Improved High-Throughput Sequencing of the Human Oral Microbiome: from Illumina to Pacbio

Hindawi Canadian Journal of Infectious Diseases and Medical Microbiology Volume 2020, Article ID 6678872, 13 pages https://doi.org/10.1155/2020/6678872

Research Article Improved High-Throughput Sequencing of the Human Oral Microbiome: From Illumina to PacBio

Jie Zhang , Lingkai Su , Yuan Wang , and Shuli Deng

e Aﬃliated Hospital of Stomatology, School of Stomatology, Zhejiang University School of Medicine and Key Laboratory of Oral Biomedical Research of Zhejiang Province, Hangzhou, Zhejiang 310006, China

Correspondence should be addressed to Shuli Deng; [email protected]

Received 21 October 2020; Revised 6 November 2020; Accepted 18 November 2020; Published 11 December 2020

Academic Editor: Tingtao Chen

Copyright © 2020 Jie Zhang et al. +is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Background. A comprehensive understanding of the commensal microflora and its relation to health is essential for preventing and combating diseases. +e aim of this study was to examine the structure of the oral microbiome by using different sequencing technologies. Material and Methods. Five preschool children with no symptoms of oral and systemic diseases were recruited. Samples of saliva were collected. A 468 bp insert size library was constructed on the MiSeq platform and then subjected to 300 bp paired-end sequencing. Libraries with longer insert sizes, including a full-length 16S rDNA gene, were sequenced on the PacBio RS II platform. Results. A total of 122.6 Mb of raw data, including 244,967 high-quality sequences, were generated by the MiSeq platform, while 134.6 Mb of raw data, including 70,030 high-quality reads, were generated by the PacBio RS II platform. Clustering of the unique sequences into OTUs at 3% dissimilarity resulted in an average of 225 OTUs on the MiSeq platform; however, the number of OTUs generated on the PacBio RS II platform was 449, far greater than the number of OTUs generated on the MiSeq platform. A total of 437 species belonging to 10 phyla and 60 genera were detected by the PacBio RS II platform, while 163 species belonging to 12 phyla and 72 genera were detected by the MiSeq platform. Conclusions. +e oral microflora of healthy Chinese children were analyzed. Compared with traditional 16S rRNA sequencing technology, the PacBio system, despite providing a lower amount of clean data, surpassed the resolution of the MiSeq platform by improving the read length and annotating the nucleotide sequences at the species or strain level. +is trial is registered with NCT02341352.

1. Introduction for understanding the biology and functional character- ization of oral microorganisms. +e human oral microbiome comprises over 700 prevalent +e emergence of the next-generation sequencers (NGS) taxa at the species level, including a large number of op- and their sequencing by synthesis have drastically trans- portunistic pathogens involved in periodontal, respiratory, formed the way scientists delve into the relationship between cardiovascular, and systemic diseases [1–5]. Identification microbiome and related diseases [8]. Since then, many of oral microorganisms at the species level is the basis and studies have used the NGS technologies, such as Roche/454 prerequisite for analyzing microbial communities of the [9], ABI/Solid, Illumina [10], and its upgrade platforms oral cavity. +e 16S rRNA gene is considered the gold including Illumina/HiSeq and MiSeq for microbial ecosys- standard for phylogenetic studies of microbial communi- tem analysis [9–15]. When it comes to the resolution and ties and high-throughput sequencing of the 16S rRNA gene accuracy of the sequencing results, lengths and quantity of could provide snapshots of microbial communities, re- reads are very important factors [16–18]. Unfortunately, the vealing phylogeny and the abundances of microbial pop- NGS came with this drawback. Compared with the previous ulations across diverse ecosystems [6, 7]. For this reason, methods (e.g., Sanger sequencing), the reads generated are the sequencing techniques had become an important tool short. +is became a major challenge for the assembly, 2 Canadian Journal of Infectious Diseases and Medical Microbiology especially in the case of large repetitive genomes [19]. +us, 2.2. Saliva Sampling and Isolation of Bacterial DNAs. +e in spite of the low cost and extremely high-throughput, the subjects were instructed neither to eat and drink nor to NGS platform is sometimes less accurate as a result of short perform any oral hygiene procedure two hours before read lengths and long repeats present in multiple copies [17]. sampling. Saliva samples were collected from all subjects in Besides, although the explosion of sequence data brought the morning between 9 : 00 am and 11 : 00 am. about by high-throughput sequencing technologies is Unstimulated saliva samples were collected according to highlighting a richness of microbes not previously antici- a protocol, modified from a previous study. +e children pated, not all of the novel organisms discovered by the NGS were initially asked to rinse their mouth thoroughly with can be named by taxonomists because the existing tools are deionized water prior to sampling, followed by collection of not sufficient to provide species names or phylogenetic at least 5 mL unstimulated saliva in a plastic cup. Finally, the information for the millions of short reads [20]. Operational samples were transferred into sterile cryogenic vials. +en, taxonomic units (OTUs) at the 97% similarity is recognized the samples were placed into liquid nitrogen and stored at as providing differentiation of bacterial organisms below the −80°C until use. genus level [12]; however, it was still inaccurate for the Bacterial DNAs were extracted using the E.Z.N.A.™ Soil reason that this level of clustering defines either microbial DNA Kit (Qiagen, Omega, USA), according to the in- species or strains. structions of the manufacturer. +e enriched microbial +ird-generation sequencing (TGS), PacBio single DNAs were purified by ethanol precipitation. DNA con- molecule, real-time (SMRT) sequencing technology cir- centration was measured using NanoDrop, and its molecular cumvented this problem by greatly increasing read lengths size was estimated by agarose gel electrophoresis. DNAs that have the ability to sequence the full length of the 16S were stored at −20°C until use. rRNA gene [16, 18]. It involves a DNA fragment sequenced by a single DNA polymerase molecule connected to the bottom of a zero-mode waveguide [18]. During DNA 2.3. PCR Amplification of the 16S rRNA Gene. PCR ampli- synthesis, each of the nucleotides is illuminated upon in- fication of the 16S rRNA gene hypervariable V3-V4 regions corporation, which can enable for identification. +e PacBio was performed with universal bacterial primers 338F (5′- RS II can yield average sequence reads of greater than ACTCCTACGGGAGGCAGCA-3′) and 806R (5′-GGAC- 2500 bp; however, some research data show that circular TACHVGGGTWTCTAAT-3′). +e V1-V9 hypervariable consensus sequencing (CCS) of shorter fragments region was performed with primers 27F (5′-AGAGTTT- (<1500 bp) can decrease the sequencing errors [21]. Some GATCCTGGCTCAG-3′) and 1492R (5′- studies have shown that the longer reads generated from GGTTACCTTGTTACGACTT-3′). +e products were sequencing the entire 16S rRNA gene provide a higher extracted with the AxyPrep DNA Gel Extraction kit (Qiagen, resolution of organisms and higher estimates of richness USA) and were then examined by agarose gel electropho- [17]. A previous study has shown that PacBio outperformed resis. According to the electrophoretic results, the PCR the other sequencers such as Roche 454 and MiSeq in terms products were quantified by Quantifluo™-ST (Promega, of the length of contigs and reconstructed the greatest USA). +en, the products from different samples were then portion of the genome when sequencing the genome of mixed at equal ratios for pyrosequencing on the two dif- Vibrio parahaemolyticus [22]. However, there have been few ferent platforms. studies that aim at comparing the next-generation sequencing technology with PacBio RS II in oral microbiome. 2.4. DNA Library Construction and Sequencing. In this study, we explore the microbiota of oral cavity using Construction of DNA library was carried out by following sequences amplified V3-V4 and the V1-V9 small subunit the manufacturer’s instructions (Illumina and PacBio). A ribosomal RNA (16S) hypervariable regions by two different 468 bp insert size library was constructed on the MiSeq platforms. +e aim of this study was to evaluate the per- platform and then applied to 300 bp paired-end sequencing. formance of TGS technology PacBio RS II in comparison Libraries with longer insert size (1540 bp) were performed with NGS technology Illumina/MiSeq for the structure of on the PacBio RS II platform, including full length of 16S oral microbiome in 5 healthy preschool children in China. rDNA gene. Barcoded 16S rRNA amplicons (V3-V4 and V1- V9 hypervariable regions) of the five Chinese children were 2. Materials and Methods sequenced on MiSeq and PacBio RS II platforms, respectively. Raw data were generated, and low-quality reads were 2.1. Patient Information. Five preschool children aged 63–74 then removed by quality control (Figure 1). months, lacking evidence of oral and systematic diseases were recruited based on a list of exclusion criteria on Nov 26, 2014. +e subjects with a history of chronic antibiotic used 2.5. Bioinformatic Analysis. We used QIIME software to within 8 weeks before enrollment were excluded from the cluster filtered reads into operational taxonomic units study. All subjects’ legally authorized representatives pro- (OTUs) from PacBio and MiSeq platforms [23] by applying vided written informed consent upon enrollment. +e study a 97% identity threshold relative to a centroid sequence. +e was approved by the Institutional Review Board of the generated OTUs were used for alpha-diversity (Shannon and Affiliated Stomatology Hospital of Zhejiang University in Simpson), richness (Chao, ACE), coverage, and rarefaction accordance with the Declaration of Helsinki principles. curves using Mothur software (version v.1.30.1) [24]. We Canadian Journal of Infectious Diseases and Medical Microbiology 3

MiSeq PacBio V3-V4 V1-V9 338F 806R 27F 1492R

338F: 5’-ACTCCTACGGGAGGCAGCA-3’ 27F: 5’-AGAGTTTGATCCTGGCTCAG-3’ 806R: 5’-GGACTACHVGGGTWTCTAAT-3’ 1492R: 5’-GGTTACCTTGTTGTTACGACTT-3’

MiSeq PacBio Insert size (bp) 468 Insert size (bp) 1540 Sequencing length (bp) PE300 Sequencing length (bp) Full length of 16SrRNA gene Sample ID Reads Sequences Bases Average length Sample ID Reads Sequences Bases Average length 1 49212 56649 25312520 446.83 1 7894 10656 15570128 1461.2 2 50054 56238 25086400 446.08 2 8790 10772 15866046 1472.9 3 41843 48103 21409856 445.08 3 16198 21044 31096186 1477.7 4 48838 54939 24489025 445.75 4 12350 15542 22794132 1466.6 5 55020 58892 26279440 446.23 5 24798 33310 49260212 1478.8

Figure 1: Sequencing results from 5 oral samples. Partial 16S amplicons (V3-V4) were sequenced on the Illumina/MiSeq; and full-length 16S amplicons (V1-V9) were sequenced on PacBio. then assigned the resulting OTUs using a BLAST-based PacBio RS II platform was 449, almost twice as that of the method implemented in QIIME, employing the SILVA MiSeq platform (Figure 3). Other indices (Chao estimate (version 119) database as the reference for taxonomic and Ace index) revealed that the PacBio RS II platform analysis [25, 26]. +e species-level operational taxonomic detected more species. +e comparisons of alpha-diversity units (OTUs) and relative richness of phylum, class, order, indices (Shannon and Simpson) of the oral microbiota were family, genus, and species for each sample between the two significantly different between the two platforms. +e platforms were compared. Statistical analysis was performed Shannon index of the MiSeq group was lower than that of using SPSS for Windows (version 19.0; SPSS Inc., Chicago, the PacBio RS II group, and the Simpson index of the MiSeq IL, USA). group was higher than that of the PacBio RS II group. It was demonstrated that the PacBio RS II platform exhibited a significant higher level of α-diversity when compared with 3. Results the MiSeq platform (Figure 4). In spite of less clean reads, the PacBio RS II system discovered more species than the MiSeq 3.1. Increased Diversity of Oral Microbiota Sequenced by TGS. sequencing platform (Figures 2 and 3). By high-throughput pyrosequencing of 5 samples syn- chronously on two different platforms, a total of 122.6 Mb raw data including 244,967 high-quality sequences were 3.2. Taxonomic Analysis of Different Platforms. 437 species generated by the MiSeq platform, while 134.6 Mb raw data derived from 10 phyla, 17 classes, 24 orders, 31 families, and including 70,030 high-quality reads were generated by the 60 genera were detected by the PacBio RS II platform, while PacBio RS II platform. For the MiSeq platform, 99.99% of 163 species derived from 12 phyla, 21 classes, 29 orders, 42 the clean reads distribution ranged from 401 to 500 bp, and families, and 72 genera were detected by the MiSeq platform. for the PacBio RS II platform, 94.24% of the clean reads were At the phylum level, Firmicutes, Bacteroidetes, Proteo- distributed from 1401 to 1600 bp. bacteria, Actinobacteria, Fusobacteria, and TM7 shared +e average lengths of quality reads were 446 bp and 95.7% of oral microbiome and 1.17% of oral bacteria cannot 1471 bp on MiSeq and PacBio RS II platforms, respectively. be classified by the MiSeq platform. However, on the Pac- With accurate read lengths of 1471 base pairs, the PacBio BioRS II platform, Firmicutes, Proteobacteria, Bacteroidetes, system opens up the possibility of identifying microor- Fusobacteria, Actinobacteria, and TM7 comprised 99.96% of ganisms to the species level in oral cavity (Figure 1). the community and all of the bacteria were annotation to A slightly higher coverage was observed in the PacBio RS phylum (Figure 5(a)). II platform, and the level of coverage indicated that the 16S +e overall structure of oral microbiota for each plat- rRNA gene sequences identified by the two sequencing form at the phylum level is shown in Figure 6(a). Ten phyla platforms represented the majority of bacterial sequences were shared by the two platforms, and Candidate_divi- present in the oral saliva samples. +e rarefaction curves and sion_SR1 were found only on the MiSeq platform. richness indices (Chao and ACE) that estimated the richness At the class level, the majority of the sequences of MiSeq of the total oral microbiota also show that enough se- belonged to Betaproteobacteria, Bacteroidia, Negativicutes, quencing data were generated by the two platforms (Fig- Actinobacteria, and Bacilli, which contributed 93.3% of the ures 2 and 3). whole community. +e unknown and unclassified class Clustering the unique sequences into OTUs at 3% dis- proportion accounted for 0.79%. For the PacBio platform, similarity resulted in an average of 225 OTUs on the MiSeq Betaproteobacteria, Bacilli, Negativicutes, Gammaproteo- platform; however, the number of OTUs generated on the bacteria, and Epsilonproteobacteria shared 92.9% of oral 4 Canadian Journal of Infectious Diseases and Medical Microbiology

600 300 PacBio 3 550 MiSeq 5 500 250 2 450 35 2 4 400 4 200 1

350 1 300 150 250 Rarefaction Rarefaction 200 100 150 100 50 50 0 0 0 5000 10000 15000 20000 25000 30000 0 5000 10000 15000 20000 25000 30000 35000 40000 45000 50000 55000 60000 Number of reads sampled Number of reads sampled (a) (b)

Figure 2: Rarefaction curves for (a) PacBio and (b) MiSeq platforms. +e average number of OTUs in each sample was calculated. Samples from the two platforms displayed similar phylogenetic diversity at a 97% identity level.

OTU 600 600

400 400 Number

Number 200 200

0 0 OTU ACE Chao 12345 MiSeq MiSeq PacBio PacBio

(a) (b)

Figure 3: Richness of oral saliva. (a) OTU distribution of the 5 samples sequenced by MiSeq and PacBio platforms. (b) Comparison of OTU number and richness indices (Chao and ACE) between PacBio and MiSeq platforms. Different colors indicate different platforms. microbiome and a minuscule proportion (0.25%) of un- be classified by the MiSeq and PacBio platform, respectively known classes was generated (Figure 5(b)). +e overall (Figure 6(d)). structure of oral microbiota for each platform at the class At the genus level, the majority of the sequences of the level was shown in Figure 6(b). Betaproteobacteria two platforms belonged to Neisseria, Prevotella, Veillonella, accounted for the largest proportion of the total community Streptococcus, Haemophilus, and Fusobacterium, which in both of the two groups, while the abundance of the contributed 79.4% and 86.8% of the MiSeq and PacBio abundance of Bacilli and Bacteroidia were different between community. +e unknown and unclassified genera of the the two platforms. MiSeq platform accounted for 0.68% (Figure 5(e)). +e At the order level, Neisseriales, Bacteroidales, Seleno- overall structure and portion of oral microbiota for each monadales, Lactobacillales, Fusobacteriales, Pasteurellales, platform were shown in Figure 6(e). and Clostridiales dominated the community in both groups At the species level, 68 species were shared by the two (Figure 5(c)). +e overall structure and portion of oral platforms and 368 species were detected only by the PacBio microbiota for each platform were shown in Figure 6(c). +e RS II platform. Forty-two genera cannot be classified into unknown and unclassified order proportion sequencing by special strains on the MiSeq platform, which accounted for MiSeq was 0.79%; however, only 0.25% order was unclas- nearly half of the whole community (Figure 6(f)); however, sified by the PacBio platform (Figure 6(c)). only 0.03% of microorganisms were unidentified when using At the family level, Neisseriaceae, Prevotellaceaes, Veil- the PacBio RS II platform. lonellaceae, Streptococcaceae, Pasteurellaceae, and Fuso- Figure 5(f) shows the top 15 species generated by the bacteriaceae shared 82.4% and 88.0% of oral microbiome by two platforms. As is shown in the figure, unlike the other the MiSeq and PacBio platforms, respectively (Figure 5(d)). levels, there was a distinction between the most abundant 0.39% and 0.25% of oral bacteria were unknown or cannot bacteria sequenced by the two platforms. Speculation was Canadian Journal of Infectious Diseases and Medical Microbiology 5

5 Shannon index 0.25 Simpson index

4 0.20

3 0.15

2 0.10

1 0.05

0 0.00 MiSeq PacBio MiSeq PacBio MiSeq MiSeq PacBio PacBio

(a) (b) Coverage 1.05

1.00

0.95

0.90 MiSeq PacBio MiSeq PacBio

(c)

Figure 4: Comparison of α-diversity and coverage between MiSeq and PacBio platforms. (a) Shannon index, which can reﬂect how many OTUs there are in saliva and simultaneously take into account how evenly the OTUs are distributed among the oral microbiome. (b) Simpson index, which is used to measure the degree of concentration when oral microbiota are classiﬁed into OTUs. (c) Coverage, which is calculated from the length of the original genome (G), the number of reads (N), and the average read length (L) as N∗L/G.

Phylum-PacBio Phylum-MiSeq 0.8 0.8

0.6 0.6

0.4 0.4 Percent Percent

0.2 0.2

0.0 0.0 BD1-5 Bacteria_ Firmicutes Firmicutes Tenericutes Candidate_ Candidate_ Candidate_ unclassifed Spirochaetae Fusobacteria Spirochaetae Fusobacteria division_SR1 Bacteroidetes Bacteroidetes division_TM7 division_TM7 Cyanobacteria Cyanobacteria Proteobacteria Proteobacteria Actinobacteria Actinobacteria

PacBio MiSeq MiSeq PacBio (a) Figure 5: Continued. 6

Percent Percent Percent 0.0 0.2 0.4 0.6 0.0 0.2 0.4 0.6 0.0 0.2 0.4 0.6 0.8 MiSeq PacBio MiSeq PacBio Neisseriaceae MiSeq PacBio Lactobacillales Betaproteobacteria

Streptococcaceae Neisseriales Bacilli

Veillonellaceae Selenomonadales Negativicutes

Pasteurellaceae Pasteurellales Gammaproteobacteria Family-PacBio Order-PacBio Class-PacBio Campylobacteraceae Campylobacterales Epsilonproteobacteria

Carnobacteriaceae Clostridiales Clostridia

Family_XI Bacillales Bacteroidia

Lachnospiraceae Fusobacteriales Fusobacteriia Figure Prevotellaceae Bacteroidales Flavobacteriia

Fusobacteriaceae Flavobacteriales Actinobacteria Microbiology Medical and Diseases Infectious of Journal Canadian :Continued. 5: (d) (b) (c) Percent Percent Percent 0.0 0.2 0.4 0.6 0.0 0.2 0.4 0.6 0.8 0.0 0.2 0.4 0.6 PacBio MiSeq PacBio MiSeq Neisseriaceae Neisseriales PacBio MiSeq Betaproteobacteria

Prevotellaceae Bacteroidales Bacteroidia

Veillonellaceae Selenomonadales Negativicutes

Micrococcaceae Lactobacillales Actinobacteria Order-MiSeq Streptococcaceae Micrococcales Bacilli Class-MiSeq Family-MiSeq

Porphyromonadaceae Fusobacteriales Fusobacteriia

Pasteurellaceae Pasteurellales Gammaproteobacteria

Candidate_division Candidate_division Candidate_division_ _TM7_norank _TM7_norank TM7_norank Fusobacteriaceae Clostridiales Clostridia

Leptotrichiaceae Burkholderiales Flavobacteriia Canadian Journal of Infectious Diseases and Medical Microbiology 7

Genus-PacBio Genus-MiSeq 0.6 0.6

0.4 0.4 Percent Percent 0.2 0.2

0.0 0.0 Rothia Gemella Neisseria Neisseria Prevotella Prevotella Veillonella Veillonella Leptotrichia Selenomonas Haemophilus Haemophilus TM7_norank Streptococcus Streptococcus Granulicatella Fusobacterium Fusobacterium Campylobacter Porphyromonas

PacBio Candidate_division_ MiSeq MiSeq PacBio (e) Species-PacBio Species-MiSeq 0.25 0.5

0.20 0.4

0.15 0.3

Percent 0.10 Percent 0.2

0.05 0.1

0.00 0.0 _T3T1 _bacterium _bacterium _bacterium _bacterium ATCC_25996 Haemophilus_ parainfuenzae _ATCC_25845 _prevotella_sp. aromaticivorans Prevotella_pallens Neisseria_subfava Veillonella_atypica Neisseria_mucosa_ Veillonella_parvula Terrahaemophilus_ Prevotella_histicola Streptococcus_mitis Neisseria_favescens Streptococcus_oralis subsp._thermophilus Neisseria_uncultured Neisseria_unclassifed Prevotella_uncultured Gemella_morbillorum Neisseria_sp._R-22841 Veillonella_uncultured Prevotella_unclassifed Veillonella_unclassified Streptococcus_salivarius Streptococcus_sanguinis Campylobacter_concisus Streptococcus_unclassifed Prevotella_melaninogenica Streptococcuse_uncultured Streptococcus_pneumoniae Porphyromonas_uncultured Haemophilus_parainfuenzae Rothia_uncultured_organism Fusobacterium_periodonticum PacBio MiSeq MiSeq PacBio

(f)

Figure 5: +e relative abundance of top 10 phyla, classes, orders, families, and genera and top 15 species. (a) Top 10 phyla. (b) Top 10 classes. (c) Top 10 orders. (d) Top 10 families. (e) Top 10 genera. (f) Top 15 species. that a large proportion of the total bacteria was unclas- As to the species of Campylobacter, 2.2% of the bacteria sified by the MiSeq platform. +e structure and compo- was unclassified by the MiSeq platform. Campylobacter sition of saliva microbiota shown in Figure 7 lists concisus and Campylobacter showae were shared by the two comparison of some species sequenced by the two plat- platforms, and 8 species were unique to the PacBio RS II forms. As is shown in Figure 7, unclassified species ac- platform (Figure 7(b)). counts for a considerable proportion on the MiSeq For species of Rothia, 10.1% of the bacteria was un- platform. +e PacBio RS II platform, by contrast, had classified by the MiSeq platform. Rothia uncultured bacte- higher resolution and could provide more information at rium was the only species shared by the two platforms, and 8 the species level. unique species were generated only by the PacBio RS II For species of Actinomyces, 16.2% of the bacteria was platform (Figure 7(c)). unclassified by the MiSeq platform. Actinomyces odontoly- When it comes to Haemophilus, 5.7% of the bacteria was ticus and Actinomyces uncultured bacterium were shared by unclassified by the MiSeq platform. Haemophilus para- the two platforms, and 7 unique species were generated only haemolyticus, Haemophilus parainfluenzae T3T1, and Hae- by the PacBio RS II platform (Figure 7(a)). mophilus uncultured bacterium were shared by the two 8 Canadian Journal of Infectious Diseases and Medical Microbiology

PacBio MiSeq PacBio MiSeq

Candidate_division_SR1_norank Sphingobacteriia Bacteria_unclassified Candidate_division_SR1 Firmicutes_unclassified Bacteria_unclassified Unknown_Class Tenericutes 1 BD1-5_norank BD1-5 Mollicutes 1 1 Spirochaetes 1 Cyanobacteria 2 Spirochaetae Cyanobacteria Candidate_division_TM7 2 Erysipelotrichia Phylum Fusobacteria Class Epsilonproteobacteria Flavobacteriia Actinobacteria Clostridia Proteobacteria 16 Candidate_division_TM7_norank Bacteroidetes 10 Gammaproteobacteria Firmicutes Fusobacteriia 16 10 Bacilli Actinobacteria Negativicutes Shared phylum Shared class Bacteroidia M-special phylum M-special class Betaproteobacteria Unclassified phylum Unclassified class Unknown class

(a) (b)

PacBio MiSeq PacBio MiSeq Actinomycetaceae Aerococcaceae Bifidobacteriaceae Candidate_division_SR1_norank Candidate_division_TM7_norank Bifidobacteriales Defluviitaleaceae Candidate_division_SR1_norank WCHB1-69 Sphingobacteriales Bacteria_unclassified Firmicutes_unclassified Bacteria_unclassified Lactobacillales_unclassified Firmicutes_unclassified Micrococcales_unclassified Unknown_Order Unknown_Family RF9 RF9_norank BD1-5_norank BD1-5_norank 1 Cardiobacteriales 1 Comamonadaceae Cardiobacteriaceae 1 Spirochaetales 2 4 1 P5D1-392 Pseudomonadales Peptococcaceae 3 Coriobacteriales Spirochaetaceae Corynebacteriales 7 Moraxellaceae Order Cyanobacteria_norank Family Coriobacteriaceae Erysipelotrichales Corynebacteriaceae Bacillales Cyanobacteria_norank 23 Actinomycetales 30 Erysipelotrichaceae Ruminococcaceae Campylobacterales Peptostreptococcaceae 23 Flavobacteriales 30 Family_XIII Burkholderiales Family_XI Clostridiales Carnobacteriaceae Shared order Candidate_division_TM7_norank Shared family Lachnospiraceae M-special order Pasteurellales Campylobacteraceae M-special family Flavobacteriaceae Fusobacteriales Unclassified order Unclassified family Burkholderiaceae Micrococcales Leptotrichiaceae Unknown order Lactobacillales Unknown family Fusobacteriaceae Selenomonadales Pasteurellaceae Bacteroidales Porphyromonadaceae Neisseriales Streptococcaceae Micrococcaceae Veillonellaceae Prevotellaceae Neisseriaceae (c) (d)

PacBio MiSeq 1 Bergeriella Treponema 68 Eikenella Abiotrophia 1 Erysipelotrichaceae_uncultured Moraxella 42 68 Neisseriaceae_uncultured Stomatobaculum Simonsiella Family_XIII_uncultured Trichococcus Candidatus_Saccharimonas Species Tropheryma Catonella Bifidobacterium Atopobium Blvii28_wastewater-sludge_group Cyanobacteria_norank 52 Candidate_division_SR1_norank Solobacterium Corynebacterium Aggregatibacter 368 Defluviitaleaceae_uncultured Actinobacillus 7 Dolosigranulum Bergeyella 8 Filifactor Megasphaera Scardovia Ruminococcaceae_uncultured Shared species 11 Shuttleworthia Kingella M-special species Tannerella Family_XIII_incertae_sedis Genus WCHB1-69_norank Peptostreptococcus P-special genus Bacteria_unclassified Lachnoanaerobaculum Unclassified species Firmicutes_unclassified Oribacterium Lachnospiraceae_unclassified Gemella Unknown species 53 Lactobacillales_unclassified Granulicatella 53 Leptotrichiaceae_unclassified Capnocytophaga Neisseriaceae_unclassified Actinomyces Prevotellaceae_unclassified Campylobacter Veillonellaceae_unclassified Lautropia Shared genus Alysiella Alloprevotella M-special genus RF9_norank Selenomonas BD1-5_norank Leptotrichia P-special genus Mogibacterium Fusobacterium Unclassified genus Comamonas Candidate_division_TM7_norank Johnsonella Haemophilus Peptostreptococcaceae_incertae_sedis Porphyromonas Cardiobacterium Streptococcus Leptotrichiaceae_uncultured Rothia Parvimonas Veillonella Lachnospiraceae_uncultured Prevotella Dialister Neisseria P5D1-392_norank Peptococcus Butyrivibrio (e) (f)

Figure 6: Community structures sequenced by PacBio and MiSeq platforms. represents the number of organisms shared by the two platforms and the detail taxonomy information was shown on the right bar chart. +e star of the same color represents the names of the shared organism. represents the number of organisms generated only by MiSeq platforms and the detail taxonomy information was shown on the right bar chart. +e star of the same color represents the names of the organism only generated by MiSeq. represents the number of unclassiﬁed organism and the detail taxonomy information was shown on the right bar chart. +e star of the same color represents the names of the unclassiﬁed organism. represents the number of unknown organism and the detail taxonomy information was shown on the right bar chart. +e star of the same color represents the names of the unknown organism. represents the number of organisms generated only by PacBio platforms and the detail taxonomy information was shown on the right bar chart. +e star of the same color represents the names of the organism only generated by PacBio. (a–f) represents phylum, class, order, family, genus and species level, respectively. Canadian Journal of Infectious Diseases and Medical Microbiology 9

MiSeq PacBio MiSeq PacBio

2 Campylobacter_unclassified 3 1 Actinomyces_unclassified Campylobacter_rectus_RM3267 Actinomyces_viscosus 1 Campylobacter_uncultured_campylobacter_sp. Actinomyces_uncultured_actinomyces_sp. Campylobacter_uncultured_organism Actinomyces_oris Campylobacter_concisus_13826 Actinomyces 2 Actinomyces_sp._ph3 Campylobacter Campylobacter_concisus_UNSWCD Actinomyces_graevenitzii Campylobacter_uncultured_bacterium Actinomyces_odontolyticus Campylobacter_showae_CC57C Actinomyces_uncultured_bacterium 1 3 Campylobacter_rectus Actinomyces_sp._oral_taxon_180_str._F0310 Campylobacter_showae Actinomyces_sp._oral_clone_CT047 8 Campylobacter_concisus_UNSW1 Actinomyces_graevenitzii_F0530 Campylobacter_concisus 7

Shared Shared Special-M Special-P Special-P Unclassified Unclassified

(a) (b)

MiSeq PacBio MiSeq PacBio

Haemophilus_unclassified Haemophilus_haemolyticus_M19107 Haemophilus_pittmaniae Haemophilus_pittmaniae_HK_85 1 3 Haemophilus_influenzae Haemophilus_uncultured_haemophilus_sp. 1 Rothia_unclassified 1 Haemophilus_uncultured_gamma_proteobacterium Rothia_aeria Haemophilus_influenzae_Rd_KW20 Rothia_dentocariosa_M567 Haemophilus_haemolyticus Rothia_sp._oral_taxon_188 Haemophilus_paraphrohaemolyticus_HK411 Rothia Rothia_sp._THG-T3 Haemophilus Rothia_mucilaginosa_DY-18 Haemophilus_sp._70334 Rothia_uncultured_bacterium Haemophilus_parainfluenzae_T3T1 Haemophilus_sp._oral_clone_BJ021 Rothia_mucilaginosa 3 1 Rothia_dentocariosa Haemophilus_parahaemolyticus Rothia_mucilaginosa_M508 Haemophilus_uncultured_organism 17 Haemophilus_paraphrohaemolyticus 8 Haemophilus_parainfluenzae_ATCC_33392 Haemophilus_sp._oral_taxon_851_str._F0397 Haemophilus_sp._oral_taxon_851 Shared Shared Haemophilus_uncultured_bacterium Special-P Special-P Haemophilus_parainfluenzae Unclassified Unclassified

MiSeq PacBio MiSeq PacBio

Fusobacterium_unclassified 2 Fusobacterium_nucleatum 1 Fusobacterium_sp._oral_clone_ASCF11 Gemella_unclassified Fusobacterium_sp._AC18 Gemella_sp._oral_clone_ASCE02 12 Fusobacterium_nucleatum_subsp._nucleatum_ATCC_23726 Gemella_sp._933-88 Fusobacterium_simiae Gemella_morbillorum_M424 Fusobacterium_uncultured_organism Gemella_uncultured_organism Fusobacterium Fusobacterium_nucleatum_subsp._animalis 1 Gemella 1 Gemella_uncultured_gemella_sp. Fusobacterium_uncultured_bacterium Gemella_sanguinis_M325 Fusobacterium_periodonticum_D10 Gemella_uncultured_bacterium Fusobacterium_nucleatum_subsp._polymorphum Gemella_sanguinis Fusobacterium_nucleatum_subsp._fusiforme Gemella_haemolysans Fusobacterium_periodonticum Gemella_morbillorum Fusobacterium_nucleatum_subsp._nucleatum 12 Fusobacterium_periodonticum_ATCC_33693 9

Shared Shared Special-P Special-P Unclassified Unclassified

(e) (f)

MiSeq PacBio MiSeq PacBio

Veillonella_unclassified Veillonella_rodentium Selenomonas_unclassified Veillonella_sp._2011_Oral_VSA_A8 Selenomonas_sp._oral_clone_DD020 Veillonella_sp._AS16 Veillonella_sp._oral_clone_ASCG01 Selenomonas_flueggei-like_sp._clone_AH132 2 Selenomonas_sp._oral_clone_AJ036 Veillonella_sp._2011_Ileo_VSA_G7 Selenomonas_uncultured_organism Veillonella_sp._oral_clone_ASCG02 1 5 Selenomonas_noxia-like_sp._oral_clone_CI002 Veillonella_tobetsuensis Selenomonas_sp._FOBRC9 Veillonella_sp._2010_Ileo_VSA_VIIc 1 Veillonella_uncultured_veillonellaceae_bacterium 5 Selenomonas_flueggei_ATCC_43531 Selenomonas_flueggei 2 Veillonella_parvula_DSM_2008 Selenomonas Veillonella_caviae Selenomonas_artemidis Veillonella Veillonella_dispar Selenomonas_noxia Veillonella_sp._oral_taxon_780 Selenomonas_sp._oral_clone_FT050 Veillonella_denticariosi Selenomonas_sp._oral_clone_DO042 Veillonella_uncultured_veillonella_sp. Selenomonas_sp._oral_taxon_137 Veillonella_sp._3_1_44 Selenomonas_sp._oral_clone_GI064 Veillonella_atypica 13 Selenomonas_infelix_ATCC_43532 21 Veillonella_rogosae Selenomonas_sputigena_ATCC_35185 Veillonella_dispar_ATCC_17748 Selenomonas_uncultured_selenomonas_sp. Veillonella_uncultured_organism Selenomonas_uncultured_bacterium Shared Shared Veillonella_sp._HPA0037 Special-P Veillonella_parvula Special-P Veillonella_uncultured_bacterium Unclassified Unclassified

(g) (h) Figure 7: Continued. 10 Canadian Journal of Infectious Diseases and Medical Microbiology

MiSeq PacBio

Prevotella_denticola Prevotella_sp._C561 Prevotella_intermedia Prevotella_sp._oral_clone_DO045 Prevotella_maculosa Prevotella_sp._oral_clone_ASCG10 Prevotella_micans Prevotella_oulorum Prevotella_nigrescens Prevotella_tannerae_ATCC_51259 1 Prevotella_oris Prevotella_sp._CD3_34 Prevotella_saccharolytica Prevotella_sp._oral_clone_DO027 Prevotella_sp._oral_clone_DO014 Prevotella_veroralis 11 Prevotella_sp._oral_clone_IK053 Prevotella_sp._SEQ065 11 Prevotella_sp._oral_clone_P4PB_83_P2 Prevotella_sp._oral_clone_BE073 Prevotella Prevotella_unclassified Prevotella_scopos 10 Prevotella_loescheii Prevotella_uncultured_bacterium 15 Prevotella_salivae Prevotella_melaninogenica_D18 Prevotella_sp._oral_clone_FW035 Prevotella_salivae Prevotella_sp._oral_taxon_306_str._F0472 Prevotella_melaninogenica Prevotella_melaninogenica_ATCC_25845 Prevotella_histicola Prevotella_shahii Prevotella_nanceiensis Prevotella_aurantiaca Shared Prevotella_pallens Special-M Prevotella_uncultured_prevotella_sp. Special-P Unclassified

(i) MiSeq PacBio

Neisseria_unclassified Neisseria_elongata_subsp._nitroreducens Neisseria_sp._5060b Neisseria_weaveri 5 Neisseria_sp._SMC-A9199 Neisseria_sp._oral_taxon_014_str._F0314 Neisseria_canis Neisseria_sp._ChDC_B385 Neisseria_elongata_subsp._glycolytica_ATCC_29315 Neisseria_subflava 1 Neisseria_macacae_ATCC_33926 Neisseria_sp._5770 Neisseria_meningitidis_98008 Neisseria_cinerea_ATCC_14685 Neisseria_meningitidis_M04-240196 Neisseria_polysaccharea 5 Neisseria_sp._oral_clone_AP060 Neisseria_uncultured_neisseria_sp. Neisseria Neisseria_meningitidis_WUE_2594 Neisseria_sp._TM10_1 Neisseria_meningitidis_8013 Neisseria_meningitidis_FAM18 Neisseria_weaveri_ATCC_51223 Neisseria_elongata Neisseria_zoodegmatis Neisseria_mucosa Neisseria_sp._oral_clone_AP132 Neisseria_flavescens_SK114 Neisseria_sicca_ATCC_29256 Neisseria_uncultured_organism 35 Neisseria_sp._oral_strain_B33KA Neisseria_meningitidis_Z2491 Neisseria_oralis Neisseria_sp._TM10_4 Neisseria_shayeganii Neisseria_mucosa_ATCC_25996 Shared Neisseria_sp._oral_clone_AP015 Neisseria_sp._R-22841 Special-P Neisseria_sicca_4320 Neisseria_uncultured_bacterium Unclassified Neisseria_flavescens

(j) MiSeq PacBio Streptococcus_unclassified Streptococcus_sp._oral_clone_ASCF09 Streptococcus_pneumoniae_GA47210 Streptococcus_sp._oral_clone_DN025 Streptococcus_sp._2010_Ileo_MS_XIXa Streptococcus_sp._2010_Ileo_MS_XXc Streptococcus_sp._oral_taxon_071 Streptococcus_sp._oral_clone_EK048 Streptococcus_sp._oral_clone_P4PA_30_P4 Streptococcus_infantis_SK1076 Streptococcus_parasanguinis Streptococcus_sp._‘group_G’ Streptococcus_uberis Streptococcus_sp._THG-M4 Streptococcus_uncultured_streptococcus_sp. Streptococcus_sp._oral_clone_ASCD01 Streptococcus_uncultured_organism Streptococcus_sp._oral_strain_B5SC Streptococcus_uncultured_lactobacillales_bacterium Streptococcus_australis_ATCC_700641 Streptococcus_cristatus Streptococcus_sp._oral_clone_ASCE06 2 Streptococcus_uncultured_streptococcaceae_bacterium Streptococcus_sp._2010_Ileo_MS_XVIIb Streptococcus_infantis Streptococcus_constellatus_subsp._constellatus Streptococcus_sp._oral_clone_ASCA09 Streptococcus_cristatus_ATCC_51100 Streptococcus_sanguinis Streptococcus_pasteuri 1 2 Streptococcus_sp._oral_clone_ASCC01 Streptococcus_vestibularis Streptococcus_uncultured_bacterium Streptococcus_anginosus Streptococcus_sp._oral_clone_ASCD09 Streptococcus_sp._oral_clone_ASCC04 Streptococcus Streptococcus_pneumoniae Streptococcus_gordonii Streptococcus_oralis Streptococcus_sp._oral_clone_ASCB12 Streptococcus_sp._oral_clone_ASCE01 Streptococcus_pseudopneumoniae Streptococcus_mitis Streptococcus_constellatus Streptococcus_sp._oral_clone_FP015 Streptococcus_uncultured_firmicutes_bacterium Streptococcus_sp._oral_clone_GM006 Streptococcus_thermophilus_ND03 Streptococcus_salivarius_subsp._thermophilus Streptococcus_dysgalactiae 77 Streptococcus_peroris Streptococcus_sp._HPH0090 Streptococcus_pneumoniae_GA41565 Streptococcus_pyogenes Streptococcus_pneumoniae_SPN034156 Streptococcus_pneumoniae_GA13856 Shared Streptococcus_sp._oral_clone_ASCD10 Streptococcus_salivarius Special-P Streptococcus_sp._oral_clone_CH016 Streptococcus_pneumoniae_ATCC_700669 Unclassified Streptococcus_suis Streptococcus_intermedius Streptococcus_sanguinis_ATCC_29667 Streptococcus_rubneri Streptococcus_sp. Streptococcus_sp._2010_Ileo_MS_XVId Streptococcus_sp._oral_clone_DP009 Streptococcus_sp._oral_clone_FO042 Streptococcus_sp._oral_clone_GK051 Streptococcus_pneumoniae_GA13637 Streptococcus_lactarius Streptococcus_sp._oral_taxon_056 Streptococcus_sp._oral_clone_ASCE03 Streptococcus_oligofermentans Streptococcus_infantis_X Streptococcus_sp._106-03c Streptococcus_infantis_SK970 Streptococcus_equinus Streptococcus_sp._oral_clone_AA007 Streptococcus_australis (k)

Figure 7: Structure and composition of some particular species sequenced by PacBio and MiSeq platforms. +e outer ring of the chart represents the number of species sequenced by the PacBio platform. +e inner ring, on the opposite, represents the number of species sequenced by the MiSeq platform. represents the number of species shared by the two platforms, and the detail information was shown on the right bar chart. +e star of the same color represents the name of the shared species. represents the number of species generated only by the MiSeq platform, and the detail information was shown on the right bar chart. +e star of the same color represents the name of the species only generated by MiSeq. represents the number of unclassified species, and the detail information was marked on the right bar chart. +e star of the same color represents the name of the unclassified species. represents the number of species generated only by the PacBio platform, and the detail information was shown on the right bar chart. +e star of the same color represents the name of the species only generated by PacBio. (a–k) represents the species of Actinomyces, Campylobacter, Rothia, Haemophilus, Fusobacterium, Gemella, Selenomonas, Veillonella, Prevotella, Neisseria, and Streptococcus, respectively. Canadian Journal of Infectious Diseases and Medical Microbiology 11 platforms, and 17 unique species were generated only by the 4. Discussion PacBio RS II platform (Figure 7(d)). For species of Fusobacterium, which are among the most A number of research studies have presented evidence for abundant bacteria in healthy oral cavity, 10.8% of the using childhood oral microbiome to predict future oral and bacteria was unclassified by the MiSeq platform. Fuso- systemic diseases [30]. +erefore, it is very important for us bacterium periodonticum and Fusobacterium uncultured to find a suitable sequencing method to study oral micro- bacterium were shared by the two platforms, and 12 unique biome. In this study, the oral saliva microbiome of five species were generated only by the PacBio RS II platform healthy Chinese children was evaluated using the NGS and (Figure 7(e)). TGS. +e oral microbiome composition sequenced by the Figure 7(f) shows the composition of Gemella sequenced two platforms was basically identical from phylum to genus by different platforms. +e comparison of sequencing results level. +e structure of oral microbiome at the species level, between MiSeq and PacBio RS II indicates that Gemella however, showed a significant difference between the two haemolysans was the only species shared by both the plat- platforms. +e possible reason we speculate is that a large forms and up to 98.8% species were unclassified by the amount of short reads generated by the MiSeq platform MiSeq platform. Nine unique species were generated only by cannot be resolved in spite of the development of the as- the PacBio RS II platform. semblers, such as the Celera Assembler, SOAPdenovo, and For species of Selenomonas, 8.6% of the bacteria was Allpath-LG. As a result, a very large proportion of bacteria unclassified by the MiSeq platform. Five species including was unclassified by the MiSeq sequencing technology. +e Selenomonas uncultured organism, Selenomonas flueggei, longer reads sequenced on the PacBio platform gave more Selenomonas artemidis, Selenomonas noxia, and Selenomo- phylogenetic resolution than 400–500 bp fragments that nas sputigena ATCC 35185 were shared by the two platforms, contain fewer hypervariable regions. and 7 unique species were generated only by the PacBio RS II Compared with our previous study on the structure of platform (Figure 7(g)). oral microbiome in healthy children, the top 10 phyla, As to species of Veillonella, 78.1% of the bacteria was genera, and species are consistent [18]. However, when unclassified by the MiSeq platform. Veillonella atypica and compared with other studies, there are some differences with Veillonella sp. oral taxon 780 were shared by the two our results [31]. In this respect, we speculated that oral platforms, and 21 unique species were generated only by the microbiome is linked to age, race, and region at the species PacBio RS II platform (Figure 7(h)). level. Some studies have also demonstrated that the oral For species of Prevotella, 10.5% of the bacteria was microbiota are better defined based on age, gender, oral unclassified by the MiSeq platform. 11 species including niches, and even the body size [32, 33]. Recent findings Prevotella loescheii, Prevotella salivae, Prevotella sp. oral indicate that the oral ecosystem of healthy children is highly clone FW035, Prevotella sp. oral taxon 306 str. F0472, heterogeneous and dynamic with substantial changes in Prevotella melaninogenica ATCC 25845, Prevotella histi- microbial composition over time and only few taxa per- cola, Prevotella shahii, Prevotella nanceiensis, Prevotella sisting across the age [34]. PacBio RS II sequencing, one aurantiaca, Prevotella pallens, and Prevotella uncultured platform of TGS, has the ability to provide longer sequences prevotella sp. were shared by the two platforms. +e and reads generated from sequencing the entire 16S rRNA number of unique species generated by the MiSeq and gene. Compared with the previous NGS, this platform can PacBio RS II platform were 15 and 11, respectively establish a higher estimate of richness and provide the ability (Figure 7(i)). to identify organisms at a higher taxonomic and phyloge- For species of Neisseria, which are the most abundant netic resolution [17, 18, 35]. At the same time, some studies species of the community in this study, 71.7% of the bacteria have shown that the PacBio sequencing error rate is in the was unclassified by the MiSeq platform. Five species in- same range of the previously widely used Roche 454 se- cluding Neisseria sp. oral strain B33KA, Neisseria oralis, quencing platform and the current MiSeq platform [36, 37]. Neisseria subflava, Neisseria elongata, and Neisseria fla- More importantly, a recent study presented a high- vescens were shared by the two platforms. +e number of throughput amplicon sequencing methodology based on unique species generated by PacBio RS II platform was up to PacBio CCS that measures the full-length 16S rRNA gene 35 (Figure 7(j)). with a near-zero error rate [38]. Streptococcus is a gram-positive bacterium belonging Compared with the traditional 16S rDNA sequencing of to the phylum Firmicutes, which is found to be associated the MiSeq platform, the PacBio RS II technology improved with many kinds of oral diseases, such as caries [18, 27], its read length and annotated the nucleotide sequence of oral pneumonia, bacteremia, and meningitis [28, 29]. In this bacteria to the species level. PacBio RS II may be optimal for study, 73.4% of Streptococcus was unclassified by the oral microbiome sequencing due to its long reads and high MiSeq platform. Only the two species Streptococcus performance, while platforms such as Illumina MiSeq will intermedius and Streptococcus sanguinis were shared by provide cost-efficient methods for sequencing projects. the two platforms. +e number of unique species gen- Previous research studies had compared the TGS PacBio erated by the PacBio RS II platform was up to 77 platform with the NGS Roche 454 pyrosequencing platform. (Figure 7(k)). Amplicons of the 16S rRNA gene from the environmental 12 Canadian Journal of Infectious Diseases and Medical Microbiology samples from streambed habitats, rocks, sediments, and a its implication in oral and systemic diseases,” Advances in riparian zone soil were analyzed [16, 17]. In this study, we Applied Microbiology, vol. 97, pp. 171–210, 2016. focus on the oral microbiome of healthy Chinese children [5] Z. Cheng, J. Meade, K. Mankia, P. Emery, and D. A. Devine, and compare the amplicons of the 16S rRNA gene between “Periodontal disease and periodontal bacteria as triggers for PacBio and MiSeq platforms. As the exact composition of rheumatoid arthritis,” Best Practice and Research Clinical the microbiome from the five Chinese children were un- Rheumatology, vol. 31, no. 1, pp. 19–30, 2017. known, it is still difficult to assess the accuracy of the PacBio [6] Z. D. Kurtz, C. L. Muller, E. R. Miraldi et al., “Sparse and compositionally robust inference of microbial ecological RS II platform at the species level. Next, we would enroll a networks,” PLoS Computational Biology, vol. 11, no. 5, Article known isolate as a positive control in high-throughput se- ID e1004226, 2015. quencing, which can provide the quality assurance of [7] P. I. Costea, F. Hildebrand, M. Arumugam et al., “Enterotypes quantifying error rates when analyzing environmental in the landscape of gut microbial community composition,” communities. Nature Microbiology, vol. 3, no. 1, pp. 8–16, 2018. [8] B. Royer-Bertrand and C. Rivolta, “Whole genome se- 5. Conclusions quencing as a means to assess pathogenic mutations in medical genetics and cancer,” Cellular and Molecular Life In our study, oral microbiome of healthy Chinese children Sciences, vol. 72, no. 8, pp. 1463–1471, 2015. was explored. For oral microbiome studies, if the goal is [9] M. L. Metzker, “Sequencing technologies - the next genera- identifying all species in a sample, PacBio appears to have tion,” Nature Reviews Genetics, vol. 11, no. 1, pp. 31–46, 2010. superior performance to MiSeq. However, if the goal is to [10] J. M. Shin, T. Luo, K. H. Lee et al., “Deciphering endodontic simply quantify relative differences in diversity, either microbial communities by next-generation sequencing,” Journal of Endodontics, vol. 44, no. 7, pp. 1080–1087, 2018. platform would be appropriate. In this article, we have [11] S. Huang, R. Li, X. Zeng et al., “Predictive modeling of compared the difference between the two platforms, how- gingivitis severity and susceptibility via oral microbiota,” e ever, with the limited sample size, the study does not provide ISME Journal, vol. 8, no. 9, pp. 1768–1780, 2014. a statistic conclusion, and more in-depth studies with larger [12] J. Zhou, X. Zhou, Y. Li, and J. Xing, “Bacterial communities in group sizes are needed to validate these results. haloalkaliphilic sulfate-reducing bioreactors under different electron donors revealed by 16S rRNA MiSeq sequencing,” Data Availability Journal of Hazardous Materials, vol. 295, pp. 176–184, 2015. [13] K.-T. Lee, W.-H. Chung, S.-Y. Lee et al., “Whole-genome +e data sets used and/or analyzed during the current study resequencing of Hanwoo (Korean cattle) and insight into available from the corresponding author on reasonable regions of homozygosity,” BMC Genomics, vol. 14, no. 1, request. +e authors have deposited the raw data and clean p. 519, 2013. data (after QC and human reads removal) to the NCBI under [14] Y. S. Bukin, Y. P. Galachyants, I. V. Morozov et al., “+e effect accession number PRJNA445629. of 16S rRNA region choice on bacterial community meta- barcoding results,” Scientific Data, vol. 6, Article ID 190007, Conflicts of Interest 2019. [15] S. Goodwin, J. D. McPherson, and W. R. McCombie, +e authors declare that there are no conflicts of interest. “Coming of age: ten years of next-generation sequencing technologies,” Nature Reviews Genetics, vol. 17, no. 6, Acknowledgments pp. 333–351, 2016. [16] J. J. Mosher, E. L. Bernberg, O. Shevchenko, J. 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