00836-16;
Laudadio I, Fulci V, Palone F, et al. Quantitative assessment of shotgun metagenomics and 16S rDNA amplicon sequencing in the study of human gut microbiome. OMICS 2018;22(4):248–254;
Leggett RM, Alcon-Giner C, Heavens D, et al. Rapid MinION metagenomic profiling of the preterm infant gut microbiota to aid in pathogen diagnostics. 2017;5(3):430–442;
Liu Z, Li J, Wei B, et al. Bacterial community and composition in Jiang-Shui and Suan-Cai revealed by high-throughput sequencing of 16S rRNA. Int J Food Microbiol 2019;306:108271;
Lu J, Breitwieser FP, Thielen P, et al. Bracken: Estimating species abundance in metagenomics data. PeerJ Computer Sci 2017;3:e104;
Macori G, Cotter PD. Novel insights into the microbiology of fermented dairy foods. Curr Opin Biotechnol 2018;49:172–178;
Matsuo Y, Komiya S, Yasumizu Y, et al. Full-length 16S rRNA gene amplicon analysis of human gut microbiota using MinION™ nanopore sequencing confers species-level resolution. BMC Microbiol 2021;21(1):35;
McHugh AJ, Feehily C, Fenelon MA, et al. Tracking the dairy microbiota from farm bulk tank to skimmed milk powder. mSystems 2020;5(2):e00226-20;
Mouillot D, Leprêtre A. A comparison of species diversity estimators. Res Popul Ecol 1999;41(2):203–215;
Murphy BP, Murphy M, Buckley JF, et al. In-line milk filter analysis: Escherichia coli O157 surveillance of milk production holdings. Int J Hyg Environ Health 2005;208(5):407–413;
Neu AT, Allen EE, Roy K. Defining and quantifying the core microbiome: Challenges and prospects. Proc Natl Acad Sci U S A 2021;118(51):e2104429118;
Numberger D, Ganzert L, Zoccarato L, et al. Characterization of bacterial communities in wastewater with enhanced taxonomic resolution by full-length 16S rRNA sequencing. Sci Rep 2019;9(1):9673;
Nygaard AB, Tunsjø HS, Meisal R, et al. A preliminary study on the potential of nanopore MinION and Illumina MiSeq 16S rRNA gene sequencing to characterize building-dust microbiomes. Sci Rep 2020;10(1):3209;
Quast C, Pruesse E, Yilmaz P, et al. The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Res 2013;41(Database issue):D590–D596;
Ranjan R, Rani A, Metwally A, et al. Analysis of the microbiome: Advantages of whole genome shotgun versus 16S amplicon sequencing. Biochem Biophys Res Commun 2016;469(4):967–977;
Rintala A, Pietilä S, Munukka E, et al. Gut microbiota analysis results are highly dependent on the 16S rRNA gene target region, whereas the impact of DNA extraction is minor. J Biomol Tech 2017;28(1):19–30;
Rubiola S, Macori G, Chiesa F, et al. Shotgun metagenomic sequencing of bulk tank milk filters reveals the role of Moraxellaceae and Enterobacteriaceae as carriers of antimicrobial resistance genes. Food Res. Int. 2022; 158:111579;
Rubiola S, Chiesa F, Dalmasso A, et al. Detection of antimicrobial resistance genes in the milk production environment: Impact of host DNA and sequencing depth. Front Microbiol 2020;11:1983;
Schloss PD, Westcott SL, Ryabin T, et al. Introducing mothur: Open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl Environ Microbiol 2009;75(23):7537–7541;
Schwenker JA, Friedrichsen M, Waschina S, et al. Bovine milk microbiota: Evaluation of different DNA extraction protocols for challenging samples. Microbiol Open 2022;11(2):e1275;
Shah N, Tang H, Doak TG, et al. Comparing bacterial communities inferred from 16S rRNA gene sequencing and shotgun metagenomics. Pac Symp Biocomput 2011;165–176;
Sonnier JL, Karns JS, Lombard JE, et al. Prevalence of Salmonella enterica, Listeria monocytogenes, and pathogenic Escherichia coli in bulk tank milk and milk filters from US dairy operations in the National Animal Health Monitoring System Dairy 2014 study. J Dairy Sci 2018;101(3):1943–1956;
Soriano-Lerma A, Pérez-Carrasco V, Sánchez-Marañón M, et al. Influence of 16S rRNA target region on the outcome of microbiome studies in soil and saliva samples. Sci Rep 2020;10(1):13637;
Tessler M, Neumann JS, Afshinnekoo E, et al. Large-scale differences in microbial biodiversity discovery between 16S amplicon and shotgun sequencing. Sci Rep 2017;7(1):6589;
Vogtmann E, Hua X, Zeller G, et al. Colorectal cancer and the human gut microbiome: Reproducibility with whole-genome shotgun sequencing. PLoS One 2016;11(5):e0155362;
Wood DE, Lu J, Langmead B. Improved metagenomic analysis with Kraken 2. Genome Biol 2019;20(1):257;
Yap M, Feehily C, Walsh CJ, et al. Evaluation of methods for the reduction of contaminating host reads when performing shotgun metagenomic sequencing of the milk microbiome. Sci Rep 2020;10(1):21665;

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Published In

cover image Foodborne Pathogens and Disease
Foodborne Pathogens and Disease
Volume 19Issue Number 7July 2022
Pages: 495 - 504
PubMed: 35819265


Published online: 11 July 2022
Published in print: July 2022


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Data Availability Statement

The datasets presented in this study can be found in the following online repository:, BioProject ID PRJNA808865, PRJNA809009.



Selene Rubiola*
Department of Veterinary Sciences, University of Turin, Grugliasco, Italy.
Guerrino Macori*
University College Dublin-Centre for Food Safety, School of Public Health, Physiotherapy & Sports Science, Dublin, Ireland.
Tiziana Civera
Department of Veterinary Sciences, University of Turin, Grugliasco, Italy.
Séamus Fanning
University College Dublin-Centre for Food Safety, School of Public Health, Physiotherapy & Sports Science, Dublin, Ireland.
University College Dublin-Centre for Food Safety, School of Public Health, Physiotherapy & Sports Science, Dublin, Ireland.
Department of Veterinary Sciences, University of Turin, Grugliasco, Italy.


Both these authors contributed equally to this study.
Address correspondence to: Francesco Chiesa, DVM, PhD, Department of Veterinary Sciences, University of Turin, Largo Paolo Braccini 2, 10095 Grugliasco (TO), Italy [email protected]

Authors' Contributions

Writing—original draft, formal analysis, investigation, and conceptualization by S.R. Writing—review and editing, resources, validation, and conceptualization by G.M. Writing—review and editing, and funding acquisition by T.C. Writing—review and editing, and resources by S.F. Writing—review and editing, and formal analysis by M.M. Writing—review and editing, visualization, supervision, and conceptualization by F.C.

Disclosure Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Funding Information

This study was supported by Ministero dell'Istruzione, dell'Università e della Ricerca (MIUR) under the programme “Dipartimenti di Eccellenza ex L.232/2016” to the “Department of Veterinary Science, University of Turin.” This study was supported by European Regional Development Funds (FESR 2014-2020—D24I19000980002)—TECH4MILK.

Ethical Statement

This study does not require Institutional Review Board (IRB) approval.

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