FastQC [83] followed by MultiQC [84] were used to examine data quality. Trimmomatic [85] was used to remove adaptors and trim low quality reads using the parameters: TRAILING:3 SLIDINGWINDOW:4:15 MINLEN:75. An average of 85% of reads mapped to various host DNA databases, but reads were not filtered before functional classification. Microbial communities were then profiled using MetaPhlAn2 [86] . Differentially abundant bacteria were calculated using MetaPhlAn2’s hclust2.py function by hierarchical clustering (based on Bray-Curtis dissimilarity) of the top 25 most abundant species according to the 90th percentile of the abundance in each clade as well as DESeq2. Functional, metabolic profiles were analyzed using HUMANn2, which aligns reads from UniRef [87] and clusters abundances to the ChocoPhlAn [57] database. This generates three outputs: UniRef IDs for gene families in reads per million, MetaCyc pathway coverage, and MetaCyc pathway abundances in copies per million (Supplemental File 9). To identify differential pathways between sample groups, associations between cotton rat species were identified by the HUMANn2.associate script and statistical testing using the Kruskal-Wallis H-test. Data presented (generated by HUMANn2.barplot script) is from pathway abundances (normalized as relative abundance) within each sample with unmapped/unintegrated pathways removed and was found statistically significant (p < 0.05 and q < 0.05). Superclasses distribution of identified MetaCyc pathways was manually generated using the online MetaCyc database.

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