Re-examination of reported biological patterns through a shallow
shotgun metagenomic lens
In the second half of our analyses, we sought to determine whether
shallow shotgun sequencing data (rarefied to 0.38 million bacterial read
pairs) could replicate the biological patterns reported in a recently
published 16S amplicon dataset We used mantel tests to test for
correlations between spatial and Bray-Curtis dissimilarity matrices,
PERMANOVAs to test for the effect of environmental variables on
microbiome beta-diversity, and ANCOM-BC tests to identify taxa which
differ in abundance between horses with access to sandwort versus horses
without access to sandwort (with false discovery rate adjustments and a
conservative variance estimate; To allow for comparisons between 16S
amplicon and shotgun metagenomic results, ANCOM-BC tests for taxon
differential abundance were performed on datasets binned to bacterial
family. However, to prevent large imbalances in false discovery rate
penalties between datasets, we further filtered the shallow shotgun
dataset to only families which were present at a percent abundance of
0.1% in at least 1 sample. This resulted in the retention of 144
families, which was comparable to the 147 families observed in the 16S
amplicon dataset. Notably, although Kaiju accurately estimates abundance
weighted patterns in the microbiome , it overestimates richness and can
result in a large number of spurious, low abundance taxa. Abundance
filtering of count tables is therefore prudent. Similar analyses as
described above were used to analyze differential abundances in MetaCyc
reactions and metabolic pathways. For all functional analyses, reads
which could not be classified or those which could not be integrated
into pathways (in the case of pathway abundance estimates) were removed.
Datasets were normalized via rarefaction for all analyses other than
ANCOM-BC tests, which possess a built-in normalization process . Unless
otherwise stated, all analyses were completed in R (v. 4.1.2) using the
package phyloseq v. 1.38.0 and vegan v. 2.5–7