ORIGINAL ARTICLE Illumina-based analysis of microbial community diversity Patrick H Degnan and Howard Ochman Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT, USA Microbes commonly exist in milieus of varying complexity and diversity. Although cultivation-based techniques have been unable to accurately capture the true diversity within microbial communities, these deficiencies have been overcome by applying molecular approaches that target the universally conserved 16S ribosomal RNA gene. The recent application of 454 pyrosequencing to simultaneously sequence thousands of 16S rDNA sequences (pyrotags) has revolutionized the characterization of complex microbial communities. To date, studies based on 454 pyrotags have dominated the field, but sequencing platforms that generate many more sequence reads at much lower costs have been developed. Here, we use the Illumina sequencing platform to design a strategy for 16S amplicon analysis (iTags), and assess its generality, practicality and potential complications. We fabricated and sequenced paired-end libraries of amplified hyper-variable 16S rDNA fragments from sets of samples that varied in their contents, ranging from a single bacterium to highly complex communities. We adopted an approach that allowed us to evaluate several potential sources of errors, including sequencing artifacts, amplification biases, non-corresponding paired-end reads and mistakes in taxonomic classification. By considering each source of error, we delineate ways to make biologically relevant and robust conclusions from the millions of sequencing reads that can be readily generated by this technology. The ISME Journal (2012) 6, 183–194; doi:10.1038/ismej.2011.74; published online 16 June 2011 Subject Category: microbial ecology and functional diversity of natural habitats Keywords: iTags; pyrotags; 16S ribosomal RNA Introduction Microbes in nature typically exist in complex com- munities, which can be highly variable in both the composition and the abundance of their constituent species. Initial surveys of microbial community diversity applied cultivation-based methods, but these were often biased and of limited value owing to the inability to cultivate many (more likely, the majority of) naturally occurring species. Such meth- ods were supplanted by molecular approaches that did not rely on cultivation but were based rather on the PCR amplification, cloning and Sanger sequen- cing of universally conserved molecules, usually the 16S ribosomal RNA gene. Current sequence databases contain over a million full-length 16S rRNA sequences spanning a broad phylogenetic spectrum than can serve as a benchmark for assessing the bacterial taxa (also referred to as ‘phylotypes’ or ‘ribotypes’) present in environments worldwide (Cole et al., 2009). Although the traditional 16S-cloning-and-sequen- cing approach has the potential to provide an in- depth view of the richness and evenness of bacterial species within a community, its application is somewhat laborious and costly, with the result that most applications have assayed on the order of only 100 sequences per sample, which may not be sufficient to fully characterize all but the simplest communities. This has led to the development of alternative techniques for assessing rRNA variation (for example, ARISA, DGGE, tRFLPs) (Muyzer et al., 1993; Liu et al., 1997; Fisher and Triplett, 1999); however, the most revolutionary innovation has been the application of 454 pyrosequencing to survey hundreds of thousands of 16S rRNA gene amplicons (termed ‘pyrotags’) in a single sequencing run (Sogin et al., 2006). Since its initial introduc- tion, pyrotag analysis has been widely implemented (for example, Dethlefsen et al., 2008; Bowers et al., 2009; Cardenas et al., 2010; McLellan et al., 2010) and further optimized in order to obtain thousands of sequences from multiple samples in parallel (Parameswaran et al., 2007; Hamady et al., 2008; Costello et al., 2009; Engelbrektson et al., 2010). Advancements in sequencing technologies, by offering greater numbers of sequencing reads at much lower costs, might eventually supplant pyrotag analysis for the characterization of microbial Received 14 March 2011; revised 2 May 2011; accepted 2 May 2011; published online 16 June 2011 Correspondence: PH Degnan, Department of Ecology and Evolu- tionary Biology, Yale University West Campus, PO Box 27388, West Haven, CT 06516-7388, USA. E-mail: [email protected]The ISME Journal (2012) 6, 183–194 & 2012 International Society for Microbial Ecology All rights reserved 1751-7362/12 www.nature.com/ismej
Transcript
ORIGINAL ARTICLE
Illumina-based analysis of microbial communitydiversity
Patrick H Degnan and Howard OchmanDepartment of Ecology and Evolutionary Biology, Yale University, New Haven, CT, USA
Microbes commonly exist in milieus of varying complexity and diversity. Although cultivation-basedtechniques have been unable to accurately capture the true diversity within microbial communities,these deficiencies have been overcome by applying molecular approaches that target theuniversally conserved 16S ribosomal RNA gene. The recent application of 454 pyrosequencing tosimultaneously sequence thousands of 16S rDNA sequences (pyrotags) has revolutionized thecharacterization of complex microbial communities. To date, studies based on 454 pyrotags havedominated the field, but sequencing platforms that generate many more sequence reads at muchlower costs have been developed. Here, we use the Illumina sequencing platform to design astrategy for 16S amplicon analysis (iTags), and assess its generality, practicality and potentialcomplications. We fabricated and sequenced paired-end libraries of amplified hyper-variable 16SrDNA fragments from sets of samples that varied in their contents, ranging from a single bacteriumto highly complex communities. We adopted an approach that allowed us to evaluate severalpotential sources of errors, including sequencing artifacts, amplification biases, non-correspondingpaired-end reads and mistakes in taxonomic classification. By considering each source of error, wedelineate ways to make biologically relevant and robust conclusions from the millions ofsequencing reads that can be readily generated by this technology.The ISME Journal (2012) 6, 183–194; doi:10.1038/ismej.2011.74; published online 16 June 2011Subject Category: microbial ecology and functional diversity of natural habitatsKeywords: iTags; pyrotags; 16S ribosomal RNA
Introduction
Microbes in nature typically exist in complex com-munities, which can be highly variable in both thecomposition and the abundance of their constituentspecies. Initial surveys of microbial communitydiversity applied cultivation-based methods, butthese were often biased and of limited value owingto the inability to cultivate many (more likely, themajority of) naturally occurring species. Such meth-ods were supplanted by molecular approaches thatdid not rely on cultivation but were based rather onthe PCR amplification, cloning and Sanger sequen-cing of universally conserved molecules, usuallythe 16S ribosomal RNA gene. Current sequencedatabases contain over a million full-length 16SrRNA sequences spanning a broad phylogeneticspectrum than can serve as a benchmark for assessingthe bacterial taxa (also referred to as ‘phylotypes’or ‘ribotypes’) present in environments worldwide(Cole et al., 2009).
Although the traditional 16S-cloning-and-sequen-cing approach has the potential to provide an in-depth view of the richness and evenness of bacterialspecies within a community, its application issomewhat laborious and costly, with the result thatmost applications have assayed on the order of only100 sequences per sample, which may not besufficient to fully characterize all but the simplestcommunities. This has led to the development ofalternative techniques for assessing rRNA variation(for example, ARISA, DGGE, tRFLPs) (Muyzer et al.,1993; Liu et al., 1997; Fisher and Triplett, 1999);however, the most revolutionary innovation hasbeen the application of 454 pyrosequencing tosurvey hundreds of thousands of 16S rRNA geneamplicons (termed ‘pyrotags’) in a single sequencingrun (Sogin et al., 2006). Since its initial introduc-tion, pyrotag analysis has been widely implemented(for example, Dethlefsen et al., 2008; Bowers et al.,2009; Cardenas et al., 2010; McLellan et al., 2010)and further optimized in order to obtain thousandsof sequences from multiple samples in parallel(Parameswaran et al., 2007; Hamady et al., 2008;Costello et al., 2009; Engelbrektson et al., 2010).
Advancements in sequencing technologies, byoffering greater numbers of sequencing reads atmuch lower costs, might eventually supplantpyrotag analysis for the characterization of microbial
Received 14 March 2011; revised 2 May 2011; accepted 2 May2011; published online 16 June 2011
Correspondence: PH Degnan, Department of Ecology and Evolu-tionary Biology, Yale University West Campus, PO Box 27388,West Haven, CT 06516-7388, USA.E-mail: [email protected]
The ISME Journal (2012) 6, 183–194& 2012 International Society for Microbial Ecology All rights reserved 1751-7362/12
communities. Recent interest has focused on theapplicability of other sequencing methodologies,most notably Solexa/Illumina (Lazarevic et al.,2009; Claesson et al., 2010; Gloor et al., 2010;Caporaso et al., 2011; Zhou et al., 2011), which iscurrently less than 1/100 the cost per read than 454pyrosequencing. Although realizing much shorterread lengths, the Illumina technology can be tailoredto yield sequences of increased lengths, as can beobtained by merging the paired-end reads generatedfrom the same amplicon (Gloor et al., 2010; Rodrigueet al., 2010; Zhou et al., 2011). By integratingsample-identifying barcodes into the amplificationprimers, the Illumina platform, like 454 pyrosequen-cing, is amenable to a high level of multiplexing,which further increases its utility for examininglarge and complex sets of samples (Gloor et al.,2010).
Here, we use the Illumina sequencing platform for16S rRNA amplicon analysis (called ‘iTags’) andassess its generality, practicality and potentialcomplications. We first fabricate and sequence apaired-end library of hyper-variable 16S rDNAfragments amplified from samples that varied incontents from a single bacterium, an artificialcommunity and a highly complex natural commu-nity. The inclusion of identifying barcodes andmultiple primer pairs, combined with the selectedamplicon lengths, allowed us to evaluate the levelof sequencing error, amplification biases and asso-ciation between paired-end reads, among othervariables. Despite recovering over 30 million reads,our imposition of stringent quality filters resulted inup to an 85% reduction in the number of potentiallyinformative reads. Nevertheless, tens to hundredsof thousands of informative reads were returnedfor each of the multiplexed samples. These resultsuncovered both the major advantages and obstaclesin the application of these methods, and provideinsights into the ways that such impediments canbe ameliorated prior to the application of theseprocedures.
Materials and methods
Designing amplification primersDuring the development of this project, the IlluminaGAIIx sequencer could produce reads of up to 100nucleotides (nt) in length from both ends of singleDNA fragments or amplicons (that is, paired-endreads). We exploited this technology to assemblereads spanning a 4100-nt region of the 16S rDNA. Toaccomplish this, we identified candidate universalprimer pairs spanning the 16S rRNA variable loops(Neefs et al., 1991) that would maximize sequenceinformation and be short enough to allow adequateread overlap.
The phylogenetic distribution of each primersequence was analyzed using the RDP Probe Match(Cole et al., 2009). The goal was to obtain broadly
distributed primers that amplify regions of approxi-mately 100–150 bp in length such that the paired-end reads from an amplicon would show someoverlap. Of primer sets meeting these criteria, twoflanking the variable loop-6 (V6) were chosen (XXXdenotes sample-specific barcodes): 967F 50-XXXCAACGCGAAGAACCTTACC-30 and 1046R 50-XXXCGACAGCCATGCANCACCT-30 (Short 1 V6 (S1V6);98 bp in Escherichia coli) (Sogin et al., 2006), and917F 50-XXX GAATTGACGGGGRCCCGC-30 and1061R 50-XXX CACGRCACGAGCTGACGAC-30
(Long V6 (LV6); 163 bp in E. coli) (Keijser et al.,2008). We further modified the S1V6 primer pair toincrease both the extent of overlap between paired-reads and the number of potentially informativesites as follows: 970F 50-XXX CGCGAAGAACCTTACC-30 and 1050R 50-XXX ACGACAGCCATGCANC-30 (Short 2 V6 (S2V6); 96 bp in E. coli).
Sample-identifying barcode sequences were in-cluded at the 50 ends on both the forward and thereverse primers. The barcodes were designed (1) tobe short, 3 or 4 bp, to minimize loss of sequenceinformation; (2) to have two or more differencesfrom one another (such that a single sequencingerror could not convert one into another); and (3) tohave the 30 nucleotide of the barcode be a low-frequency match of nucleotide upstream from the50 end of the priming site. The barcodes for theprimer pairs were as follows: S1V6–ATG, CAG, TCG,ACT, CGT, GCA, GAT, and TGA; S2V6–AGC, ATT,GTC, TCC, CCAT, CGGC, GCGT and TTAT; and LV6–GTC, AAC, CCA, TAA, AGGA, GCGC, TTGC andATAC.
Selecting sequencing templatesWe tested 16S primers on sets of samples representinga range of microbial diversity. First, we used two well-defined samples containing either DNA extractedfrom a single sequenced strain of E. coli K-12(MG1655) (Blattner et al., 1997) or from a mixture ofgenomic DNAs of known concentrations from 19cultivated strains of bacteria (Table 3). Next, weanalyzed a sample of unknown microbial diversity: afecal sample from a laboratory mouse. The QIAampDNA stool mini kit (Qiagen, Valencia, CA, USA) wasused following the manufacturer’s protocol to extracttotal DNA from the feces of an individual lab-rearedmouse, Mus domesticus strain WSB.
Preparing samples for 16S amplificationPCR amplifications were performed in triplicate 30-mlreactions using 30–50 ng of template DNA and oneof two DNA polymerases. Samples amplified withthe barcoded S1V6 primer pairs were performedusing 1.2 U of Taq DNA polymerase (5 PRIME) andfinal concentrations of 1X polymerization buffer,1 mM dNTPs and 0.4mM of each primer. The reactionmixtures were subjected to an initial denaturationcycle of 95 1C for 2 min, followed by 25 cycles at 95 1C
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for 30 s, 55 1C for 45 s and 72 1C for 1 min, and a finalextension step at 72 1C for 10 min. The samplesamplified with the barcoded S2V6 or LV6 primerpairs used the high-fidelity Advantage 2 polymerasemix and the Advantage 2 SA PCR buffer (Clontech,Mountain View, CA, USA) following the manufac-turer’s instructions. Amplifications with the S2V6and LV6 primer pairs were performed in a laminar-flow hood using screw cap tubes to minimizecontamination. The thermal cycling conditions wereidentical to those detailed above except that theannealing temperature was 58 1C.
Triplicate reactions for each sample were pooled,visualized on 1% agarose gels, purified with theMinElute PCR purification kit (Qiagen) and elutedin 11 ml of EB buffer. The concentrations of theamplification products were quantified on a Bio-analyzer using the DNA 1000 LabChip kit (AgilentTechnologies, Santa Clara, CA, USA). The barcodedproducts of the S1V6 and S2V6 primer sets werecombined in equimolar amounts, re-purified withthe MinElute kit, eluted in 30 ml of EB buffer andsubmitted for 2� 75-nt paired-end sequencing at theYale Center for Genomic Analysis. Similarly, theLV6 products were mixed in equimolar amounts, butthese longer amplicons were subjected to 2� 100-ntpaired-end Illumina sequencing.
Processing and assembly of sequencing readsInitial base-calling using Bustard (v 1.7) and qualityfiltering of paired-end reads were performed by theYale Center for Genomic Analysis. Upon receipt,these reads were evaluated for quality (mean qualityscores; presence of sites denoted as ‘N’), andidentifiable primers and barcode sequences. Readpairs with perfectly matching primer and barcodeswere extracted and binned by barcode. The resultingIllumina fastq files, containing quality valuesequivalent to Phred scores, were converted to Sangerfastq files using Maq (‘maq ill2sanger’, v 0.7.1)(Li et al., 2008). Individual read pairs were convertedto fasta and qual files, and then assembled usingPhrap (v 1.080812) using the following parameters:‘-vector_bound 0 -trim_start 0 -forcelevel 3 -preas-semble -bandwidth 10 -repeat_stringency 0.98 -ace -minmatch 5 -maxmatch 20 -minscore 10’.
To investigate the effect of quality scores onestimates of diversity, the assembled reads for eachbarcoded sample were further filtered by consensusquality scores. Barcodes were pruned from se-quences and the data sets were aligned in the RDPpyrosequence aligner (Cole et al., 2009), whichimplements the Infernal algorithm (Nawrocki andEddy, 2007). The resulting alignments were editedto remove primer sequences and clustered togenerate operational taxonomic units (OTUs) usingthe RDP clustering algorithm. The taxonomic assign-ments of OTUs were derived from the RDP Classifierusing a 50% bootstrap cutoff, as recommended forreads less than 100 bp. Custom PERL scripts were
written to generate a pipeline to analyze, organizeand format these data. The PERL scripts and rawsequence data presented and analyzed here areavailable at www.yale.edu/ochman/data.
Results
High read recovery, low read usabilityOver 30 million paired-reads were collected fromeach of the two multiplexed Illumina sequencingruns. Initial filtering removed 85% of the readsfrom run1 and 54% from run2 owing to errorsstemming from several sources (Table 1), which iscomparable to other studies that report filtering40–70% of their initial reads (Gloor et al., 2010;Caporaso et al., 2011). We secured a total of 4.5and 16.0 million paired-end reads for the two runsin spite of the high number of reads that werediscarded because they did not meet our thresholdsfor accuracy; this, in turn yielded tens of thous-ands to millions of raw read-pairs per multi-plexed sample (run1 x¼ 380 253±176 319; run2x¼ 3 936 966±2 299 439) (Table 2). Because the runswere founded with equimolar concentrations ofamplicons, some of the variation in the number ofreads returned per sample is most likely attributableto amplification biases that occurred during thesynthesis of the Illumina sequencing library. Futureexperiments might avoid this bias by implementingan amplification-free library approach (Kozarewaet al., 2009).
We used a strict set of quality filters to minimizethe impact of erroneous reads on measures ofmicrobial diversity (Kunin et al., 2010). Errors inthe barcode/primer sequence of either of the paired-end reads necessitates the removal of both reads inthe pair; and in the majority of read-pairs that wereculled, either only one of the reads perfectlymatched the primer sequence or the paired readslacked identical barcodes. The primer and barcodesare not informative for sequence classificationand together constitute over 20% of the sequencingread, but the inclusion of both sequences provide auseful filter for removing erroneous reads andestimating sequencing error rates.
Table 1 Summary statistics of Illumina paired-end runs
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Error frequencies were estimated for the barcode/primer regions of all reads with high sequencequality (Xq30) for the first 25 nt and with single-nucleotide differences from the expected sequence.This included reads initially filtered out for notcontaining a perfect primer or barcode sequence (seeabove). Across the 14 barcode/primer combinationsanalyzed from run1, the error rates per nucleotidevaried over three-fold (1.6� 10�4 to 5.6� 10�4), andrates varied only two-fold (4.1� 10�4 to 8.4� 10�4)among the four barcode/primer combinations fromrun2 (Supplementary Table 1). For two reasons, weascribe these variants to errors generated duringsequencing rather than to artifacts that arose duringPCR amplification: (1) Oligonucleotide synthesis istypically very accurate, such that errors in primerregions are more likely generated during the sequen-cing step; (2) we performed independent experi-ments using two different Taq polymerases eachhaving unique error rates, but obtained approxi-mately the same rate (analysis of covariance;F1,15¼ 1.07, P¼ 0.317) and spectrum of mutationswith each. The fact that error rates were indepen-dent of the source of Taq polymerase furthersuggests that errors are introduced during DNAsequencing.
There were large numbers of paired-end readswith mismatched barcodes, but owing to our use of
barcodes that require at least two mutations toconvert one barcode into another, not many of thesemismatched read-pairs were attributable to sequen-cing errors. In fact, many read-pairs containedrecognizable barcodes that differed between thecorresponding reads, which could result from(1) over-clustering during the sequencing run;(2) the production of chimeric amplicons duringthe amplification step of the Illumina library or(3) primer contamination, the likelihood of each ofwhich is discussed below.
Over-clustering can result from low nucleotidediversity in the first four sequenced nucleotides andhigh cluster density on the flow cell. Although thefirst four bases of our barcodes and primers did notcontain equal frequencies of each of the fournucleotides, over-clustering is an unlikely cause ofthe mismatched barcodes detected. An analysis ofthe run showed that read signal intensities and thepercentage of clusters passing filter were both abovenormal expectations (intensity 4200 for first cycle,480% clusters passing filter). Alternatively, chi-meric amplicons are a common by-product of 16SrDNA amplifications (Qiu et al., 2001); however, ourprotocols used a low number of amplification cycles(10–12) and the high-fidelity Taq polymerase forlibrary construction, both of which reduce thefrequency of chimeric amplicons. This leaves primer
Table 2 Binning, assembly and quality filtering of iTags for the analyzed samples
Abbreviation: OTU, operational taxonomic unit.aPercentage based on the fraction of total paired-end reads not containing an ‘N’ run1¼29 938 439; run2¼33 595 900.
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contamination during the initial PCR amplificationsas the principal contributor of mismatched bar-codes. Although we implemented precautionary
measures intended to minimize contamination(using screw cap tubes; removing primers andtemplates individually; setting up the reactions in
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a laminar flow hood), contamination occurred inthese samples. With current interest in multiplexingscores (or even hundreds) of samples, we let theseresults serve to forewarn investigators that highlystringent procedures are necessary to prevent cross-contamination.
Recapitulation of E. coli OTUsWe first evaluated the efficacy and accuracy of theiTag approach by sequencing a single template,E. coli K-12 (MG1655). The E. coli genome containsseven rRNA operons, six of which have identicalalleles at V6 and the seventh encoding a V6 allelewith nine nucleotide differences (Figure 1a). At both100% and 97% identity thresholds only two OTUsare expected.
The V6 loop of E. coli was amplified using threeprimer pairs (S1V6, S2V6 and LV6), and weperformed a technical replicate of S2V6 (Table 2).Nearly all reads assembled with well-supportedoverlaps of their distal 30 regions (S1V6 and S2V6,E50 bp; LV6, E30 bp). Alignment and clusteringof the assembled iTag sequences showed that eachof the E. coli samples contained much more thanthe two predicted phylotypes (n¼ 4090–159 224;Table 2).
Previous work has shown that quality trimmingof pyrotags is essential for accurate prediction ofcommunity composition (Kunin et al., 2010);therefore, we filtered the assembled reads accordingto consensus quality scores estimated by Phrap.We compared two quality filters, requiring that allsites had error probabilities of either p10�2 orp10�3 (corresponding to Phred scores of Xq20 orXq30). These measures reduced the number ofunique phylotypes (aka 100% OTUs), but failed toremove all of the spurious OTUs (Table 2).
Among these confounding OTUs, we found that3–7% of high-quality reads (Xq30) differed by only1 nt from the expected E. coli alleles (Figures 1b–e).Variants occurred at every nucleotide positionregardless of the primer pair or the source of Taqpolymerase. Given the numbers of variants, it is notlikely that each represents a new mutation thatoccurred during growth in culture. Therefore, thesephylotypes are attributable to errors that occurredduring sequencing.
The very large number of sequence variants madethem impossible to cluster into the two expectedOTUs, even when applying very relaxed thresholds
(for example, 90% OTUs). Most of the remainingOTUs (Figure 1) represent phylotypes that weredetected in other samples and are best explainedby primer contamination (discussed above). Inaddition, some of the contaminants in the LV6samples were because of nonspecific amplificationof other chromosomal loci; for example, we recov-ered a 123-bp fragment of uridylyltransferase (glnD)from E. coli and a 78-bp fragment of an ATP trans-port permease from Streptococcus sp.
To rid samples of artifactual phylotypes generatedby sequencing errors and PCR contamination, weused a sequence abundance threshold in a mannersimilar to that of Caporaso et al. (2011). We based thisthreshold on the representation of a unique phylo-type (100% OTU) given the number of q30 reads forthat barcode. Through stepwise increments in theabundance threshold, we followed the degree towhich spurious reads were eliminated (Figures 1 and 2).Eventually, by applying a 1.0% abundance thresh-old (that is, removing all OTUs occurring atfrequencies less than 1.0%) we were able to recapi-tulate the two phylotypes expected to occur in theE. coli genome.
Diversity in a defined communityHaving addressed the sequence variation detectedwithin a single species by the iTag approach, weanalyzed the data generated for a synthetic commu-nity of 19 strains of bacteria (Table 3). This samplewas amplified using each of the three primer sets,which should yield 19 100% OTUs, and 16 (S1V6,S2V6) or 15 (LV6) 97% OTUs. A similar patternshowing an excess of erroneous and contaminatinghigh-quality OTUs emerged even after stringentquality filtering (Table 3). Although the applicationof a 1% abundance threshold removed artifactualreads, it also removed several of the expected OTUsthat occurred below this threshold (Figure 2). Theinitial template DNA concentrations of 12 of the 19strains represented less than 1% of the total DNAmass, so this result was anticipated (Table 3);however, we also found that the actual readabundances for the 19 100% OTUs departed fromthe expected values (Figure 3). The likely cause ofthe deviation between the relative amount of input DNA and the relative number of reads couldbe primer specificity. For example, the Strepto-coccus cristatus OTU was similarly abundant in theCAG–S1V6 (1.7%¼ 2754/162 125) and ATT–S2V6
Figure 1 iTag design and diversity in E. coli. (a) Three primer combinations were tested mapping to E100- or E160-bp regionsspanning the V6 loop of bacterial 16S rRNA. The schematic representation illustrates the two alleles (black bars differing at ninepositions) in the 16S rRNA genes of E. coli MG1655 (rrsA-H) that are differentiated by the resulting amplicons (LV6, S1V6 and S2V6, withforward and reverse primer positions designated). In panels (b–e), the location and count of high-quality, 100% OTUs with singlesequencing errors relative to the expected E. coli reference alleles are plotted for each of four E. coli iTag samples. The dashed and solidlines indicate the numbers of mutations after application of increasingly stringent sequence abundance thresholds (10�4, 10�3, 10�2) usedto remove erroneous OTUs. The pie charts indicate the relative abundance of iTags corresponding to expected E. coli OTUs (white), tagswith 1-bp errors (gray) and putative contaminants (black) without the application of an abundance threshold.
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(1.8%¼ 10 207/556 184) data sets, but nearly absentfrom the AAC–LV6 data set (0.0022%¼ 9/407 785).
Short 16S tags can be difficult to assign taxo-nomically and alone provide limited phylogeneticresolution (Liu et al., 2007). We found thatthe RDP Classifier accurately identified all of the19 expected OTUs to taxonomic Class, but itssuccess rate progressively decreased for lowertaxonomic divisions, with only about 60–90%assigned correctly. Alternative BLAST-based classi-fication approaches can increase accuracy providedrelated full-length 16S rRNA sequences are available(Huse et al., 2008). Of course, neither of these
approaches are able to distinguish among speciesthat have identical V6 loop sequences (for example,E. coli, Escherichia fergusonii and Salmonellaenterica; Figure 3).
Microbial diversity in a mammalian hostBy way of analyzing a complex microbial communitywhose composition and constituents are uncharac-terized, we assessed the V6 sequence diversity withina fecal sample from the house mouse M. domesticusstrain WSB/EiJ. This sample was amplified withGCA–S1V6 and TCC–S2V6, generating, respectively,384 480 and 223 708 binned reads. As before, evenwith rigorous quality filtering, a large number of low-frequency 100% OTUs remained (Table 2). Weapplied similar abundance thresholds (10�5 to10�2), which resulted in a dramatic reduction inphylotypes (Figure 4). The utilization of the samethreshold as that applied to the E. coli samples (thatis, 10�2) leaves only 12 OTUs, which represent 67%(GCA–S1V6) and 74% (TCC–S2V6) of the high-qualityreads. Thus, it appears that application of a 1.0%threshold is too stringent for actual biologicalsamples and probably removes many tags that arelikely to represent the actual microbial constituentsof these communities. The two amplified samplesshow an extensive overlap in 100% OTUs (Sørensendistance, 0.010; Jaccard distance, 0.020), whichresults in a highly similar taxonomic distributionregardless of the abundance threshold applied(Figure 4).
Discussion
Massively parallel sequencing platforms supply anefficient and sensitive means of characterizingmicrobial communities, but the sequencing depthsoffer by these methods are accompanied by artifactsthat can affect the interpretation of community
Figure 2 The thresholds required to attain the actual rRNA diversity in a sample. Observed species estimates (OTUs) at 100% (filled)and 97% (open) levels are plotted at a series of sequence abundance thresholds for (a) E. coli and (b) 19 Strain samples. The solid anddashed lines represent the expected numbers of OTUs at 100% and 97% identity thresholds, respectively. An identical pattern emergeswhen the numbers of estimated species (Chao1) are used (data not shown).
1.0
Figure 3 Frequencies of OTUs from defined communities. Thestacked bar (left) indicates the expected frequencies of each of the19 species based on input DNA, followed by the frequenciesobtained in each of three iTag analyses. The abbreviations are asfollows: X., Xylella; S., Salmonella; E., Escherichia; A., Agrobacterium;Strep., Streptococcus; Staph., Staphylococcus; B., Bacillus.
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diversity. We have identified and quantified severalsources of errors inherent to 16S amplicon sequen-cing (iTags) on the Illumina platform (contamina-tion, amplification and sequencing errors), therebyenhancing the utility of these data for assessing
community diversity. Most recent efforts to inter-rogate microbial communities have relied upon 16SrRNA gene sequences generated by 454 pyrosequen-cing (for example, Dethlefsen et al., 2008; Bowerset al., 2009; Costello et al., 2009; Cardenas et al.,2010; McLellan et al., 2010); however, Illuminasequencing technologies currently produce muchlarger numbers of much shorter reads for a fractionof the cost and will likely supplant the use of 454pyrotags.
Our experimental iTag design used barcodedprimers flanking the V6 segment of 16S rRNA, aregion commonly amplified in pyrotag experiments(Sogin et al., 2006; Keijser et al., 2008; Galand et al.,2009). Individual samples were amplified, mixedand then used as templates to construct andsequence two standard Illumina paired-endlibraries. Although Illumina sequence quality de-cays along the length of reads, our motivation forassembling paired-end reads was to increase thequality and confidence of the overlapping region(Gloor et al., 2010; Rodrigue et al., 2010; Zhou et al.,2011). Therefore the amplified V6 region wasrestricted to 100–160 bp to ensure an adequateoverlap of the forward and reverse paired-end reads.We subsequently filtered out low-quality tags,leaving only assembled reads that contained bar-code and primer sequences at each end, whichbounded a phylogenetically informative amplifiedregion. This design allowed the simultaneousestimation of sequencing error rates and microbialdiversity.
Over half of 430 million paired-end reads fromeach of Illumina runs were subsequently discardedowing to either (1) sequencing errors in one or bothof the primer regions or (2) paired-end reads thatcontained mismatched barcodes (Table 1). OtherIllumina-based 16S rRNA studies have encounteredsimilarly high sequencing error rates, making suchextreme read filtering necessary, but these studiesdid not report a high incidence of mismatchedbarcodes (Gloor et al., 2010; Caporaso et al., 2011).After ruling out over-clustering and sequencechimeras, primer contamination during the initialsample amplifications was viewed as the most likelycause of the mismatched barcodes (see above).For example, 460% of the contaminants detectedin the E. coli libraries were present in one or more ofthe other samples, and frequently these contami-nants represented the more abundant phylotypesin the other samples (data not shown). The sequen-cing depth afforded by the Illumina platform greatlyincreases the likelihood of detecting contaminants;however, we stress the utility of barcoding andsequencing both ends of multiplexed ampliconsin order to detect this source of errors. In the absenceof paired barcodes, removing reads that lack asignificant match to a reference database can beimplemented. Although this latter method is oflimited utility when examining novel or poorlycharacterized communities.
Figure 4 iTag diversity and taxonomic representation in thecolon of house mouse. (a) The pie charts indicate the relativeabundances and taxonomic affiliation of the resultant OTUs basedon the RDP Classifier labeled according to the key. The plot showsthe numbers of OTUs after application of different abundance andclustering thresholds on the 100% OTUs derived for theM. domesticus WSB samples GCA–S1V6 and TCC–S2V6. (b) Thenumbers of phylotypes common to the 100% OTUs predicted atthree increasingly stringent sequence abundance thresholds(10�4, 10�3, 10�2).
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Analysis of contrived samplesTo establish baseline levels of sensitivity of the iTagapproach, we performed several controls, includingthe amplicons generated from a single bacterialgenome and from a defined mixture of DNA from 19strains. In both cases, we found an excess of high-quality, 100% phylotypes regardless of the primerpair, Taq polymerase or sequencing run. However,sequencing reads bearing a single mutation relativeto the two expected E. coli phylotypes, or to the 19phylotypes from the mixture of strains, represented2.2–8.2% of the reads. Based on these reads weestimate a sequencing error rate of 1.3� 10�3 to4.1� 10�4 per nucleotide (Supplementary Table 2),which is slightly greater that the rate estimated fromerrors confined to the primer regions (run1x¼ 3.8� 10�4; run2 x¼ 6.4� 10�4). This differenceis in line with expectations as, in the Illuminaplatform, error rates are known to increase withdistance from the priming site (Rodrigue et al., 2010;Zhou et al., 2011). These errors impact taxonomicidentification using the RDP Classifier, particularlyreducing the ability to assign Order, Family andGenus identities to the shorter S1V6 and S2V6 tags.
To account for the large number of spurious readsin these samples, we applied strict abundancethresholds to our samples. We came the closest torecapitulating our expected diversity from thesesamples when we applied an abundance thresholdat which all phylotypes at frequencies o1% wereremoved. This cutoff is based on the total numberof high-quality reads for a given sample and more-or-less corresponds to the 0.01% threshold appliedby Caporaso et al. (2011), whose threshold is basedon the total number of read-pairs recovered for theirentire experiment (10 000 out of 87 507 177 reads).Application of such stringent thresholds is notparticularly satisfying, but will remain a necessarymeasure until sequencing error rates are improved.
Measurements of mouse intestinal floraMammalian guts house a diverse array of residentmicrobes and are numerically dominated by theFirmicutes and Bacteriodetes (Ley et al., 2008). Aprevious pyrosequencing effort identified 1000bacterial phylotypes (97% OTUs) in healthy Musmusculus individuals using the same S1V6 primers(Antonopoulos et al., 2009); however, our samplefrom a lab-reared, wild-derived inbred strain ofM. domesticus WSB yielded only 12 phylotypes(97% OTUs) after application of the equivalentquality and abundance filters derived from thecontrived samples (q30 and 10�2). Reducing thestringency of the abundance threshold increased thenumber of recovered iTag phylotypes, but did notalter the taxonomic distribution of the iTags (Fig-ure 4). (Lowering the abundance threshold from10�2 to 10�4 yielded about 20% more iTags contain-ing only a single-nucleotide difference relative tothe 12 high abundance OTUs.) The source of the
difference in the number of phylotypes detected inthe Mus fecal samples is unknown, and directcomparisons between iTags and pyrotags can onlybe assessed from the corresponding analyses of 16Sdiversity within E. coli (Kunin et al., 2010; thisstudy). Therefore, we suggest the amplification andsequencing of a control sample (for example, E. coli)during each multiplex experiment to empiricallydetermine appropriate abundance thresholds and toaccount for differences in sequencing error ratesbetween runs.
Alternative iTag implementationsInterest in leveraging the Illumina sequencing plat-form to examine 16S rRNA gene diversity has led tothe recent development of several strategies andobservations about its implementation (Claessonet al., 2010; Gloor et al., 2010; Caporaso et al.,2011; Zhou et al., 2011). Until recently, the Illuminasequence read length was limited to E100 nt. As aresult, paired-end sequencing of single, short vari-able loops (V6) has been assembled (Gloor et al.,2010; Zhou et al., 2011; this study), and longervariable region(s) (255–465 bp) were artificiallymerged or analyzed separately (Claesson et al.,2010; Caporaso et al., 2011). Assembly of over-lapping, paired-end reads results in phylotypes ofconsiderably better quality because it identifies andrectifies problems that arise from the deterioration ofread quality as a function of read length (Claessonet al., 2010; Gloor et al., 2010; Zhou et al., 2011).But because PCR and sequencing errors persist,application of abundance thresholds are still neces-sary to avoid inflated estimates of species richness(Gloor et al., 2010; Zhou et al., 2011). Moreover,conservative estimates of error rates using contrivedDNA samples vary six-fold, from 2.4� 10�3 to4.1� 10�4 per nucleotide (Zhou et al., 2011;this study).
The iTag approach holds enormous promise asmeans to investigate microbial diversity, but in itscurrent applications there are three major obstacles:(1) contamination, (2) the utility and classificationof short read lengths, and (3) sequencing error rates.Contamination can likely be mitigated by changesin laboratory procedures, including use of liquid-handling robots and sterile work environments.Although not considered here, contaminationderived from reagents or consumables is also possi-ble and would require alternative strategies for itsdetection and elimination. Despite the prevalence ofstudies using the V6 region and evidence that shortreads perform adequately for community analyses(Liu et al., 2007), short V6 tags appear to system-atically overestimate species richness (Youssef et al.,2009). Current Illumina read lengths (E150 nt) willallow the recovery and assembly of larger (V4) ormore (V6þV7) 16S variable regions that betterreflect the microbial diversity obtained when ana-lyzing the entire 16S rRNA molecule. Additionally,
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longer read lengths will facilitate the use of addi-tional barcodes that will allow multiplexing hun-dreds of samples in a single run. Although theobserved error rates are of little consequence tomost genomic studies, as errors are resolved throughsequencing depth, amplicon tagging approachestreat each unique sequence read as a novel OTU.Until there are improvements to the Illuminasequencing chemistry and analysis programs, erro-neous reads will need to be filtered through theapplication of abundance thresholds (Gloor et al.,2010; Caporaso et al., 2011). The cost and capacity ofthe Illumina sequencing make it the preeminentplatform for assessing microbial community diver-sity, and we have called attention to the errors thatneed to be resolved in its application and in theinterpretation of results.
Acknowledgements
We thank Michael Nachman, Nancy Moran and ZakeeSabree for contributing the template material used forlibrary construction, and Zakee Sabree for coining theterm ‘iTag’. Funding for this project was provided by theNational Institutes of Health to HO.
References
Antonopoulos DA, Huse SM, Morrison HG, Schmidt TM,Sogin ML, Young VB. (2009). Reproducible commu-nity dynamics of the gastrointestinal microbiotafollowing antibiotic perturbation. Infect Immun 77:2367–2375.
Blattner FR, Plunkett III G, Bloch CA, Perna NT,Burland V, Riley M et al. (1997). The complete genomesequence of Escherichia coli K-12. Science 277:1453–1462.
Bowers RM, Lauber CL, Wiedinmyer C, Hamady M,Hallar AG, Fall R et al. (2009). Characterization ofairborne microbial communities at a high-elevationsite and their potential to act as atmospheric icenuclei. Appl Environ Microbiol 75: 5121–5130.
Caporaso JG, Lauber CL, Walters WA, Berg-Lyons D,Lozupone CA, Turnbaugh PJ et al. (2011). Globalpatterns of 16S rRNA diversity at a depth of millionsof sequences per sample. Proc Natl Acad Sci USA108(Suppl 1): 4516–4522.
Cardenas E, Wu WM, Leigh MB, Carley J, Carroll S,Gentry T et al. (2010). A combined massively parallelsequencing—indicator species approach revealed sig-nificant association between sulfate-reducing bacteriaand uranium-reducing microbial communities. ApplEnviron Microbiol 76: 6778–6786.
Claesson MJ, Wang Q, O’Sullivan O, Greene-Diniz R,Cole JR, Ross RP et al. (2010). Comparison of two next-generation sequencing technologies for resolvinghighly complex microbiota composition using tandemvariable 16S rRNA gene regions. Nucleic Acids Res 38:e200.
Cole JR, Wang Q, Cardenas E, Fish J, Chai B, Farris RJ et al.(2009). The Ribosomal Database Project: improved
alignments and new tools for rRNA analysis. NucleicAcids Res 37: D141–D145.
Costello EK, Lauber CL, Hamady M, Fierer N, Gordon JI,Knight R. (2009). Bacterial community variation inhuman body habitats across space and time. Science326: 1694–1697.
Dethlefsen L, Huse S, Sogin ML, Relman DA. (2008). Thepervasive effects of an antibiotic on the human gutmicrobiota, as revealed by deep 16S rRNA sequencing.PLoS Biol 6: e280.
Engelbrektson A, Kunin V, Wrighton KC, Zvenigorodsky N,Chen F, Ochman H et al. (2010). Experimental factorsaffecting PCR-based estimates of microbial speciesrichness and evenness. ISME J 4: 642–647.
Fisher MM, Triplett EW. (1999). Automated approach forribosomal intergenic spacer analysis of microbialdiversity and its application to freshwater bacterialcommunities. Appl Environ Microbiol 65: 4630–4636.
Galand PE, Casamayor EO, Kirchman DL, Lovejoy C. (2009).Ecology of the rare microbial biosphere of the ArcticOcean. Proc Natl Acad Sci USA 106: 22427–22432.
Gloor GB, Hummelen R, Macklaim JM, Dickson RJ,Fernandes AD, MacPhee R et al. (2010). Microbiomeprofiling by Illumina sequencing of combinatorialsequence-tagged PCR products. PLoS One 5: e15406.
Hamady M, Walker JJ, Harris JK, Gold NJ, Knight R. (2008).Error-correcting barcoded primers for pyrosequencinghundreds of samples in multiplex. Nat Methods 5:235–237.
Huse SM, Dethlefsen L, Huber JA, Mark Welch D,Relman DA, Sogin ML. (2008). Exploring microbialdiversity and taxonomy using SSU rRNA hypervari-able tag sequencing. PLoS Genet 4: e1000255.
Keijser BJ, Zaura E, Huse SM, van der Vossen JM,Schuren FH, Montijn RC et al. (2008). Pyrosequencinganalysis of the oral microflora of healthy adults. J DentRes 87: 1016–1020.
Kozarewa I, Ning Z, Quail MA, Sanders MJ, Berriman M,Turner DJ. (2009). Amplification-free Illumina sequen-cing-library preparation facilitates improved mappingand assembly of (G+C)-biased genomes. Nat Methods6: 291–295.
Kunin V, Engelbrektson A, Ochman H, Hugenholtz P.(2010). Wrinkles in the rare biosphere: pyrosequen-cing errors can lead to artificial inflation of diversityestimates. Environ Microbiol 12: 118–123.
Lazarevic V, Whiteson K, Huse S, Hernandez D,Farinelli L, Osteras M et al. (2009). Metagenomicstudy of the oral microbiota by Illumina high-through-put sequencing. J Microbiol Methods 79: 266–271.
Ley RE, Hamady M, Lozupone C, Turnbaugh PJ,Ramey RR, Bircher JS et al. (2008). Evolution of mammalsand their gut microbes. Science 320: 1647–1651.
Li H, Ruan J, Durbin R. (2008). Mapping short DNAsequencing reads and calling variants using mappingquality scores. Genome Res 18: 1851–1858.
Liu W, Marsh T, Cheng H, Forney L. (1997). Characteriza-tion of microbial diversity by determining terminalrestriction fragment length polymorphisms of genesencoding 16S rRNA. Appl Environ Microbiol 63:4516–4522.
Liu Z, Lozupone C, Hamady M, Bushman FD, Knight R.(2007). Short pyrosequencing reads suffice for accu-rate microbial community analysis. Nucleic Acids Res35: e120.
McLellan SL, Huse SM, Mueller-Spitz SR, AndreishchevaEN, Sogin ML. (2010). Diversity and population
Estimating microbial diversity with iTagsPH Degnan and H Ochman
193
The ISME Journal
structure of sewage-derived microorganisms in waste-water treatment plant influent. Environ Microbiol 12:378–392.
Muyzer G, de Waal EC, Uitterlinden AG. (1993). Profilingof complex microbial populations by denaturinggradient gel electrophoresis analysis of polymerasechain reaction-amplified genes coding for 16S rRNA.Appl Environ Microbiol 59: 695–700.
Neefs JM, Van de Peer Y, De Rijk P, Goris A, De Wachter R.(1991). Compilation of small ribosomal subunit RNAsequences. Nucleic Acids Res 19(Suppl): 1987–2015.
Parameswaran P, Jalili R, Tao L, Shokralla S,Gharizadeh B, Ronaghi M et al. (2007). A pyrosequen-cing-tailored nucleotide barcode design unveils op-portunities for large-scale sample multiplexing.Nucleic Acids Res 35: e130.
Qiu X, Wu L, Huang H, McDonel PE, Palumbo AV,Tiedje JM et al. (2001). Evaluation of PCR-generated
chimeras, mutations, and heteroduplexes with 16SrRNA gene-based cloning. Appl Environ Microbiol 67:880–887.
Rodrigue S, Materna AC, Timberlake SC, Blackburn MC,Malmstrom RR, Alm EJ et al. (2010). Unlockingshort read sequencing for metagenomics. PLoS One5: e11840.
Sogin ML, Morrison HG, Huber JA, Mark Welch D,Huse SM, Neal PR et al. (2006). Microbial diversityin the deep sea and the underexplored ‘rare bio-sphere’. Proc Natl Acad Sci USA 103: 12115–12120.
Youssef N, Sheik CS, Krumholz LR, Najar FZ, Roe BA,Elshahed MS. (2009). Comparison of species richnessestimates obtained using nearly complete fragmentsand simulated pyrosequencing-generated fragmentsin 16S rRNA gene-based environmental surveys.Appl Environ Microbiol 75: 5227–5236.
Zhou H-W, Li D-F, Tam NF-Y, Jiang XT, Zhang H,Sheng H-F et al. (2011). BIPES, a cost-effective high-throughput method for assessing microbial diversity.ISME J 5: 741–749.
Supplementary Information accompanies the paper on The ISME Journal website (http://www.nature.com/ismej)
Estimating microbial diversity with iTagsPH Degnan and H Ochman
