Computational protocol: Geographical Distribution of Methanogenic Archaea in Nine Representative Paddy Soils in China

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Protocol publication

[…] The methanogenic archaeal 16S rRNA gene data were processed using the Quantitative Insights Into Microbial Ecology (QIIME) 1.7.0-dev pipeline () using default parameters unless otherwise noted. In brief, the sequences were denoised () and binned into OTUs using a 97% identity threshold, and the most abundant sequence from each OTU was selected as a representative sequence for that OTU. Taxonomy was assigned to bacterial OTUs against a subset of the Silva 104 database. OTU representative sequences were aligned using PyNAST. A phylogenetic tree was then constructed using FastTree2 () to the support phylogenetic diversity (PD) calculations.A richness of phylotypes (Chao1) was calculated to compare community-level bacterial diversity at a single level of taxonomic resolution. We also estimated the PD using Faith’s index (), which provides an integrated index of the phylogenetic breadth across taxonomic levels. We obtained total 74,921 methanogenic archaeal 16S rRNA sequences, and between 404 and 9,059 sequences per sample. Because an even depth of sampling is required for beta diversity calculations, we reduced the datasets to the lowest number available to correct for differences in survey effort between the samples. Namely, we calculated both diversity metrics using a randomly selected subset of 400 sequences per soil sample. This approach allows us to compare general diversity patterns among sites even though it is highly unlikely that we surveyed the full extent of diversity in each community (). The weighted pairwise UniFrac distances () were calculated for community comparisons using QIIME and were visualized using non-metric multidimensional scaling (NMDS) plots as implemented in PRIMER v6 (). [...] Statistical procedures were calculated using the IBM Statistical Product and Service Solutions (SPSS) Statistics for windows (Version 13). The data were expressed as the means with standard deviation (SD), and the letters indicated significant differences between the results of the different samples. Mean separation was conducted based on Tukey’s multiple range test. Differences at P < 0.05 were considered statistically significant. Correlation between the abundances and the diversity of methanogenic archaeal community and environmental variables were quantified using analysis of variance (ANOVA). R software (Version 3.1.2) was utilized to estimate the correlations between environmental variables and methanogenic archaeal community composition by Mantel test and partial Mantel test (vegan package), to conduct Anosim, mrpp, and Adonis analyses (vegan package; ) and to build multivariate regression tree (MRT) to identify the most important abiotic factors for methanogenic archaeal community composition (mvpart package; ). MRT approach explains the variation of a single numeric response variable using explanatory variables that may be numeric and/or categorical. It is the natural extension of univariate regression trees, by summing the univariate impurity measured over the multivariate response. Mechanically, it grows a tree structure that partitions the data set into mutually exclusive groups. Starting with all the data represented by a single node at the top of the tree, the tree is grown by repeated binary splitting of the data. Each split is defined by a simple rule, usually based on a single explanatory variable, and forms two nodes. Splits are chosen to maximize the homogeneity of the resulting two nodes. […]

Pipeline specifications

Software tools QIIME, PyNAST, FastTree, UniFrac, SPSS
Applications Miscellaneous, Phylogenetics, 16S rRNA-seq analysis
Chemicals Methane