Profiling bacterial communities by MinION sequencing of ribosomal operons
© The Author(s). 2017
Received: 25 April 2017
Accepted: 30 August 2017
Published: 15 September 2017
An approach utilizing the long-read capability of the Oxford Nanopore MinION to rapidly sequence bacterial ribosomal operons of complex natural communities was developed. Microbial fingerprinting employs domain-specific forward primers (16S rRNA subunit), reverse primers (23S rRNA subunit), and a high-fidelity Taq polymerase with proofreading capabilities. Amplicons contained both ribosomal subunits for broad-based phylogenetic assignment (~ 3900 bp of sequence), plus the intergenic spacer (ITS) region (~ 300 bp) for potential strain-specific identification.
To test the approach, bacterial rRNA operons (~ 4200 bp) were amplified from six DNA samples employing a mixture of farm soil and bioreactor DNA in known concentrations. Each DNA sample mixture was barcoded, sequenced in quadruplicate (n = 24), on two separate 6-h runs using the MinION system (R7.3 flow cell; MAP005 and 006 chemistry). From nearly 90,000 MinION reads, roughly 33,000 forward and reverse sequences were obtained. This yielded over 10,000 2D sequences which were analyzed using a simplified data analysis pipeline based on NCBI Blast and assembly with Geneious software. The method could detect over 1000 operational taxonomic units in the sample sets in a quantitative manner. Global sequence coverage for the various rRNA operons ranged from 1 to 1951x. An iterative assembly scheme was developed to reconstruct those rRNA operons with > 35x coverage from a set of 30 operational taxonomic units (OTUs) among the Proteobacteria, Actinobacteria, Acidobacteria, Firmicutes, and Gemmatimonadetes. Phylogenetic analysis of the 16S rRNA and 23S rRNA genes from each operon demonstrated similar tree topologies with species/strain-level resolution.
This sequencing method represents a cost-effective way to profile microbial communities. Because the MinION is small, portable, and runs on a laptop, the possibility of microbiota characterization in the field or on robotic platforms becomes realistic.
Molecular biological approaches for the genetic analysis of environmental samples have become the most widely accepted way to characterize microbial communities. Initially, a clone and sequence scheme was largely used to characterize 5S or 16S rRNA genes [1, 2]. Later, direct profiling methods such as denaturing gradient gel electrophoresis (DGGE), terminal restriction fragment length polymorphism (TRFLP), or single strand conformation polymorphism (SSCP) analysis were employed to characterize complex communities [3–5]. More recently, 16S rRNA gene sequence data is collected using a suite of “next generation” sequencing platforms (e.g., 454, Illumina, Ion Torrent) [6–9]. Although a large amount of data can be obtained in this manner, these recent approaches rely on expensive machines, bioinformatics training, and specialized computing facilities. In order to analyze the sequence data, a working knowledge of UNIX commands and Python scripts seems essential. Despite the computer software being freely available (e.g., QIIME and Mothur), the programs require the use of command lines and training in the proper UNIX syntax in order to function properly. Often the installation scripts and software dependencies become outdated quickly, and it is not always straightforward to install and/or operate. Furthermore, a server is generally needed to perform the analyses. Each of these requirements can place a significant monetary burden and a steep learning curve onto laboratories hoping to characterize bacterial communities. Finally, most second-generation sequencing platforms provide relatively short read lengths (200–400 bp) which limit the phylogenetic depth that can be achieved (with the exception of the PacBio system).
As an alternative approach, we tested if the portable DNA sequencer (MinION) from Oxford Nanopore Technologies (ONT) could be used to profile the microbiota using tools that can be purchased for a low cost and data analysis methods that are readily available to many laboratories. The MinION is a third-generation platform for direct sequencing of individual strands of DNA translocating nanoscale pores in a semiconductor membrane [10, 11]. A major advantage of the MinION is that it currently costs ~ $1000, connects to a laptop, collects/analyzes data in real time, and does not require specialized computer equipment or training for data analysis. For MinION sequencing, each DNA molecule has an adaptor ligated to one end, which interacts with a docking protein and binds to a nanopore. This docking protein regulates the speed by which the DNA traverses the membrane. The other end of the DNA fragment is ligated to a hairpin structure, which allows for the complementary strand to be sequenced as it flows through the pore. The DNA sequence is determined from 5-bp segments (k-mers) by measuring the change in electrical conductivity across the membrane as the DNA strand flows along the nanopore channel using hidden Markov models and Metrichor base calling software which is available to MinION users on the web. This approach generates 2D (double stranded; template plus complement) reads for single DNA molecules possessing both adaptor and hairpin, while 1D (single stranded; template or complement) reads are generated for DNA molecules possessing only the adaptor. (Those DNA molecules without adaptor or hairpin are removed during library preparation and are not detected in the analysis.)
For this study, we tested whether the MinION could be used to rapidly sequence bacterial ribosomal operons from complex environmental samples. To validate the approach, we generated a mixture of complex genomic DNA from two different sources where a large number of unknown microorganisms exist rather than a simple mock community of a few model organisms. After rRNA operon sequencing, each individual read was assigned to an operational taxonomic unit (OTU) by screening against an NCBI 16S rRNA gene database. An rRNA consensus sequence was then reconstructed for a particular OTU using an iterative alignment approach with a commercially available DNA software program (Geneious; < $900 per academic license) which can be run on Windows, Mac, or Linux operating systems. These efforts were designed to test if consensus building would yield data for environmental rRNA operons that are reproducible, quantitative, and similar to known rRNA genes within online databases.
Discontinuous MegaBLAST results for the rRNA consensus operons
NCBI top hit
16S % ID*
Acidobacteria bacterium IGE-003
83.8 ± 3.1
1345 ± 161
82.6 ± 5.0
1445 ± 83
Acidobacteria bacterium IGE-003 ***
90.0 ± 4.7
622 ± 251
Acidobacteria bacterium IGE-003 ***
84.7 ± 2.7
1324 ± 128
86.6 ± 2.6
1483 ± 69
88.3 ± 3.7
1472 ± 85
87.6 ± 1.4
1497 ± 33
87.2 ± 1.6
1509 ± 41
86.1 ± 3.5
1446 ± 88
89.3 ± 1.9
1279 ± 38
Enterococcus sp. CSL 7544–3
96.7 ± 2.1
1491 ± 52
97.0 ± 2.1
1489 ± 51
84.0 ± 2.7
1039 ± 6
98.2 ± 0.8
1530 ± 24
94.4 ± 1.0
1496 ± 40
96.3 ± 1.4
1501 ± 43
Castellaniella sp. ADC-27
95.2 ± 1.5
1502 ± 51
95.1 ± 1.4
1513 ± 43
95.4 ± 0.8
1521 ± 30
97.6 ± 0.2
1521 ± 16
85.5 ± 0.4
1551 ± 10
89.1 ± 3.7
1499 ± 45
Xanthomonas sp. R-20819
95.1 ± 1.0
1521 ± 27
94.7 ± 1.0
1531 ± 27
Citrobacter freundii CAV1741
98.2 ± 0.3
1534 ± 11
92.5 ± 0.5
1253 ± 5
Xanthomonas axonopodis ***
94.2 ± 1.2
1457 ± 30
Citrobacter freundii P10159
97.9 ± 0.4
1536 ± 14
Citrobacter freundii P10159 ***
97.2 ± 0.3
1540 ± 11
93.7 ± 0.5
1545 ± 16
Because the 2D sequencing error rate for MinION reads has been reported at 12% , we performed a sensitivity analysis to determine whether MinION reads with comparable errors could be accurately assigned to an OTU by Discontinuous MegaBLAST. Three 16S rRNA gene sequences from the NCBI database (Stenotrophomonas maltophilia, Comomonas nitrativorans, Comomonas denitrificans) had random errors and indels introduced along the entire length creating copies with similarities ranging from 79 to 100% (Additional file 1: Figure S5). All these test sequences were screened by Discontinuous MegaBLAST as described above and were assigned to the proper source OTU with the appropriate substitution rate (Additional file 1: Table S2).
Direct amplification of 16S rRNA genes from genomic DNA has revolutionized our understanding of the complexity of microbial communities. However, most recent efforts devoted to 16S rRNA gene discovery have focused more on the volume of sequences rather than the length of the sequence obtained from the molecule. It is now common to use high-throughput sequencing methods (e.g., Illumina, Ion Torrent, Pyrosequencing) to generate millions of short reads (often < 200–400 bp) and to report results at the phylum-order-family level. This approach inherently groups all members of a bacterial phyla-order-family together into a single unit and obscures species or strain-level dynamics that may be occurring in an environmental or experimental perturbation (e.g., light, temperature, nutrient addition). In this study, we tested a portable sequencing technology for the ability to distinguish bacterial species or strains in environmental samples. The Oxford MinION sequences single DNA molecules and enables very long reads to be obtained, compared to most second-generation sequencing approaches (> 10,000 bp vs 200 bp). When applied to rRNA gene characterization, this approach can provide nearly full-length rRNA operon sequence data yielding robust species resolution as demonstrated by both the tree topologies and the bootstrap values in Figs. 5 and 6. A comparable phylogenetic analysis using only the V4 and V5 regions of the 16S rRNA gene (~ 400 bp) did not provide robust species node resolution (Additional file 1: Figures S14–S15).
Although MinION sequencing of individual DNA molecules represents a major advance in characterizing entire operons and does not require in silico assembly, it should be noted that the Nanopore R5-R7 error rates (~ 15%; ) are often higher than the error rates for other sequencing systems, such as PacBio (25–160x higher; ). To date, nanopore sequencing has mostly been used to re-sequence known genomes for testing the ability to provide long reads and improve error correction. For example, there are reports of complete genomes being assembled using only R7 Nanopore sequence data with accuracies of 99.5% for Escherichia coli K12  and 99.8% for Francisella strains . Additionally, a combination of nanopore reads recruiting Illumina short reads to create a synthetic consensus for assembly/alignment (NaS fragments up to 60 kb in length) has been described with 99.99% accuracy for Acinetobacter baylyi . However, it is conceivable that the higher MinION error rate could overestimate the number of OTUs that are detected, as has been reported for the V3–V5 regions of the 16S rRNA genes using MiSeq approaches . Using the shorter MiSeq reads, the OTUs in a model bacterial community were overestimated by a factor of 1.1–9.6x, depending on the variable region and chimera removal using UCHIME. It is possible that the large number of singleton OTUs detected by the MinION reflect a higher sequence error. Interestingly, analysis of our DMegaBLAST results from the singleton MinION reads (> 40% query coverage) indicated that the average identity was 81 ± 5% over 1170 ± 150 bp for the 16S rRNA gene (n = 2409). Based on the sensitivity analysis (Additional file 1: Figure S5), the singleton DMegaBLAST results suggest that many of our rare reads are potentially being correctly assigned to an OTU. Furthermore, ONT has recently released R9 flow cells and chemistry for the MinION with improved throughput and lower error rates which promise to enable more accurate OTU assignment and a much greater number of near complete rRNA operons to be assembled from other complex environments.
Finally, other researchers have begun using the MinION to determine near a full-length sequence of 16S rRNA gene amplicons. Most of these studies have also tested model communities to demonstrate proof of concept. Specifically, researchers have tested E. coli K12 , a 3-member bacterial system containing Streptococcus and Parvimonas , a 20-member model community using representative DNA from different bacterial phyla (e.g., Proteobacteria, Firmicutes, Bacteroides, Deinococcus, Actinobacteria) [30, 31]. All studies found that the MinION could provide a nearly full-length sequence of 16S rRNA gene amplicons with accuracies ranging from 80 to 94% and could often obtain species-level resolution. Only a single study has utilized a complex “environmental”-type sample (mouse fecal material)  comparing Illumina and ONT Nanopore sequencing). While Shin et al. (2016) found nearly 1000 OTUs by Illumina methods, and they did not report the number of OTUs in their MinION data. However, they described the identification of more bacterial species using the nanopore data compared with the MiSeq (n = 16), and the authors could demonstrate robust phylogenetic resolution of species of Bifidobacterium and Bacteroides. In contrast with these prior studies, we used purified DNA from complex environment settings (soils and bioreactors), containing a large number of unknown bacterial species and grouped the various MinION sequences by OTU to remove sequencing errors using an iterative consensus building approach.
Our analysis demonstrated that the MinION has the ability to provide rRNA operon sequence data of sufficient quality for characterizing the microbiota of complex environmental samples and provided results that are reproducible, quantitative, and consistent. Over 1000 OTUs could be detected from our test environmental sample mixture. However, further analysis of the errors in rare reads may be necessary to ensure accurate OTU assignment. The long-read capability of MinION allowed for robust bacterial species and strain resolution combining both 16S and 23S rRNA genes, consistent with previous reports [30, 32]. Additionally, improvements in chemistry and library prep have led to increasing accuracy from 66 to 92% within the last few years  and ONT has released a newer version of their analysis software (MinKnow v51.3) that allows for local base calling on the host computer, rather than in the cloud using Metrichor. Given the MinION’s low cost, small size, improving chemistry, and ability to analyze the nucleic acid data in real time, genetic analysis on mobile, and robotic platforms becomes feasible, as connectivity to the web is no longer required to analyze a sequence run.
DNA from Rutgers farm soil and bioreactor samples  were extracted in triplicate (twice) using a modified CTAB extraction method . Briefly, samples were amended with 100 μl of solution 1 (50 mM glucose, 10 mM EDTA, 25 mM Tris-Cl; pH 8.0) and subjected to five quick freeze/thaw cycles between liquid nitrogen temperatures and a 55 °C water bath. After these freeze/thaw cycles, 450 μL CTAB solution (4% CTAB, 100 mM Tris [pH 8.2], 20 mM EDTA, 1.4 M NaCl), 0.14 M β-mercaptoethanol was added. The samples were quickly extracted 2x with 800 μl phenol: chloroform: isoamyl alcohol; 25:24:1 (> pH 7.0). The aqueous phase of each extract was ethanol precipitated with the addition of 2 μg of glycogen. The triplicate extracts were combined into a single stock for the farm soil and bioreactor to create end-member DNAs of very different microbial communities for this study. These end-member DNAs were brought to the same concentration and combined in different ratios, respectively: 0/100, 10/90, 20/80, 50/50, 75/25, and 100/0 (farm soil DNA/bioreactor DNA) for MinION sequencing (Fig. 1). Further DNA purification was done by combining 20 μl of DNA, 20 μl of 6 M NaI, and 20 μl of Ampure beads (Beckman Coulter; Brea, CA, USA). After DNA binding on a vortexer mixer for 10 min, the beads were separated using a magnet and washed twice with freshly made 70% ethanol. DNA elution employed sterile water with a 55 °C treatment for 10 min.
Amplification of rRNA operons
Bacterial ribosomal operons were amplified using modified 16S rRNA-27Forward primer (5′ TTT CTG TTG GTG CTG ATA TTG C-[barcode overhang for PCR labeling]-AGA GTT TGA TCC TGG CTC AG 3′)  and modified 23S rRNA-2241Reverse primer (5′ ACT TGC CTG TCG CTC TAT CTT C-[barcode overhang for PCR labeling]-ACC GCC CCA GTH AAA CT 3′) . Ribosomal operon amplicons were generated using AMPure bead purified DNA as follows: 10 ng template DNA was combined with dNTP’s, five units of Universe High-Fidelity Hot Start DNA polymerase (Biomake LLC, Houston, TX, USA), primers, and PCR buffer. Amplification conditions were 5 min at 94 °C, followed by 27 cycles at 94 °C for 0.5 min and 72 °C for 1.5 min. At 18 cycles, 10 μl of amplification mixture was removed and stored at −80 °C. The amplification was allowed to proceed until 27 cycles and the product was visualized by agarose gel electrophoresis. Once clean, PCR product was observed, the 18 cycle mixture was cleaned with AMPure beads by bringing the volume up to 50 μl with water, adding 50 μl of 5 M NaCl, 50 μl of 30% PEG/1.5 M NaCl, and 7 μl of Ampure Beads. Ethanol washing and resuspension in 10 μl of water were done as described above. Purified DNA (1 μl) after 18 PCR cycles was added to a tube containing the ONT barcodes, and the amplification was repeated.
Library construction for the MinION relies on ligation of adaptor and hairpin to rRNA amplicons in order to perform nanopore sequencing. For this study, 100 ng of each barcoded amplicon were combined (1200 ng total) into DNA Lo-Bind tubes at a volume of 85 μl (by adding reagent grade PCR water) with 10 μl end-repair buffer and 5 μl of the end-repair enzyme (New England Biolabs, Ipswich, MA, USA). After a 20 min incubation at room temperature, the end-repair reaction was concentrated/purified by adding 100 μl of 5 M NaCl, 100 μl of 30% PEG/1.5 M NaCl, and 15 μl of Ampure Beads and allowed to bind for 10 min on a vortex shaker. The beads were removed from the supernatant using a magnet and washed twice with freshly made 70% ethanol. The end-repaired DNA was eluted in 25 μl of water at 55 °C for 10 min and dA-tailing was done by adding 3 μl of tailing buffer and 2 μl of enzyme (NEB) and incubating at 37 °C for 10 min. The DNA was re-purified on AMPure beads using the NaCl/PEG protocol as above and re-suspended in 15 μl of water at 55 °C for 10 min.
For the ligation, “half-reactions” were utilized with slight modifications. Fifteen microliters of DNA was combined with 9 μl of water, 5 μl of ONT adaptor mix, 1 μl of HP adaptor, and 25 μl of Blunt/TA ligase master mix (NEB). Additionally, a critical modification was to add 1–2 μl of freshly prepared ATP solution (~ 4 mg/ml). The mixture was incubated for 10 min at room temperature, then 0.5 μl of HP tether was added, and the reaction was allowed to incubate an additional 10 min. The library was then purified using streptavidin C1 magnetic beads as per ONT instructions with the exception that the elution was done by incubating the bead in 25 μl elution buffer overnight at 4 °C then by a 30-min incubation at 37 °C. The library was loaded into R7 flow cells and run as per the manufacturer’s instructions.
QA/QC on Geneious
After sequencing on the MinION, the 2D reads were opened using Poretools  and the corresponding fastA files were exported. These sequence files were subjected to QA/QC analysis by annotating in Geneious using six pairs each of universal 16S rRNA primer sequences (27F, 343F, 518F, 907F, 1392F, and 1492F)  and 23S rRNA primer sequences (129F, 473F, 820F, 1623F, 2069F, and 2758F) [35, 36]. Only those files between 4 and 5 kb and containing at least two rRNA priming sites were retained for further analysis (~ 85% of the 2D sequences). These files were oriented in a uniform direction, and the 16S rRNA sequences were extracted in Geneious (Additional file 1: Figure S16).
The MinION 16S rRNA genes for each barcode were screened against an NCBI 16S rRNA gene bacterial and archaeal database (Bioproject 33175) using Discontinuous MegaBLAST in Geneious 10.1.2. Settings included a word size of 11, gap cost of 5/2, scoring of 2/−3, and a seed length of 18. The top BLAST output was exported as .csv files and opened in a spreadsheet program (e.g., Numbers, Excel) to group by best BLAST hit, count the number of OTUs, and parse for comparisons across samples. Additionally, the MinION sequences were analyzed by SINA online at the Arb-SILVA website (https://www.arb-silva.de/aligner). Settings included rejecting sequences < 70% identity, search-kmer candidates 100, lca-quorum 0.8, search-kmer length 10 using the SILVA, RDP, and Greengenes RefNR databases.
Thirty sequences representing a single OTU with the same top DMegaBLAST scores were copied into a separate folder and used to build a consensus rRNA operon from the host organism via an iterative LastZ alignment approach . Initially, ten sequences were selected to build a consensus sequence by MUSCLE alignment using Geneious. This consensus was exported as text and imported in Pages to remove gaps. This MUSCLE consensus was used to re-align the original 10 operon sequences into a new LastZ consensus (termed “con 1A”). “Con 1A” was then used to align 20 of the rRNA operon sequences with LastZ to create “con 2A”. “Con 2A” was then used to align 30 of the rRNA operons to create a final consensus. The process was repeated with the next set of operon sequences (e.g., con 1B) and with the final set of 10 sequences (con 1C, etc.) All three final consensus sequences (A, B, C) were assessed for coverage and sequence length to choose a final consensus that best represents the OTU (Fig. 4). This final rRNA consensus sequence was annotated by selecting regions excluding the priming sites and screened by BLAST against the NR database to determine the full extent of the 16S and 23S rRNA genes.
Phylogenetic tree analysis
A maximum likelihood method (FastTree 2.1.5 with default settings) was used to reconstruct phylogenetic trees by first aligning full-length sequence for the ribosomal subunits with MUSCLE. The alignment was edited in Geneious to retain only unambiguously aligned bases (16S rRNA genes-1292 bp and 23S rRNA genes-1767 bp).
The authors wish to thank Oxford Nanopore Technologies for creating the MinION Access Program and supporting the Nanopore Community. Particular thanks to James Breyer and Andy Davies for their interest in our research projects and Concetta Dipace and Mike Micorescu for their helpful suggestions for LastZ data analysis.
This research was primarily funded by the National Science Foundation through an Ocean Technology and Interdisciplinary Program grant to LJK (NSF #1131022).
Availability of data and materials
All data is currently being made available at NCBI SRA (BioProject #PRJNA383904).
LJK conceived and designed the experiments. KPD, LRM, and LJK performed the experiments. LJK and LRM designed the Apple Scripts. LJK analyzed the data. LJK, LRM, and MMH discussed the findings and interpreted the results. LJK, KPD, LRM, and MMH wrote the paper. All authors read and approved the final manuscript.
Ethics approval and consent to participate
The authors declare that they have no competing interests.
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- Stahl DA, Lane DJ, Olsen GJ, Pace NR. Characterization of a Yellowstone hot spring microbial community by 5S rRNA sequences. Appl Environ Microbiol. 1985;49:1379–84.PubMedPubMed CentralGoogle Scholar
- Olsen GJ, Lane DJ, Giovannoni SJ, Pace NR, Stahl DA. Microbial ecology and evolution: a ribosomal RNA approach. Ann Rev Microbiol. 1986;40:337–65.View ArticleGoogle 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;59:695–700.PubMedPubMed CentralGoogle Scholar
- Avaniss-Aghajani E, Jones K, Chapman D, Brunk C. A molecular technique for identification of bacteria using small subunit ribosomal RNA sequences. BioTechniques. 1994;17:144–6.PubMedGoogle Scholar
- Widjojoatmodljo MN, Fluit ADC, Verhoer J. Molecular identification of bacteria by fluorescence-based PCR-single-strand conformation polymorphism analysis of the 16S rRNA gene. J Clin Microbiol. 1995;33:2601–6.Google Scholar
- Sogin ML, Morrison HG, Huber JA, Welch DM, Huse SM, Neal PR, Arrieta JM, Herndl GJ. Microbial diversity in the deep sea and the underexplored “rare biosphere”. Proc Natl Acad Sciences. 2006;103:12115–20.View ArticleGoogle Scholar
- Roesch LFW, Fulthorpe RR, Riva A, Casella G, Hadwin AKM, Kent AD, Daroub SH, Camargo FAO, Farmerie WG, Triplett EW. Pyrosequencing enumerates and contrasts soil microbial diversity. ISME J. 2007;1:283–90.PubMedPubMed CentralGoogle Scholar
- Lazarevic V, Whiteson K, Huse S, Hernandez D, Farinelli L, Østerås M, Schrenzel J, François P. Metagenomic study of the oral microbiota by Illumina high-throughput sequencing. J Microbiol Meth. 2009;79:266–71.View ArticleGoogle Scholar
- Whiteley AS, Jenkins S, Waite I, Kresoje N, Payne H, Mullan B, Allcock R, O'Donnell A. Microbial 16S rRNA ion tag and community metagenome sequencing using the ion torrent (PGM) platform. J Microbiol Meth. 2012;91:80–8.View ArticleGoogle Scholar
- Schneider GF, Dekker C. DNA sequencing with nanopores. Nature Biotech. 2012;30:326–8.View ArticleGoogle Scholar
- Wang Y, Yang Q, Wang Z. The evolution of nanopore sequencing. Front Gen. 2015;449:1–20. doi:10.3389/fgene.2014.00449.Google Scholar
- McGuinness LM, Salganik M, Vega L, Pickering KD, Kerkhof LJ. Replicability of bacterial communities in denitrifying bioreactors as measured by PCR/T-RFLP analysis. Env Science and Tech. 2006;40:509–15.View ArticleGoogle Scholar
- Jiang XT, Peng X, Deng GH, Sheng HF, Wang Y, Zhou HW, Tam NFY. Illumina sequencing of 16S rRNA tag revealed spatial variations of bacterial communities in a mangrove wetland. Microb Ecol. 2013;66:96–104.View ArticlePubMedGoogle Scholar
- Hong C, Si Y, Xing Y, Li Y. Illumina MiSeq sequencing investigation on the contrasting soil bacterial community structures in different iron mining areas. Environ Sci Pollut Res. 2015;22:10788–99.View ArticleGoogle Scholar
- Wu X, Zhang H, Chen J, Shang S, Wei Q, Yan J, Tu X. Comparison of the fecal microbiota of dholes high-throughput Illumina sequencing of the V3–V4 region of the 16S rRNA gene. Appl Microbiol Biotechnol. 2016;100:3577–86.View ArticlePubMedGoogle Scholar
- Li LT, Yan BL, Li SH, Xu JT, An XH. A comparison of bacterial community structure in seawater pond with shrimp, crab, and shellfish cultures and in non-cultured pond in Ganyu. Eastern China Ann Microbiol. 2016;66:317–28.View ArticleGoogle Scholar
- Ye L, Shao MF, Zhang T, Tong AHY, Lok S. Analysis of the bacterial community in a laboratory-scale nitrification reactor and a wastewater treatment plant by 454-pyrosequencing. Water Res. 2011;45:4390–8.View ArticlePubMedGoogle Scholar
- Hand D, Wallis C, Colyer A, Penn CW. Pyrosequencing the canine faecal microbiota: breadth and depth of biodiversity. PLoS One. 2013;8:e53115.View ArticlePubMedPubMed CentralGoogle Scholar
- Wang A, Yao Z, Zheng W, Zhang H. Bacterial communities in the gut and reproductive organs of Bactrocera minax (Diptera: Tephritidae) based on 454 pyrosequencing. PLoS One. 2014;9:e106988.View ArticlePubMedPubMed CentralGoogle Scholar
- Myer PR, MS FHC, TPL S. Evaluation of 16S rRNA amplicon sequencing using two next-generation sequencing technologies for phylogenetic analysis of the rumen bacterial community in steers. J Microbiol Meth. 2016;127:132–40.View ArticleGoogle Scholar
- CLC I, Loose M, Tysone JR, de Cesare M, Brown BL, Jain M, Leggett RM, Eccles DA, Zalunin V, Urban JM, Piazza P, Bowden RJ, Paten B, Mwaigwisya S, Batty EM, Simpson JT, Snutch TP, Birney E, Buck D, Jansen HJ GS, O’Grady J, Olsen HE. MinION analysis and reference consortium: phase 1 data release and analysis. F1000Research. 2015;4:1075.Google Scholar
- Harris RS Improved pairwise alignment of genomic DNA. PhD thesis. The Center for Comparative Genomics and Bioinformatics, The Pennsylvania State University, 501 Wartik Laboratory, University Park, PA 16802; 2007.Google Scholar
- Loman NJ, Watson M. Successful tSest launch for nanopore sequencing. Nat Methods. 2015;12:303–4.View ArticlePubMedGoogle Scholar
- Karlsson E, Lärkeryd A, Sjödin A, Forsman M, Stenberg P. Scaffolding of a bacterial genome using MinION nanopore sequencing. Nature Scientific Reports. 2015; doi:10.1038/srep11996.
- Loman NJ, Quick J, Simpson JT. A complete bacterial genome assembled de novo using only nanopore sequencing data. Nature Meth. 2015; doi:10.1038/nMeth.3444.
- Madoui M-A, Engelen S, Cruaud C, Belser C, Bertrand L, Alberti A, Lemainque A, Wincker P, Aury J-M. Genome assembly using Nanopore-guided long and error-free DNA reads. BMC Genomics. 2015;16:327. doi:10.1186/s12864-015-1519-z.View ArticlePubMedPubMed CentralGoogle Scholar
- Kozich JJ, Westcott SL, Baxter NT, Highlander SK, Schloss PD. Development of a dual-index sequencing strategy and curation pipeline for analyzing amplicon sequence data on the MiSeq Illumina sequencing platform. Appl Environ Microbiol. 2013;79:5112–20.View ArticlePubMedPubMed CentralGoogle Scholar
- Kilianski A, Haas JL, Corriveau EJ, Liem AT, Willis KL, Kadavy DR, Rosenzweig CN, Minot SS. Bacterial and Viral identification and differentiation by amplicon sequencing on the MinION nanopore sequencer. GigaScience. 2015;4:12; doi:10.1186/s13742-015-0051-z.
- Li C, Chang KR, Hui Boey EJ, Qi Ng AH, Wilm A, Nagarajan N. INC-Seq: accurate single molecule reads using nanopore sequencing. GigaScience. 2016;5:34. doi:10.1186/s13742-016-0140-7.View ArticlePubMedPubMed CentralGoogle Scholar
- Benitez-Paez A, Portune KJ, Sanz Y. Species-level resolution of 16S rRNA gene amplicons sequenced through the MinION portable nanopore sequencer. GigaScience. 2016;5:4. doi:10.1186/s13742-016-0111-z.View ArticlePubMedPubMed CentralGoogle Scholar
- Brown BL, Watson M, Minot SS, Rivera MC, Franklin RB. MinIONTM nanopore sequencing of environmental metagenomes: a synthetic approach. GigaScience. 2017;6:1–10.View ArticlePubMedGoogle Scholar
- Shin J, Lee S, Go M-J, Lee SY, Kim SC, Lee C-H, Cho B-K. Analysis of the mouse gut microbiome using full-length 16S rRNA amplicon sequencing. Nature Sci Rep. 2016;6:29681. doi:10.1038/srep29681.View ArticleGoogle Scholar
- Jain M, Olsen HE, Paten B, Akeson M. The Oxford Nanopore MinION: delivery of nanopore sequencing to the genomics community. Genome Biol. 2016;17:239. doi:10.1186/s13059-016-1103-0.View ArticlePubMedPubMed CentralGoogle Scholar
- Männistö MK, Tiirola M, Häggblom MM. Effect of freeze-thaw cycles on bacterial communities of arctic tundra soil. Microb Ecol. 2009;58:621–31.View ArticlePubMedGoogle Scholar
- Lane, D. J. 1991. 16S/23S rRNA sequencing. In: Stackebrandt E and Goodfellow M, editors. Nucleic acid techniques in bacterial systematics. Chichester, England: John Wiley & Sons Ltd.; 1991. p. 115–175.Google Scholar
- Hunt DE, Klepac-Ceraj V, Acinas SG, Gautier C, Bertilsson S, Polz MF. Evaluation of 23S rRNA PCR primers for use in phylogenetic studies of bacterial diversity. Appl Environ Microbiol. 2006;72:2221–5.View ArticlePubMedPubMed CentralGoogle Scholar
- Loman NJ, Quinlan AR. Poretools: a toolkit for analyzing nanopore sequence data. Bioinformatics. 2014;30:3399–401.View ArticlePubMedPubMed CentralGoogle Scholar