Literature DB >> 29563060

Reproducibility and repeatability of six high-throughput 16S rDNA sequencing protocols for microbiota profiling.

Sajan C Raju1, Sonja Lagström2, Pekka Ellonen3, Willem M de Vos4, Johan G Eriksson5, Elisabete Weiderpass6, Trine B Rounge7.   

Abstract

Culture-independent molecular techniques and advances in next generation sequencing (NGS) technologies make large-scale epidemiological studies on microbiota feasible. A challenge using NGS is to obtain high reproducibility and repeatability, which is mostly attained through robust amplification. We aimed to assess the reproducibility of saliva microbiota by comparing triplicate samples. The microbiota was produced with simplified in-house 16S amplicon assays taking advantage of large number of barcodes. The assays included primers with Truseq (TS-tailed) or Nextera (NX-tailed) adapters and either with dual index or dual index plus a 6-nt internal index. All amplification protocols produced consistent microbial profiles for the same samples. Although, in our study, reproducibility was highest for the TS-tailed method. Five replicates of a single sample, prepared with the TS-tailed 1-step protocol without internal index sequenced on the HiSeq platform provided high alpha-diversity and low standard deviation (mean Shannon and Inverse Simpson diversity was 3.19 ± 0.097 and 13.56 ± 1.634 respectively). Large-scale profiling of microbiota can consistently be produced by all 16S amplicon assays. The TS-tailed-1S dual index protocol is preferred since it provides repeatable profiles on the HiSeq platform and are less labour intensive.
Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.

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Year:  2018        PMID: 29563060     DOI: 10.1016/j.mimet.2018.03.003

Source DB:  PubMed          Journal:  J Microbiol Methods        ISSN: 0167-7012            Impact factor:   2.363


  8 in total

1.  A Quantitative Metagenomic Sequencing Approach for High-Throughput Gene Quantification and Demonstration with Antibiotic Resistance Genes.

Authors:  Bo Li; Xu Li; Tao Yan
Journal:  Appl Environ Microbiol       Date:  2021-07-27       Impact factor: 4.792

2.  High abundance of sugar metabolisers in saliva of children with caries.

Authors:  Muhammed Manzoor; Sohvi Lommi; Jussi Furuholm; Catharina Sarkkola; Elina Engberg; Sajan Raju; Heli Viljakainen
Journal:  Sci Rep       Date:  2021-02-24       Impact factor: 4.379

3.  Evaluating gut microbiota profiles from archived fecal samples.

Authors:  Trine B Rounge; Roger Meisal; Jan Inge Nordby; Ole Herman Ambur; Thomas de Lange; Geir Hoff
Journal:  BMC Gastroenterol       Date:  2018-11-08       Impact factor: 3.067

4.  Gender-Specific Associations Between Saliva Microbiota and Body Size.

Authors:  Sajan C Raju; Sonja Lagström; Pekka Ellonen; Willem M de Vos; Johan G Eriksson; Elisabete Weiderpass; Trine B Rounge
Journal:  Front Microbiol       Date:  2019-04-10       Impact factor: 5.640

5.  Highly Reproducible 16S Sequencing Facilitates Measurement of Host Genetic Influences on the Stickleback Gut Microbiome.

Authors:  Clayton M Small; Mark Currey; Emily A Beck; Susan Bassham; William A Cresko
Journal:  mSystems       Date:  2019-08-13       Impact factor: 6.496

6.  The Composition and Functional Capacities of Saliva Microbiota Differ Between Children With Low and High Sweet Treat Consumption.

Authors:  Sohvi Lommi; Muhammed Manzoor; Elina Engberg; Nitin Agrawal; Timo A Lakka; Jukka Leinonen; Kaija-Leena Kolho; Heli Viljakainen
Journal:  Front Nutr       Date:  2022-04-25

7.  Meal Regularity Plays a Role in Shaping the Saliva Microbiota.

Authors:  Jannina Viljakainen; Sajan C Raju; Heli Viljakainen; Rejane Augusta de Oliveira Figueiredo; Eva Roos; Elisabete Weiderpass; Trine B Rounge
Journal:  Front Microbiol       Date:  2020-04-24       Impact factor: 5.640

8.  Quantitative PCR provides a simple and accessible method for quantitative microbiota profiling.

Authors:  Ching Jian; Panu Luukkonen; Hannele Yki-Järvinen; Anne Salonen; Katri Korpela
Journal:  PLoS One       Date:  2020-01-15       Impact factor: 3.240

  8 in total

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