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Improved microbial community characterization of 16S rRNA via metagenome hybridization capture enrichment

View ORCID ProfileMegan Sarah Beaudry, Jincheng Wang, View ORCID ProfileTroy Kieran, Jesse Thomas, Natalia Juliana Bayona-Vasquez, Bei Gao, Alison Devault, Brian Brunelle, Kun Lu, Jia-Sheng Wang, Olin E. Rhodes, View ORCID ProfileTravis C. Glenn
doi: https://doi.org/10.1101/2020.12.18.423101
Megan Sarah Beaudry
1 University of Georgia;
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  • For correspondence: megan.beaudry@uga.edu
Jincheng Wang
2 Rutgers University;
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Troy Kieran
1 University of Georgia;
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Jesse Thomas
1 University of Georgia;
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Natalia Juliana Bayona-Vasquez
1 University of Georgia;
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Bei Gao
1 University of Georgia;
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Alison Devault
3 Daicel Arbor Biosciences;
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Brian Brunelle
3 Daicel Arbor Biosciences;
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Kun Lu
1 University of Georgia;
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Jia-Sheng Wang
1 University of Georgia;
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Olin E. Rhodes
4 Savannah River Ecology Lab
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Travis C. Glenn
1 University of Georgia;
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Abstract

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. However, shotgun metagenomic sequencing is 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 < 80% sequence similarity 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 in part to the extent and curation of the reference databases considered.

Competing Interest Statement

The EHS DNA lab provides oligonucleotide aliquots and library preparation services at cost, including some oligonucleotides and services used in this manuscript (baddna.uga.edu). Brian Brunelle and Alison Devault are employed by, and thereby have financial interest in, Daicel Arbor Biosciences, who provided the in-solution capture reagents used in this work.

Footnotes

  • https://www.dropbox.com/sh/exg0kow6pyghlmx/AAAIn7R93EawGUDO7TQ6NDIYa?dl=0

  • https://www.dropbox.com/s/ndnfcc59ggnapxu/Supplemental_Data-01_Lindgreen_genomes.fasta?dl=0

  • https://www.dropbox.com/s/5196mzctbwjz7cx/Supplemental_Data-02_Zymo_genomes.fasta?dl=0

  • https://www.dropbox.com/s/neu04baa5wili25/Supplemental_Data-03_BEI_genomes.fasta?dl=0

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted December 19, 2020.
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Improved microbial community characterization of 16S rRNA via metagenome hybridization capture enrichment
Megan Sarah Beaudry, Jincheng Wang, Troy Kieran, Jesse Thomas, Natalia Juliana Bayona-Vasquez, Bei Gao, Alison Devault, Brian Brunelle, Kun Lu, Jia-Sheng Wang, Olin E. Rhodes, Travis C. Glenn
bioRxiv 2020.12.18.423101; doi: https://doi.org/10.1101/2020.12.18.423101
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Improved microbial community characterization of 16S rRNA via metagenome hybridization capture enrichment
Megan Sarah Beaudry, Jincheng Wang, Troy Kieran, Jesse Thomas, Natalia Juliana Bayona-Vasquez, Bei Gao, Alison Devault, Brian Brunelle, Kun Lu, Jia-Sheng Wang, Olin E. Rhodes, Travis C. Glenn
bioRxiv 2020.12.18.423101; doi: https://doi.org/10.1101/2020.12.18.423101

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