Computational protocol: Diets Alter the Gut Microbiome of Crocodile Lizards

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[…] Raw tags were filtered using the QIIME V1.7.0 package () in order to remove the low-quality sequences and chimeras. Then, sequences with ≥97% similarity were assigned to the same operational taxonomic units (OTUs) using UCLUST in QIIME V1.7.0 package (). A representative sequence for each OTU was annotated with threshold 0.8 using RDP Classifier 2.2 by searching the SILVA database (; ).For comparisons between samples, the OTU abundances were normalized by the number obtained from the sample with the lowest counts.For each sample, alpha diversity was estimated by calculating the Shannon and abundance-based coverage estimator (ACE) indices. These indices were calculated by QIIME 1.7.0 () and displayed using R software. Alpha diversity indices were compared among samples using the Tukey method (P = 0.05) with R software.Beta diversity was measured by principal coordinate analysis (PCoA) on unweighted and weighted UniFrac distances and were displayed using R software. The unweighted and weighted UniFrac distances were calculated by QIIME 1.7.0 (). In addition, unweighted pair-group method with arithmetic means (UPGMA) clustering was also performed using QIIME 1.7.0 (). The unweighted UniFrac distance accounts for membership in a community whereas the weighted UniFrac distance considers both membership and the relative abundance. Permutational multivariate analysis of variance (PERMANOVA) statistical analyses were conducted based on unweighted and weighted UniFrac distances with 999 permutations using function adonis in R’s vegan package.To identify microbes accounting for the effects of disease and diet, the linear discriminatory analysis (LDA) effect size (LEfSe) method was used to compare the differential abundances of bacteria among groups at family and genus levels. LEfSe analysis emphasizes statistical significance, biological consistency, and effect relevance. It first robustly identifies taxa that are statistically different among groups. Then it investigates biological consistent using a set of pairwise tests among subgroups. At last, it uses LDA to estimate the effect size of each selected taxon. LEfSe analysis was performed using LEfSe software (). The threshold of P-value in the Kruskal–Wallis test among groups was 0.05. Only those taxa with a log LDA score >4 (more than four orders of magnitude) were considered in this study.All raw sequences obtained in this study have been deposited in the Sequence Read Archive (SRA) under accession number SRP107074. […]

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