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Development of an Analysis Pipeline Characterizing Multiple Hypervariable Regions of 16S rRNA Using Mock Samples

OBJECTIVES: 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...

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Autores principales: Barb, Jennifer J., Oler, Andrew J., Kim, Hyung-Suk, Chalmers, Natalia, Wallen, Gwenyth R., Cashion, Ann, Munson, Peter J., Ames, Nancy J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4734828/
https://www.ncbi.nlm.nih.gov/pubmed/26829716
http://dx.doi.org/10.1371/journal.pone.0148047
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author Barb, Jennifer J.
Oler, Andrew J.
Kim, Hyung-Suk
Chalmers, Natalia
Wallen, Gwenyth R.
Cashion, Ann
Munson, Peter J.
Ames, Nancy J.
author_facet Barb, Jennifer J.
Oler, Andrew J.
Kim, Hyung-Suk
Chalmers, Natalia
Wallen, Gwenyth R.
Cashion, Ann
Munson, Peter J.
Ames, Nancy J.
author_sort Barb, Jennifer J.
collection PubMed
description OBJECTIVES: 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 (D(KL)), 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 D(KL) while the V4 gave the lowest (best) average D(KL). In addition to having a high D(KL), 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.
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spelling pubmed-47348282016-02-04 Development of an Analysis Pipeline Characterizing Multiple Hypervariable Regions of 16S rRNA Using Mock Samples Barb, Jennifer J. Oler, Andrew J. Kim, Hyung-Suk Chalmers, Natalia Wallen, Gwenyth R. Cashion, Ann Munson, Peter J. Ames, Nancy J. PLoS One Research Article OBJECTIVES: 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 (D(KL)), 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 D(KL) while the V4 gave the lowest (best) average D(KL). In addition to having a high D(KL), 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. Public Library of Science 2016-02-01 /pmc/articles/PMC4734828/ /pubmed/26829716 http://dx.doi.org/10.1371/journal.pone.0148047 Text en https://creativecommons.org/publicdomain/zero/1.0/ This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 (https://creativecommons.org/publicdomain/zero/1.0/) public domain dedication.
spellingShingle Research Article
Barb, Jennifer J.
Oler, Andrew J.
Kim, Hyung-Suk
Chalmers, Natalia
Wallen, Gwenyth R.
Cashion, Ann
Munson, Peter J.
Ames, Nancy J.
Development of an Analysis Pipeline Characterizing Multiple Hypervariable Regions of 16S rRNA Using Mock Samples
title Development of an Analysis Pipeline Characterizing Multiple Hypervariable Regions of 16S rRNA Using Mock Samples
title_full Development of an Analysis Pipeline Characterizing Multiple Hypervariable Regions of 16S rRNA Using Mock Samples
title_fullStr Development of an Analysis Pipeline Characterizing Multiple Hypervariable Regions of 16S rRNA Using Mock Samples
title_full_unstemmed Development of an Analysis Pipeline Characterizing Multiple Hypervariable Regions of 16S rRNA Using Mock Samples
title_short Development of an Analysis Pipeline Characterizing Multiple Hypervariable Regions of 16S rRNA Using Mock Samples
title_sort development of an analysis pipeline characterizing multiple hypervariable regions of 16s rrna using mock samples
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4734828/
https://www.ncbi.nlm.nih.gov/pubmed/26829716
http://dx.doi.org/10.1371/journal.pone.0148047
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