Advertisement

16S rRNA Amplicon Sequencing for Metagenomics

  • Henrik Christensen
  • Anna Jasmine Andersson
  • Steffen Lynge Jørgensen
  • Josef Korbinian Vogt
Chapter
Part of the Learning Materials in Biosciences book series (LMB)

Abstract

The 16S rRNA amplicon sequencing technique is a microbiome analysis where different samples are analyzed at the same time using multiplexing. The results can be used to evaluate microbial diversity at genus, family, order, class, and phylum levels. The resolution is normally insufficient to evaluate the species level. The different steps in the bioinformatical analysis allow both the analysis of all samples combined and comparisons between samples. The bioinformatical analysis focuses on quality control of reads, merging of identical reads, and grouping of reads into operational taxonomic units (OTUs) with a threshold of 97%. The threshold is inherited from the species threshold for classification of species based on 16S rRNA sequence comparison. The distribution of reads and OTUs within and between samples can be used to estimate α- and β-diversity, respectively. The relative occurrence of the taxonomic units at the levels of genus, family, order, class, and phylum can be compared between samples. These distributions can be related to metadata by principal component analysis.

References

  1. Anderson EL, Li W, Klitgord N, Highlander SK, Dayrit M, Seguritan V, Yooseph S, Biggs W, Venter JC, Nelson KE, Jones MB. 2016. A robust ambient temperature collection and stabilization strategy: Enabling worldwide functional studies of the human microbiome. Sci Rep. 25;6:31731.CrossRefGoogle Scholar
  2. Ashelford KE, Chuzhanova NA, Fry JC, Jones AJ, Weightman AJ. 2005. At least 1 in 20 16S rRNA sequence records currently held in public repositories is esti-mated to contain substantial anomalies. Appl Environ Microbiol. 71, 7724–36.CrossRefPubMedGoogle Scholar
  3. Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, Fierer N, Peña AG, Goodrich JK, Gordon JI, Huttley GA, Kelley ST, Knights D, Koenig JE, Ley RE, Lozupone CA, McDonald D, Muegge BD, Pirrung M, Reeder J, Sevinsky JR, Turnbaugh PJ, Walters WA, Widmann J, Yatsunenko T, Zaneveld J, Knight R. 2010. QIIME allows analysis of high-throughput community sequencing data. Nat Methods. 7, 335–336.CrossRefPubMedGoogle Scholar
  4. Cole JR, Wang Q, Fish JA, Chai B, McGarrell DM, Sun Y, Brown CT, Porras-Alfaro A, Kuske CR, Tiedje JM. 2014. Ribosomal Database Project: data and tools for high throughput rRNA analysis. Nucleic Acids Res 42, D633–42.CrossRefGoogle Scholar
  5. Danzeisen JL, Kim HB, Isaacson RE, Tu ZJ, Johnson TJ. 2011. Modulations of the chicken cecal microbiome and metagenome in response to anticoccidial and growth promoter treatment. PLoS One. 6:e27949.CrossRefPubMedGoogle Scholar
  6. Davis NM, Proctor D, Holmes SP, Relman DA, & Callahan BJ. 2017. Simple statistical identification and removal of contaminant sequences in marker-gene and metagenomics data. bioRxiv preprint.  https://doi.org/10.1101/221499
  7. DeSantis TZ, Hugenholtz P, Larsen N, Rojas M, Brodie EL, Keller K, Huber T, Dalevi D, Hu P, Andersen GL. 2006. Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl Environ Microbiol. 72, 5069–5072.CrossRefPubMedGoogle Scholar
  8. Edgar RC. 2010. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461.CrossRefGoogle Scholar
  9. Edgar RC. 2013. UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nat Methods. 10:996–998.CrossRefGoogle Scholar
  10. Edgar RC, Haas BJ, Clemente JC, Quince C, Knight R. 2011. UCHIME improves sensitivity and speed of chimera detection. Bioinformatics 27:2194–2200.CrossRefPubMedGoogle Scholar
  11. Faith DP. 1992. Conservation evaluation and phylogenetic diversity. Biological Conservation 64, 1–10.CrossRefGoogle Scholar
  12. Hamady M, Walker JJ, Harris JK, Gold NJ, Knight R. 2008. Error-correcting barcoded primers for pyrosequencing hundreds of samples in multiplex. Nat Methds. 5:235–7.CrossRefGoogle Scholar
  13. Karst SM, Dueholm MS, McIlroy SJ, Kirkegaard RH, Nielsen PH, & Albertsen M. 2018. Retrieval of a million high-quality, full-length microbial 16S and 18S rRNA gene sequences without primer bias. Nat Biotechnol. 36:190–195.CrossRefGoogle Scholar
  14. Lane DJ. 1991. 16S/23S rRNA Sequencing. In: Stackebrandt, E. and Goodfellow, M., Eds., Nucleic Acid Techniques in Bacterial Systematic, John Wiley and Sons, New York, 115–175.Google Scholar
  15. Langille MG, Zaneveld J, Caporaso JG, McDonald D, Knights D, Reyes JA, Clemente JC, Burkepile DE, Vega Thurber RL, Knight R, Beiko RG, Huttenhower, C. 2013. Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences. Nat Biotechnol 31, 814–821.CrossRefPubMedGoogle Scholar
  16. Liu Z, Lozupone C, Hamady M, Bushman FD & Knight R. 2007. Short pyrosequencing reads suffice for accurate microbial community analysis. Nucleic Acids Res. 35, e120.CrossRefPubMedGoogle Scholar
  17. Liu Z, DeSantis TZ, Andersen GL, Knight R. 2008. Accurate taxonomy assignments from 16S rRNA sequences produced by highly parallel pyrosequencers. Nucleic Acids Res. 36:e120.CrossRefPubMedGoogle Scholar
  18. McDonald D, Price MN, Goodrich J, Nawrocki EP, DeSantis TZ, Probst A, Andersen GL, Knight R, Hugenholtz P. 2012. An improved Greengenes taxonomy with explicit ranks for ecological and evolutionary analyses of bacteria and archaea. ISME J. 6, 610–618.CrossRefGoogle Scholar
  19. McKenna P, Hoffmann C, Minkah N, Aye PP, Lackner A, Liu Z, Lozupone CA, Hamady M, Knight R, Bushman FD. 2008. The macaque gut microbiome in health, lentiviral infection, and chronic enterocolitis. PLoS Pathog. 4:e20.CrossRefPubMedGoogle Scholar
  20. McMurdie PJ, & Holmes S. 2012. Phyloseq: a bioconductor package for handling and analysis of high-throughput phylogenetic sequence data. Pac Symp Biocomput. 12:235–46.Google Scholar
  21. Nossa et al. 2010. World J. Gastroenterol. 16, 4135–44.Google Scholar
  22. Popp et al. 2017. Biospektrum Abstractbook, p. 199, 2017.Google Scholar
  23. Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, Peplies J, Glöckner FO. 2013. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 41:D590–6.CrossRefPubMedGoogle Scholar
  24. Rognes T, Flouri T, Nichols B, Quince C, Mahé F. 2016. VSEARCH: a versatile open source tool for metagenomics. PeerJ. 18:e2584.CrossRefGoogle Scholar
  25. Schloss PD, Westcott SL, Ryabin T, Hall JR, Hartmann M, Hollister EB, Lesniewski RA, Oakley BB, Parks DH, Robinson CJ, Sahl JW, Stres B, Thallinger GG, Van Horn DJ, Weber CF. 2009. Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl. Environ. Microbiol. 75, 7537–7541.CrossRefPubMedGoogle Scholar
  26. Schloss PD, Jenior ML, Koumpouras CC, Westcott SL & Highlander SK. 2016. Sequencing 16S rRNA gene fragments using the PacBio SMRT DNA sequencing system. Peer J 4:e1869.CrossRefGoogle Scholar
  27. Soergel DA, Dey N, Knight R & Brenner SE. 2012. Selection of primers for optimal taxonomic classification of environmental 16S rRNA gene sequences. ISME J. 6, 1440–4.CrossRefPubMedGoogle Scholar
  28. Tang Y, Underwood A, Gielbert A, Woodward MJ, Petrovska L. 2014. Metaproteomics analysis reveals the adaptation process for the chicken gut microbiota. Appl Environ Microbiol. 80, 478–485.CrossRefPubMedGoogle Scholar
  29. Wagner J, Coupland P, Browne HP, Lawley TD, Francis SC, Parkhill J. 2016. Evaluation of PacBio sequencing for full-length bacterial 16S rRNA gene classification. BMC Microbiol. 16:274.CrossRefPubMedGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Henrik Christensen
    • 1
  • Anna Jasmine Andersson
    • 2
  • Steffen Lynge Jørgensen
    • 1
  • Josef Korbinian Vogt
    • 3
  1. 1.Department of Veterinary Animal SciencesUniversity of CopenhagenCopenhagenDenmark
  2. 2.HovedOrtoCentret, Øjenklinikken, ØjenforskningenGlostrupDenmark
  3. 3.National Food institute, Technical University of DenmarkLyngbyDenmark

Personalised recommendations