Computational protocol: Bacterial community diversity of the deep-sea octocoral Paramuricea placomus

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Protocol publication

[…] A fully commented workflow describing the analysis conducted, as well as all the resulting output files, is available as a USGS data release at http://dx.doi.org/10.5066/F7HQ3WZZ (). The software QIIME 1.9 () was used to process and analyze the sequence data, and specific python scripts listed below were run within QIIME. The libraries were split and then quality checked using the following parameters: length between 200 and 700, quality score of 25 with a 50 bp running quality window, one primer mismatch allowed, and a maximum homopolymer run of 6 (). The data were denoised (; ) and then operational taxonomic units (OTUs) were picked using the pick_open_reference_otus.py script () which combines a closed OTU picking against the reference database (Greengenes release 13_8; ) with a de novo method so as not to lose novel OTUs. We employed the usearch61 OTU-picking method () in this script since it incorporates chimera-checking. Alignment was performed with PyNAST (version 1.2.2) () and taxonomy was assigned with uclust (). Absolute singletons (OTUs that occur only once in the dataset) were removed from the OTU table as a default in this method. Any non-bacterial sequences (i.e., archaeal, eukaryotic, chloroplast, or mitochondrial) were removed in a post-OTU picking step. Two of the libraries sequenced poorly (), so the remaining three libraries were rarefied to 4,300 sequences and rarefaction curves were generated (). Diversity calculations were accomplished using alpha_diversity.py and beta_diversity.py. Effective number of species (the number of equally abundant species needed to obtain the same mean proportional species abundance as that observed in the data) was calculated by taking the inverse of the natural logarithm of the Shannon diversity value. Taxa relative abundance summaries were generated using summarize_taxa_through_plots.py, with the resulting data passed to Excel and then R-Studio () using the vegan () and ggplot2 () packages to produce taxa summary plots. The core microbiome was analyzed with compute_core_microbiome.py with the minimum fraction of samples set at 100%. Comparisons of bacterial community dissimilarity between coral individuals were calculated using Similarity Percentages (SIMPER) from PRIMER (). SIMPER was run using abundance data with no transformation, using the sample names (NF12.19Q1, NF12.19Q2, NF12.19Q5) as factors. […]

Pipeline specifications

Software tools QIIME, USEARCH, PyNAST, UCLUST, Ggplot2
Databases Greengenes
Applications Miscellaneous, 16S rRNA-seq analysis
Chemicals Nitrogen