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Improved Microbial Community Characterization of 16S rRNA via Metagenome Hybridization Capture Enrichment

Environmental microbial diversity is often investigated from a molecular perspective using 16S ribosomal RNA (rRNA) gene amplicons and shotgun metagenomics. While amplicon methods are fast, low-cost, and have curated reference databases, they can suffer from amplification bias and are limited in gen...

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Autores principales: Beaudry, Megan S., Wang, Jincheng, Kieran, Troy J., Thomas, Jesse, Bayona-Vásquez, Natalia J., Gao, Bei, Devault, Alison, Brunelle, Brian, Lu, Kun, Wang, Jia-Sheng, Rhodes, Olin E., Glenn, Travis C.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8110821/
https://www.ncbi.nlm.nih.gov/pubmed/33986735
http://dx.doi.org/10.3389/fmicb.2021.644662
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author Beaudry, Megan S.
Wang, Jincheng
Kieran, Troy J.
Thomas, Jesse
Bayona-Vásquez, Natalia J.
Gao, Bei
Devault, Alison
Brunelle, Brian
Lu, Kun
Wang, Jia-Sheng
Rhodes, Olin E.
Glenn, Travis C.
author_facet Beaudry, Megan S.
Wang, Jincheng
Kieran, Troy J.
Thomas, Jesse
Bayona-Vásquez, Natalia J.
Gao, Bei
Devault, Alison
Brunelle, Brian
Lu, Kun
Wang, Jia-Sheng
Rhodes, Olin E.
Glenn, Travis C.
author_sort Beaudry, Megan S.
collection PubMed
description Environmental microbial diversity is often investigated from a molecular perspective using 16S ribosomal RNA (rRNA) gene amplicons and shotgun metagenomics. While amplicon methods are fast, low-cost, and have curated reference databases, they can suffer from amplification bias and are limited in genomic scope. In contrast, shotgun metagenomic methods sample more genomic regions with fewer sequence acquisition biases, but are much more expensive (even with moderate sequencing depth) and computationally challenging. Here, we develop a set of 16S rRNA sequence capture baits that offer a potential middle ground with the advantages from both approaches for investigating microbial communities. These baits cover the diversity of all 16S rRNA sequences available in the Greengenes (v. 13.5) database, with no sequence having <78% sequence identity to at least one bait for all segments of 16S. The use of our baits provide comparable results to 16S amplicon libraries and shotgun metagenomic libraries when assigning taxonomic units from 16S sequences within the metagenomic reads. We demonstrate that 16S rRNA capture baits can be used on a range of microbial samples (i.e., mock communities and rodent fecal samples) to increase the proportion of 16S rRNA sequences (average > 400-fold) and decrease analysis time to obtain consistent community assessments. Furthermore, our study reveals that bioinformatic methods used to analyze sequencing data may have a greater influence on estimates of community composition than library preparation method used, likely due in part to the extent and curation of the reference databases considered. Thus, enriching existing aliquots of shotgun metagenomic libraries and obtaining modest numbers of reads from them offers an efficient orthogonal method for assessment of bacterial community composition.
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spelling pubmed-81108212021-05-12 Improved Microbial Community Characterization of 16S rRNA via Metagenome Hybridization Capture Enrichment Beaudry, Megan S. Wang, Jincheng Kieran, Troy J. Thomas, Jesse Bayona-Vásquez, Natalia J. Gao, Bei Devault, Alison Brunelle, Brian Lu, Kun Wang, Jia-Sheng Rhodes, Olin E. Glenn, Travis C. Front Microbiol Microbiology Environmental microbial diversity is often investigated from a molecular perspective using 16S ribosomal RNA (rRNA) gene amplicons and shotgun metagenomics. While amplicon methods are fast, low-cost, and have curated reference databases, they can suffer from amplification bias and are limited in genomic scope. In contrast, shotgun metagenomic methods sample more genomic regions with fewer sequence acquisition biases, but are much more expensive (even with moderate sequencing depth) and computationally challenging. Here, we develop a set of 16S rRNA sequence capture baits that offer a potential middle ground with the advantages from both approaches for investigating microbial communities. These baits cover the diversity of all 16S rRNA sequences available in the Greengenes (v. 13.5) database, with no sequence having <78% sequence identity to at least one bait for all segments of 16S. The use of our baits provide comparable results to 16S amplicon libraries and shotgun metagenomic libraries when assigning taxonomic units from 16S sequences within the metagenomic reads. We demonstrate that 16S rRNA capture baits can be used on a range of microbial samples (i.e., mock communities and rodent fecal samples) to increase the proportion of 16S rRNA sequences (average > 400-fold) and decrease analysis time to obtain consistent community assessments. Furthermore, our study reveals that bioinformatic methods used to analyze sequencing data may have a greater influence on estimates of community composition than library preparation method used, likely due in part to the extent and curation of the reference databases considered. Thus, enriching existing aliquots of shotgun metagenomic libraries and obtaining modest numbers of reads from them offers an efficient orthogonal method for assessment of bacterial community composition. Frontiers Media S.A. 2021-04-27 /pmc/articles/PMC8110821/ /pubmed/33986735 http://dx.doi.org/10.3389/fmicb.2021.644662 Text en Copyright © 2021 Beaudry, Wang, Kieran, Thomas, Bayona-Vásquez, Gao, Devault, Brunelle, Lu, Wang, Rhodes and Glenn. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Microbiology
Beaudry, Megan S.
Wang, Jincheng
Kieran, Troy J.
Thomas, Jesse
Bayona-Vásquez, Natalia J.
Gao, Bei
Devault, Alison
Brunelle, Brian
Lu, Kun
Wang, Jia-Sheng
Rhodes, Olin E.
Glenn, Travis C.
Improved Microbial Community Characterization of 16S rRNA via Metagenome Hybridization Capture Enrichment
title Improved Microbial Community Characterization of 16S rRNA via Metagenome Hybridization Capture Enrichment
title_full Improved Microbial Community Characterization of 16S rRNA via Metagenome Hybridization Capture Enrichment
title_fullStr Improved Microbial Community Characterization of 16S rRNA via Metagenome Hybridization Capture Enrichment
title_full_unstemmed Improved Microbial Community Characterization of 16S rRNA via Metagenome Hybridization Capture Enrichment
title_short Improved Microbial Community Characterization of 16S rRNA via Metagenome Hybridization Capture Enrichment
title_sort improved microbial community characterization of 16s rrna via metagenome hybridization capture enrichment
topic Microbiology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8110821/
https://www.ncbi.nlm.nih.gov/pubmed/33986735
http://dx.doi.org/10.3389/fmicb.2021.644662
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