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. 2014 Apr 8;9(4):e93827.
doi: 10.1371/journal.pone.0093827. eCollection 2014.

Strengths and limitations of 16S rRNA gene amplicon sequencing in revealing temporal microbial community dynamics

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Strengths and limitations of 16S rRNA gene amplicon sequencing in revealing temporal microbial community dynamics

"V体育官网" Rachel Poretsky et al. PLoS One. .

"V体育平台登录" Abstract

This study explored the short-term planktonic microbial community structure and resilience in Lake Lanier (GA, USA) while simultaneously evaluating the technical aspects of identifying taxa via 16S rRNA gene amplicon and metagenomic sequence data VSports手机版. 16S rRNA gene amplicons generated from four temporally discrete samples were sequenced with 454 GS-FLX-Ti yielding ∼40,000 rRNA gene sequences from each sample and representing ∼300 observed OTUs. Replicates obtained from the same biological sample clustered together but several biases were observed, linked to either the PCR or sequencing-preparation steps. In comparisons with companion whole-community shotgun metagenome datasets, the estimated number of OTUs at each timepoint was concordant, but 1. 5 times and ∼10 times as many phyla and genera, respectively, were identified in the metagenomes. Our analyses showed that the 16S rRNA gene captures broad shifts in community diversity over time, but with limited resolution and lower sensitivity compared to metagenomic data. We also identified OTUs that showed marked shifts in abundance over four close timepoints separated by perturbations and tracked these taxa in the metagenome vs. 16S rRNA amplicon data. A strong summer storm had less of an effect on community composition than did seasonal mixing, which revealed a distinct succession of organisms. This study provides insights into freshwater microbial communities and advances the approaches for assessing community diversity and dynamics in situ. .

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Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Figure 1
Figure 1. Diversity estimates for the four Lake Lanier timepoints
. A) Alpha diversity based on observed species (97% OTUs) from 16S amplicons for each of the nine samples. Error bars represent the variation observed among duplicate sequencing runs. B) Redundancy curves of the metagenomes of the four timepoints using (see Methods for details). The curves show that NOV is a more diverse sample, e.g., with the same sequencing effort it results in a lower coverage.
Figure 2
Figure 2. Community composition shifts over time as revealed by 16S data.
Taxonomic binning of 16S amplicon sequences for each of the 14 individual datasets at the phylum (top) and genus (middle) levels were based on the July 2011 version of the Greengenes database . Freshwater lineages (bottom) were based on a freshwater database according to the taxonomy framework described in Newton et al., 2011. Datasets are ordered left to right by date, technical sequencing replicate (lane 1 and lane 2), and DNA replicate (A, B and C). Taxa identified as major drivers of the differences between timepoints (SIMPER analysis) are labeled (see figure key).
Figure 3
Figure 3. Sequence diversity of the samples used in this study
. Chao1 diversity estimates of datasets based on phylum (A) and genus (B) level taxonomic classification are shown for all four metagenomic timepoints and seven selected 16S amplicon datasets.
Figure 4
Figure 4. Individual genera abundance shifts over time based on 16S and metagenomes.
Genus-level taxonomic trends for a subset of genera identified within the metagenomic contigs (A) and 16S rRNA amplicon (B) datasets, based on NCBI taxonomy, are shown. The lines represent the general temporal trends of two genera, Synechococcus and Legionella, in each dataset.

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