Comprehensive single-PCR 16S and 18S rRNA community analysis validated with mock communities,and estimation of sequencing bias against 18S

Universal primers for SSU rRNA genes allow profiling of natural communities by simultaneously amplifying templates from Bacteria, Archaea, and Eukaryota in a single PCR reaction. Despite the potential to show relative abundance for all rRNA genes, universal primers are rarely used, due to various concerns including amplicon length variation and its effect on bioinformatic pipelines. We thus developed 16S and 18S rRNA mock communities and a bioinformatic pipeline to validate this approach. Using these mocks, we show that universal primers (515Y/926R) outperformed eukaryote-specific V4 primers in observed versus expected abundance correlations (slope = 0.88 vs. 0.67–0.79), and mock community members with single mismatches to the primer were strongly underestimated (threefold to eightfold). Using field samples, both primers yielded similar 18S beta-diversity patterns (Mantel test, p < 0.001) but differences in relative proportions of many rarer taxa. To test for length biases, we mixed mock communities (16S + 18S) before PCR and found a twofold underestimation of 18S sequences due to sequencing bias. Correcting for the twofold underestimation, we estimate that, in Southern California field samples (1.2–80 μm), there were averages of 35% 18S, 28% chloroplast 16S, and 37% prokaryote 16S rRNA genes. These data demonstrate the potential for universal primers to generate comprehensive microbiome profiles.     

New Insights into the Diversity of Marine Picoeukaryotes

Over the last decade, culture-independent surveys of marine picoeukaryotic diversity based on 18S ribosomal DNA clone libraries have unveiled numerous sequences of novel high-rank taxa. This newfound diversity has significantly altered our understanding of marine microbial food webs and the evolution of eukaryotes. However, the current picture of marine eukaryotic biodiversity may be significantly skewed by PCR amplification biases, occurrence of rDNA genes in multiple copies within a single cell, and the capacity of DNA to persist as extracellular material. In this study we performed an analysis of the metagenomic dataset from the Global Ocean Survey (GOS) expedition, seeking eukaryotic ribosomal signatures. This PCR-free approach revealed similar phylogenetic patterns to clone library surveys, suggesting that PCR steps do not impose major biases in the exploration of environmental DNA. The different cell size fractions within the GOS dataset, however, displayed a distinct picture. High protistan diversity in the <0.8 µm size fraction, in particular sequences from radiolarians and ciliates (and their absence in the 0.8–3 µm fraction), suggest that most of the DNA in this fraction comes from extracellular material from larger cells. In addition, we compared the phylogenetic patterns from rDNA and reverse transcribed rRNA 18S clone libraries from the same sample harvested in the Mediterranean Sea. The libraries revealed major differences, with taxa such as pelagophytes or picobiliphytes only detected in the 18S rRNA library. MAST (Marine Stramenopiles) appeared as potentially prominent grazers and we observed a significant decrease in the contribution of alveolate and radiolarian sequences, which overwhelmingly dominated rDNA libraries. The rRNA approach appears to be less affected by taxon-specific rDNA copy number and likely better depicts the biogeochemical significance of marine protists.     

Reducing the effects of PCR amplification and sequencing artifacts on 16S rRNA-based studies

The advent of next generation sequencing has coincided with a growth in interest in using these approaches to better understand the role of the structure and function of the microbial communities in human, animal, and environmental health. Yet, use of next generation sequencing to perform 16S rRNA gene sequence surveys has resulted in considerable controversy surrounding the effects of sequencing errors on downstream analyses. We analyzed 2.7×10(6) reads distributed among 90 identical mock community samples, which were collections of genomic DNA from 21 different species with known 16S rRNA gene sequences; we observed an average error rate of 0.0060. To improve this error rate, we evaluated numerous methods of identifying bad sequence reads, identifying regions within reads of poor quality, and correcting base calls and were able to reduce the overall error rate to 0.0002. Implementation of the PyroNoise algorithm provided the best combination of error rate, sequence length, and number of sequences. Perhaps more problematic than sequencing errors was the presence of chimeras generated during PCR. Because we knew the true sequences within the mock community and the chimeras they could form, we identified 8% of the raw sequence reads as chimeric. After quality filtering the raw sequences and using the Uchime chimera detection program, the overall chimera rate decreased to 1%. The chimeras that could not be detected were largely responsible for the identification of spurious operational taxonomic units (OTUs) and genus-level phylotypes. The number of spurious OTUs and phylotypes increased with sequencing effort indicating that comparison of communities should be made using an equal number of sequences. Finally, we applied our improved quality-filtering pipeline to several benchmarking studies and observed that even with our stringent data curation pipeline, biases in the data generation pipeline and batch effects were observed that could potentially confound the interpretation of microbial community data.     

Incorporating 16S Gene Copy Number Information Improves Estimates of Microbial Diversity and Abundance

The abundance of different SSU rRNA (“16S”) gene sequences in environmental samples is widely used in studies of microbial ecology as a measure of microbial community structure and diversity. However, the genomic copy number of the 16S gene varies greatly – from one in many species to up to 15 in some bacteria and to hundreds in some microbial eukaryotes. As a result of this variation the relative abundance of 16S genes in environmental samples can be attributed both to variation in the relative abundance of different organisms, and to variation in genomic 16S copy number among those organisms. Despite this fact, many studies assume that the abundance of 16S gene sequences is a surrogate measure of the relative abundance of the organisms containing those sequences. Here we present a method that uses data on sequences and genomic copy number of 16S genes along with phylogenetic placement and ancestral state estimation to estimate organismal abundances from environmental DNA sequence data. We use theory and simulations to demonstrate that 16S genomic copy number can be accurately estimated from the short reads typically obtained from high-throughput environmental sequencing of the 16S gene, and that organismal abundances in microbial communities are more strongly correlated with estimated abundances obtained from our method than with gene abundances. We re-analyze several published empirical data sets and demonstrate that the use of gene abundance versus estimated organismal abundance can lead to different inferences about community diversity and structure and the identity of the dominant taxa in microbial communities. Our approach will allow microbial ecologists to make more accurate inferences about microbial diversity and abundance based on 16S sequence data.     

Molecular evidence for widespread occurrence of Foraminifera in soils

Environmental SSU rDNA‐based surveys are contributing to the dramatic revision of eukaryotic high‐level diversity and phylogeny as the number of sequence data increases. This ongoing revolution gives the opportunity to test for the presence of some eukaryotic taxa in environments where they have not been found using classical microscopic observations. Here, we test whether the foraminifera, a group of single‐celled eukaryotes, considered generally as typical for the marine ecosystems are present in soil. We performed foraminiferal‐specific nested PCR on 20 soil DNA samples collected in contrasted environments. Unexpectedly, we found that the majority of the samples contain foraminiferal SSU rDNA sequences. In total, we obtained 49 sequences from 17 localities. Phylogenetic analysis clusters them in four groups branching among the radiation of early foraminiferal lineages. Three of these groups also include sequences originated from previous freshwater surveys, suggesting that there were up to four independent colonization events of terrestrial and/or freshwater ecosystems by ancestral foraminifera. As shown by our data, foraminifera are a widespread and diverse component of soil microbial communities. Yet, identification of terrestrial foraminiferal species and understanding of their ecological role represent an exciting challenge for future research.     

Extreme differences in rates of molecular evolution of foraminifera revealed by comparison of ribosomal DNA sequences and the fossil record

Foraminifera have one of the best known fossil records among the unicellular eukaryotes. However, the origin and phylogenetic relationships of the extant foraminiferal lineages are poorly understood. To test the current paleontological hypotheses on evolution of foraminifera, we sequenced about 1,000 base pairs from the 3' end of the small subunit rRNA gene (SSU rDNA) in 22 species representing all major taxonomic groups. Phylogenies were derived using neighbor- joining, maximum-parsimony, and maximum-likelihood methods. All analyses confirm the monophyletic origin of foraminifera. Evolutionary relationships within foraminifera inferred from rDNA sequences, however, depend on the method of tree building and on the choice of analyzed sites. In particular, the position of planktonic foraminifera shows important variations. We have shown that these changes result from the extremely high rate of rDNA evolution in this group. By comparing the number of substitutions with the divergence times inferred from the fossil record, we have estimated that the rate of rDNA evolution in planktonic foraminifera is 50 to 100 times faster than in some benthic foraminifera. The use of the maximum-likelihood method and limitation of analyzed sites to the most conserved parts of the SSU rRNA molecule render molecular and paleontological data generally congruent.      

Quantitative metagenomic analyses based on average genome size normalization

Over the past quarter-century, microbiologists have used DNA sequence information to aid in the characterization of microbial communities. During the last decade, this has expanded from single genes to microbial community genomics, or metagenomics, in which the gene content of an environment can provide not just a census of the community members but direct information on metabolic capabilities and potential interactions among community members. Here we introduce a method for the quantitative characterization and comparison of microbial communities based on the normalization of metagenomic data by estimating average genome sizes. This normalization can relieve comparative biases introduced by differences in community structure, number of sequencing reads, and sequencing read lengths between different metagenomes. We demonstrate the utility of this approach by comparing metagenomes from two different marine sources using both conventional small-subunit (SSU) rRNA gene analyses and our quantitative method to calculate the proportion of genomes in each sample that are capable of a particular metabolic trait. With both environments, to determine what proportion of each community they make up and how differences in environment affect their abundances, we characterize three different types of autotrophic organisms: aerobic, photosynthetic carbon fixers (the Cyanobacteria); anaerobic, photosynthetic carbon fixers (the Chlorobi); and anaerobic, nonphotosynthetic carbon fixers (the Desulfobacteraceae). These analyses demonstrate how genome proportionality compares to SSU rRNA gene relative abundance and how factors such as average genome size and SSU rRNA gene copy number affect sampling probability and therefore both types of community analysis.     

Single Cell Analysis Linking Ribosomal (r)DNA and rRNA Copy Numbers to Cell Size and Growth Rate Provides Insights into Molecular Protistan Ecology

Ribosomal (r)RNA and rDNA have been golden molecular markers in microbial ecology. However, it remains poorly understood how ribotype copy number (CN)‐based characteristics are linked with diversity, abundance, and activity of protist populations and communities observed at organismal levels. Here, we applied a single‐cell approach to quantify ribotype CNs in two ciliate species reared at different temperatures. We found that in actively growing cells, the per‐cell rDNA and rRNA CNs scaled with cell volume (CV) to 0.44 and 0.58 powers, respectively. The modeled rDNA and rRNA concentrations thus appear to be much higher in smaller than in larger cells. The observed rRNA:rDNA ratio scaled with CV^0.14. The maximum growth rate could be well predicted by a combination of per‐cell ribotype CN and temperature. Our empirical data and modeling on single‐cell ribotype scaling are in agreement with both the metabolic theory of ecology and the growth rate hypothesis, providing a quantitative framework for linking cellular rDNA and rRNA CNs with body size, growth (activity), and biomass stoichiometry. This study also demonstrates that the expression rate of rRNA genes is constrained by cell size, and favors biomass rather than abundance‐based interpretation of quantitative ribotype data in population and community ecology of protists.     

Molecular Identification of Nanoplanktonic Protists Based on Small Subunit Ribosomal RNA Gene Sequences for Ecological Studies1

Nanoplanktonic protists are comprised of a diverse assemblage of species which are responsible for a variety of trophic processes in marine and freshwater ecosystems. Current methods for identifying small protists by electron microscopy do not readily permit both identification and enumeration of nanoplanktonic protists in field samples. Thus, one major goal in the application of molecular approaches in protistan ecology has been the detection and quantification of individual species in natural water samples. Sequences of small subunit ribosomal RNA (SSU rRNA) genes have proven to be useful towards achieving this goal. Comparison of sequences from clone libraries of protistan SSU rRNA genes amplified from natural assemblages of protists by the polymerase chain reaction (PCR) can be used to examine protistan diversity. Furthermore, oligonucleotide probes complementary to short sequence regions unique to species of small protists can be designed by comparative analysis of rRNA gene sequences. These probes may be used to either detect the RNA of particular species of protists in total nucleic acid extracts immobilized on membranes, or the presence of target species in water samples via in situ hybridization of whole cells. Oligonucleotide probes may also serve as primers for the selective amplification of target sequences from total population DNA by PCR. Thus, molecular sequence information is becoming increasingly useful for identifying and enumerating protists, and for studying their spatial and temporal distribution in nature. Knowledge of protistan species composition, abundance and variability in an environment can ultimately be used to relate community structure to various aspects of community function and biogeochemical activity.     

Protistan microbial observatory in the Cariaco Basin,Caribbean. I. Pyrosequencing vs Sanger insights into species richness

Microbial diversity and distribution are topics of intensive research. In two companion papers in this issue, we describe the results of the Cariaco Microbial Observatory (Caribbean Sea, Venezuela). The Basin contains the largest body of marine anoxic water, and presents an opportunity to study protistan communities across biogeochemical gradients. In the first paper, we survey 18S ribosomal RNA (rRNA) gene sequence diversity using both Sanger- and pyrosequencing-based approaches, employing multiple PCR primers, and state-of-the-art statistical analyses to estimate microbial richness missed by the survey. Sampling the Basin at three stations, in two seasons, and at four depths with distinct biogeochemical regimes, we obtained the largest, and arguably the least biased collection of over 6000 nearly full-length protistan rRNA gene sequences from a given oceanographic regime to date, and over 80 000 pyrosequencing tags. These represent all major and many minor protistan taxa, at frequencies globally similar between the two sequence collections. This large data set provided, via the recently developed parametric modeling, the first statistically sound prediction of the total size of protistan richness in a large and varied environment, such as the Cariaco Basin: over 36 000 species, defined as almost full-length 18S rRNA gene sequence clusters sharing over 99% sequence homology. This richness is a small fraction of the grand total of known protists (over 100 000–500 000 species), suggesting a degree of protistan endemism.     

PCR biases distort bacterial and archaeal community structure in pyrosequencing datasets

As 16S rRNA gene targeted massively parallel sequencing has become a common tool for microbial diversity investigations, numerous advances have been made to minimize the influence of sequencing and chimeric PCR artifacts through rigorous quality control measures. However, there has been little effort towards understanding the effect of multi-template PCR biases on microbial community structure. In this study, we used three bacterial and three archaeal mock communities consisting of, respectively, 33 bacterial and 24 archaeal 16S rRNA gene sequences combined in different proportions to compare the influences of (1) sequencing depth, (2) sequencing artifacts (sequencing errors and chimeric PCR artifacts), and (3) biases in multi-template PCR, towards the interpretation of community structure in pyrosequencing datasets. We also assessed the influence of each of these three variables on α- and β-diversity metrics that rely on the number of OTUs alone (richness) and those that include both membership and the relative abundance of detected OTUs (diversity). As part of this study, we redesigned bacterial and archaeal primer sets that target the V3-V5 region of the 16S rRNA gene, along with multiplexing barcodes, to permit simultaneous sequencing of PCR products from the two domains. We conclude that the benefits of deeper sequencing efforts extend beyond greater OTU detection and result in higher precision in β-diversity analyses by reducing the variability between replicate libraries, despite the presence of more sequencing artifacts. Additionally, spurious OTUs resulting from sequencing errors have a significant impact on richness or shared-richness based α- and β-diversity metrics, whereas metrics that utilize community structure (including both richness and relative abundance of OTUs) are minimally affected by spurious OTUs. However, the greatest obstacle towards accurately evaluating community structure are the errors in estimated mean relative abundance of each detected OTU due to biases associated with multi-template PCR reactions.     

The rpb2 gene represents a viable alternative molecular marker for the analysis of environmental fungal communities

Although the commonly used internal transcribed spacer region of rDNA (ITS) is well suited for taxonomic identification of fungi, the information on the relative abundance of taxa and diversity is negatively affected by the multicopy nature of rDNA and the existence of ITS paralogues. Moreover, due to high variability, ITS sequences cannot be used for phylogenetic analyses of unrelated taxa. The part of single‐copy gene encoding the second largest subunit of RNA polymerase II (rpb2) was thus compared with first spacer of ITS as an alternative marker for the analysis of fungal communities in spruce forest topsoil, and their applicability was tested on a comprehensive mock community. In soil, rpb2 exhibited broad taxonomic coverage of the entire fungal tree of life including basal fungal lineages. The gene exhibited sufficient variation for the use in phylogenetic analyses and taxonomic assignments, although it amplifies also paralogues. The fungal taxon spectra obtained with rbp2 region and ITS1 corresponded, but sequence abundance differed widely, especially in the basal lineages. The proportions of OTU counts and read counts of major fungal groups were close to the reality when rpb2 was used as a molecular marker while they were strongly biased towards the Basidiomycota when using the ITS primers ITS1/ITS4. Although the taxonomic placement of rbp2 sequences is currently more difficult than that of the ITS sequences, its discriminative power, quantitative representation of community composition and suitability for phylogenetic analyses represent significant advantages.     

An optimised PCR/T-RFLP fingerprinting approach for the investigation of protistan communities in groundwater environments

Due to the scarcity or complete absence of higher organisms, protists may represent an important higher trophic level (above Prokaryotes) in the food webs of groundwater habitats. Nevertheless, the importance of aquifer protists, especially in contaminated groundwater environments, is poorly understood. Partly, this may be due to a lack of adequate PCR and fingerprinting approaches for protists in aquifers, which can be considered low in protistan or high in non-target rRNA gene copy numbers. Therefore, we have validated the suitability of distinct eukaryote-targeted primer pairs and restriction endonucleases for T-RFLP fingerprinting of protistan communities. By in silico predictions, and by fingerprinting, cloning and sequencing of microeukaryote amplicons from hydrocarbon-contaminated aquifer sediment DNA, we show that the Euk20f/Euk516r primer set in combination with Bsh1236I digestion is best suited for the recovery of diverse protistan 18S rRNA lineages. In contrast to other tested primer sets, a preferred recovery of fungal and archaeal non-target amplicons was not observed. In summary, we present an optimised microeukaryote-targeted PCR/T-RFLP fingerprinting approach which may be of value for the characterisation of protistan communities in groundwater and other habitats.     

Intra-Genomic Variation in the Ribosomal Repeats of Nematodes

Ribosomal loci represent a major tool for investigating environmental diversity and community structure via high-throughput marker gene studies of eukaryotes (e.g. 18S rRNA). Since the estimation of species’ abundance is a major goal of environmental studies (by counting numbers of sequences), understanding the patterns of rRNA copy number across species will be critical for informing such high-throughput approaches. Such knowledge is critical, given that ribosomal RNA genes exist within multi-copy repeated arrays in a genome. Here we measured the repeat copy number for six nematode species by mapping the sequences from whole genome shotgun libraries against reference sequences for their rRNA repeat. This revealed a 6-fold variation in repeat copy number amongst taxa investigated, with levels of intragenomic variation ranging from 56 to 323 copies of the rRNA array. By applying the same approach to four C. elegans mutation accumulation lines propagated by repeated bottlenecking for an average of ~400 generations, we find on average a 2-fold increase in repeat copy number (rate of increase in rRNA estimated at 0.0285-0.3414 copies per generation), suggesting that rRNA repeat copy number is subject to selection. Within each Caenorhabditis species, the majority of intragenomic variation found across the rRNA repeat was observed within gene regions (18S, 28S, 5.8S), suggesting that such intragenomic variation is not a product of selection for rRNA coding function. We find that the dramatic variation in repeat copy number among these six nematode genomes would limit the use of rRNA in estimates of organismal abundance. In addition, the unique pattern of variation within a single genome was uncorrelated with patterns of divergence between species, reflecting a strong signature of natural selection for rRNA function. A better understanding of the factors that control or affect copy number in these arrays, as well as their rates and patterns of evolution, will be critical for informing estimates of global biodiversity.     

Environmental rRNA inventories miss over half of protistan diversity

Background  

The main tool to discover novel microbial eukaryotes is the rRNA approach. This approach has important biases, including PCR discrimination against certain rRNA gene species, which makes molecular inventories skewed relative to the source communities. The degree of this bias has not been quantified, and it remains unclear whether species missed from clone libraries could be recovered by increasing sequencing efforts, or whether they cannot be detected in principle. Here we attempt to discriminate between these possibilities by statistically analysing four protistan inventories obtained using different general eukaryotic PCR primers.     

Accelerated methylation of ribosomal RNA genes during the cellular senescence of Werner syndrome fibroblasts.

Ribosomal DNA (rDNA) metabolism has been implicated in cellular and organismal aging. The role of rDNA in premature and normal human aging was investigated by measuring rDNA gene copy number, the level of rDNA methylation, and rRNA expression during the in vitro senescence of primary fibroblasts from normal (young and old) donors and from Werner syndrome (WS) patients. In comparison to their normal counterparts, WS fibroblasts grew slowly and reached senescence after fewer doublings. The rDNA copy number did not change significantly throughout the life span of both normal and WS fibroblasts. However, in senescent WS and normal old fibroblasts, we detected rDNA species with unusually slow electrophoretic mobility. Cellular aging in Saccharomyces cerevisiae is accompanied by the formation and accumulation of rDNA circles. Our analysis revealed that the rDNA species observed in this study were longer, linear rDNA molecules attributable to the inhibition of ECO:RI cleavage by methylation. Furthermore, isoschizomeric restriction analysis confirmed that in vitro senescence of fibroblasts is accompanied by significant increases in cytosine methylation within rDNA genes. This increased methylation is maximal during the abbreviated life span of WS fibroblasts. Despite increased methylation of rDNA in senescent cells, the steady-state levels of 28S rRNA remained constant over the life span of both normal and WS fibroblasts.     

Rethinking microbial diversity analysis in the high throughput sequencing era

The analysis of amplified and sequenced 16S rRNA genes has become the most important single approach for microbial diversity studies. The new sequencing technologies allow for sequencing thousands of reads in a single run and a cost-effective option is split into a single run across many samples. However for this type of investigation the key question that needs to be answered is how many samples can be sequenced without biasing the results due to lack of sequence representativeness? In this work we demonstrated that the level of sequencing effort used for analyzing soil microbial communities biases the results and determines the most effective type of analysis for small and large datasets. Many simulations were performed with four independent pyrosequencing-generated 16S rRNA gene libraries from different environments. The analysis performed here illustrates the lack of resolution of OTU-based approaches for datasets with low sequence coverage. This analysis should be performed with at least 90% of sequence coverage. Diversity index values increase with sample size making normalization of the number of sequences in all samples crucial. An important finding of this study was the advantage of phylogenetic approaches for examining microbial communities with low sequence coverage. However, if the environments being compared were closely related, a deeper sequencing would be necessary to detect the variation in the microbial composition.     

VITCOMIC2: visualization tool for the phylogenetic composition of microbial communities based on 16S rRNA gene amplicons and metagenomic shotgun sequencing

Background

The 16S rRNA gene-based amplicon sequencing analysis is widely used to determine the taxonomic composition of microbial communities. Once the taxonomic composition of each community is obtained, evolutionary relationships among taxa are inferred by a phylogenetic tree. Thus, the combined representation of taxonomic composition and phylogenetic relationships among taxa is a powerful method for understanding microbial community structure; however, applying phylogenetic tree-based representation with information on the abundance of thousands or more taxa in each community is a difficult task. For this purpose, we previously developed the tool VITCOMIC (VIsualization tool for Taxonomic COmpositions of MIcrobial Community), which is based on the genome-sequenced microbes’ phylogenetic information. Here, we introduce VITCOMIC2, which incorporates substantive improvements over VITCOMIC that were necessary to address several issues associated with 16S rRNA gene-based analysis of microbial communities.

Results

We developed VITCOMIC2 to provide (i) sequence identity searches against broad reference taxa including uncultured taxa; (ii) normalization of 16S rRNA gene copy number differences among taxa; (iii) rapid sequence identity searches by applying the graphics processing unit-based sequence identity search tool CLAST; (iv) accurate taxonomic composition inference and nearly full-length 16S rRNA gene sequence reconstructions for metagenomic shotgun sequencing; and (v) an interactive user interface for simultaneous representation of the taxonomic composition of microbial communities and phylogenetic relationships among taxa. We validated the accuracy of processes (ii) and (iv) by using metagenomic shotgun sequencing data from a mock microbial community.

Conclusions

The improvements incorporated into VITCOMIC2 enable users to acquire an intuitive understanding of microbial community composition based on the 16S rRNA gene sequence data obtained from both metagenomic shotgun and amplicon sequencing.

     

Groundtruthing next-gen sequencing for microbial ecology-biases and errors in community structure estimates from PCR amplicon pyrosequencing

Analysis of microbial communities by high-throughput pyrosequencing of SSU rRNA gene PCR amplicons has transformed microbial ecology research and led to the observation that many communities contain a diverse assortment of rare taxa-a phenomenon termed the Rare Biosphere. Multiple studies have investigated the effect of pyrosequencing read quality on operational taxonomic unit (OTU) richness for contrived communities, yet there is limited information on the fidelity of community structure estimates obtained through this approach. Given that PCR biases are widely recognized, and further unknown biases may arise from the sequencing process itself, a priori assumptions about the neutrality of the data generation process are at best unvalidated. Furthermore, post-sequencing quality control algorithms have not been explicitly evaluated for the accuracy of recovered representative sequences and its impact on downstream analyses, reducing useful discussion on pyrosequencing reads to their diversity and abundances. Here we report on community structures and sequences recovered for in vitro-simulated communities consisting of twenty 16S rRNA gene clones tiered at known proportions. PCR amplicon libraries of the V3-V4 and V6 hypervariable regions from the in vitro-simulated communities were sequenced using the Roche 454 GS FLX Titanium platform. Commonly used quality control protocols resulted in the formation of OTUs with >1% abundance composed entirely of erroneous sequences, while over-aggressive clustering approaches obfuscated real, expected OTUs. The pyrosequencing process itself did not appear to impose significant biases on overall community structure estimates, although the detection limit for rare taxa may be affected by PCR amplicon size and quality control approach employed. Meanwhile, PCR biases associated with the initial amplicon generation may impose greater distortions in the observed community structure.