Comparisons of deep metagenomic, shallow metagenomic, and 16S
amplicon sequencing
Unless specified, only reads identified as bacterial were retained for
taxonomic analyses, and all reports of realized sampling depths
hereafter refer to the fraction of reads classified as bacteria. All
datasets were normalized prior to analysis via rarefaction, except for
datasets used in ANCOM-BC differential abundance testing. A schematic of
our study design can be found in the supporting information (Figure S1).
To determine the requisite shotgun metagenomic sequencing depth required
to accurately characterize the Sable Island horse microbiome, we first
analyzed a deeply sequenced 16 sample Nextera XT prepared library.
Successively rarefied versions of this dataset were benchmarked against
a minimally rarefied dataset (9.3 million read-pairs). Similarly, to
determine the affect of sequencing depth on functional profile
reconstruction, we compared successively rarefied datasets of MetaCyc
reaction and pathway abundance tables.
Next, to identify discrepancies between prevailing and newer
high-throughput library preparation methods, we compared the results of
shotgun metagenomics libraries prepared from the same DNA extracts,
using Nextera XT and iGenomx Riptide methods. For this comparison,
datasets were rarefied to the lowest sequencing depth observed amongst
these 13 paired sample datasets, 1.6 million read pairs.
Finally, we identified differences between metagenomic and
amplicon-based characterization of the bacterial microbiome using the
same DNA extracts from 13 samples. As above, these datasets were
rarefied to the same minimal sequencing depth, in this case 35,000 read
pairs. Comparisons of taxon relative abundances between shotgun
metagenomic and amplicon datasets occurred primarily at the level of
family, since this was the finest resolution to which most 16S rRNA
amplicon reads could be classified. Analyses across these methodological
comparisons were comprised of general linear models to quantify
correlations in alpha diversity (Shannon diversity indices) and taxon
relative abundances between dataset types. We also tested for
correlations between beta-diversity estimates using mantel tests of
Bray-Curtis dissimilarity matrices.