title: Development of an Analysis Pipeline Characterizing Multiple Hypervariable Regions of 16S rRNA Using Mock Samples.
abstract: OBJECTIVE
  There is much speculation on which hypervariable region provides the highest bacterial specificity in 16S rRNA sequencing. The optimum solution to prevent bias and to obtain a comprehensive view of complex bacterial communities would be to sequence the entire 16S rRNA gene; however, this is not possible with second generation standard library design and short-read next-generation sequencing technology.
  
  
  METHODS
  This paper examines a new process using seven hypervariable or V regions of the 16S rRNA (six amplicons: V2, V3, V4, V6-7, V8, and V9) processed simultaneously on the Ion Torrent Personal Genome Machine (Life Technologies, Grand Island, NY). Four mock samples were amplified using the 16S Ion Metagenomics Kit™ (Life Technologies) and their sequencing data is subjected to a novel analytical pipeline.
  
  
  RESULTS
  Results are presented at family and genus level. The Kullback-Leibler divergence (DKL), a measure of the departure of the computed from the nominal bacterial distribution in the mock samples, was used to infer which region performed best at the family and genus levels. Three different hypervariable regions, V2, V4, and V6-7, produced the lowest divergence compared to the known mock sample. The V9 region gave the highest (worst) average DKL while the V4 gave the lowest (best) average DKL. In addition to having a high DKL, the V9 region in both the forward and reverse directions performed the worst finding only 17% and 53% of the known family level and 12% and 47% of the genus level bacteria, while results from the forward and reverse V4 region identified all 17 family level bacteria.
  
  
  CONCLUSIONS
  The results of our analysis have shown that our sequencing methods using 6 hypervariable regions of the 16S rRNA and subsequent analysis is valid. This method also allowed for the assessment of how well each of the variable regions might perform simultaneously. Our findings will provide the basis for future work intended to assess microbial abundance at different time points throughout a clinical protocol.
authors.affiliation: Mathematical and Statistical Computing Laboratory, Center for Information Technology, National Institutes of Health, Bethesda, Maryland, United States of America.
authors.forename: Jennifer J
authors.initials: JJ
authors.lastname: Barb
authors.affiliation: Bioinformatics and Computational Biosciences Branch, Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland, United States of America.
authors.forename: Andrew J
authors.initials: AJ
authors.lastname: Oler
authors.affiliation: National Institute of Nursing Research, National Institutes of Health, Bethesda, Maryland, United States of America.
authors.forename: Hyung-Suk
authors.initials: HS
authors.lastname: Kim
authors.affiliation: National Institute of Dental and Craniofacial Research, National Institutes of Health, Bethesda, Maryland, United States of America.
authors.forename: Natalia
authors.initials: N
authors.lastname: Chalmers
authors.affiliation: Clinical Center Nursing Department, National Institutes of Health, Bethesda, Maryland, United States of America.
authors.forename: Gwenyth R
authors.initials: GR
authors.lastname: Wallen
authors.affiliation: National Institute of Nursing Research, National Institutes of Health, Bethesda, Maryland, United States of America.
authors.forename: Ann
authors.initials: A
authors.lastname: Cashion
authors.affiliation: Mathematical and Statistical Computing Laboratory, Center for Information Technology, National Institutes of Health, Bethesda, Maryland, United States of America.
authors.forename: Peter J
authors.initials: PJ
authors.lastname: Munson
authors.affiliation: Clinical Center Nursing Department, National Institutes of Health, Bethesda, Maryland, United States of America.
authors.forename: Nancy J
authors.initials: NJ
authors.lastname: Ames
mesh_terms: D001419:Bacteria / Q000145:classification / Q000235:genetics*; D019295:Computational Biology / Q000379:methods; D058893:DNA Barcoding, Taxonomic; D023061:Gene Order; D056426:Genetic Loci; D014644:Genetic Variation; D059014:High-Throughput Nucleotide Sequencing; D056186:Metagenomics / Q000379:methods; D009876:Operon; D012336:RNA, Ribosomal, 16S / Q000235:genetics*; D017422:Sequence Analysis, DNA
chemical_list: D012336:RNA, Ribosomal, 16S
references.citation: PLoS One. 2013;8(10):e76185
references.pmid: 24146837
references.citation: J Microbiol Methods. 2014 Dec;107:30-7
references.pmid: 25193439
references.citation: ISME J. 2012 Jul;6(7):1440-4
references.pmid: 22237546
references.citation: Nature. 2012 Jun 14;486(7402):215-21
references.pmid: 22699610
references.citation: Nat Methods. 2013 Oct;10(10):996-8
references.pmid: 23955772
references.citation: Nucleic Acids Res. 2013 Jan 7;41(1):e1
references.pmid: 22933715
references.citation: Appl Environ Microbiol. 2014 Dec;80(24):7583-91
references.pmid: 25261520
references.citation: Clin Microbiol Rev. 2004 Oct;17(4):840-62, table of contents
references.pmid: 15489351
references.citation: PLoS One. 2012;7(6):e39315
references.pmid: 22720093
references.citation: Nucleic Acids Res. 2010 Dec;38(22):e200
references.pmid: 20880993
references.citation: Bioinformatics. 2011 Aug 15;27(16):2194-200
references.pmid: 21700674
references.citation: PLoS One. 2012;7(6):e34242
references.pmid: 22719824
references.citation: Proc Natl Acad Sci U S A. 1985 Oct;82(20):6955-9
references.pmid: 2413450
references.citation: J Microbiol Methods. 2014 Sep;104:59-60
references.pmid: 24978594
references.citation: ISME J. 2012 Aug;6(8):1621-4
references.pmid: 22402401
references.citation: PLoS One. 2013;8(8):e71360
references.pmid: 23977026
references.citation: Microbiol Rev. 1987 Jun;51(2):221-71
references.pmid: 2439888
references.citation: Genome Res. 2011 Mar;21(3):494-504
references.pmid: 21212162
references.citation: Microb Ecol. 2014 Feb;67(2):237-41
references.pmid: 24173537
references.citation: J Microbiol Methods. 2009 Dec;79(3):266-71
references.pmid: 19796657
references.citation: PLoS One. 2011;6(12):e27310
references.pmid: 22194782
references.citation: Nat Methods. 2010 May;7(5):335-6
references.pmid: 20383131
references.citation: BMC Microbiol. 2013;13:208
references.pmid: 24034943
references.citation: Microbiome. 2013 Jun 05;1(1):17
references.pmid: 24450808
references.citation: BMC Microbiol. 2014;14:316
references.pmid: 25515234
references.citation: PLoS One. 2013;8(1):e53608
references.pmid: 23308262
references.citation: Nucleic Acids Res. 2007;35(21):7188-96
references.pmid: 17947321
references.citation: J Biotechnol. 2014 Nov 20;190:30-9
references.pmid: 24670254
references.citation: Clin Microbiol Infect. 2008 Oct;14(10):908-34
references.pmid: 18828852
references.citation: Curr Protoc Bioinformatics. 2011 Dec;Chapter 10:Unit 10.7.
references.pmid: 22161565
references.citation: Nucleic Acids Res. 2008 Oct;36(18):e120
references.pmid: 18723574
references.citation: PLoS One. 2011;6(6):e20956
references.pmid: 21738596
references.citation: Bioinformatics. 2010 Oct 1;26(19):2460-1
references.pmid: 20709691
references.citation: PLoS One. 2015;10(4):e0125356
references.pmid: 25915756
references.citation: Appl Environ Microbiol. 2009 Dec;75(23):7537-41
references.pmid: 19801464
references.citation: J Mol Biol. 1975 May 25;94(3):441-8
references.pmid: 1100841
references.citation: Periodontol 2000. 1994 Jun;5:26-51
references.pmid: 9673161
references.citation: Genome Biol. 2015;16:114
references.pmid: 26025440
references.citation: J Clin Microbiol. 2014 Nov;52(11):3913-21
references.pmid: 25143582
references.citation: Proc Natl Acad Sci U S A. 2011 Mar 15;108 Suppl 1:4516-22
references.pmid: 20534432
references.citation: PLoS One. 2012;7(8):e42671
references.pmid: 22880076