phyloseq (version 1.16.2)

data-soilrep: (Data) Reproducibility of soil microbiome data (2011)

Description

Published in early 2011, this work compared 24 separate soil microbial communities under four treatment conditions via multiplexed/barcoded 454-pyrosequencing of PCR-amplified 16S rRNA gene fragments. The authors found differences in the composition and structure of microbial communities between soil treatments. As expected, the soil microbial communities were highly diverse, with a staggering 16,825 different OTUs (species) observed in the included dataset. Interestingly, this study used a larger number of replicates than previous studies of this type, for a total of 56 samples, and the putatively low resampling rate of species between replicated sequencing trials (``OTU overlap'') was a major concern by the authors.

Details

This dataset contains an experiment-level (phyloseq-class) object, which in turn contains the taxa-contingency table and soil-treatment table as otu_table-class and sample_data-class components, respectively.

This data was imported from raw files supplied directly by the authors via personal communication for the purposes of including as an example in the phyloseq-package. As this data is sensitive to choices in OTU-clustering parameters, attempts to recreate the otu_table from the raw sequencing data may give slightly different results than the table provided here.

abstract from research article (quoted):

To determine the reproducibility and quantitation of the amplicon sequencing-based detection approach for analyzing microbial community structure, a total of 24 microbial communities from a long-term global change experimental site were examined. Genomic DNA obtained from each community was used to amplify 16S rRNA genes with two or three barcode tags as technical replicates in the presence of a small quantity (0.1% wt/wt) of genomic DNA from Shewanella oneidensis MR-1 as the control. The technical reproducibility of the amplicon sequencing-based detection approach is quite low, with an average operational taxonomic unit (OTU) overlap of 17.2%+/-2.3% between two technical replicates, and 8.2%+/-2.3% among three technical replicates, which is most likely due to problems associated with random sampling processes. Such variations in technical replicates could have substantial effects on estimating beta-diversity but less on alpha-diversity. A high variation was also observed in the control across different samples (for example, 66.7-fold for the forward primer), suggesting that the amplicon sequencing-based detection approach could not be quantitative. In addition, various strategies were examined to improve the comparability of amplicon sequencing data, such as increasing biological replicates, and removing singleton sequences and less-representative OTUs across biological replicates. Finally, as expected, various statistical analyses with preprocessed experimental data revealed clear differences in the composition and structure of microbial communities between warming and non-warming, or between clipping and non-clipping. Taken together, these results suggest that amplicon sequencing-based detection is useful in analyzing microbial community structure even though it is not reproducible and quantitative. However, great caution should be taken in experimental design and data interpretation when the amplicon sequencing-based detection approach is used for quantitative analysis of the beta-diversity of microbial communities.

(end quote)

References

Zhou, J., Wu, L., Deng, Y., Zhi, X., Jiang, Y.-H., Tu, Q., Xie, J., et al. Reproducibility and quantitation of amplicon sequencing-based detection. The ISME Journal. (2011) 5(8):1303-1313. doi:10.1038/ismej.2011.11

The article can be accessed online at http://www.nature.com/ismej/journal/v5/n8/full/ismej201111a.html

Examples

# Load the data
data(soilrep)
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# Alpha diversity (richness) example. Accept null hypothesis: 
# No convincing difference in species richness between warmed/unwarmed soils.
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# Graphically compare richness between the different treatments.
man.col <- c(WC="red", WU="brown", UC="blue", UU="darkgreen")
plot_richness(soilrep, x="Treatment", color="Treatment", measures=c("Observed", "Chao1", "Shannon"))