Operational Taxonomic Unit Data Research Articles

A sparse Bayesian hierarchical vector autoregressive model for microbial dynamics in a wastewater treatment plant

Proper function of a wastewater treatment plant (WWTP) relies on maintaining a delicate balance between a multitude of competing microorganisms. Gaining a detailed understanding of the complex network of interactions therein is essential to maximising not only current operational efficiencies, but also for the effective design of new treatment technologies. Metagenomics offers an insight into these dynamic systems through the analysis of the microbial DNA sequences present. Unique taxa are deduced through sequence clustering to form operational taxonomic units (OTUs), with per-taxa abundance estimates obtained from corresponding sequence counts. The data in this study comprise weekly OTU counts from an activated sludge (AS) tank of a WWTP along with corresponding measurements of chemical and environmental (CE) covariates. Directly fitting a model to the OTU data is incredibly challenging because of the high dimensionality and sparsity of the observations. The first step is therefore to aggregate the OTUs into twelve microbial communities or “bins” using a seasonal phase-based clustering approach. The mean abundances in the twelve bins are assumed to vary over time according to a multivariate linear regression on the CE covariates. Deviations from the mean are then modelled using a vector autoregressive (VAR) model of order one, which is a linear approximation to the commonly used generalised Lotka-Volterra (gLV) model. Sparsity is assumed in the interactions between microbial communities by carrying out inference in a hierarchical Bayesian framework which uses a shrinkage prior for the autoregressive coefficient matrix of the VAR model. Different shrinkage priors are explored by analysing simulated data sets before selecting the regularised horseshoe prior for the biological application. It is found that ammonia and chemical oxygen demand have a positive relationship with several bins and pH has a positive relationship with one bin. These results are supported by findings in the biological literature. Several negative interactions are also identified. These novel biological findings suggest OTUs in different bins may be competing for resources and that these relationships are complex. Although simpler than a gLV model, the VAR model is still able to offer valuable insight into the microbial dynamics of the WWTP.

Comparison of tumor-associated and nontumor-associated esophageal mucosa microbiota in patients with esophageal squamous cell carcinoma.

Esophageal microbiota plays important roles in esophageal squamous cell carcinoma (ESCC). The aims of this study were to clarify the changes in the bacterial community during ESCC development and identify latent pathogenic bacteria which may contribute to esophageal carcinogenesis and progression. Fresh tumor and nontumor esophageal mucosal samples were collected from 31 men with ESCC. High-throughput 16s rRNA sequencing was performed, and the operational taxonomic unit data and bacterial classification annotation were obtained and analyzed. The Ace, Chao, Shannon, Simpson indexes, and operational taxonomic unit numbers were higher in nontumor tissues than in tumor tissues, although without statistical significance. There were 4 phyla and 28 genera found to show significant differences between tumor and nontumor samples. The general probiotic Lactobacillus was 1.98-fold higher in nontumor tissues, while the general pathogenic genera Fusobacterium was 4.35-fold higher in tumor tissues. For tumor tissue samples, the genera Treponema and Brevibacillus were significantly higher in N1 and N2 stages, respectively, and Acinetobacter was significantly higher in T3 stage. For nontumor tissues, the genus Fusicatenibacter was significantly higher in T2 stage, and Corynebacterium, Aggregatibacter, Saccharimonadaceae-TM7x, and Cupriavidus were significantly higher in T4 stage. Additionally, bacteria related to nitrotoluene degradation were enriched in nontumor tissues, while bacteria related to base excision repair were enriched in tumor tissues. The relative abundance of several phyla and genera are different between tumor and nontumor tissue samples. The altered bacterial microbiota is correlated with different tumor stages and some microbes may take part in the carcinogenesis and development of ESCC.

1064. Treatment Success in Reducing Recurrent Clostridioides difficile Infection with Investigational Live Biotherapeutic RBX2660 Is Associated with Microbiota Restoration: Consistent Evidence from a Phase 3 Clinical Trial

BackgroundSeveral investigational microbiota-based live biotherapeutics are in clinical development for reducing recurrence of Clostridioides difficile infection (rCDI), including RBX2660 a liquid suspension of a broad consortium of microbiota, which includes Bacteridetes and Firmicutes. RBX2660 has been evaluated in >600 participants in 6 clinical trials. Here we report that RBX2660 induced significant shifts to the intestinal microbiota of treatment-responsive participants in PUNCH CD3—a Phase 3 randomized, double-blinded, placebo-controlled trial.MethodsPUNCH CD3 participants received a single dose of RBX2660 or placebo between 24 to 72 hours after completing rCDI antibiotic treatment. Clinical response was the absence of CDI recurrence at eight weeks after treatment. Participants voluntarily submitted stool samples prior to blinded study treatment (baseline), 1, 4 and 8 weeks, 3 and 6 months after receiving study treatment. Samples were extracted and sequenced using shallow shotgun methods. Operational taxonomic unit (OTU) data were used to calculate relative taxonomic abundance, alpha diversity, and the Microbiome Health Index (MHI)—a biomarker of antibiotic-induced dysbiosis and restoration.ResultsClinically, RBX2660 demonstrated superior efficacy versus placebo (70.4% versus 58.1%). From before to after treatment, RBX2660-treated clinical responders’ microbiome diversity shifted significantly (Mann-Whitney), and so did microbiome composition (Generalized Wald Test). Post-treatment changes were characterized by increased Bacteroidia and Clostridia and decreased Gammaproteobacteria and Bacilli, changes and were durable to at least 6 months. Repeated measures analysis confirmed that shifts were greater among RBX2660 responders compared to placebo responders (DMRepeat). The majority of responders’ MHI values shifted from a range common to antibiotic dysbiosis to a range common in healthy populations.Figure 1 Left panel. Mean relative abundance taxonomic class level at timepoints for participants in PUNCH CD3 before and after RBX2660 treatment, and for doses of RBX2660 administered in PUNCH CD3. The four taxonomic classes that change most from before to after treatment are shown with the mean and confidence intervals based on fitting OTU data to a Dirichlet multinomial distribution. Right panel, MHI biomarker for the same time points and investigational product groups, shown as median (red) and individual samples. A previously calculated threshold of MHI = 7.2 is shown (dotted line), above which MHI values predict healthy, below which MHI values predict antibiotic-induced dysbiosis.ConclusionAmong PUNCH CD3 clinical responders, RBX2660 significantly restored microbiota from less to more healthy compositions, and this restoration was durable to at least 6 months. These clinically-correlated microbiome shifts are highly consistent with results from multiple prior trials of RBX2660.Disclosures Ken Blount, PhD, Rebiotix Inc., a Ferring Company (Employee) Dana M. Walsh, PhD, Rebiotix (Employee)

Investigation of Adiposity Measures and Operational Taxonomic unit (OTU) Data Transformation Procedures in Stool Samples from a German Cohort Study Using Machine Learning Algorithms.

The analysis of the gut microbiome with respect to health care prevention and diagnostic purposes is increasingly the focus of current research. We analyzed around 2000 stool samples from the KORA (Cooperative Health Research in the Region of Augsburg) cohort using high-throughput 16S rRNA gene amplicon sequencing representing a total microbial diversity of 2089 operational taxonomic units (OTUs). We evaluated the combination of three different components to assess the reflection of obesity related to microbiota profiles: (i) four prediction methods (i.e., partial least squares (PLS), support vector machine regression (SVMReg), random forest (RF), and M5Rules); (ii) five OTU data transformation approaches (i.e., no transformation, relative abundance without and with log-transformation, as well as centered and isometric log-ratio transformations); and (iii) predictions from nine measurements of obesity (i.e., body mass index, three measures of body shape, and five measures of body composition). Our results showed a substantial impact of all three components. The applications of SVMReg and PLS in combination with logarithmic data transformations resulted in considerably predictive models for waist circumference-related endpoints. These combinations were at best able to explain almost 40% of the variance in obesity measurements based on stool microbiota data (i.e., OTUs) only. A reduced loss in predictive performance was seen after sex-stratification in waist–height ratio compared to other waist-related measurements. Moreover, our analysis showed that the contribution of OTUs less prevalent and abundant is minor concerning the predictive power of our models.

A taxonomy-free approach based on machine learning to assess the quality of rivers with diatoms

Diatoms are a compulsory biological quality element in the ecological assessment of rivers according to the Water Framework Directive. The application of current official indices requires the identification of individuals to species or lower rank under a microscope based on the valve morphology. This is a highly time-consuming task, often susceptible of disagreements among analysts. In alternative, the use of DNA metabarcoding combined with High-Throughput Sequencing (HTS) has been proposed. The sequences obtained from environmental DNA are clustered into Operational Taxonomic Units (OTUs), which can be assigned to a taxon using reference databases, and from there calculate biotic indices. However, there is still a high percentage of unassigned OTUs to species due to the incompleteness of reference libraries. Alternatively, we tested a new taxonomy-free approach based on diatom community samples to assess rivers. A combination of three machine learning techniques is used to build models that predict diatom OTUs expected in test sites, under reference conditions, from environmental data. The Observed/Expected OTUs ratio indicates the deviation from reference condition and is converted into a quality class. This approach was never used with diatoms neither with OTUs data. To evaluate its efficiency, we built a model based on OTUs lists (HYDGEN) and another based on taxa lists from morphological identification (HYDMORPH), and also calculated a biotic index (IPS). The models were trained and tested with data from 81 sites (44 reference sites) from central Portugal. Both models were considered accurate (linear regression for Observed and Expected richness: R2 ≈ 0.7, interception ≈ 0.8) and sensitive to global anthropogenic disturbance (Rs2 > 0.30 p < 0.006 for global disturbance). Yet, the HYDGEN model based on molecular data was sensitive to more types of pressures (such as, changes in land use and habitat quality), which gives promising insights to its use for bioassessment of rivers.

Bacterial metagenome analysis of Mytilus galloprovincialiscollected from Istanbul and Izmir coastal stations of Turkey.

Mytilus galloprovincialis is a marine mollusk belonging to the Bivalvia class. It has been distributed largely in Turkish shores and worldwide aquatic environments. Besides being known as an environmental pollution indicator, it is highly consumed as a food and has a high economic value. Due to their nutritional mechanisms by filtering water, they are affected by pollution in seawater and mussels can host-microbial diversity of environmental origin as well as pathogenic bacteria. Therefore, in this study, bacterial species found in Mediterranean mussels collected from the coastal stations of Istanbul [Rumeli Kavagi (RK), Kucukcekmece (KC)], and Izmir [(Foca (MF), Urla (MU)] were investigated and compared with microbiological and metagenomic analyses. According to microbiological analysis results, 34 mussel-associated Enterobacteriaceae and Vibrionaceae family members were identified. As a result of the culture-independent metagenomic analysis, taxonomic groups for each station were identified and compared based on Operational Taxonomic Unit data. For all stations, the most abundant bacterial genera were the unclassified bacterial genera. The total number of mussel-related total richness identified in all groups was 4889 (RK = 1605; KC = 1930; MF = 1508; and MU = 1125). According to the metagenomic data obtained in this study, different relative amounts of Lachnospiraceae and Bacteroidetes taxa groups were reported for all stations. The pathogenic bacterial genera identified by metagenomic analyses which may be significant for the public health are Arcobacter, Clostridium, Aeromonas, Vibrio, Escherichia_Shigella, Klebsiella, Campylobacter, Helicobacter, Pseudomonas, Morganella, Serratia, Corynebacterium, Enterococcus, Staphylococcus, Yersinia, Mycoplasma, Brucellaceae_unclassified, Pantoea, and Proteus.

Piphillin predicts metagenomic composition and dynamics from DADA2-corrected 16S rDNA sequences

BackgroundShotgun metagenomic sequencing reveals the potential in microbial communities. However, lower-cost 16S ribosomal RNA (rRNA) gene sequencing provides taxonomic, not functional, observations. To remedy this, we previously introduced Piphillin, a software package that predicts functional metagenomic content based on the frequency of detected 16S rRNA gene sequences corresponding to genomes in regularly updated, functionally annotated genome databases. Piphillin (and similar tools) have previously been evaluated on 16S rRNA data processed by the clustering of sequences into operational taxonomic units (OTUs). New techniques such as amplicon sequence variant error correction are in increased use, but it is unknown if these techniques perform better in metagenomic content prediction pipelines, or if they should be treated the same as OTU data in respect to optimal pipeline parameters.ResultsTo evaluate the effect of 16S rRNA sequence analysis method (clustering sequences into OTUs vs amplicon sequence variant error correction into amplicon sequence variants (ASVs)) on the ability of Piphillin to predict functional metagenomic content, we evaluated Piphillin-predicted functional content from 16S rRNA sequence data processed through OTU clustering and error correction into ASVs compared to corresponding shotgun metagenomic data. We show a strong correlation between metagenomic data and Piphillin-predicted functional content resulting from both 16S rRNA sequence analysis methods. Differential abundance testing with Piphillin-predicted functional content exhibited a low false positive rate (< 0.05) while capturing a large fraction of the differentially abundant features resulting from corresponding metagenomic data. However, Piphillin prediction performance was optimal at different cutoff parameters depending on 16S rRNA sequence analysis method. Using data analyzed with amplicon sequence variant error correction, Piphillin outperformed comparable tools, for instance exhibiting 19% greater balanced accuracy and 54% greater precision compared to PICRUSt2.ConclusionsOur results demonstrate that raw Illumina sequences should be processed for subsequent Piphillin analysis using amplicon sequence variant error correction (with DADA2 or similar methods) and run using a 99% ID cutoff for Piphillin, while sequences generated on platforms other than Illumina should be processed via OTU clustering (e.g., UPARSE) and run using a 96% ID cutoff for Piphillin. Piphillin is publicly available for academic users (Piphillin server. http://piphillin.secondgenome.com/.)

The Gut Microbiota in Collagenous Colitis Shares Characteristics With Inflammatory Bowel Disease-Associated Dysbiosis.

INTRODUCTION:In inflammatory bowel disease (IBD), an aberrant immune response to gut microbiota is important, but the role of the microbiota in collagenous colitis (CC) is largely unknown. We aimed to characterize the microbiota of patients with CC compared with that of healthy control and patients with IBD.METHODS:Fecal samples were collected from patients with CC (n = 29), age- and sex-matched healthy controls (n = 29), patients with Crohn's disease (n = 32), and patients with ulcerative colitis (n = 32). Sequence data were obtained by 454 sequencing of 16S rRNA gene amplicons, and the obtained sequences were subsequently taxonomically classified.RESULTS:Analysis of similarity statistics showed a segregation between patients with CC and healthy controls with increasing taxonomic resolution, becoming significant comparing operational taxonomic unit data (P = 0.006). CC had a lower abundance of 10 different taxa. Taxa-specific analyses revealed a consistent lower abundance of several operational taxonomic units belonging to the Ruminococcaceae family in patients with CC, q < 0.05 after false discovery rate correction. Loss of these taxa was seen in patients with CC with active disease and/or corticosteroid treatment only and resembled the findings in patients with IBD.DISCUSSION:CC is associated with a specific fecal microbiome seen primarily in patients with active disease or ongoing corticosteroid treatment, whereas the microbiome of CC patients in remission resembled that of healthy controls. Notably, the shift in key taxa, including the Ruminococcaceae family, was also observed in IBD. There may be common mechanisms in the pathogenesis of CC and IBD.

Associations Between Race, Perceived Psychological Stress, and the Gut Microbiota in a Sample of Generally Healthy Black and White Women: A Pilot Study on the Role of Race and Perceived Psychological Stress.

Racial health disparities persist among black and white women for colorectal cancer. Understanding racial differences in the gut microbiota and related covariates (e.g., stress) may yield new insight into unexplained colorectal cancer disparities. Healthy non-Hispanic black or white women (age ≥19 years) provided survey data, anthropometrics, and stool samples. Fecal DNA was collected and isolated from a wipe. Polymerase chain reaction was used to amplify the V4 region of the 16SrRNA gene and 250 bases were sequenced using the MiSeq platform. Microbiome data were analyzed using QIIME. Operational taxonomic unit data were log transformed and normalized. Analyses were conducted using linear models in R Package "limma." Fecal samples were analyzed for 80 women (M (SD) age = 39.9 (14.0) years, 47 black, 33 white). Blacks had greater average body mass index (33.3 versus 27.5 kg/m, p < .01) and waist circumference (98.3 versus 86.6 cm, p = .003) than whites. Whites reported more stressful life events (p = .026) and greater distress (p = .052) than blacks. Final models accounted for these differences. There were no significant differences in dietary variables. Unadjusted comparisons revealed no racial differences in alpha diversity. Racial differences were observed in beta diversity and abundance of top 10 operational taxonomic units. Blacks had higher abundances than whites of Faecalibacterium (p = .034) and Bacteroides (p = .038). Stress was associated with abundances of Bifidobacterium. The association between race and Bacteroides (logFC = 1.72, 0 = 0.020) persisted in fully adjusted models. Racial differences in the gut microbiota were observed including higher Bacteroides among blacks. Efforts to cultivate an "ideal" gut microbiota may help reduce colorectal cancer risk.

Gut Microbiome Associates With Lipid-Lowering Effect of Rosuvastatin in Vivo.

Background: Statin has been widely used to treat hyperlipidemia because of its high potency in decreasing cholesterol levels. The present study aimed to examine the lipid-lowering effect of rosuvastatin and the composition, diversity and species abundance of gut microbiome in association with rosuvastatin efficacy. Trial registration: ChiCTR-ORC-17013212 at the First Affiliated Hospital of Dalian Medical University, November 2, 2017.Results: Totally 64 patients with hyperlipidemia were treated with 10 mg/day of rosuvastatin for 4–8 weeks. Blood lipid indicators triglycerides (TG), total cholesterol (TC), high density lipoprotein (HDL), low-density lipoprotein cholesterol (LDL-C) were measured before and after the treatment. Stool samples were collected right after the treatment. Following total DNA extraction and PCR amplification of 16S rRNA gene, Illumina sequencing was performed for gut microbiome identification, classification and characterization. All the patients showed a significant blood lipid reduction after the treatment. The patients were grouped according to parallel manner design. Group I had 33 patients whose blood lipid levels dropped to the normal levels from week 4, with 58.5% reduction in LDL-C and 26.6% reduction in TC. Group II had 31 patients whose blood lipid levels were still above the normal levels after 8 weeks therapy, but with 41.9% reduction in LDL-C and 31.2% reduction in TC. Based on Operational Taxonomic Unit data, Alpha-diversity by Shannon Index was different between the two groups, and beta-diversity by Principle Component Analysis exhibited separated patterns of the two groups. The differences were also observed in the relative-abundance at phylum, family, and genus levels of the two groups. Linear discriminate analysis illustrated that the abundance of 29 taxa was higher in group I, while the abundance of other 13 taxa was higher in group II. Phyla Firmicutes and Fusobacteria had negative correlation to LDL-C level, but Cyanobacteria and Lentisphaerae had a positive correlation to LDL-C level. Moreover, gender and age were also found somehow correlated to microbial community composition.Conclusion: Rosuvastatin therapy had different blood lipid-lowering effect on hyperlipidemia. The gut microbiota exhibited variation in community composition, diversity and taxa in association to rosuvastatin hypolipidemic effect. These results indicate that modulation of gut microflora, especially the negative/positive correlated species might strengthen statin efficacy in statin-inadequate patients.

Characterization of spatial distribution of the bacterial community in the South Sea of Korea.

In order to investigate the importance of spatial and environmental factors on the structure and diversity of bacterial communities, high-resolution 16S rRNA gene tag pyrosequencing was applied to bacterial communities in the littoral sea. Seawater samples were prepared from seven different stations in the South Sea of Korea, the marginal sea in the western Pacific Ocean, and were divided into three groups according to distances from the coastline. The majority of 19,860 sequences were affiliated with Alphaproteobacteria (58.2%), Gammaproteobacteria (7.9%), and Bacteroidetes (13.9%). The bacterioplankton community at each station was highly diverse and varied among the samples. Major bacterial lineages showed different niche preferences among three locational groups. Alphaproteobacteria was the most abundant bacterial class, and it harbored the most frequently recorded operational taxonomic units (OTUs) in all sampling stations. However, dominant groups at the order levels showed a clear difference among the samples. The SAR11 clade was more abundant in coastal waters while the Roseobacter clade prevailed at stations far away from the coastline. Furthermore, members of Actinobacteria and Cyanobacteria also exhibited spatial variability. The OM1 clade in Actinobacteria constituted a predominant fraction in coastal samples, but it was essentially absent at the distal stations closer to open ocean. In contrast, Synechococcus was the predominant taxon in the distal samples, accounting for 7.1–19.5%, but was hardly detected in coastal waters, representing less than 0.7%. In Bacteroidetes, NS5 and NS9 groups tended to inhabit coastal waters while the genera Polaribacter and Ulvibacter were more abundant in distal stations. Clustering analysis and principle coordinates analysis based on OTU data indicated that bacterial communities in the studied area were separated into three groups that coincided with locational grouping. Statistical analysis showed that phosphate and dissolved oxygen concentration had a significant influence on the bacterial community composition.

Technical Aspects of Nominal Partitions on Accuracy of Data Mining Classification of Intestinal Microbiota - Comparison between 7 Restriction Enzymes.

The application of data mining analyses (DM) is effective for the quantitative classification of human intestinal microbiota (HIM). However, there remain various technical problems that must be overcome. This paper deals with the number of nominal partitions (NP) of the target dataset, which is a major technical problem. We used here terminal restriction fragment length polymorphism data, which was obtained from the feces of 92 Japanese men. Data comprised operational taxonomic units (OTUs) and subject smoking and drinking habits, which were effectively classified by two NP (2-NP; Yes or No). Using the same OTU data, 3-NP and 5-NP were examined here and results were obtained, focusing on the accuracies of prediction, and the reliability of the selected OTUs by DM were compared to the former 2-NP. Restriction enzymes for PCR were further affected by the accuracy and were compared with 7 enzymes. There were subjects who possess HIM at the border zones of partitions, and the greater the number of partitions, the lower the obtained DM accuracy. The application of balance nodes boosted and duplicated the data, and was able to improve accuracy. More accurate and reliable DM operations are applicable to the classification of unknown subjects for identifying various characteristics, including disease.

Pyrosequencing the Canine Faecal Microbiota: Breadth and Depth of Biodiversity

Mammalian intestinal microbiota remain poorly understood despite decades of interest and investigation by culture-based and other long-established methodologies. Using high-throughput sequencing technology we now report a detailed analysis of canine faecal microbiota. The study group of animals comprised eleven healthy adult miniature Schnauzer dogs of mixed sex and age, some closely related and all housed in kennel and pen accommodation on the same premises with similar feeding and exercise regimes. DNA was extracted from faecal specimens and subjected to PCR amplification of 16S rDNA, followed by sequencing of the 5′ region that included variable regions V1 and V2. Barcoded amplicons were sequenced by Roche-454 FLX high-throughput pyrosequencing. Sequences were assigned to taxa using the Ribosomal Database Project Bayesian classifier and revealed dominance of Fusobacterium and Bacteroidetes phyla. Differences between animals in the proportions of different taxa, among 10,000 reads per animal, were clear and not supportive of the concept of a “core microbiota”. Despite this variability in prominent genera, littermates were shown to have a more similar faecal microbial composition than unrelated dogs. Diversity of the microbiota was also assessed by assignment of sequence reads into operational taxonomic units (OTUs) at the level of 97% sequence identity. The OTU data were then subjected to rarefaction analysis and determination of Chao1 richness estimates. The data indicated that faecal microbiota comprised possibly as many as 500 to 1500 OTUs.

Chronic N-amended soils exhibit an altered bacterial community structure in Harvard Forest, MA, USA

At the Harvard Forest, Petersham, MA, the impact of 20 years of annual ammonium nitrate application to the mixed hardwood stand on soil bacterial communities was studied using 16S rRNA genes pyrosequencing. Amplification of 16S rRNA genes was done using DNA extracted from 30 soil samples (three treatments × two horizons × five subplots) collected from untreated (control), low N-amended (50 kg ha(-1) year(-1)) and high N-amended (150 kg ha(-1) year(-1)) plots. A total of 1.3 million sequences were processed using qiime. Although Acidobacteria represented the most abundant phylum based on the number of sequences, Proteobacteria were the most diverse in terms of operational taxonomic units (OTUs). UniFrac analyses revealed that the bacterial communities differed significantly among soil horizons and treatments. Microsite variability among the five subplots was also evident. Nonmetric multidimensional scaling ordination of normalized OTU data followed by permutational manova further confirmed these observations. Richness indicators and indicator species analyses revealed higher bacterial diversity associated with N amendment. Differences in bacterial diversity and community composition associated with the N treatments were also observed at lower phylogenetic levels. Only 28-35% of the 6 936 total OTUs identified were common to three treatments, while the rest were specific to one treatment or common to two.

OTUbase: an R infrastructure package for operational taxonomic unit data

OTUbase is an R package designed to facilitate the analysis of operational taxonomic unit (OTU) data and sequence classification (taxonomic) data. Currently there are programs that will cluster sequence data into OTUs and/or classify sequence data into known taxonomies. However, there is a need for software that can take the summarized output of these programs and organize it into easily accessed and manipulated formats. OTUbase provides this structure and organization within R, to allow researchers to easily manipulate the data with the rich library of R packages currently available for additional analysis. OTUbase is an R package available through Bioconductor. It can be found at http://www.bioconductor.org/packages/release/bioc/html/OTUbase.html.