Computational protocol: Diversity and resilience of the wood‐feeding higher termite Mironasutitermes shangchengensis gut microbiota in response to temporal and diet variations

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Protocol publication

[…] The pyrosequencing reads obtained from the sequencer were mainly processed with MOTHUR (Schloss et al., ). Raw reads were discarded if they: (1) had incorrect or unmatched primer sequences; (2) were <150 bp; (3) had an average quality score <30; or (4) had any ambiguous bases or homopolymers of <8 nucleotides. After the preliminary filtering process, the resulting sequences were denoised using the “pre.cluster” command ( in MOTHUR. Chimera sequences were detected and removed by UCHIME (Edgar, Haas, Clemente, Quince, & Knight, ). To obtain the taxonomic assignments, all of the trimmed reads were clustered into OTUs with ≥97% similarity and aligned to the Greengenes database (McDonald et al., ) using the “claasify.seq” command in MOTHUR, with an 80% confidence threshold. The sequences associated with taxonomy classifications were classified according to the following levels: kingdom, phylum, class, order, family, genus, and unclassified if their levels were not clearly defined. The relative abundances of bacterial taxa were determined based on the number of sequences belonging to each OTU, which were calculated using R (version 3.1.2). All of the samples were rarefied, and only OTUs present in >90% samples were considered for further analysis.Alpha diversity, including the analysis of Good's coverage, diversity estimators (Shannon's and Invsimpson), richness estimators (ACE and Chao1), rarefaction curves, heatmaps, and Venn diagrams, was calculated based on OTUs with ≥97% identity using the summary single command in MOTHUR ( Significant differences in Shannon's diversity index and PC1 values between treatment groups were determined using Sigmaplot with Wilcoxon's rank‐sum test. The phylogenetic beta diversity measure was used to investigate potential clustering of the microbial communities among different treatment samples, including unweighted and weighted UniFrac distance metrics analysis (Hamady, Lozupone, & Knight, ), where we used the OTUs from each sample in the analysis and visualization with QIIME software (Caporaso et al., ). To determine significant correlations along PC1 based on the OTUs and taxonomy, all of the data were normalized initially before removing the low‐abundance OTUs or phyla (variables that comprised >90% zeroes or mean relative abundance <0.01%), and the data were transformed into logarithm values (log2) and tested (Spearman |ρ| > .5, FDR q < 0.2) for significance using R ( Wilcoxon's rank‐sum test was performed in R, and p < .05 was considered statistically significant. […]

Pipeline specifications

Software tools mothur, UCHIME, SigmaPlot, UniFrac, QIIME
Applications Miscellaneous, Phylogenetics, 16S rRNA-seq analysis
Organisms Bacteria, Firmicutes
Diseases Callosities