Covid-19 Research

Mini Review

OCLC Number/Unique Identifier:

Decoding Microbial Networks: An Insight into 16S rRNA and Whole Genome Sequencing Approaches in Metagenomic Studies

Biology Group    Start Submission

Cinzia Alfonsi, Fabio Di Pietro, Filomena Tiziana Papa and Federico Gabrielli*

Dates: Received: 2023-10-09 | Accepted: 2023-10-20 | Published: 2023-10-21
Pages: 1443-1446


The exploration of microbial communities is pivotal in understanding environmental, human, and animal ecosystem dynamics. Advances in high-throughput sequencing technologies have significantly enriched our insights into microbiomes, with 16S rRNA and Whole Genome Sequencing (WGS) being central methodologies. This review delineates a comparative analysis of these sequencing techniques, particularly focusing on different hypervariable regions of 16S rRNA (V3-V4 and V2-3-4-6-7-8-9) and WGS. The 16S rRNA sequencing, despite being cost-effective and less computationally demanding, often limits identification to the genus level. In contrast, WGS, while being resource-intensive, provides a broader spectrum of microbial identification including bacteria, viruses, fungi, and parasites, alongside a deeper insight into microbial functional attributes. The challenges associated with sequencing depth in WGS are discussed, alongside emerging mitigating strategies like host DNA depletion. The choice between these methodologies hinges on the project objectives and available resources. This comparative assessment aims to guide researchers in selecting the apt sequencing approach for their metagenomic studies, thereby facilitating a more extensive understanding of microbial ecosystems.

FullText HTML FullText PDF DOI: 10.37871/jbres1816

Certificate of Publication


© 2023 Alfonsi C, et al. Distributed under Creative Commons CC-BY 4.0

How to cite this article

Alfonsi C, Di Pietro F, Papa FT, Gabrielli F. Decoding Microbial Networks: An Insight into 16S rRNA and Whole Genome Sequencing Approaches in Metagenomic Studies. J Biomed Res Environ Sci. 2023 Oct 21; 4(10): 1443-1446. doi: 10.37871/ jbres1816, Article ID: JBRES1816, Available at:

Subject area(s)


  1. Sanschagrin S, Yergeau E. Next-generation sequencing of 16S ribosomal RNA gene amplicons. J Vis Exp. 2014 Aug 29;(90):51709. doi: 10.3791/51709. PMID: 25226019; PMCID: PMC4828026.
  2. Fadeev E, Cardozo-Mino MG, Rapp JZ, Bienhold C, Salter I, Salman-Carvalho V, Molari M, Tegetmeyer HE, Buttigieg PL, Boetius A. Comparison of Two 16S rRNA Primers (V3-V4 and V4-V5) for Studies of Arctic Microbial Communities. Front Microbiol. 2021 Feb 16;12:637526. doi: 10.3389/fmicb.2021.637526. PMID: 33664723; PMCID: PMC7920977.
  3. Kameoka S, Motooka D, Watanabe S, Kubo R, Jung N, Midorikawa Y, Shinozaki NO, Sawai Y, Takeda AK, Nakamura S. Benchmark of 16S rRNA gene amplicon sequencing using Japanese gut microbiome data from the V1-V2 and V3-V4 primer sets. BMC Genomics. 2021 Jul 10;22(1):527. doi: 10.1186/s12864-021-07746-4. PMID: 34246242; PMCID: PMC8272389.
  4. Tremblay J, Singh K, Fern A, Kirton ES, He S, Woyke T, Lee J, Chen F, Dangl JL, Tringe SG. Primer and platform effects on 16S rRNA tag sequencing. Front Microbiol. 2015 Aug 4;6:771. doi: 10.3389/fmicb.2015.00771. PMID: 26300854; PMCID: PMC4523815.
  5. Nelson MC, Morrison HG, Benjamino J, Grim SL, Graf J. Analysis, optimization and verification of Illumina-generated 16S rRNA gene amplicon surveys. PLoS One. 2014 Apr 10;9(4):e94249. doi: 10.1371/journal.pone.0094249. PMID: 24722003; PMCID: PMC3983156.
  6. Thijs S, Op De Beeck M, Beckers B, Truyens S, Stevens V, Van Hamme JD, Weyens N, Vangronsveld J. Comparative Evaluation of Four Bacteria-Specific Primer Pairs for 16S rRNA Gene Surveys. Front Microbiol. 2017 Mar 28;8:494. doi: 10.3389/fmicb.2017.00494. PMID: 28400755; PMCID: PMC5368227.
  7. Treangen TJ, Salzberg SL. Repetitive DNA and next-generation sequencing: computational challenges and solutions. Nat Rev Genet. 2011 Nov 29;13(1):36-46. doi: 10.1038/nrg3117. Erratum in: Nat Rev Genet. 2012 Feb;13(2):146. PMID: 22124482; PMCID: PMC3324860.
  8. Barb JJ, Oler AJ, Kim HS, Chalmers N, Wallen GR, Cashion A, Munson PJ, Ames NJ. Development of an Analysis Pipeline Characterizing Multiple Hypervariable Regions of 16S rRNA Using Mock Samples. PLoS One. 2016 Feb 1;11(2):e0148047. doi: 10.1371/journal.pone.0148047. PMID: 26829716; PMCID: PMC4734828.
  9. Malczynski M, Zhu A, Zembower T, Qi C. Diagnostic performance of Ion 16S metagenomics kit and Ion reporter metagenomics workflow for bacterial pathogen detection in culture-negative clinical specimens from sterile sources. Diagn Microbiol Infect Dis. 2021 Oct;101(2):115451. doi: 10.1016/j.diagmicrobio.2021.115451. Epub 2021 Jun 11. PMID: 34237647.
  10. Wood DE, Lu J, Langmead B. Improved metagenomic analysis with Kraken 2. Genome Biol. 2019 Nov 28;20(1):257. doi: 10.1186/s13059-019-1891-0. PMID: 31779668; PMCID: PMC6883579.
  11. Wensel CR, Pluznick JL, Salzberg SL, Sears CL. Next-generation sequencing: insights to advance clinical investigations of the microbiome. J Clin Invest. 2022 Apr 1;132(7):e154944. doi: 10.1172/JCI154944. PMID: 35362479; PMCID: PMC8970668.
  12. Shi Y, Wang G, Lau HC, Yu J. Metagenomic Sequencing for Microbial DNA in Human Samples: Emerging Technological Advances. Int J Mol Sci. 2022 Feb 16;23(4):2181. doi: 10.3390/ijms23042181. PMID: 35216302; PMCID: PMC8877284.
  13. Ross MG, Russ C, Costello M, Hollinger A, Lennon NJ, Hegarty R, Nusbaum C, Jaffe DB. Characterizing and measuring bias in sequence data. Genome Biol. 2013 May 29;14(5):R51. doi: 10.1186/gb-2013-14-5-r51. PMID: 23718773; PMCID: PMC4053816.
  14. Robins HS, Campregher PV, Srivastava SK, Wacher A, Turtle CJ, Kahsai O, Riddell SR, Warren EH, Carlson CS. Comprehensive assessment of T-cell receptor beta-chain diversity in alphabeta T cells. Blood. 2009 Nov 5;114(19):4099-107. doi: 10.1182/blood-2009-04-217604. Epub 2009 Aug 25. PMID: 19706884; PMCID: PMC2774550.
  15. Feldgarden M, Brover V, Haft DH, Prasad AB, Slotta DJ, Tolstoy I, Tyson GH, Zhao S, Hsu CH, McDermott PF, Tadesse DA, Morales C, Simmons M, Tillman G, Wasilenko J, Folster JP, Klimke W. Validating the AMRFinder Tool and Resistance Gene Database by Using Antimicrobial Resistance Genotype-Phenotype Correlations in a Collection of Isolates. Antimicrob Agents Chemother. 2019 Oct 22;63(11):e00483-19. doi: 10.1128/AAC.00483-19. Erratum in: Antimicrob Agents Chemother. 2020 Mar 24;64(4): PMID: 31427293; PMCID: PMC6811410.
  16. Blin K, Shaw S, Augustijn HE, Reitz ZL, Biermann F, Alanjary M, Fetter A, Terlouw BR, Metcalf WW, Helfrich EJN, van Wezel GP, Medema MH, Weber T. antiSMASH 7.0: new and improved predictions for detection, regulation, chemical structures and visualisation. Nucleic Acids Res. 2023 Jul 5;51(W1):W46-W50. doi: 10.1093/nar/gkad344. PMID: 37140036; PMCID: PMC10320115.
  17. Oyola SO, Gu Y, Manske M, Otto TD, O'Brien J, Alcock D, Macinnis B, Berriman M, Newbold CI, Kwiatkowski DP, Swerdlow HP, Quail MA. Efficient depletion of host DNA contamination in malaria clinical sequencing. J Clin Microbiol. 2013 Mar;51(3):745-51. doi: 10.1128/JCM.02507-12. Epub 2012 Dec 5. PMID: 23224084; PMCID: PMC3592063.


Swift, Reliable, and studious. We aim to cherish the world by publishing precise knowledge.

  • asd
  • Brown University Library
  • University of Glasgow Library
  • University of Pennsylvania, Penn Library
  • University of Amsterdam Library
  • The University of British Columbia Library
  • UC Berkeley’s Library
  • MIT Libraries
  • Kings College London University
  • University of Texas Libraries
  • UNSW Sidney Library
  • The University of Hong Kong Libraries
  • UC Santa Barbara Library
  • University of Toronto Libraries
  • University of Oxford Library
  • Australian National University
  • ScienceOpen
  • UIC Library
  • KAUST University Library
  • Cardiff University Library
  • Ball State University Library
  • Duke University Library
  • Rutgers University Library
  • Air University Library
  • UNT University of North Texas
  • Washington Research Library Consortium
  • Penn State University Library
  • Georgetown Library
  • Princeton University Library
  • Science Gate
  • Internet Archive
  • WashingTon State University Library
  • Dimensions
  • Zenodo
  • OpenAire
  • Index Copernicus International
  • icmje
  •  International Scientific Indexing (ISI)
  • Sherpa Romeo
  • ResearchGate
  • Universidad De Lima
  • WorldCat
  • JCU Discovery
  • McGill
  • National University of Singepore Libraries
  • SearchIT
  • Scilit
  • SemantiScholar
  • Base Search
  • VU
  • KB
  • Publons
  • oaji
  • Harvard University
  • sjsu-library
  • UWLSearch
  • Florida Institute of Technology
  • CrossRef
  • LUBsearch
  • Universitat de Paris
  • Technical University of Denmark
  • ResearchBIB
  • Google Scholar
  • Microsoft Academic Search