Bacterial community comparisons bytaxonomy-supervised analysis independentof sequence alignment and clusteringWoo Jun Sula, James R. Colea, Ederson da C. Jesusa,b, Qiong Wanga, Ryan J. Farrisa, Jordan A. Fisha,and James M. Tiedjea,c,1

aCenter for Microbial Ecology, Michigan State University, East Lansing, MI 48824; bEmbrapa Agrobiologia, Seropédica, 23890-000, Rio de Janeiro, Brazil;and cWorld Class University Center for Green Metagenomics, Yonsei University, Seoul 120-749, Republic of Korea

Contributed by James M. Tiedje, July 28, 2011 (sent for review March 8, 2011)

High-throughput sequencing of 16S rRNA genes has increased ourunderstanding of microbial community structure, but now evenhigher-throughput methods to the Illumina scale allow the crea-tion of much larger datasets with more samples and orders-of-magnitude more sequences that swamp current analytic methods.We developed a method capable of handling these larger datasetson the basis of assignment of sequences into an existing taxon-omy using a supervised learning approach (taxonomy-supervisedanalysis). We compared this method with a commonly used clus-tering approach based on sequence similarity (taxonomy-unsuper-vised analysis). We sampled 211 different bacterial communitiesfrom various habitats and obtainedw1.3 million 16S rRNA sequen-ces spanning the V4 hypervariable region by pyrosequencing. Bothmethodologies gave similar ecological conclusions in that β-diver-sity measures calculated by using these two types of matriceswere significantly correlated to each other, as were the ordinationconfigurations and hierarchical clustering dendrograms. In addition,our taxonomy-supervised analyses were also highly correlatedwith phylogenetic methods, such as UniFrac. The taxonomy-super-vised analysis has the advantages that it is not limited by theexhaustive computation required for the alignment and clusteringnecessary for the taxonomy-unsupervised analysis, is more toler-ant of sequencing errors, and allows comparisons when sequencesare from different regions of the 16S rRNA gene. With the tre-mendous expansion in 16S rRNA data acquisition underway, thetaxonomy-supervised approach offers the potential to providemore rapid and extensive community comparisons across habitatsand samples.

taxonomy bin | operational taxonomic unit

The increasing abundance of 16S rRNA gene sequences stim-ulated by reduced sequencing costs and greatly expanded

parallel capacities is providing a more encompassing view of mi-crobial communities (1). Although the short read lengths pro-vided by the current technologies make it more challenging toassign sequences to bacterial taxonomy, the depth and replicationprovided are powerful advantages (2–4).Information on bacterial community structure can be compiled

in a matrix where different communities are represented as rowsand “species” as columns, i.e., a community-by-species matrix.When describing bacterial community relationships based on 16SrRNA gene sequences, each sequence is allocated to a species,usually termed an operational taxonomic unit (OTU), by align-ment-based clustering at a specified nucleotide distance, oftenat a 97% identity. This community-by-OTU matrix, which isbased exclusively on nucleotide distances among 16S rRNA genesequences, has bacterial communities as rows with OTU as col-umns. This community-by-OTU matrix can be used to measuredissimilarities between bacterial communities (β-diversity) eitherby presence/absence or abundance data. These dissimilaritiescombined in a distance matrix can be used for bacterial commu-nity comparisons by ordination and clustering methods. This

process, termed “taxonomy-unsupervised analysis,” originatesfrom the distribution of 16S rRNA gene sequences into OTUs.When applying taxonomy-unsupervised analysis to very large

numbers of sequences (>106) produced by the new sequencingtechnologies, much larger computational capacities are requiredto analyze the data (5). The alignment and clustering of se-quences that require calculation of pairwise nucleotide distancesis one bottleneck. Even though taxonomy-unsupervised analysisis advantageous in that it includes sequences that are not yetassignable to bacterial taxonomy, the current computationallimitations make pursuing comparisons among bacterial com-munities difficult.We investigated an alternative analysis, i.e., “taxonomy-su-

pervised analysis,” which consists of allocating sequences intotaxonomy-supervised “taxonomy bins” on the basis of the existingbacterial taxonomy, which is rooted in polyphasic taxonomy (6)and hence also reflects physiological, morphological, and geneticinformation. Currently, several ribosomal RNA databases [i.e.,Ribosomal Database Project, RDP (7), Greengenes (8), SILVA(9), and GAST (10)] are dedicated to sequence deposition andprovide 16S rRNA gene classification tools.Here we compared taxonomy-unsupervised (OTU) and tax-

onomy-supervised analyses for two β-diversity measures, ordi-nation configurations, and hierarchical clustering dendrograms,using w1.3 million (M) V4-region 16S rRNA gene sequencesobtained by pyrosequencing of 211 bacterial communities. Weshow that the taxonomy-supervised analysis gives similar eco-logical conclusions as taxonomy-unsupervised analysis, is moreadvantageous in avoiding computational limitations, can be usedfor comparisons with different sequenced regions of the rRNAgene, and has greater tolerance to sequencing errors.

ResultsObtaining V4-16S rRNA Gene Sequences by Barcoded Pyrosequencing.Regions in the 16S rRNA gene suitable for pyrosequencing wereidentified, which exhibited: (i) an appropriate amplicon lengthfor pyrosequencing reads, (ii) high coverage by bacterial universalprimers, (iii) high resolution and accuracy for bacterial classifi-cation and identification, and (iv) a low frequency of insertionsand deletions to simplify sequence alignment. Especially, the re-gion used in this study (corresponding to Escherichia coli 16SrRNA gene positions 563–802) spanning the hypervariable V4region, with an average length of 207 bp between primer sequen-ces, is one of the regions with the high classification accuracy using

Author contributions: W.J.S. and J.M.T. designed research; W.J.S. and E.d.C.J. performedresearch; W.J.S., J.R.C., Q.W., R.J.F., and J.A.F. analyzed data; and W.J.S., J.R.C., E.d.C.J.,and J.M.T. wrote the paper.

The authors declare no conflict of interest.1To whom correspondence should be addressed: tiedjej@msu.edu.

This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1111435108/-/DCSupplemental.

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the RDP’s naïve Bayesian rRNA classifier (11). Classification ac-curacy declines with length, but is accurate at higher taxonomiclevels even for 50-bp fragments. Its applicability for pyrosequenc-ing was further supported by in silico Unifrac analysis (12). Theuniversality of the primers was determined by internal alignmentof perfect matches against 16S rRNA gene sequences in theRDP (94.6% coverage) and from themetagenomic database of theSorcerer II Global Ocean Sampling Expedition (94.7% coverage)(13). Specifically, the primers designed in this study targeted anoverwhelming majority of known 16S rRNA gene sequencesthroughout all phyla, while providing deep taxonomic classificationuseful for community comparisons (Fig. S1).The w1.3 M 16S rRNA gene sequences we used were from

211 bacterial communities from a variety of studies and habitatsthat reflected a random collection of pyrosequencing projectdata. Each bacterial assemblage was assigned into 11 habitatgroups by a priori classification (a classification made beforeexperimentation) (Table 1), using two-level habitat definitionsfrom the Genomic Standards Consortium’s “Habitat-Lite” ver-sion 0.4 ontology (14). Detailed sample descriptions are pro-vided in Table S1.

Allocation of 1.3 M 16S rRNA Gene Sequences to Taxonomy Bins andto OTUs at 97% Identity. For taxonomy-supervised analysis, each16S rRNA gene query sequence was assigned to a set of taxon-omy bins, 1,400 genera, and 492 artificial “unclassified” taxa,using the RDP classifier (11). When the classifier cutoffs were setat 80, 50, and 0% threshold (the latter forced all sequences intogenus bins), 48, 64, and 100% of the sequences were classified tothe genus level (Fig. 1), producing 903, 1,170, and 1,259 bins

for the respective thresholds. The maximum distance amongsequences within each bin increased when the RDP classifierthreshold was set lower (Fig. S2). For taxonomy-unsupervisedanalysis, all sequences clustered into 112,233 OTUs at 97% 16SrRNA gene sequence identity.A total of 22,154 pairwise β-diversity calculations using Chao’s

adjusted Sørensen (15) and Jaccard (16) indices among 211bacterial communities were performed with both community-by-OTU and community-by-taxonomy bin matrices. The β-diversitydistance matrix based on OTUs was highly correlated with thosebased on taxonomy bins at three different thresholds as con-firmed by Mantel tests (Table 2 and Fig. 2; SI Results and TableS2 for 90% OTUs) (17). Ordinations from β-diversity matricesbased both on OTU and taxonomy bins were also correlated toeach other when configurations two axes of nonmetric multidi-mensional scaling (NMDS) were compared by Procrustes anal-ysis (correlation r > 0.51, P value <0.001) (Figs. 3 and 4 andTable S3). The dendrograms produced by hierarchical clustering(unweighted pair-group method using arithmetric averages,UPGMA) of the matrices on the basis of OTU and taxonomybins were highly correlated to each other as measured bycophenetic correlation (Table 3). Also, comparisons betweena phylogenetic distance matrix created using UniFrac (18) andthe β-diversity distance matrix based on taxonomy bins showedthese matrices were highly correlated using a Mantel test(Table 2).

Comparison of Communities with Artificial Sequencing Errors. Todetermine tolerance to sequencing errors, artificial sequencingerrors were added to copies of the parent library, consistingof V4 regions extracted from a library of 2,483 near full-length16S rRNA Sanger-based sequences (19). The resulting altered

Table 1. The eleven habitat groups as defined followingHabitat-Lite criteria before sampling (sequencing)

Habitat groupNumbers ofsamples Habitat-Lite description

G01 116 Terrestrial*, soil†

G02 8 Extreme*, soil†

G03 10 Terrestrial*, extreme*, soil†

G04 16 Organism associated*G05 6 Freshwater*, waste water†

G06 7 Freshwater*, sediment†

G07 2 Fossil*, organism associated*G08 10 Marine*, sediment†

G09 14 Cultured*, soil†, or sediment†

G10 20 Extreme*, freshwater*, sediment†

G11 2 Extreme*, microbial mat†

Detailed habitat descriptions are provided in Table S1.*Top level terms in Habitat-Lite version 0.3 (14).†Second level terms.

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at 0% Confidence Threshold

at 50% Confidence Threshold

at 80% Confidence Threshold

Fig. 1. Qualified sequence classification percentages at different confi-dence thresholds determined by the RDP classifier for the indicated taxo-nomic levels.

Table 2. Comparison of two distance matrices by Mantel tests

r statistic

Similarity index usedTaxonomy bins- (at 80%)based distance matrix

Taxonomy bins- (at 50%)based distance matrix

Taxonomy bins- (at 0%)based distance matrix

97% OTU-baseddistance matrix

Chao’s adjustedSørensen 0.779*** 0.803*** 0.817***Jaccard 0.863*** 0.860*** 0.787***

UniFrac distancematrix

Chao’s adjustedSørensen 0.737*** 0.732*** 0.737***Jaccard 0.866*** 0.873*** 0.873***

The Mantel statistic was based on Spearman’s rank correlation. The correlation r reflects the relationship between two matrices. ***P < 0.001.The permutations (n = 999) assessed for the significance.

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libraries were compared with the unaltered parent library usingChao’s adjusted Sørensen and Jaccard indices after either un-supervised OTUs clustering or taxonomy binning with the RDPclassifier. In comparisons using unsupervised OTU clustering,both β-diversity indices dropped to near zero (similar commu-nities have β-diversity indices close to 1) by around 3% added“sequencing errors,” whereas supervised taxonomic binningshowed only a small decrease with up to 10% of added se-quencing errors (Fig. 5).

DiscussionThere are major advantages in the taxonomy-supervised analysis:(i) it is capable of comparing the data coming from differentregions in the 16S rRNA gene, (ii) it avoids the computationallyintensive alignment and clustering, and (iii) it is easy to combinedata sets classified separately. Depending on the length of the16S rRNA gene region sequenced and the resolution of thebacterial taxonomy classification, the taxonomy-supervised anal-ysis can compare the bacterial communities of 16S rRNA genesequences spanning other hypervariable regions and bacterialcommunities with previously deposited sequences. For example,the RDP classifier returned similar classification results for V3,V4, and V6 regions compared with full-length queries at the ge-nus level (Table 4). Therefore, one can obtain comparable dataregardless of the sequenced region. When doing such compar-isons, one must be aware that primer sets for different regionscan sample somewhat different populations.Another advantage of the taxonomy-supervised analysis is that,

due to the fixed number of taxonomy bins, it is simple to addand delete bacterial communities from a preformulated bacterialcommunities comparison. Using taxonomy-unsupervised analysis,the addition and deletion of bacterial communities affects thecommunity-by-OTUmatrix because the number and compositionof OTUs are affected by realignment and reclustering. Further-more, sequence allocation into taxonomy bins is computationallyfaster than into taxonomy-unsupervised OTUs as the time forthe former method increases linearly with the number of se-quences, whereas the time for the latter increases quadratically,thus requiring significantly longer processing times. For instance,taxonomy-supervised analysis took 1 h for generating community-by-taxonomy bins using the RDP classifier with 1.3 M sequences(480,000 unique sequences), whereas taxonomy-unsupervisedanalysis took w10 h for sequence alignment using Infernal and144 h for complete-linkage clustering on an Intel XEONMacPro.Also, the taxonomy-unsupervised analysis is very sensitive tosequencing errors (20–22) (Fig. 5). With taxonomy-supervised

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Fig. 2. Scatter plot of β-diversity distance orders (the highest β-diversity isconsidered as rank 1) calculated using community-by-taxonomy bins matrix(x axis) RDP’s classifier at 0% threshold and community-by-OTU matrix(y axis). In this plot, β-diversity distance is 1 − Chao’s adjusted Sørensensimilarity index.

A B

C D

Stress = 24.31

Stress = 24.31 Stress = 23.271

Stress = 23.51

Fig. 3. Comparison of NMDS plots based on abundance-based distance (1 − Chao’s adjusted Sørensen similarity index). The habitat groups defined by usingthe ontology of Habitat-Lite are indicated by the different color and shape points (key below). Community-by-taxonomy bins at (A) 80%, (B) 50%, (C) 0%, and(D) community-by-OTU.

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analysis, it is statistically very unlikely that random sequencingerrors will cause a sequence from one organism to “mutate” toresemble the sequence from an unrelated organism.We focused on defining the differences between using taxon-

omy-supervised and taxonomy-unsupervised analyses when com-paring bacterial communities. Both β-diversity distance matrices,the configuration of ordinations, and the comparisons of hierar-chical clustering results confirmed that the two analyses are sig-nificantly correlated such that similar conclusions would be drawn(i.e., Fig. S3, distance-based redundancy analysis (db-RDA) withboth matrices showed the similar correlations with bacterial com-munities and environmental variable). The resolution, however, ismore limited with the taxonomy-supervised analysis due to thecoarser average distance among taxa. The median of maximumdistance within taxonomy bins was 10.8, 15, and 31.9% at 80, 50,and 0% RDP classifier thresholds, respectively (SI Results andFig. S2). For example, there was a decreased resolution of habitatgroup G01 (terrestrial and soil) to other groups in taxonomy-su-pervised (Fig. 4A) compared with taxonomy-unsupervised anal-ysis (Fig. 4D), especially in occurrence-based NMDS plots. This isdue to the more limited number of taxonomy bins in the phylumAcidobacteria (26 genera and four unclassified taxa), Verrucomi-crobia (10 genera and eight unclassified taxa), and Gemmatimo-nadetes (2 genera and five unclassified taxa). These bins have

a relatively large number of sequences in habitat group G01(soils) due to the low number of isolated bacteria or describedclusters. In contrast, the communities in habitat group G04 (or-ganism associated; specifically animal feces) were mostly com-posed of well-characterized groups and exhibited better separationfrom other groups with the taxonomy-supervised analysis ratherthan with the taxonomy-unsupervised analysis (Figs. 3 and 4).When a more complete bacterial taxonomy is available for thesephyla and the unclassified taxa, the bacterial communities com-parison should exhibit a higher resolution, more accurately reflectbacterial community composition, and, if the polyphasic founda-tion of taxonomy has validity, better reflect the physiologies.As revolutionary sequencing technologies continue to emerge,

generating tremendous numbers of 16S rRNA and other markergene sequences, the limited flexibility of current clustering toolsand the computational requirements will be major bottlenecks.For example, two recent studies have produced millions of 16Samplicon sequences of 150–200 bases in length using Illuminapaired-end technology (23, 24). In related work, we have used thetechnique presented here to analyze 9.7 million paired-end Illu-mina sequence reads from five soil samples in less than 2 h. Thetaxonomy-based method has the potential to overcome theselimitations as a fast and simple bacterial assemblage comparison

A B

C D

Stress = 13.25

Stress = 14.80 Stress = 20.91

Stress = 14.59

Fig. 4. Comparison of NMDS plots based on occurrence-based distance (1 − Jaccard similarity index). The habitat groups defined by using the ontologyof Habitat-Lite are indicated by the different color and shape points (key below). Community-by-taxonomy bins at (A) 80%, (B) 50%, (C) 0%, and (D)community-by-OTU.

Table 3. Cophenetic correlations of unweighted pair-group method using arithmetric averages (UPGMA) clustering dendrograms

Cophenetic matrix of UPGMA clustering with

Similarity index usedTaxonomy bins- (at 80%)based distance matrix

Taxonomy bins- (at 50%)based distance matrix

Taxonomy bins- (at 0%)based distance matrix

Cophenetic matrix of UPGMAclustering with 97% OTU-baseddistance matrix

Chao’s adjustedSørensen 0.748 0.709 0.694Jaccard 0.816 0.839 0.907

Cophenetic correlation was computed using Spearman’s rank from two matrices of cophenetic distance, which is the distance where two samples becamethe same group in the clustering dendrogram. Higher cophenetic correlation means the UPGMA clustering dendrograms are more similar.

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method, but its value would be improved if microbiologists ad-vance the taxonomy for the poorly characterized groups.

Materials and MethodsSample Preparation. DNA of 211 bacterial communities was obtained viavarious extraction methods. Amplicon preparations for pyrosequencing andsequencing 16S rRNA gene by Genome Sequencer FLX system (454 Life Sci-ences) were performed. The detailed method is described in SI Materialsand Methods.

Binning Sequences into Species. For the taxonomy-unsupervised analysis, thecommunity-by-OTU matrix was generated as follows: briefly, all sequenceswere aligned by secondary structure using Infernal (25), clustered by com-plete-linkage clustering, and then allocated into OTUs of 97% nucleotideidentity through RDP’s pyrosequencing pipeline (26). For the taxonomy-supervised analysis, all sequences were allocated into taxonomy bins ofgenus and artificial unclassified taxa provided by the RDP classifier at 80,50, and 0% confidence thresholds (11). The RDP classifier was trained usingthe Taxonomic Outline of the Bacteria and Archaea (TOBA), release 7.8 (27)augmented with unofficial taxa to cover regions of diversity not covered inthe formal bacterial taxonomy. Each of the lowest taxonomy units, i.e.,genera and unclassified taxa were considered as taxonomy bins. The re-liability of classification of each sequence was estimated by bootstrapping,and sequences that could not be assigned, as they were below a bootstrapconfidence threshold, were allocated to an artificial unclassified taxon. Inthe same manner, the community-by-taxonomy bin matrix was generatedwith communities as rows and with taxonomy bins as columns.

β-Diversity Measures and Other Statistical Analyses. β-Diversity measures ofthe 211 communities were calculated on the basis of pairwise Chao’s ad-justed Sørensen similarity index (quantitative measures corrected for unseenspecies) (15) and Jaccard similarity index (presence/absence measures) (16)using EstimateS (http://viceroy.eeb.uconn.edu/EstimateS). Each of the twodistance matrices of bacterial communities (1 − Chao’s adjusted Sorensensimilarity index and 1 − Jaccard similarity index) from the community-by-OTU and community-by-taxonomy bin matrices were compared by Manteltests (17) on the basis of Spearman’s rank correlation ρ. Bacterial commu-nities were displayed by two axes of NMDS to represent the greatest vari-ability. The configuration of the NMDS plots was compared by Procrustesanalysis (28), a statistical shape analysis that compares the distribution ofpoints’ shapes with all 211 points in NMDS dimensions. UPGMA hierarchicalclustering was performed, and clustering dendrograms were compared bythe cophenetic correlation. For measuring UniFrac distance (18), the ap-proximately maximum-likelihood phylogenetic trees were generated usingFastTree (29).

To test bacterial classification variability of the different 16S rRNA genehypervariable regions, four sets of near full-length (>1,200 bp) 16S rRNAgene sequence collections were used: one each of human gut, soil, bovinerumen, and ocean (30–34). All sequences were aligned and V3, V4, and V6hypervariable regions extracted on the basis of the reference positions ofthe Escherichia coli 16S rRNA gene. Classification results for the full-lengthsequences were compared with results for the corresponding V3, V4, and V6hypervariable regions.

To measure the effects of sequencing error on β-diversity measures, theregion corresponding to the V4 amplicon region was extracted from a li-brary of 2,483 near full-length 16S rRNA Sanger-based sequences (19) toproduce a V4 region high-quality library. Artificial sequencing errors wereadded to copies of this library by randomly modifying various percentages ofbases in each sequence. The original and modified libraries were assigned tosupervised taxonomic bins with the RDP classifier as described above. Un-supervised OTU bins were calculated individually for each modified library incombination with the original library as described above.

NMDS, Mantel test, Procrustes analysis, UPGMA clustering, and copheneticcorrelation were performed using the R statistical program (R DevelopmentCore Team) running the vegan package (Vegan: Community Ecology Packageversion 1.8-6). Except where otherwise indicated, processing software waswritten in Java (API v1.5.0) and executed on the Macintosh (OS 10.4) or Linux(2.4.23) operating systems running Java virtual machines from Apple orSun, respectively.

ACKNOWLEDGMENTS. We thank Stella Asuming-Brempong, Mary BethLeigh, David Emerson, Chris Blackwood, Erick Cardenas, Ryan Penton, BlazStres, Stephan Gantner, Claudia Etchebehere, Thad Stanton, DeboraRodrigues, Aviaja Hansen, Mathew Marshall, Alexandre Soares Rosado,and Dan Fisher whose projects provided the 211 DNA samples. This studyis funded by grants from the Department of Energy, the Department ofAgriculture, and the National Institute of Environmental Health Sciences,with samples coming from grants from these agencies plus the NationalScience Foundation, the National Aeronautics and Space Administration,and several international sources, including the World Class Universityprogram through the National Research Foundation of Korea, funded bythe Ministry of Education, Science, and Technology (R33-10076).

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Simulated 'Sequencing errors' Distance

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Chao's Sorensen with unsupervised OTU

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Chao's Sorensen with taxonomy-bin

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