- Research article
- Open Access
Improved taxonomic assignment of human intestinal 16S rRNA sequences by a dedicated reference database
© Ritari et al. 2015
- Received: 11 March 2015
- Accepted: 1 December 2015
- Published: 12 December 2015
Current sequencing technology enables taxonomic profiling of microbial ecosystems at high resolution and depth by using the 16S rRNA gene as a phylogenetic marker. Taxonomic assignation of newly acquired data is based on sequence comparisons with comprehensive reference databases to find consensus taxonomy for representative sequences. Nevertheless, even with well-characterised ecosystems like the human intestinal microbiota it is challenging to assign genus and species level taxonomy to 16S rRNA amplicon reads. A part of the explanation may lie in the sheer size of the search space where competition from a multitude of highly similar sequences may not allow reliable assignation at low taxonomic levels. However, when studying a particular environment such as the human intestine, it can be argued that a reference database comprising only sequences that are native to the environment would be sufficient, effectively reducing the search space.
We constructed a 16S rRNA gene database based on high-quality sequences specific for human intestinal microbiota, resulting in curated data set consisting of 2473 unique prokaryotic species-like groups and their taxonomic lineages, and compared its performance against the Greengenes and Silva databases. The results showed that regardless of used assignment algorithm, our database improved taxonomic assignation of 16S rRNA sequencing data by enabling significantly higher species and genus level assignation rate while preserving taxonomic diversity and demanding less computational resources.
The curated human intestinal 16S rRNA gene taxonomic database of about 2500 species-like groups described here provides a practical solution for significantly improved taxonomic assignment for phylogenetic studies of the human intestinal microbiota.
- Next-generation sequencing
- Ribosomal RNA
- Human intestinal microbiota
As the most genetically diverse and functionally complex microbial ecosystem of the human body the intestinal microbiota has become one of the major areas of interest in microbial ecology . In particular, efforts have been undertaken to understand how individual composition and variation of the microbiota together with host genetic and environmental factors influence human health [2, 3]. Over the past decade it has become evident that the microbiota exerts various beneficial effects to the host physiology during the development and in adulthood, notably through immunity and nutrition [4, 5], and deviations from a balanced microbial composition are related to systemic problems, such as diabetes, obesity and allergy [6–8]. Progress in molecular analysis of the microbiota has been made possible largely by the advance of next-generation sequencing technology, which has allowed studying the composition and dynamics of microbial communities with unforeseen scale and resolution [9, 10].
The bacterial and archaeal 16S small subunit ribosomal RNA (16S rRNA) gene has been established as the most widely used phylogenetic marker due to its conserved and variable regions and universal presence in prokaryotes. By sequencing the pool of 16S rRNA genes, community composition can be investigated in a comprehensive and rapid manner by high-throughput sequencing platforms harbouring the capacity for millions of reads per single run [11, 12]. As a result of increasing read length, sample multiplexing capability and reducing costs, 16S sequence data is being accumulated from various microbial ecosystems, and vast reference databases like Silva [13, 14], Greengenes (GG)  and RDP  have been built to enable phylogenetic analysis of high-throughput data.
While being highly successful at gathering data, the high-throughput technologies also present challenges for data analysis by requiring sophisticated computational methods not only in correcting technical artifacts but also for organizing the output and extraction of biologically meaningful features. A crucial step in deciphering 16S rRNA reads data is the taxonomic annotation of the discovered sequences. This holds true especially because current sequencing technologies typically cover only a part of the 16S rRNA gene, the large number of reference sequences and limited resolution at genus and species levels [17, 18]. Taxonomic annotations have been shown to depend on several factors, including sequence length, target region of the 16S gene, OTU classification method and assignment algorithm. Although many comparative studies have addressed these technical factors [19–22], the effect of the reference database on the accuracy of taxonomic assignment remains less well known. The standard approach has been to use as comprehensive a database as possible to minimize the number of unclassified sequences . However, increasing database size also makes it potentially more difficult to assign taxonomy at genus and species levels as the likelihood of ambiguous assignment increases due to larger number of competing sequences in the search space. On the other hand, better taxonomic resolution would be valuable in profiling the human gut microbiota because different species and genera can associate with different conditions and outcomes . Furthermore, the 16S rRNA gene has been shown to have considerably higher ambiguous assignment rate at lower taxonomic levels compared with other taxonomic marker genes , making its use somewhat problematic despite extensive reference data sets.
We hypothesized that by reducing the size of the reference database to encompass only the sequences innate to the environment under study would lead to improved taxonomic assignations at lower taxonomic levels due to less competition among targets. In this respect, the human intestinal microbiota presents an advantageous model system because it is already well characterized by sequencing [25, 26] while genus and species level taxonomic assignment of new sequencing data remains challenging . Moreover, a curated set of over 1000 cultured bacterial and archaeal species from the human intestinal ecosystem has recently been reported . To this end, we constructed a custom human intestinal 16S rRNA database, termed HITdb, including all currently known cultivable gastrointestinal prokaryotes as well as operational taxonomic units (OTUs) generated from high-quality 16S rRNA sequences originating from human intestinal tract. Here, we have evaluated the taxonomic assignment performance of the custom database by comparing it with the current standard, the Greengenes database, and demonstrate that the custom database improves taxonomic assignation of human intestinal 16S high-throughput reads. The 2473 species-like 16S rRNA sequences present in the HITdb also provide a minimum estimate for the number of species present in the human intestinal ecosystem.
Finally, the HITdb sequences were taxonomically assigned based on the cultivated species’ taxonomy, Greengenes and manual curating. A nearest neighbour cultivable species was determined for each OTU to facilitate the interpretation of the OTUs. Phylogenetic trees constructed from bacterial and archaeal sequences (Additional file 1) were found to correspond with the nearest neighbour information.
In order to evaluate how comprehensively the HITdb represents taxonomic diversity we performed a computational rarefaction analysis based on the sequence data used for constructing the HITdb (see Methods). The obtained rarefaction curve shows that the number of 97 % OTUs is not quite saturated at current sequence data (Additional file 2), which would indicate that the full species-level diversity is not fully covered. On the other hand, actual rarefaction by sampling random subsets from the sequence data, defining OTUs for each sampled subset and calculating the number of known unique species and genera represented by the OTU clusters showed that the numbers were not significantly lower in samples constituting about 80 % of the original sequence data (Additional file 3), suggesting that the data is close to reaching saturation. Altogether, these results suggest that the HITdb is able to capture the diversity of known taxa quite well, because in all tests with actual rarefaction the observed numbers would be expected to drop significantly if the sampling depth was a limiting factor. However, the sequence coverage in clusters representing known species was typically higher than in OTUs (data not shown), which could explain why saturation is seen with known species in actual rarefaction, but not when all OTUs are considered in computational rarefaction. Since most of the 16S sequences are assigned to clusters of known species, it also implies that if some species-like groups were missing from HITdb, they would be increasingly rare and therefore probably not highly relevant.
The number of entries in the curated HITdb, viz. a total of 2473, can be seen as the present estimate for the minimal number of species expected to be present in the human intestinal tract. Since according to the rarefaction analysis the number of OTUs is probably not quite saturated yet, the number may still increase with new data. However, there may be only a limited number of new OTUs emerging, similar to the situation with metagenomic data that shows only a limited increase to the known information pool with the addition of new metagenome sequences . In any case, an earlier estimate of about 1800 human intestinal species remaining uncultured  is consistent with HITdb because the number of cultured species has increased since then , leaving about 1500 species still uncultured. This is an important estimate to keep in mind when designing strategies to culture the not-yet cultured species from the human intestinal tract.
Computational resources required by taxonomic annotation depend on the reference database, assignment algorithm and the number of sequences to be assigned. We found that HITdb with RDP classifier is both faster and takes less memory than Greengenes using Uclust or RDP classifiers (Additional file 8). Although Uclust is very fast, it is quite memory intensive, while RDP (and Mothur) consume less memory but are slower. Since HITdb performs faster and with less memory than either algorithm with Greengenes, it may be expected to scale well for increasingly large datasets for future needs.
Profiling the composition of intestinal microbiota relies on accurate taxonomic annotation of sequencing reads. However, large reference databases may not be able to provide optimal species- and genus-level resolution due to increasing competition in the search space. To improve low-level resolution without abandoning comprehensiveness in taxonomic assignation we propose using a dedicated reference database specific to the ecosystem under study. By constructing a human intestinal 16S database and comparing its performance with Greengenes we found that the dedicated reference database improves the assignment rate at genus and species level, suggesting that large search space may be a limiting factor in low-level taxonomic assignment. Our study provides a practical solution with considerable performance improvements, is readily applicable in human gut microbiota profiling studies and paves the way for developing similar focused databases for other model systems.
To create a reference data set the 16S sequences of cultivable bacterial and archaeal human intestinal resident species  were obtained from NCBI Genbank. In addition, a Cyanobacteria related Melainabacterium species from curated metagenome , a cultivable representative of phylum TM7 from human oral cavity , Intestinimonas butyriproducens and Methanomassiliicoccus intestinalis  were included to the reference set of known species.
To obtain a comprehensive set of near full-length 16S sequences originating from human intestinal microbiota a search was performed against the NCBI Genbank nucleotide database using the command ((“Homo sapiens”[Organism] OR human[All Fields]) AND (intestinal[All Fields] OR gut[All Fields]) AND 16S[All Fields]) AND (“bacteria”[porgn] OR “archaea”[porgn]) AND 1000:2000[SLEN]. The extracted sequences were matched against the Greengenes 13_5  and Silva [13, 14] (SSURef_NR99_115_tax_silva_trunc) 16S databases at 97 % identity using Usearch v. 7.0.1001 command usearch_global. The matched sequences were extracted from both databases and subjected to chimeric sequence removal by UCHIME v. 7.0.1001 (command uchime_ref; default parameters) using the 16S reference database available at http://drive5.com/uchime/gold.fa . The non-chimeric sequences were length filtered to exclude sequences shorter than 1.3 kb. The filtered sequence data was then clustered to OTUs using the cultivable species’ sequences as a reference but allowing non-matching sequences to cluster de novo. At minimum two sequences were required for each de novo OTU. The OTU clustering was performed at 97 % identity threshold in Qiime v. 1.8.0  using the command pick_open_reference_otus.py with parameters suppress_taxonomy_assignment, min_otu_size = 2, prefilter_percent_id = 0.0, percent_subsample = 0.1 and suppress_align_and_tree. Next, the representative sequences of OTU clusters were matched back to the reference species’ sequences using Usearch v. 7.0.1001 command usearch_global with parameters id = 0.5 and maxhits = 1. OTUs having a match over 97 % similar to any of the cultivable species were removed (i.e. collapsed with the corresponding species). Furthermore, for each OTU, the nearest cultivable species was determined from the sequence match results. The final sequence content of HITdb consisted of representative sequences of the processed de novo OTUs and cultivable species.
HITdb OTU representative sequences were assigned taxonomy from the taxonomy of known species and Greengenes by using RDP classifier. In cases where HITdb was able to give a lower level assignation than Greengenes, and where all taxonomic levels were in agreement between HITdb and Greengenes, the OTU was assigned the taxonomy given by the known species. The lineages of species and OTUs were manually checked for consistency, and to adapt them to current naming convention as well as possible.
The HITdb bacterial and archaeal sequences were separately aligned using Muscle v. 3.8.31 with default settings . The alignments were filtered in Qiime v. 1.8.0 using command filter_alignment.py with parameter suppress_lane_mask_filter. Newick formatted phylogenetic trees were built from the filtered alignments using FastTree . The trees were visualized with FigTree v. 1.4.0 (http://tree.bio.ed.ac.uk/software/figtree/ ).
The 16S sequences of 953 cultivable human intestinal bacterial species were aligned to 518R  and 338R  primer sequences allowing 2 or 3 mismatches, respectively, in order to extract V1-V3 and V4-V6 gene regions from the sequences. For V1-V3, target sequence from its start until the end of 518R alignment position was extracted. For V4-V6, sequence from 338R alignment start position until 500 bp downstream of the target was extracted. The synthetic read sequences are given in Additional file 4.
Both biological and synthetic 16S reads were taxonomically assigned using in-built functions of Qiime v. 1.8.0 (assign_taxonomy.py, make_otu_table.py, summarize_taxa_through_plots.py)  with default parameters except for reference database where in addition to Greengenes 13_5  also HITdb and Silva were used, and assignment algorithm where RDP  and Mothur  were used along with the default Uclust.
Biological samples and 16S amplicon sequencing
Two sets of fecal samples obtained from children were sequenced for evaluating the HITdb performance with real data. Sample collection and DNA extraction were performed as described before [48, 49]. For data set 1 (119 samples) , PCR amplicons from bacterial 16S rRNA gene region V4-V6 were generated with forward (5’-AYTGGGYDTAAAGNG-3’) and reverse (5’-TGCTGCCTCCCGTAGGAGT-3’) primers. For data set 2 (40 samples) , amplicons from V1-V3 region were generated with forward (5’-AGAGTTTGATCMTGGCTCAG-3’) and reverse (5’-GTATTACCGCGGCTGCTG-3’) primers. The PCR primers contained 18-mer overhangs added to the 5’ ends . Replicate PCR products were pooled and purified with Agencourt AMPure XP magnetic beads (Agencourt Bioscience) and subjected to a second PCR round with barcoded forward primers and a reverse primer, both of which attached to the respective 18-mer overhang sequences from the primers of the first PCR amplification. Phusion polymerase (Thermo Fisher Scientific/Finnzymes) with HF buffer and 2.5 % DMSO were used. Cycling conditions for both PCR reactions consisted of an initial denaturation at 98 °C for 30 s, followed by 15 cycles at 98 °C for 10 s, 65 °C for 30 s, and 72 °C for 10 s, and then a final extension for 5 min. Between 3.6 and 60 ng of template DNA were used in the initial reaction. DNA concentration and quality were measured with Qubit (Invitrogen) and Bioanalyzer 2100 (Agilent). Sample set 1 was sequenced on 454 FLX Titanium instrument and set 2 in paired-end mode (R1 = 326 bp, R2 = 286 bp) on Illumina MiSeq instrument with standard library preparation protocol. Sequencing was carried out at the DNA sequencing and genomics laboratory, Institute of Biotechnology, University of Helsinki, Finland.
Pre-processing of 16S amplicon sequencing data
The raw pyrosequencing reads were subjected to reference-based chimera filtering using UCHIME v. 7.0.1001  (command uchime_ref; default parameters) with 16S reference database available at http://drive5.com/uchime/gold.fa. The non-chimeric reads were length filtered to exclude reads shorter than 500 nt. Thereafter the read numbers were rarefied by randomly sampling the lowest common read number (4246) from each sample using the Biostrings library  in R v. 3.1.1 . The reads from paired-end Illumina MiSeq sequencing data set were treated in a similar manner, except for merging of read pairs which was performed using Usearch v. 7.0.1001 command fastq_mergepairs  with parameters fastq_truncqual = 4, minhsp = 9, fastq_minovlen = 10, fastq_maxdiffs = 3 and fastq_minmergelen = 440. The quality filtering of the merged read pairs was done with Usearch fastq_filter with parameters fastq_truncqual = 10 and fastq_maxee = 0.75. The merged and filtered Illumina reads were rarefied to 13,303 reads per sample.
HMP data analysis
16S data of 192 Human Microbiome Project fecal samples were obtained from Sequence Read Archive (http://sra.dnanexus.com/). The SRA sample ID codes are given in Additional file 7. The data were preprocessed as described above, except for using minimum sequence length cutoff of 400 nt and rarefaction cutoff of 4000 reads. The data were analysed in Qiime v. 1.9 with default parameter settings. The data were taxonomically assigned by Uclust and Greengenes v.13_8, and by HITdb and RDP classifier in Qiime.
Metagenomic, taxonomically profiled data from HMP were obtained from HMSMCP - Shotgun MetaPHlAn Community Profiling (http://www.hmpdacc.org/HMSMCP/). The samples with the same SRS codes as in 16S HMP data were selected for taxonomic comparison. For HITdb, OTUs were excluded from comparative analysis at species level to make comparisons equal.
In order to estimate completeness of sequence data used to define the HITdb OTUs, sampling from multinomial distribution was performed. The number of sequences binned to each species-like cluster constituted the event probabilities of the multinomial model. For each draw from the multinomial, an OTU was accepted to be present if at least two reads were binned to it. The number of OTUs was calculated from 5000 draws. The sample size was varied starting from the number of all sequences (531,442) to lower numbers at a decrement of 10,000, and the mean number of OTUs over 5000 draws was calculated for each sampling. Quantiles for OTU numbers in each sample of 5000 draws was calculated for probabilities 0.95 and 0.05.
To estimate the sampling distribution of numbers of assigned taxa in synthetic reads data, the results of taxonomic assignment were bootstrapped 1000 times for each taxonomic level and employed database/assignment algorithm combination at that level. The proportion of present vs. absent taxa was calculated for each bootstrap sampling iteration.
To compare absolute and relative numbers of assigned taxa between databases in 454 and Illumina sequencing data sets, paired two-way Wilcoxon signed rank test was performed. All analyses were performed in R software v. 3.1.1 .
Availability of supporting data
HITdb is available in GitHub at https://github.com/microbiome/HITdb.git.
For direct download, use https://github.com/microbiome/HITdb/archive/master.zip.
The contained README file provides instructions and other information.
The study was financially supported by the Academy of Finland and the European Research Council (grant 250172, MicrobesInside). LL was supported by the Academy of Finland (grant 256950).
We would like to thank Dr. Anne Salonen for providing sequencing data.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
- Human Microbiome Project Consortium. Structure, function and diversity of the healthy human microbiome. Nature. 2012;486(7402):207–14.View ArticleGoogle Scholar
- Cheng J, Palva AM, de Vos WM, Satokari R. Contribution of the intestinal microbiota to human health: from birth to 100 years of age. Curr Top Microbiol Immunol. 2013;358:323–46.PubMedGoogle Scholar
- Guarner F, Malagelada JR. Gut flora in health and disease. Lancet. 2003;361(9356):512–9.View ArticlePubMedGoogle Scholar
- O’Hara AM, Shanahan F. The gut flora as a forgotten organ. EMBO Rep. 2006;7(7):688–93.PubMed CentralView ArticlePubMedGoogle Scholar
- Kau AL, Ahern PP, Griffin NW, Goodman AL, Gordon JI. Human nutrition, the gut microbiome and the immune system. Nature. 2011;474(7351):327–36.PubMed CentralView ArticlePubMedGoogle Scholar
- Hart AL, Lammers K, Brigidi P, Vitali B, Rizzello F, Gionchetti P, et al. Modulation of human dendritic cell phenotype and function by probiotic bacteria. Gut. 2004;53(11):1602–9.PubMed CentralView ArticlePubMedGoogle Scholar
- Kalliomaki M, Collado MC, Salminen S, Isolauri E. Early differences in fecal microbiota composition in children may predict overweight. Am J Clin Nutr. 2008;87(3):534–8.PubMedGoogle Scholar
- Le Chatelier E, Nielsen T, Qin J, Prifti E, Hildebrand F, Falony G, et al. Richness of human gut microbiome correlates with metabolic markers. Nature. 2013;500(7464):541–6.View ArticlePubMedGoogle Scholar
- Caporaso JG, Lauber CL, Walters WA, Berg-Lyons D, Huntley J, Fierer N, et al. Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms. ISME J. 2012;6(8):1621–4.PubMed CentralView ArticlePubMedGoogle Scholar
- Hamady M, Knight R. Microbial community profiling for human microbiome projects: Tools, techniques, and challenges. Genome Res. 2009;19(7):1141–52.PubMed CentralView ArticlePubMedGoogle Scholar
- Mosher JJ, Bernberg EL, Shevchenko O, Kan J, Kaplan LA. Efficacy of a 3rd generation high-throughput sequencing platform for analyses of 16S rRNA genes from environmental samples. J Microbiol Methods. 2013;95(2):175–81.View ArticlePubMedGoogle Scholar
- Binladen J, Gilbert MT, Bollback JP, Panitz F, Bendixen C, Nielsen R, et al. The use of coded PCR primers enables high-throughput sequencing of multiple homolog amplification products by 454 parallel sequencing. PLoS One. 2007;2(2):e197.PubMed CentralView ArticlePubMedGoogle Scholar
- Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2013;41(Database issue):D590–6.PubMed CentralView ArticlePubMedGoogle Scholar
- Yilmaz P, Parfrey LW, Yarza P, Gerken J, Pruesse E, Quast C, et al. The SILVA and “All-species Living Tree Project (LTP)” taxonomic frameworks. Nucleic Acids Res. 2014;42(Database issue):D643–8.PubMed CentralView ArticlePubMedGoogle Scholar
- DeSantis TZ, Hugenholtz P, Larsen N, Rojas M, Brodie EL, Keller K, et al. Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl Environ Microbiol. 2006;72(7):5069–72.PubMed CentralView ArticlePubMedGoogle Scholar
- Cole JR, Wang Q, Fish JA, Chai B, McGarrell DM, Sun Y, et al. Ribosomal Database Project: data and tools for high throughput rRNA analysis. Nucleic Acids Res. 2014;42(Database issue):D633–42.PubMed CentralView ArticlePubMedGoogle Scholar
- Janda JM, Abbott SL. 16S rRNA gene sequencing for bacterial identification in the diagnostic laboratory: pluses, perils, and pitfalls. J Clin Microbiol. 2007;45(9):2761–4.PubMed CentralView ArticlePubMedGoogle Scholar
- Mende DR, Sunagawa S, Zeller G, Bork P. Accurate and universal delineation of prokaryotic species. Nat Methods. 2013;10(9):881–4.View ArticlePubMedGoogle Scholar
- Edgar RC. UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nat Methods. 2013;10(10):996–8.View ArticlePubMedGoogle Scholar
- Liu Z, DeSantis TZ, Andersen GL, Knight R. Accurate taxonomy assignments from 16S rRNA sequences produced by highly parallel pyrosequencers. Nucleic Acids Res. 2008;36(18):e120.PubMed CentralView ArticlePubMedGoogle Scholar
- Huse SM, Dethlefsen L, Huber JA, Mark Welch D, Relman DA, Sogin ML. Exploring microbial diversity and taxonomy using SSU rRNA hypervariable tag sequencing. PLoS Genet. 2008;4(11):e1000255.PubMed CentralView ArticlePubMedGoogle Scholar
- Bowen De Leon K, Ramsay BD, Fields MW. Quality-score refinement of SSU rRNA gene pyrosequencing differs across gene region for environmental samples. Microb Ecol. 2012;64(2):499–508.PubMed CentralView ArticlePubMedGoogle Scholar
- Werner JJ, Koren O, Hugenholtz P, DeSantis TZ, Walters WA, Caporaso JG, et al. Impact of training sets on classification of high-throughput bacterial 16s rRNA gene surveys. ISME J. 2012;6(1):94–103.PubMed CentralView ArticlePubMedGoogle Scholar
- Lahti L, Salojarvi J, Salonen A, Scheffer M, de Vos WM. Tipping elements in the human intestinal ecosystem. Nat Commun. 2014;5:4344.PubMed CentralView ArticlePubMedGoogle Scholar
- Qin J, Li R, Raes J, Arumugam M, Burgdorf KS, Manichanh C, et al. A human gut microbial gene catalogue established by metagenomic sequencing. Nature. 2010;464(7285):59–65.PubMed CentralView ArticlePubMedGoogle Scholar
- Eckburg PB, Bik EM, Bernstein CN, Purdom E, Dethlefsen L, Sargent M, et al. Diversity of the human intestinal microbial flora. Science. 2005;308(5728):1635–8.PubMed CentralView ArticlePubMedGoogle Scholar
- Rajilic-Stojanovic M, de Vos WM. The first 1000 cultured species of the human gastrointestinal microbiota. FEMS Microbiol Rev. 2014;38(5):996–1047.PubMed CentralView ArticlePubMedGoogle Scholar
- Schloss PD, Gevers D, Westcott SL. Reducing the effects of PCR amplification and sequencing artifacts on 16S rRNA-based studies. PLoS One. 2011;6(12):e27310.PubMed CentralView ArticlePubMedGoogle Scholar
- Kunin V, Engelbrektson A, Ochman H, Hugenholtz P. Wrinkles in the rare biosphere: pyrosequencing errors can lead to artificial inflation of diversity estimates. Environ Microbiol. 2010;12(1):118–23.View ArticlePubMedGoogle Scholar
- Koeppel AF, Wu M. Surprisingly extensive mixed phylogenetic and ecological signals among bacterial Operational Taxonomic Units. Nucleic Acids Res. 2013;41(10):5175–88.PubMed CentralView ArticlePubMedGoogle Scholar
- Schloss PD, Westcott SL. Assessing and improving methods used in operational taxonomic unit-based approaches for 16S rRNA gene sequence analysis. Appl Environ Microbiol. 2011;77(10):3219–26.PubMed CentralView ArticlePubMedGoogle Scholar
- Drancourt M, Bollet C, Carlioz A, Martelin R, Gayral JP, Raoult D. 16S ribosomal DNA sequence analysis of a large collection of environmental and clinical unidentifiable bacterial isolates. J Clin Microbiol. 2000;38(10):3623–30.PubMed CentralPubMedGoogle Scholar
- Schloss PD, Handelsman J. Toward a census of bacteria in soil. PLoS Comput Biol. 2006;2(7):e92.PubMed CentralView ArticlePubMedGoogle Scholar
- Schloss PD, Westcott SL, Ryabin T, Hall JR, Hartmann M, Hollister EB, et al. Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl Environ Microbiol. 2009;75(23):7537–41.PubMed CentralView ArticlePubMedGoogle Scholar
- Li J, Jia H, Cai X, Zhong H, Feng Q, Sunagawa S, et al. An integrated catalog of reference genes in the human gut microbiome. Nat Biotechnol. 2014;32(8):834–41.View ArticlePubMedGoogle Scholar
- Zoetendal EG, Rajilic-Stojanovic M, de Vos WM. High-throughput diversity and functionality analysis of the gastrointestinal tract microbiota. Gut. 2008;57(11):1605–15.View ArticlePubMedGoogle Scholar
- Wang Q, Garrity GM, Tiedje JM, Cole JR. Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl Environ Microbiol. 2007;73(16):5261–7.PubMed CentralView ArticlePubMedGoogle Scholar
- Di Rienzi SC, Sharon I, Wrighton KC, Koren O, Hug LA, Thomas BC, et al. The human gut and groundwater harbor non-photosynthetic bacteria belonging to a new candidate phylum sibling to Cyanobacteria. Elife. 2013;2:e01102.PubMed CentralView ArticlePubMedGoogle Scholar
- Soro V, Dutton LC, Sprague SV, Nobbs AH, Ireland AJ, Sandy JR, et al. Axenic culture of a candidate division TM7 bacterium from the human oral cavity and biofilm interactions with other oral bacteria. Appl Environ Microbiol. 2014;80(20):6480–9.PubMed CentralView ArticlePubMedGoogle Scholar
- Borrel G, Harris HM, Parisot N, Gaci N, Tottey W, Mihajlovski A, et al. Genome Sequence of “Candidatus Methanomassiliicoccus intestinalis” Issoire-Mx1, a Third Thermoplasmatales-Related Methanogenic Archaeon from Human Feces. Genome Announc. 2013, 1(4):10.1128/genomeA.00453-13.
- Edgar RC, Haas BJ, Clemente JC, Quince C, Knight R. UCHIME improves sensitivity and speed of chimera detection. Bioinformatics. 2011;27(16):2194–200.PubMed CentralView ArticlePubMedGoogle Scholar
- Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, et al. QIIME allows analysis of high-throughput community sequencing data. Nat Methods. 2010;7(5):335–6.PubMed CentralView ArticlePubMedGoogle Scholar
- Edgar RC. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 2004;32(5):1792–7.PubMed CentralView ArticlePubMedGoogle Scholar
- Price MN, Dehal PS, Arkin AP. FastTree: computing large minimum evolution trees with profiles instead of a distance matrix. Mol Biol Evol. 2009;26(7):1641–50.PubMed CentralView ArticlePubMedGoogle Scholar
- Muyzer G, de Waal EC, Uitterlinden AG. Profiling of complex microbial populations by denaturing gradient gel electrophoresis analysis of polymerase chain reaction-amplified genes coding for 16S rRNA. Appl Environ Microbiol. 1993;59(3):695–700.PubMed CentralPubMedGoogle Scholar
- Amann RI, Ludwig W, Schleifer KH. Phylogenetic identification and in situ detection of individual microbial cells without cultivation. Microbiol Rev. 1995;59(1):143–69.PubMed CentralPubMedGoogle Scholar
- Kumpu M, Kekkonen RA, Kautiainen H, Jarvenpaa S, Kristo A, Huovinen P, et al. Milk containing probiotic Lactobacillus rhamnosus GG and respiratory illness in children: a randomized, double-blind, placebo-controlled trial. Eur J Clin Nutr. 2012;66(9):1020–3.View ArticlePubMedGoogle Scholar
- Kuitunen M, Kukkonen K, Juntunen-Backman K, Korpela R, Poussa T, Tuure T, et al. Probiotics prevent IgE-associated allergy until age 5 years in cesarean-delivered children but not in the total cohort. J Allergy Clin Immunol. 2009;123(2):335–41.View ArticlePubMedGoogle Scholar
- Edwards U, Rogall T, Blocker H, Emde M, Bottger EC. Isolation and direct complete nucleotide determination of entire genes. Characterization of a gene coding for 16S ribosomal RNA. Nucleic Acids Res. 1989;17(19):7843–53.PubMed CentralView ArticlePubMedGoogle Scholar
- Pages H, Aboyoun P, Gentleman R, DebRoy S. Biostrings: String objects representing biological sequences, and matching algorithms. 2014. p. 2.32.1.Google Scholar
- R Development Core Team. R: A language and environment for statistical computing. 2014. p. 3.1.1.Google Scholar
- Edgar RC. Search and clustering orders of magnitude faster than BLAST. Bioinformatics. 2010;26(19):2460–1.View ArticlePubMedGoogle Scholar