Abstract

The microbiome is known to play a role in many human diseases, but identifying key microbes and their functions generally requires large studies due to the vast number of species and genes, and the high levels of intra-individual and inter-individual variation. 16S amplicon sequencing of the rRNA gene is commonly used for large studies due to its comparatively low sequencing cost, but it has poor taxonomic and functional resolution. Deep shotgun sequencing is a more accurate and comprehensive alternative for small studies, but can be cost-prohibitive for biomarker discovery in large populations. Shallow or moderate-depth shotgun metagenomics may serve as a viable alternative to 16S sequencing for large-scale and/or dense longitudinal studies, but only if resolution and reproducibility are comparable. Here we applied both 16S and shallow shotgun stool microbiome sequencing to a cohort of 5 subjects sampled twice daily and weekly, with technical replication at the DNA extraction and the library preparation/sequencing steps, for a total of 80 16S samples and 80 shallow shotgun sequencing samples. We found that shallow shotgun sequencing produced lower technical variation and higher taxonomic resolution than 16S sequencing, at a much lower cost than deep shotgun sequencing. These findings suggest that shallow shotgun sequencing provides a more specific and more reproducible alternative to 16S sequencing for large-scale microbiome studies where costs prohibit deep shotgun sequencing and where bacterial species are expected to have good coverage in whole-genome reference databases.

Details

Title
Shallow shotgun sequencing reduces technical variation in microbiome analysis
Author
La Reau, Alex J. 1 ; Strom, Noah B. 1 ; Filvaroff, Ellen 2 ; Mavrommatis, Konstantinos 2 ; Ward, Tonya L. 3 ; Knights, Dan 4 

 Diversigen, Inc., New Brighton, USA 
 Bristol Myers Squibb, San Francisco, USA (GRID:grid.450559.8) (ISNI:0000 0004 0457 284X) 
 Diversigen, Inc., New Brighton, USA (GRID:grid.450559.8) 
 Diversigen, Inc., New Brighton, USA (GRID:grid.450559.8); University of Minnesota, Department of Computer Science and Engineering, Minneapolis, USA (GRID:grid.17635.36) (ISNI:0000 0004 1936 8657); University of Minnesota, Biotechnology Institute, College of Biological Sciences, Minneapolis, USA (GRID:grid.17635.36) (ISNI:0000000419368657) 
Pages
7668
Publication year
2023
Publication date
2023
Publisher
Nature Publishing Group
e-ISSN
20452322
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2812332462
Copyright
© The Author(s) 2023. corrected publication 2024. corrected publication 2024. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.