Phylogenetic classification of short environmental DNA fragments

Metagenomics is providing striking insights into the ecology of microbial communities. The recently developed massively parallel 454 pyrosequencing technique gives the opportunity to rapidly obtain metagenomic sequences at a low cost and without cloning bias. However, the phylogenetic analysis of the short reads produced represents a significant computational challenge. The phylogenetic algorithm CARMA for predicting the source organisms of environmental 454 reads is described. The algorithm searches for conserved Pfam domain and protein families in the unassembled reads of a sample. These gene fragments (environmental gene tags, EGTs), are classified into a higher-order taxonomy based on the reconstruction of a phylogenetic tree of each matching Pfam family. The method exhibits high accuracy for a wide range of taxonomic groups, and EGTs as short as 27 amino acids can be phylogenetically classified up to the rank of genus. The algorithm was applied in a comparative study of three aquatic microbial samples obtained by 454 pyrosequencing. Profound differences in the taxonomic composition of these samples could be clearly revealed.     

Annotation of metagenome short reads using proxygenes

MOTIVATION: A typical metagenome dataset generated using a 454 pyrosequencing platform consists of short reads sampled from the collective genome of a microbial community. The amount of sequence in such datasets is usually insufficient for assembly, and traditional gene prediction cannot be applied to unassembled short reads. As a result, analysis of such datasets usually involves comparisons in terms of relative abundances of various protein families. The latter requires assignment of individual reads to protein families, which is hindered by the fact that short reads contain only a fragment, usually small, of a protein. RESULTS: We have considered the assignment of pyrosequencing reads to protein families directly using RPS-BLAST against COG and Pfam databases and indirectly via proxygenes that are identified using BLASTx searches against protein sequence databases. Using simulated metagenome datasets as benchmarks, we show that the proxygene method is more accurate than the direct assignment. We introduce a clustering method which significantly reduces the size of a metagenome dataset while maintaining a faithful representation of its functional and taxonomic content.     

NoDe: a fast error-correction algorithm for pyrosequencing amplicon reads

Background

The popularity of new sequencing technologies has led to an explosion of possible applications, including new approaches in biodiversity studies. However each of these sequencing technologies suffers from sequencing errors originating from different factors. For 16S rRNA metagenomics studies, the 454 pyrosequencing technology is one of the most frequently used platforms, but sequencing errors still lead to important data analysis issues (e.g. in clustering in taxonomic units and biodiversity estimation). Moreover, retaining a higher portion of the sequencing data by preserving as much of the read length as possible while maintaining the error rate within an acceptable range, will have important consequences at the level of taxonomic precision.

Results

The new error correction algorithm proposed in this work - NoDe (Noise Detector) - is trained to identify those positions in 454 sequencing reads that are likely to have an error, and subsequently clusters those error-prone reads with correct reads resulting in error-free representative read. A benchmarking study with other denoising algorithms shows that NoDe can detect up to 75% more errors in a large scale mock community dataset, and this with a low computational cost compared to the second best algorithm considered in this study. The positive effect of NoDe in 16S rRNA studies was confirmed by the beneficial effect on the precision of the clustering of pyrosequencing reads in operational taxonomic units.

Conclusions

NoDe was shown to be a computational efficient denoising algorithm for pyrosequencing reads, producing the lowest error rates in an extensive benchmarking study with other denoising algorithms.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0520-5) contains supplementary material, which is available to authorized users.     

A diverse bacterial community in an anoxic quinoline-degrading bioreactor determined by using pyrosequencing and clone library analysis

There is a concern of whether the structure and diversity of a microbial community can be effectively revealed by short-length pyrosequencing reads. In this study, we performed a microbial community analysis on a sample from a high-efficiency denitrifying quinoline-degrading bioreactor and compared the results generated by pyrosequencing with those generated by clone library technology. By both technologies, 16S rRNA gene analysis indicated that the bacteria in the sample were closely related to, for example, Proteobacteria, Actinobacteria, and Bacteroidetes. The sequences belonging to Rhodococcus were the most predominant, and Pseudomonas, Sphingomonas, Acidovorax, and Zoogloea were also abundant. Both methods revealed a similar overall bacterial community structure. However, the 622 pyrosequencing reads of the hypervariable V3 region of the 16S rRNA gene revealed much higher bacterial diversity than the 130 sequences from the full-length 16S rRNA gene clone library. The 92 operational taxonomic unit (OTUs) detected using pyrosequencing belonged to 45 families, whereas the 37 OTUs found in the clone library belonged to 25 families. Most sequences obtained from the clone library had equivalents in the pyrosequencing reads. However, 64 OTUs detected by pyrosequencing were not represented in the clone library. Our results demonstrate that pyrosequencing of the V3 region of the 16S rRNA gene is not only a powerful tool for discovering low-abundance bacterial populations but is also reliable for dissecting the bacterial community structure in a wastewater environment.     

Analysis of 16S rRNA amplicon sequencing options on the Roche/454 next-generation titanium sequencing platform

Background

16S rRNA gene pyrosequencing approach has revolutionized studies in microbial ecology. While primer selection and short read length can affect the resulting microbial community profile, little is known about the influence of pyrosequencing methods on the sequencing throughput and the outcome of microbial community analyses. The aim of this study is to compare differences in output, ease, and cost among three different amplicon pyrosequencing methods for the Roche/454 Titanium platform

Methodology/Principal Findings

The following three pyrosequencing methods for 16S rRNA genes were selected in this study: Method-1 (standard method) is the recommended method for bi-directional sequencing using the LIB-A kit; Method-2 is a new option designed in this study for unidirectional sequencing with the LIB-A kit; and Method-3 uses the LIB-L kit for unidirectional sequencing. In our comparison among these three methods using 10 different environmental samples, Method-2 and Method-3 produced 1.5–1.6 times more useable reads than the standard method (Method-1), after quality-based trimming, and did not compromise the outcome of microbial community analyses. Specifically, Method-3 is the most cost-effective unidirectional amplicon sequencing method as it provided the most reads and required the least effort in consumables management.

Conclusions

Our findings clearly demonstrated that alternative pyrosequencing methods for 16S rRNA genes could drastically affect sequencing output (e.g. number of reads before and after trimming) but have little effect on the outcomes of microbial community analysis. This finding is important for both researchers and sequencing facilities utilizing 16S rRNA gene pyrosequencing for microbial ecological studies.     

Profiling of the bacteria responsible for pyogenic liver abscess by 16S rRNA gene pyrosequencing

Pyogenic liver abscess (PLA) is a severe disease with considerable mortality and is often polymicrobial. Understanding the pathogens that cause PLA is the basis for PLA treatment. Here, we profiled the bacterial composition in PLA fluid by pyrosequencing the 16S ribosomal RNA (rRNA) gene based on next-generation sequencing (NGS) technology to identify etiological agents of PLA and to provide information of their 16S rRNA sequences for application to DNA-based techniques in the hospital. Twenty patients with PLA who underwent percutaneous catheter drainage, abscess culture, and blood culture for isolates were included. Genomic DNAs from abscess fluids were subjected to polymerase chain reaction and pyrosequencing of the 16S rRNA gene with a 454 GS Junior System. The abscess and blood cultures were positive in nine (45%) and four (20%) patients, respectively. Pyrosequencing of 16S rRNA gene showed that 90% of the PLA fluid samples contained single or multiple genera of known bacteria such as Klebsiella, Fusobacterium, Streptococcus, Bacteroides, Prevotella, Peptostreptococcus, unassigned Enterobacteriaceae, and Dialister. Klebsiella was predominantly found in the PLA fluid samples. All samples that carried unassigned bacteria had 26.8% reads on average. We demonstrated that the occurrence of PLA was associated with eight known bacterial genera as well as unassigned bacteria and that 16S rRNA gene sequencing was more useful than conventional culture methods for accurate identification of bacterial pathogens from PLA.     

Massively parallel rRNA gene sequencing exacerbates the potential for biased community diversity comparisons due to variable library sizes

Technologies for massively parallel sequencing are revolutionizing microbial ecology and are vastly increasing the scale of ribosomal RNA (rRNA) gene studies. Although pyrosequencing has increased the breadth and depth of possible rRNA gene sampling, one drawback is that the number of reads obtained per sample is difficult to control. Pyrosequencing libraries typically vary widely in the number of sequences per sample, even within individual studies, and there is a need to revisit the behaviour of richness estimators and diversity indices with variable gene sequence library sizes. Multiple reports and review papers have demonstrated the bias in non-parametric richness estimators (e.g. Chao1 and ACE) and diversity indices when using clone libraries. However, we found that biased community comparisons are accumulating in the literature. Here we demonstrate the effects of sample size on Chao1, ACE, CatchAll, Shannon, Chao-Shen and Simpson's estimations specifically using pyrosequencing libraries. The need to equalize the number of reads being compared across libraries is reiterated, and investigators are directed towards available tools for making unbiased diversity comparisons.     

Variations in 16S rRNA-based microbiome profiling between pyrosequencing runs and between pyrosequencing facilities

Pyrosequencing of 16S rRNA gene amplicons on the 454 FLX Titanium platform has been widely used to analyze microbiomes in various environments. However, different results may stem from variations among sequencing runs or among sequencing facilities. This study aimed to evaluate these variations between different pyrosequencing runs by sequencing 16S rRNA gene amplicon libraries generated from three sets of rumen samples twice each on the 454 FLX Titanium system at two independent sequencing facilities. Similar relative abundances were found for predominant taxa represented by large numbers of sequence reads but not for minor taxa represented by small numbers of sequence reads. The two sequencing facilities revealed different bacterial profiles with respect to both predominant taxa and minor taxa, including the most predominant genus Prevotella, the family Lachnospiraceae, and the phylum Proteobacteria. Differences in primers used to generate amplicon libraries may be a major source of variations in microbiome profiling. Because different primers and regions of 16S rRNA genes are often used by different researchers, significant variations likely exist among studies. Quantitative interpretation for relative abundance of taxa, especially minor taxa, from prevalence of sequence reads and comparisons of results from different studies should be done with caution.     

Genetic diversity and composition of a plasmid metagenome from a wastewater treatment plant

Plasmid metagenome nucleotide sequence data were recently obtained from wastewater treatment plant (WWTP) bacteria with reduced susceptibility to selected antimicrobial drugs by applying the ultrafast 454-sequencing technology. The sequence dataset comprising 36,071,493 bases (346,427 reads with an average read length of 104 bases) was analysed for genetic diversity and composition by using a newly developed bioinformatic pipeline based on assignment of environmental gene tags (EGTs) to protein families stored in the Pfam database. Short amino acid sequences deduced from the plasmid metagenome sequence reads were compared to profile hidden Markov models underlying Pfam. Obtained matches evidenced that many reads represent genes having predicted functions in plasmid replication, stability and plasmid mobility which indicates that WWTP bacteria harbour genetically stabilised and mobile plasmids. Moreover, the data confirm a high diversity of plasmids residing in WWTP bacteria. The mobile organic peroxide resistance plasmid pMAC from Acinetobacter baumannii was identified as reference plasmid for the most abundant replication module type in the sequenced sample. Accessory plasmid modules encode different transposons, insertion sequences, integrons, resistance and virulence determinants. Most of the matches to Transposase protein families were identified for transposases similar to the one of the chromate resistance transposon Tn5719. Noticeable are hits to beta-lactamase protein families which suggests that plasmids from WWTP bacteria encode different enzymes possessing beta-lactam-hydrolysing activity. Some of the sequence reads correspond to antibiotic resistance genes that were only recently identified in clinical isolates of human pathogens. EGT analysis thus proofed to be a very valuable method to explore genetic diversity and composition of the present plasmid metagenome dataset.     

Metagenomics: Read Length Matters

Obtaining an unbiased view of the phylogenetic composition and functional diversity within a microbial community is one central objective of metagenomic analysis. New technologies, such as 454 pyrosequencing, have dramatically reduced sequencing costs, to a level where metagenomic analysis may become a viable alternative to more-focused assessments of the phylogenetic (e.g., 16S rRNA genes) and functional diversity of microbial communities. To determine whether the short (~100 to 200 bp) sequence reads obtained from pyrosequencing are appropriate for the phylogenetic and functional characterization of microbial communities, the results of BLAST and COG analyses were compared for long (~750 bp) and randomly derived short reads from each of two microbial and one virioplankton metagenome libraries. Overall, BLASTX searches against the GenBank nr database found far fewer homologs within the short-sequence libraries. This was especially pronounced for a Chesapeake Bay virioplankton metagenome library. Increasing the short-read sampling depth or the length of derived short reads (up to 400 bp) did not completely resolve the discrepancy in BLASTX homolog detection. Only in cases where the long-read sequence had a close homolog (low BLAST E-score) did the derived short-read sequence also find a significant homolog. Thus, more-distant homologs of microbial and viral genes are not detected by short-read sequences. Among COG hits, derived short reads sampled at a depth of two short reads per long read missed up to 72% of the COG hits found using long reads. Noting the current limitation in computational approaches for the analysis of short sequences, the use of short-read-length libraries does not appear to be an appropriate tool for the metagenomic characterization of microbial communities.     

Assessing the Consequences of Denoising Marker-Based Metagenomic Data

Early marker-based metagenomic studies were performed without properly accounting for the effects of noise (sequencing errors, PCR single-base errors, and PCR chimeras). Denoising algorithms have been developed, but they were validated using data derived from mock communities, in which the true sequences were known. Since the algorithms were designed to be used in real community studies, it is important to evaluate the results in such cases. With this goal in mind, we processed a real 16S rRNA metagenomic dataset through five denoising pipelines. By reconstituting the sequence reads at each stage of the pipelines, we determined how the reads were being altered. In one denoising pipeline, AmpliconNoise, we found that the algorithm that was designed to remove pyrosequencing errors changed the reads in a manner inconsistent with the known spectrum of these errors, until one of the parameters was increased substantially from its default value. Additionally, because the longest read was picked as the representative for each cluster, sequences were added to the 3′ ends of shorter reads that were often dissimilar from what had been removed by the truncations of the previous filtering step. In QIIME, the denoising algorithm caused a much larger number of changes to the reads unless the parameters were changed from their defaults. The denoising pipeline in mothur avoided some of these negative side-effects because of its strict default filtering criteria, but these criteria also greatly limited the sequence information produced at the end of the pipeline. We recommend that those using these denoising pipelines be cognizant of these issues and examine how their reads are being transformed by the denoising process as a component of their analysis.     

Macrolide resistance determination and molecular typing of Mycoplasma pneumoniae by pyrosequencing

The first choice antibiotics for treatment of Mycoplasma pneumoniae infections are macrolides. Several recent studies, however, have indicated that the prevalence of macrolide (ML)-resistance, which is determined by mutations in the bacterial 23S rRNA, is increasing among M. pneumoniae isolates. Consequently, it is imperative that ML-resistance in M. pneumoniae is rapidly detected to allow appropriate and timely treatment of patients. We therefore set out to determine the utility of pyrosequencing as a convenient technique to assess ML-resistance. In addition, we studied whether pyrosequencing could be useful for molecular typing of M. pneumoniae isolates. To this end, a total of four separate pyrosequencing assays were developed. These assays were designed such as to determine a short genomic sequence from four different sites, i.e. two locations within the 23S rRNA gene, one within the MPN141 (or P1) gene and one within the MPN528a gene. While the 23S rRNA regions were employed to determine ML-resistance, the latter two were used for molecular typing. The pyrosequencing assays were performed on a collection of 108 M. pneumoniae isolates. The ML-resistant isolates within the collection (n = 4) were readily identified by pyrosequencing. Moreover, each strain was correctly typed as either a subtype 1 or subtype 2 strain by both the MPN141 and MPN528a pyrosequencing test. Interestingly, two recent isolates from our collection, which were identified as subtype 2 strains by the pyrosequencing assays, were found to carry novel variants of the MPN141 gene, having rearrangements in each of the two repetitive elements (RepMP4 and RepMP2/3) within the gene. In conclusion, pyrosequencing is a convenient technique for ML-resistance determination as well as molecular typing of M. pneumoniae isolates.     

Assessment of Microbial Richness in Pelagic Sediment of Andaman Sea by Bacterial Tag Encoded FLX Titanium Amplicon Pyrosequencing (bTEFAP)

Microbial diversity of 1,000 m deep pelagic sediment from off Coast of Andaman Sea was analyzed by a culture independent technique, bacterial tag encoded FLX titanium amplicon pyrosequencing. The hypervariable region of small subunit ribosomal rRNA gene covering V6–V9, was amplified from the metagenomic DNA and sequenced. We obtained 19,271 reads, of which 18,206 high quality sequences were subjected to diversity analysis. A total of 305 operational taxonomic units (OTUs) were obtained corresponding to the members of firmicutes, proteobacteria, plantomycetes, actinobacteria, chloroflexi, bacteroidetes, and verucomicrobium. Firmicutes was the predominant phylum, which was largely represented with the family bacillaceae. More than 44 % of sequence reads could not be classified up to the species level and more than 14 % of the reads could not be assigned to any genus. Thus, the data indicates the possibility for the presence of uncultivable or unidentified novel bacterial species. In addition, the community structure identified in this study significantly differs with other reports from marine sediments.     

Communities of Arbuscular Mycorrhizal Fungi Detected in Forest Soil Are Spatially Heterogeneous but Do Not Vary throughout the Growing Season

Despite the important ecosystem role played by arbuscular mycorrhizal fungi (AMF), little is known about spatial and temporal variation in soil AMF communities. We used pyrosequencing to characterise AMF communities in soil samples (n = 44) from a natural forest ecosystem. Fungal taxa were identified by BLAST matching of reads against the MaarjAM database of AMF SSU rRNA gene diversity. Sub-sampling within our dataset and experimental shortening of a set of long reads indicated that our approaches to taxonomic identification and diversity analysis were robust to variations in pyrosequencing read length and numbers of reads per sample. Different forest plots (each 10×10 m and separated from one another by 30 m) contained significantly different soil AMF communities, and the pairwise similarity of communities decreased with distance up to 50 m. However, there were no significant changes in community composition between different time points in the growing season (May-September). Spatial structure in soil AMF communities may be related to the heterogeneous vegetation of the natural forest study system, while the temporal stability of communities suggests that AMF in soil represent a fairly constant local species pool from which mycorrhizae form and disband during the season.     

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

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

基于高通量测序的花马盐湖原核微生物及其耐盐基因

【目的】揭示陕北花马盐湖沉积物原核微生物群落组成,并分析其潜在的耐盐功能基因。【方法】构建盐湖沉积物宏基因组16S r RNA文库和fosmid文库,利用Illumina HiSeq高通量测序及生物信息技术分析细菌古菌群落组成和耐盐菌株(5-5)外源宏基因组的潜在耐盐基因。【结果】获得18978条有效的16Sr RNA序列,共5221个OTUs,包括23个门,155个属,其中广古菌门(Euryarchaeota)和变形菌门(Proteobacteria)为优势菌门,盐杆状菌属(Halorhabdus)、盐红菌属(Halorubrum)及假单胞菌属(Pseudomonas)等16个属为优势属,以及嗜盐单胞菌属(Halomonas)、冷弯菌属(Psychroflexus)及不动细菌属(Acinetobacter)等139个属为非优势属。从4126个fosmid文库菌株中筛选出37株耐盐菌株,其中菌株5-5、2E4和2F4对不同浓度的NaCl、CuSO_4、ZnSO_4及CdSO_4具有耐受性,从5-5的外源宏基因组序列中获得61个Unigene,其中12个Unigene的同源基因编码的蛋白质如无机焦磷酸酶、转座酶、亚碲酸钾抗性蛋白及钙调蛋白等广泛参与其他生物的耐盐逆境。【结论】盐湖沉积物中蕴藏着丰富多样的细菌古菌类群以及潜在耐盐功能基因资源。     

High-resolution SAR11 ecotype dynamics at the Bermuda Atlantic Time-series Study site by phylogenetic placement of pyrosequences

Advances in next-generation sequencing technologies are providing longer nucleotide sequence reads that contain more information about phylogenetic relationships. We sought to use this information to understand the evolution and ecology of bacterioplankton at our long-term study site in the Western Sargasso Sea. A bioinformatics pipeline called PhyloAssigner was developed to align pyrosequencing reads to a reference multiple sequence alignment of 16S ribosomal RNA (rRNA) genes and assign them phylogenetic positions in a reference tree using a maximum likelihood algorithm. Here, we used this pipeline to investigate the ecologically important SAR11 clade of Alphaproteobacteria. A combined set of 2.7 million pyrosequencing reads from the 16S rRNA V1–V2 regions, representing 9 years at the Bermuda Atlantic Time-series Study (BATS) site, was quality checked and parsed into a comprehensive bacterial tree, yielding 929 036 Alphaproteobacteria reads. Phylogenetic structure within the SAR11 clade was linked to seasonally recurring spatiotemporal patterns. This analysis resolved four new SAR11 ecotypes in addition to five others that had been described previously at BATS. The data support a conclusion reached previously that the SAR11 clade diversified by subdivision of niche space in the ocean water column, but the new data reveal a more complex pattern in which deep branches of the clade diversified repeatedly across depth strata and seasonal regimes. The new data also revealed the presence of an unrecognized clade of Alphaproteobacteria, here named SMA-1 (Sargasso Mesopelagic Alphaproteobacteria, group 1), in the upper mesopelagic zone. The high-resolution phylogenetic analyses performed herein highlight significant, previously unknown, patterns of evolutionary diversification, within perhaps the most widely distributed heterotrophic marine bacterial clade, and strongly links to ecosystem regimes.     

BIPES,a cost-effective high-throughput method for assessing microbial diversity

Pyrosequencing of 16S rRNA (16S) variable tags has become the most popular method for assessing microbial diversity, but the method remains costly for the evaluation of large numbers of environmental samples with high sequencing depths. We developed a barcoded Illumina paired-end (PE) sequencing (BIPES) method that sequences each 16S V6 tag from both ends on the Illumina HiSeq 2000, and the PE reads are then overlapped to obtain the V6 tag. The average accuracy of Illumina single-end (SE) reads was only 97.9%, which decreased from ∼99.9% at the start of the read to less than 85% at the end of the read; nevertheless, overlapping of the PE reads significantly increased the sequencing accuracy to 99.65% by verifying the 3′ end of each SE in which the sequencing quality was degraded. After the removal of tags with two or more mismatches within the medial 40–70 bases of the reads and of tags with any primer errors, the overall base sequencing accuracy of the BIPES reads was further increased to 99.93%. The BIPES reads reflected the amounts of the various tags in the initial template, but long tags and high GC tags were underestimated. The BIPES method yields 20–50 times more 16S V6 tags than does pyrosequencing in a single-flow cell run, and each of the BIPES reads costs less than 1/40 of a pyrosequencing read. As a laborsaving and cost-effective method, BIPES can be routinely used to analyze the microbial ecology of both environmental and human microbiomes.     

Short pyrosequencing reads suffice for accurate microbial community analysis

Pyrosequencing technology allows us to characterize microbial communities using 16S ribosomal RNA (rRNA) sequences orders of magnitude faster and more cheaply than has previously been possible. However, results from different studies using pyrosequencing and traditional sequencing are often difficult to compare, because amplicons covering different regions of the rRNA might yield different conclusions. We used sequences from over 200 globally dispersed environments to test whether studies that used similar primers clustered together mistakenly, without regard to environment. We then tested whether primer choice affects sequence-based community analyses using UniFrac, our recently-developed method for comparing microbial communities. We performed three tests of primer effects. We tested whether different simulated amplicons generated the same UniFrac clustering results as near-full-length sequences for three recent large-scale studies of microbial communities in the mouse and human gut, and the Guerrero Negro microbial mat. We then repeated this analysis for short sequences (100-, 150-, 200- and 250-base reads) resembling those produced by pyrosequencing. The results show that sequencing effort is best focused on gathering more short sequences rather than fewer longer ones, provided that the primers are chosen wisely, and that community comparison methods such as UniFrac are surprisingly robust to variation in the region sequenced.     

Generation of multimillion-sequence 16S rRNA gene libraries from complex microbial communities by assembling paired-end illumina reads

Microbial communities host unparalleled taxonomic diversity. Adequate characterization of environmental and host-associated samples remains a challenge for microbiologists, despite the advent of 16S rRNA gene sequencing. In order to increase the depth of sampling for diverse bacterial communities, we developed a method for sequencing and assembling millions of paired-end reads from the 16S rRNA gene (spanning the V3 region; ～200 nucleotides) by using an Illumina genome analyzer. To confirm reproducibility and to identify a suitable computational pipeline for data analysis, sequence libraries were prepared in duplicate for both a defined mixture of DNAs from known cultured bacterial isolates (>1 million postassembly sequences) and an Arctic tundra soil sample (>6 million postassembly sequences). The Illumina 16S rRNA gene libraries represent a substantial increase in number of sequences over all extant next-generation sequencing approaches (e.g., 454 pyrosequencing), while the assembly of paired-end 125-base reads offers a methodological advantage by incorporating an initial quality control step for each 16S rRNA gene sequence. This method incorporates indexed primers to enable the characterization of multiple microbial communities in a single flow cell lane, may be modified readily to target other variable regions or genes, and demonstrates unprecedented and economical access to DNAs from organisms that exist at low relative abundances.