Computational protocol: Effects of Short-Term Warming and Altered Precipitation on Soil Microbial Communities in Alpine Grassland of the Tibetan Plateau

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Protocol publication

[…] Data were analyzed using the QIIME 1.8.0 pipeline (Caporaso et al., ). Briefly, bacterial and fungal sequences were quality trimmed, and the 7 bp barcode was used to assign sequences to soil samples. Zero mismatches were allowed during filtering, and sequences < 200 bp were removed for bacteria; sequences < 150 bp were removed for fungi. Bacterial and fungal raw reads were denoised by QIIME's implementation of denoising (Reeder and Knight, ). Chimera checking was performed using UCHIME (Edgar et al., ) and sequences were binned into operational taxonomic units (OTUs) using 97% similarity identity with a de novo clustering-usearch algorithm. Representative sequences, the most abundant sequences showing up in each OTU, were aligned by PyNAST. The taxonomic identities were determined using RDP classifier (Wang et al., ). OTUs containing < 2 reads (singletons) were removed. Each bacterial OTU representative sequence was assigned taxonomy against the Greengenes database gg_13_5 (DeSantis et al., ). To compare all of the soils at the same level of sampling effort (subsampling), 2200 16S rRNA gene sequences were randomly selected for alpha- and beta-diversity analyses. Each fungal OTU representative sequence was assigned taxonomy against the UNITE database its_12_11 (Seifert, ). To compare all of the soils at the same level of sampling effort, 1900 ITS sequences were randomly selected for alpha- and beta-diversity analyses. The subsampling sequencing depth for each dataset was determined by the minimum number of sequences observed in any one sample. ITS OTUs that were not assigned to fungi were removed before subsampling. […]

Pipeline specifications

Software tools QIIME, UCHIME, USEARCH, PyNAST, RDP Classifier
Databases Greengenes
Application 16S rRNA-seq analysis
Diseases Mitochondrial Diseases