Matsuo et al. BMC Microbiology(2021) 21:35
https://doi.org/10.1186/s12866-021-02094-5

METHODOLOGY ARTICLE

Open AccessFull-length 16S rRNA gene ampliconanalysis of human gut microbiota using
MinION™ nanopore sequencing confers
species-level resolution
Yoshiyuki Matsuo1* , Shinnosuke Komiya2,3, Yoshiaki Yasumizu4,5, Yuki Yasuoka5, Katsura Mizushima6,
Tomohisa Takagi6, Kirill Kryukov7,8, Aisaku Fukuda9, Yoshiharu Morimoto2, Yuji Naito6, Hidetaka Okada3,
Hidemasa Bono10,11, So Nakagawa7 and Kiichi Hirota1

Abstract
Background: Species-level genetic characterization of complex bacterial communities has important clinicalapplications in both diagnosis and treatment. Amplicon sequencing of the 16S ribosomal RNA (rRNA) gene has
proven to be a powerful strategy for the taxonomic classification of bacteria. This study aims to improve themethod for full-length 16S rRNA gene analysis using the nanopore long-read sequencer MinION™. We compared it
to the conventional short-read sequencing method in both a mock bacterial community and human fecal samples.
Results: We modified our existing protocol for full-length 16S rRNA gene amplicon sequencing by MinION™. A newstrategy for library construction with an optimized primer set overcame PCR-associated bias and enabled taxonomic
classification across a broad range of bacterial species. We compared the performance of full-length and short-read
16S rRNA gene amplicon sequencing for the characterization of human gut microbiota with a complex bacterial
composition. The relative abundance of dominant bacterial genera was highly similar between full-length andshort-read sequencing. At the species level, MinION™ long-read sequencing had better resolution for discriminating
between members of particular taxa such as Bifidobacterium, allowing an accurate representation of the sample
bacterial composition.
Conclusions: Our present microbiome study, comparing the discriminatory power of full-length and short-readsequencing, clearly illustrated the analytical advantage of sequencing the full-length 16S rRNA gene.
Keywords: 16S rRNA, Gut microbiota, MinION™, Nanopore sequencing* Correspondence: ysmatsuo-kyt@umin.ac.jp
1
Department of Human Stress Response Science, Institute of Biomedical
Science, Kansai Medical University, 2-5-1 Shin-machi, Hirakata, Osaka573-1010, Japan
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which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give
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Matsuo et al. BMC Microbiology(2021) 21:35

Background
Recent advances in DNA sequencing technology have
had a revolutionary impact on clinical microbiology [1].
Next-generation sequencing (NGS) technology enables
parallel sequencing of DNA on a massive scale to generate vast quantities of accurate data. NGS platforms are
now increasingly used in the field of clinical research [2].
Metagenomic sequencing offers numerous advantages
over traditional culture-based techniques that have long
been the standard test for detecting pathogenic bacteria.
This method is particularly useful for characterizing uncultured bacteria and novel pathogens [3].
Among sequence-based bacterial analyses, ampliconsequencing of the 16S ribosomal RNA (rRNA) gene has
proven to be a reliable and efficient option for taxonomic classification [4, 5]. The bacterial 16S rRNA genecontains nine variable regions (V1 to V9) that are separated by highly conserved sequences across different
taxa. For bacterial identification, the 16S rRNA gene isfirst amplified by polymerase chain reaction (PCR) withprimers annealing to conserved regions and then sequenced. The sequencing data are subjected to bioinformatic analysis in which the variable regions are used todiscriminate between bacterial taxa [6].
Since the conventional parallel-type short-read sequencer cannot yield reads covering the full length of
the 16S rRNA gene [7], several regions of it have beentargeted for sequencing, which often causes ambiguity intaxonomic classification [8]. New sequencing platforms
have overcome these technical restrictions, particularly
those affecting read length. A prime example is the MinION™ sequencer from Oxford Nanopore Technologies,
which is capable of producing long sequences with notheoretical read length limit [9–11]. MinION™ sequencing targets the entire 16S rRNA gene, allowing theidentification of bacteria with more accuracy and sensitivity [12, 13]. Furthermore, MinION™ produces sequencing data in real time, which reduces turnaround time
for data processing [14, 15]. Currently, the MinION™ sequencing technology has some drawbacks, such as arelatively lower throughput and higher error rates compared to other sequencing platforms [16]. Despite thelower per read accuracy, the nearly unrestricted read
length provided by the MinION™ sequencer offers promise in sequence-based bacterial analyses.
Given these features of MinION™ sequencing, we had
previously conducted full-length 16S rRNA gene amplicon sequencing analyses using the MinION™ platform
coupled to a bioinformatics pipeline [17, 18] (Additional File 1), which allowed us to identify bacterial
pathogens with a total analysis time of under two hours[19]. However, we also found that the approach of using
the commercial 16S Barcoding Kit (SQK-RAB204) available from Oxford Nanopore Technologies has a limited

Page 2 of 13

ability to detect particular taxa such as Bifidobacterium[19]. This is probably due to sequence mismatches in
the primer used for 16S rRNA gene amplification [20].
Deviations or aberrancies in the Bifidobacterium composition in the human gut have been reported in several
diseases including obesity, allergy, and inflammatory disorders [21]. Based on their putative health-promoting effects, several strains of Bifidobacterium have beenutilized as probiotics [22]. Within these contexts, thespecies-level characterization of Bifidobacterium diversity in human gut microbiota is potentially important inclinical practice.
Our 16S rRNA gene sequence analysis using MinION™
has been tested only with pre-characterized mock bacterial DNA and a limited number of pathogenic bacteria
from a patient-derived sample [23]. Its applicability tohighly complex bacterial communities has not yet beenthoroughly investigated. Therefore, in this study we
modified our existing protocol for 16S rRNA gene
amplicon sequencing by MinION™ and applied it to human gut microbiota with a complex bacterial composition [24], including Bifidobacterium, to determine
whether full-length 16S rRNA gene sequencing withMinION™ is an effective characterization tool.

Results
Classification of the mock bacterial community

The 16S rRNA gene sequence of Bifidobacterium has
three base mismatches with the 27F forward primer provided in the commercial sequencing kit (16S Barcoding
Kit, SQK-RAB204, Oxford Nanopore Technologies;
Additional File 2: Supplementary Fig. S1a), which biases
amplification toward underrepresentation of Bifidobacterium species (Additional File 2: Supplementary Fig. S2,
Additional File 3: Supplementary Table S1-S3). To overcome this drawback, we introduced three degeneratebases to the 16S rRNA gene-specific sequences of theprimer (Additional File 2: Supplementary Fig. S1b). The
competence of the modified primer set was then evaluated by 16S rRNA gene sequence analysis of a tenspecies mock community. The V1-V9 region of the 16S
rRNA gene was amplified by the four-primer PCR
method with the rapid adapter attachment chemistry
and sequenced (Fig. 1a). MinION™ sequencing generated
8651 pass reads (Table 1). Following adapter trimmingand size selection, 6972 reads (80.6% of pass reads withan average read length of 1473 bp) were retained forbacterial identification. Randomly sampled 3000 reads
were aligned against our in-house genome database
GenomeSync [18]. Full-length 16S rRNA gene ampliconsequencing with the modified primer set led to the successful identification of all expected bacterial genera, including Bifidobacterium (Fig. 1b, Additional File 3:
Supplementary Table S4). The dataset was also analyzed

Matsuo et al. BMC Microbiology(2021) 21:35

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Fig. 1 16S rRNA gene sequence analysis using the MinION™ nanopore sequencer. a Workflow of 16S rRNA gene amplicon sequencing on the MinION™
platform. Sequencing libraries are generated by the four-primer PCR-based strategy, enabling simplified post-PCR adapter attachment. At the initial stage of
PCR, the 16S rRNA gene is amplified with the inner primer pairs. The resulting PCR products are targeted for amplification with the outer primers to
introduce the barcode and tag sequences at both ends, to which adapter molecules can be attached in a single-step reaction. b, c Taxonomic
assignments of a mock community analyzed by MinION™ sequencing. The V1-V9 or V3-V4 region of the 16S rRNA gene was amplified from a precharacterized mock community sample comprising ten bacterial species and sequenced on the MinION™ platform. Three thousand reads were randomly
selected from the processed data set and aligned directly to the reference genome database of 5850 representative bacterial species. The pie charts
represent taxonomic profiles at the (b) genus and (c) species levels. Even with the full-length 16S rRNA gene analysis, species-level resolution is notpossible for Bacillus and Escherichia. Slices corresponding to misclassified (assigned to bacteria not present in the mock community) or unclassified (notclassified at the given level but placed in a higher taxonomic rank) reads are exploded. The relative abundance (%) of each taxon is shown

Table 1 MinION™ sequencing statistics for the mock community sample
Primers

Pass reads

Trimmed reads

Filtered reads

No. of reads

Min (bp)

Avg (bp)

Max (bp)

No. of reads

Avg (bp)

No. of reads

Avg (bp)

Q score

V1-V9

8651

237

1497

3292

8455

1367.16972(80.6%)

1473

9.0

V3-V4

101,372

180

585.7

1977

99,937451.8

96,189(94.9%)

454.9

9.2

Min: minimum read length, Avg: average read length, Max: maximum read length, Q score: average Phred quality score. The percentage of reads retained after
size filtering is shown in parentheses

Matsuo et al. BMC Microbiology(2021) 21:35

by using the different bioinformatics workflows, where
the V1-V9 sequences were taxonomically assigned
against either the NCBI (Additional File 4) or SILVA
16S rRNA database (Additional File 5). The three alignment tools produced almost similar taxonomic profiles
for the V1-V9 MiniON™ reads at the family and genus
levels (Additional File 2: Supplementary Fig. S3a, S3b).
The majority of reads were correctly classified down to
the species level, demonstrating the excellent discriminatory power of the full-length sequencing method forbacterial identification (Fig. 1c, Additional File 2: Supplementary Fig. S3c). Bacillus species was an exception in
the analysis with both the GenomeSync and NCBI reference database (the SILVA database does not include species level information), and discrimination of Bacillus
cereus from the closely related species such as Bacillus
anthracis and Bacillus thuringiensis was not achieved(Additional File 4, Additional File 6). Likewise, Escherichia coli was not reliably distinguished from Shigella andother Escherichia species sharing the high 16S rRNA
gene sequence similarity to each other [25, 26], andspecies-level resolution was not possible.
We compared the resolution of full-length and shortread 16S rRNA gene amplicon sequencing for the taxonomic classification of bacteria. The V3-V4 region was
amplified by four-primer PCR from the ten-species
mock community DNA, and the samples were sequenced on MinION™. After removing the adapter/barcode sequences and filtering reads by length, 96,189
reads with an average length of 454.9 bp for downstream
analysis were yielded (Table 1). In contrast to full-length
sequencing with the highest resolution, a significant
number of V3-V4 reads were misclassified or assigned toa higher taxonomic rank (Fig. 1b, c, Additional File 2:
Supplementary Fig. S3). The three alignment tools
worked with some differences in assigning the V3-V4 sequences. This was notable for alignments against the
GenomeSync database, where most V3-V4 reads derived
from Enterococcus faecalis and Escherichia coli were notcorrectly assigned to each taxon, as more than one species produced the same similarity score for the sequence
read queries and the reads were ranked at the lowest
common ancestor (Additional File 3: Supplementary
Table S5, Additional File 7). We could not classify thesebacteria even at the phylum level. The results suggest an
analytical problem such as database errors, which may
give rise to assigning a distantly related organism to thequery sequences. The classifications were not affected byincreasing the number of analyzed reads to 10,000 (Additional File 2: Supplementary Fig. S4, Additional File 3:
Supplementary Table S6). These classification problems
were solved, for the most part, by the V1-V9 long-readsequencing. Thus, regardless of program and database
used, the full-length 16S rRNA gene sequencing

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appeared to give better resolution for bacterial
identification.
For seven of the ten bacterial strains constituting themock community, each subset of V1-V9 sequencing
reads classified to the specific genus was assigned with ahigh degree of accuracy (> 90%) to the corresponding
species against both the GenomeSync (Fig. 2) and NCBI
16S rRNA database (Additional File 2: Supplementary
Fig. S5). V3-V4 short-read sequencing showed a discriminatory power comparable to that of V1-V9 full-length
sequencing in the classification of Deinococcus, Rhodobacter, and Streptococcus. However, the V3-V4 region
was not suitable for species-level identification of some
genera such as Clostridium and Staphylococcus. These
results suggest a lower resolution of the V3-V4 region
for species-level classification, emphasizing the advantage of long-read sequencing for obtaining an accurate
representation of the sample bacterial composition.
Classification of human fecal bacteria

We assessed the performance of our full-length 16S
rRNA gene amplicon sequencing approach in the context of a highly complex bacterial community. The V1V9 region was amplified by four-primer PCR from six
human fecal samples (F1-F6) and analyzed by MinION™
sequencing. (Table 2). The reads were mapped against
the GenomeSync reference database for taxonomic assignment. In Fig. 3, the numbers of species detected areplotted against the numbers of reads analyzed. The
curve started to plateau at around 20,000 reads. There
was a highly significant correlation between the read
numbers 20,000 and 30,000 (Pearson’s correlation coefficient r > 0.999, Additional File 8: Supplementary Table
S7). Based on these observations, randomly sampled 20,
000 reads were used in further analysis to determine thebacterial composition of the human gut.
For comparison, amplicon sequencing of the V3-V4
region was also conducted using the MinION™ (Table 2)
and the Illumina MiSeq™ platform (Table 3). The processed reads from each data set were allocated to the reference bacterial genome using our bioinformatics
pipeline to determine the bacterial compositions (Additional File 9 for V1-V9 MinION™ sequencing, Additional File 10 for V3-V4 MinION™ sequencing, andAdditional File 11 for V3-V4 MiSeq™ sequencing). From
MiSeq™ sequencing data, the bacterial composition was
also analyzed by the operational taxonomic unit (OTU)based approach using the QIIME 2 (ver. 2019.7) pipeline(Additional File 2: Supplementary Fig. S6, Additional File 12) [27, 28]. Although Bacteroides was underrepresented in the OTU-based analysis, the twoanalytical methods (our bioinformatics pipeline andOTU-based method) produced similar taxonomic profiles in the dominant phylotypes for the MiSeq™ data.

Matsuo et al. BMC Microbiology(2021) 21:35

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Fig. 2 Accurate taxonomic assignment afforded by full-length MinION™ sequencing of the 16S rRNA gene. Classification accuracy compared
between full-length (V1-V9) and partial (V3-V4) 16S rRNA gene sequencing data obtained from composition profiling of the ten-species mockcommunity. The donut charts show the proportions of reads correctly assigned to the species constituting the mock community. The percentage
of correctly classified reads is shown in the center hole. ND: not determined (species-level resolution is not possible for Escherichia)

This result confirmed the validity of our method for thetaxonomic classification of the bacterial community.
The three sequencing methods (V1-V9 MinION™ sequencing, V3-V4 MinION™ sequencing, and V3-V4
MiSeq™ sequencing) revealed similar profiles for the six
fecal samples at the genus level (Fig. 4). Statistically significant similarities have been found in the relative genus
abundances across these sequencing methods. Thus, at
the genus level, V1-V9 full-length MinION™ sequencing
exhibited a discriminatory power comparable to that ofhigh-quality short-read sequencing with MiSeq™
technology.
The species-level taxonomic resolution achieved by fulllength sequencing of the 16S rRNA gene using MinION™

While genus classification using long versus short reads
was relatively comparable, we observed considerable differences across amplified regions in the species-levelprofiling of human gut microbiota. As shown in Fig. 5,
the number of ambiguous reads that were not assigned
to species but could be classified at a higher level was
significantly greater in the V3-V4 data set in comparison
than in the V1-V9 data set. The MinION™ V3-V4 data
had the highest proportion of ambiguous reads. In comparison with the V3-V4 reads sequenced on the MiSeq™

platform (Table 3), the MinION™ V3-V4 reads had lower
average quality scores (Table 2). The poorer read quality
gave rise to assigning multiple species to a query sequence, leading to the increased number of reads notclassified at the species level (Additional File 10).
When species compositions of the dominant taxa (Bifidobacterium, Blautia, and Bacteroides) were analyzed,
the V1-V9 and V3-V4 sequencing produced comparable
results for Blautia (Additional File 2: Supplementary Fig.
S7, Additional File 8: Supplementary Table S8) and Bacteroides genus (Additional File 2: Supplementary Fig. S8,
Additional File 8: Supplementary Table S9) in most of
the fecal samples. For Bifidobacterium, there appeared
to be considerable deviations in the relative species
abundances depending on the sequencing method used
(Fig. 6, Additional File 8: Supplementary Table S10).
Notably, most of the Bifidobacterium reads generated by
V1-V9 MinION™ sequencing were classified into the
Bifidobacterium species that were isolated from humansources [21, 29]. A significant number of the V3-V4
reads, however, were assigned erroneously to Bifidobacterium species of non-human origin in the direct read
mapping approach using the relatively shallow GenomeSync reference database (Additional File 2: Supplementary Fig. S9). Consistently, the OTU-based classification

Matsuo et al. BMC Microbiology(2021) 21:35

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Table 2 Statistics of MinION™ sequencing data for human fecal samples
SamplePass reads

Trimmed reads

Filtered reads

No. of reads

Min (bp)

Avg (bp)

Max (bp)

No. of reads

Avg (bp)

No. of reads

Avg (bp)

Q score

F1/N
V1-V9

104,895

186

1521.1

4549

103,100

1386.489,752 (85.6%)

1463.7

9.4

F2/N
V1-V9

84,065169

1393.84253

82,4581259.860,326 (71.8%)

1461.4

9.4

F3/N
V1-V9

76,9681681474.34829

74,4791343.360,713 (78.9%)

1465.5

9.4

F4/N
V1-V9

114,060

168

1541.74836

111,4361410.6

100,569 (88.2%)

1469.9

9.4

F5/N
V1-V9

85,912177

1536.04877

83,0381405.4

74,168 (86.3%)

1474.2

9.4

F6/N
V1-V9

108,938213

1525.1

4866

106,857

1393.5

93,146 (85.5%)

1467.4

9.4

F1/N
V3-V4

52,864

160

568.8

2759

52,283435.2

48,494 (91.7%)

442.5

9.1

F2/N
V3-V4

92,816

174

583.4

2886

91,989442.8

89,016 (95.9%)

444.7

9.2

F3/N
V3-V4

60,200

163

568.5

2062

59,435434.6

55,706 (92.5%)

441.1

9.2

F4/N
V3-V4

83,021

202

578.0

2050

81,734446.1

77,995 (93.9%)

450.0

9.2

F5/N
V3-V4

78,409

167

578.4

1796

76,135447.8

72,526 (92.5%)

453.1

9.2

F6/N
V3-V4

74,931

114

580.3

2246

73,946446.1

71,330 (95.2%)

449.1

9.2

N: Oxford Nanopore MinION™, Min: minimum read length, Avg: average read length, Max: maximum read length, Q score: average Phred quality score. The
percentage of reads retained after size filtering is shown in parentheses

analysis for V3-V4 MiSeq™ data using the QIIME 2 pipeline also revealed a lower resolution of short-read sequencing for taxonomic separation of Bifidobacterium
genus. Except for Bifidobacterium longum, Bifidobacterium species could not be reliably identified by the V3V4 sequencing strategy and they were ranked at thegenus level (Additional File 2: Supplementary Fig. S10).

Fig. 3 16S rRNA gene sequence analysis of human gut microbiota.
Six human fecal samples (F1-F6) were subjected to full-length 16S
rRNA gene amplicon sequencing via MinION™. Numbers of detected
species are plotted against numbers of reads used fortaxonomic classification

These results suggest that MinION™ long-read sequencing, which targets the full-length 16S rRNA gene, canprovide better resolution for discriminating between
members of particular taxa such as Bifidobacterium.

Discussion
16S rRNA gene amplicon sequencing is a powerful strategy for taxonomic classification of bacteria and has been
extensively employed for analyzing samples from environmental and clinical sources [5, 30, 31]. We assessed
the performance of MinION™ sequencing by comparing
the resolution of the V1-V9 and V3-V4 reads for thetaxonomic classification of bacteria. Due to the errorprone nature of MinION™ sequencing, the existing
OTU-based approach, requiring at least 97% sequence
identity threshold, has been considered unsuitable fortaxonomic classification of MinION™ reads [32, 33]. Instead, the reads were analyzed by the direct read mapping method that assigns sequences to taxonomic bins
based on the similarity to a reference database [14, 15].
Long-read MinION™ sequencing with the optimized primer set successfully identified Bifidobacterium species
leading to a better representation of the species composition of the mock community. For improving the classification results, the reads were filtered by length toMatsuo et al. BMC Microbiology(2021) 21:35

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Table 3 Statistics of MiSeq™ sequencing data for human fecal samples
SamplePaired reads

Merged reads

No. of reads

No. of reads

Avg (bp)

Filtered reads
No. of reads

Avg (bp)

Q Score

F1/I
V3-V4

66,242

63,821

449.3

63,778 (96.3%)

449.5

31.4

F2/I
V3-V4

68,824

66,640447.6

66,490 (96.6%)

448.3

32.3

F3/I
V3-V4

132,057

128,095446.9

127,999 (96.9%)

447.1

31.6

F4/I
V3-V4

103,532

100,945451.4

100,853 (97.4%)

451.7

32.8

F5/I
V3-V4

72,13670,521451

70,459 (97.7%)

451.3

33.2

F6/I
V3-V4

52,18250,907449

50,841 (97.4%)

449.5

33.2

I: Illumina MiSeq™, Avg: average read length, Q score: average Phred quality score. The percentage of reads retained after size filtering is shown in parentheses

eliminate those outside the expected size range. Typically, extremely short reads possess only one primerbinding site, suggesting that they are derived from incomplete sequencing. There also exist unexpectedly longer reads with a continuous sequence structure in which
two 16S rRNA gene amplicons are linked end-to-end.
Because these reads can potentially result in unclassified
reads or misclassification, they were eliminated beforealignment to the reference sequences of the bacterial
genome.
We also modified library construction for MinION™
sequencing with a four-primer PCR strategy, which enabled ligase-free adapter attachment to occur in a singlestep reaction. The four-primer PCR generates amplicons
with particular chemical modifications at the 5′ ends towhich adapter molecules can be attached nonenzymatically. Unlike the ligation-based approach, thePCR products amplified by the four-primer method are
subjected directly to the adapter attachment reaction
without repairing their 5′ ends, substantially reducing
the time required for sample preparation. Furthermore,because the protocol is free of Nanopore’s transposasebased technology (e.g. Rapid Sequencing Kit, SQKRAD004) that cleaves DNA molecules to produce chemically modified ends for library construction, the PCR
products are kept intact, enabling sequencing of the entire amplified region. Thus, the four-primer PCR-based
method allowed us to perform amplicon sequencing on
the MinION™ platform with user-defined arbitrary primer pairs, taking advantage of the rapid adapter attachment chemistry. This method can be applied to a wide
range of sequence-based analyses, including detection offunctional genetic markers like antimicrobial resistancegenes and identification of genetic variations in targetedloci [11, 34, 35].
Our present microbiome study, comparing the discriminatory power of the V1-V9 and V3-V4 reads

sequenced on the MinION™ platform, clearly illustrated
the advantage of sequencing the entire 16S rRNA gene.
The full-length 16S rRNA gene sequencing provided
better resolution than short-read sequencing for discriminating between members of certain bacterial taxa,
including Bifidobacterium, Clostridium, Enterococcus,
and Staphylococcus. Consistently, comprehensive microbiome studies using a sequencing data set consisting ofdifferent regions of the 16S rRNA gene have shown thatthe choice of the regions to be sequenced substantiallyaffects the classification results, and some bacterial species are identified only by sequencing the entire 16S
rRNA gene [6, 36, 37]. It is important to note, however,that even full-length 16S rRNA gene analysis fails to discriminate some closely related species such as membersof Bacillus cereus group and Escherichia, indicating thelimitations of the 16S rRNA gene amplicon sequencing
alone in species allocation. Long read sequencing targeting other phylogenetic markers may be an alternative to
16S rRNA gene amplicon sequencing and provide better
resolution for bacterial identification.
In the analysis of the human fecal samples, we used
the taxonomic resolution of the V3-V4 region sequenced
with MiSeq™, which generates highly accurate reads, as abenchmark for the taxonomic resolution of the fulllength 16S rRNA gene sequenced with MinION™. The
relative abundance of dominant bacterial taxa was highly
similar at the genus level between full-length MinION™
and short-read MiSeq™ sequencing. Despite the lower
read quality, the full-length sequencing by MinION™ enabled reliable identification of bacterial genera with anaccuracy comparable to MiSeq™ technology. The MiSeq™
platform enables 16S rRNA gene sequencing on a
massive scale with reduced cost (approximately 20 USD
per sample). Considering a low equipment price (1000USD) and an affordable per-run cost (approximately 50
USD per sample), the MinION™ sequencer could be a

Matsuo et al. BMC Microbiology(2021) 21:35

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Fig. 4 Comparison of taxonomic profiles of human gut microbiota between sequencing methodologies. Six fecal samples (F1-F6) were analyzed
by sequencing the entire 16S rRNA gene using MinION™ (N_V1-V9). For comparison, the V3-V4 region was sequenced on MinION™ (N_V3-V4) or
MiSeq™ platforms (I_V1-V9). Randomly sampled 20,000 reads from each data set were allocated to the reference genome database of 5850
representative bacterial species. A heat map shows the relative genus abundance (%) of classified reads. The 15 most abundant taxa are shown.
The Pearson correlation coefficient (r) between sequencing methods was computed. Asterisks indicate significant correlations at P < 0.05

viable option for practical applications in clinicalmicrobiology.
Our study has some limitations. The use of the
GenomeSync reference database with a limited number of sequences could not allow a comprehensive
survey of bacterial communities. In addition to the
database size, the analysis methods may also influence the identification results. In our bioinformatics
pipeline, a query sequence is directly mapped to thereference genome and assigned to a specific taxon
according to the alignment score. When more than
one taxon is identified, the read is assigned to thelowest common ancestor. It seemed that this approach worked well for the V1-V9 sequencing with
better resolution, and the reads had a higher probability of being assigned down to the species level.
However, as evident in the classification of Enterococcus and Escherichia, the V3-V4 sequenceMatsuo et al. BMC Microbiology(2021) 21:35

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demonstrated the analytical advantage of sequencing
the full-length 16S rRNA gene with MinION™, which
could counteract the lower sequence accuracy andprovide better resolution. With the recent progress in
nanopore sequencing chemistry and base-calling algorithms, sequencing accuracy is continuously improving [38, 39]. This will soon enable us to exploit thefull potential of MinION™ long-read sequencing technology. High-quality long sequences will allow better
discrimination between closely related species insequence-based bacterial analyses.

Methods
Mock bacterial community DNA

Fig. 5 Comparison of taxonomic resolution. The percentages ofambiguous reads not assigned to the species level are plotted forsix fecal samples analyzed by MinION™ (N_V1-V9 and N_V3-V4) or
MiSeq™ (I_V3-V4). Horizontal bars represent mean values. * P < 0.05
(statistically significant)alignment against GenomeSync produced a higher
proportion of ambiguous identification. In the caseof bifidobacteria, non-human species were erroneously identified by the V3-V4 short-read sequencing
in the analysis of human fecal samples. Such improbable errors were not observed in QIIME2 analysis, in which species-level calls were not made andthe reads were ranked at the genus level as more
specific classification was impossible. Due to the low
discriminatory power of the V3-V4 region, the direct
read mapping against a flawed or incomplete reference database may potentially give rise to assigning
an unrelated organism marked as a top hit. Since
the taxonomic assignment is a critical step for analyzing the bacterial diversity and community composition, the reference database quality and thealignment algorithm must be further evaluated foreach sequencing data set.

Conclusions
Our modified protocol for 16S rRNA gene ampliconsequencing overcame known limitations, such as theprimer-associated bias toward the underrepresentationof Bifidobacterium, and enabled taxonomic classification across a broad range of bacterial species. Benchmarking with MiSeq™ sequencing technology

A mixture of bacterial DNA (10 Strain Even Mix
Genomic Material, MSA-1000) was obtained from theAmerican Type Culture Collection (ATCC, Manassas,
VA, USA), comprising genomic DNA prepared fromthe following ten bacterial strains: Bacillus cereus(ATCC 10987), Bifidobacterium adolescentis (ATCC
15703), Clostridium beijerinckii (ATCC 35702), Deinococcus radiodurans (ATCC BAA816), Enterococcus
faecalis (ATCC 47077), Escherichia coli (ATCC
700926), Lactobacillus gasseri (ATCC 33323), Rhodobacter sphaeroides (ATCC 17029), Staphylococcus epidermidis (ATCC 12228), and Streptococcus mutans(ATCC 700610).
Fecal DNADNA was extracted from six human fecal samples
using the NucleoSpin® Microbial DNA Kit (MachereyNagel, Düren, Germany), as described previously [40].
Briefly, human feces stored using the Feces Collection
Kit (Techno Suruga Lab, Shizuoka, Japan) were subjected to mechanical disruption by bead-beating, andDNA was isolated using silica membrane spin columns. Extracted DNA was purified with the Agencourt AMPure® XP (Beckman Coulter, Brea, CA,USA).
16S rRNA gene sequencing on the MinION™ platform

Four-primer PCR with rapid adapter attachment
chemistry generated 16S rRNA gene amplicons withmodified 5′ ends for simplified post-PCR adapter attachment following the manufacturer’s instructions
with slight modifications. For amplification of the V1V9 region of the 16S rRNA gene, the following innerprimers were used, with 16S rRNA gene-specific sequences underlined: forward primer (S-D-Bact-0008-cS-20 [41]) with anchor sequence 5′-TTTCTGTTGG
TGCTGATATTGCAGRGTTYGATYMTGGCTCAG3′ and reverse primer (1492R) with anchor sequence5′-ACTTGCCTGTCGCTCTATCTTCCGGYTACCTTGTTACGACTT-3′. For amplification of the V3-V4

Matsuo et al. BMC Microbiology(2021) 21:35

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Fig. 6 Species composition of Bifidobacterium in six fecal samples. MinION™ V1-V9 sequencing confers species-level resolution for bacterial
composition profiling. Results obtained by the three sequencing methods are shown. The legends show the 14 most abundant
Bifidobacterium species

region, the following inner primers were used, with
16S rRNA gene-specific sequences underlined: 341F
with anchor sequence 5′-TTTCTGTTGGTGCTGATATTGCCCTACGGGNGGCWGCAG-3′ and 806R withanchorsequence5′-ACTTGCCTGTCGCTCTATCTTCGGACTACHVGGGTWTCTAAT-3′. PCR amplification of 16S rRNA genes was conducted using the
KAPA2G™ Robust HotStart ReadyMix PCR Kit (Kapa
Biosystems, Wilmington, MA, USA) in a total volumeof 25 μl containing inner primer pairs (50 nM each)and the barcoded outer primer mixture (3%) from thePCR Barcoding Kit (SQK-PBK004; Oxford Nanopore
Technologies, Oxford, UK). Amplification was performed with the following PCR conditions: initial denaturation at 95 °C for 3 min, 5 cycles of 95 °C for 15s, 55 °C for 15 s, and 72 °C for 30 s, 30 cycles of 95 °C
for 15 s, 62 °C for 15 s, and 72 °C for 30 s, followed bya final extension at 72 °C for 1 min. Amplified DNA

was purified using AMPure® XP (Beckman Coulter)and quantified by a NanoDrop® 1000 (Thermo Fischer
Scientific, Waltham, MA, USA). A total of 100 ng of
DNA was incubated with 1 μl of Rapid Adapter atroom temperature for 5 min. The prepared DNA library (11 μl) was mixed with 34 μl of Sequencing Buffer, 25.5 μl of Loading Beads, and 4.5 μl of water,
loaded onto the R9.4 flow cell (FLO-MIN106; Oxford
Nanopore Technologies), and sequenced on the MinION™ Mk1B. MINKNOW software ver. 1.11.5 (Oxford
Nanopore Technologies) was used for dataacquisition.
16S rRNA gene sequencing on the MiSeq™ platform

Sequencing libraries were constructed as described previously [40]. Briefly, the V3-V4 regions of the 16S rRNA
gene were amplified using a 16S (V3–V4) Metagenomic
Library Construction Kit for NGS (Takara Bio Inc.,

Matsuo et al. BMC Microbiology(2021) 21:35

Kusatsu, Japan). The following primers were used (16SrRNA gene-specific sequences are underlined): 341F
with overhang adapter 5′-TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGCCTACGGGNGGCWGCAG3′ and 806R with overhang adapter 5′-GTCTCGTGGG
CTCGGAGATGTGTATAAGAGACAGGGACTACHVGGGTWTCTAAT-3′. The second PCR was performed using the Nextera® XT Index Kit (Illumina, San
Diego, CA, USA) for sample multiplexing with index
adapters. The libraries were sequenced on the MiSeq™
platform using the MiSeq™ Reagent Kit v3 (2 × 250 bp;
Illumina).
Bioinformatics analysis

Albacore software ver. 2.3.4 (Oxford Nanopore Technologies) was used for basecalling the MinION™ sequencing data (FAST5 files) to generate pass reads(FASTQ format) with a mean quality score > 7. The
adapter and barcode sequences were trimmed using
the EPI2ME Fastq Barcoding workflow ver. 3.10.4(Oxford Nanopore Technologies). The reads were filtered by size using SeqKit software ver. 0.10.0 [42],retaining 1300–1950 bp sequences for the V1-V9 region and 350–600 bp sequences for the V3-V4 region,
based on the size distribution of 16S rRNA gene sequences in the SILVA database ver. 132 [43, 44]. The
average Phred quality score was assessed using NanoPlot ver. 1.27.0 [45]. The processed reads from each
set were analyzed using our bioinformatics pipeline[17], as described previously [14, 15]. Briefly, FASTQ
files were converted to FASTA files. Simple repetitive
sequences were masked using the TANTAN programver. 18 with default parameters [46]. To remove reads
derived from human DNA, we searched each readagainst the human genome (GRCh38) using minimap2
ver. 2.14 with a map-ont-option [47]. Then, unmatched reads were regarded as reads derived frombacteria. For each read, a minimap2 search with 5850
representative bacterial genome sequences stored in
the GenomeSync database (Additional File 1) [18] wasperformed. For each read, the species showing thehighest minimap2 score were assigned to the query
sequence. When more than one species showed thesame similarity score, the reads were classified at any
higher taxonomic rank covering all the identified species. Taxa were determined based on the NCBI taxonomy database [48]. Low-abundance taxa with lessthan 0.01% of total reads were discarded from theanalysis.
Statistical analyses

Differences between groups were evaluated by oneway analysis of variance (ANOVA) followed byDunnett’s test for multiple comparisons. The

Page 11 of 13

Pearson correlation coefficient was computed tocompare the bacterial compositions analyzed by different sequencing methods. Statistical significancewas defined by a P-value < 0.05. Statistical analyses
were performed with Prism8 (GraphPad Software,
Inc. La Jolla, CA, USA).

Supplementary Information
The online version contains supplementary material available at https://doi.
org/10.1186/s12866-021-02094-5.
Additional file 1. Representative bacterial genomes stored in the
GenomeSync database.
Additional file 2 Fig. S1. Sequence heterogeneities of the 27F primerannealing site in 16S rRNA genes. Fig. S2. Evaluation of 16S rRNA PCR
primers for identification of bacterial species. Fig. S3. Classification resultsof a mock community analyzed by the different bioinformatics workflows.
Fig. S4. Effect of read number on taxonomic classification. Fig. S5. Species composition of a mock community analyzed by FASTQ 16S workflow. Fig. S6. Rarefaction curves of observed OTUs in V3-V4 16S rRNA
gene amplicon sequencing of human fecal samples using the MiSeq™
platform. Fig. S7. Species composition of Blautia in human fecal samples.
Fig. S8. Species composition of Bacteroides in human fecal samples. Fig.
S9. Deviations in the relative abundances of Bifidobacterium species inhuman fecal samples. Fig. S10. Comparison of species composition of
fecal Bifidobacterium between classification methods.
Additional file 3 Tables S1-S6. Taxonomic assignment of the mockcommunity analyzed by MinION™ sequencing.
Additional file 4. Mock community data analysis using FASTQ 16S
workflow and the NCBI bacterial 16S rRNA database.
Additional file 5. Mock community data analysis using Kraken 2 andthe SILVA 16S rRNA reference database.
Additional file 6. Alignment search results for V1-V9 amplicon sequencing of the mock community.
Additional file 7. Alignment search results for V3-V4 amplicon sequencing of the mock community.
Additional file 8 Table S7. Correlations between numbers of reads andnumbers of detected species in 16S rRNA gene sequencing of human
fecal samples. Table S8. Comparison of species composition of fecal
Blautia between sequencing methods. Table S9. Comparison of species
composition of fecal Bacteroides between sequencing methods. Table
S10. Comparison of species composition of fecal Bifidobacterium
between sequencing methods.
Additional file 9. Taxonomic profile of human fecal samples fromMinION™ sequencing (amplicons: V1-V9).
Additional file 10. Taxonomic profile of human fecal samples fromMinION™ sequencing (amplicons: V3-V4).
Additional file 11. Taxonomic profile of human fecal samples from
MiSeq™ sequencing (amplicons: V3-V4).
Additional file 12. Taxonomic profiles of human fecal samples from
MiSeq™ sequencing (amplicons: V3-V4, taxonomic classification by OTUbased analysis using the QIIME 2 pipeline).

Abbreviations
NGS: next-generation sequencing; OTU: operational taxonomic unit;
PCR: polymerase chain reaction; rRNA: ribosomal RNA

Acknowledgements
We are grateful to Tadashi Imanishi (Tokai University School of Medicine) andShino Matsukawa (Kyoto University Hospital) for helpful comments anddiscussion. We would like to thank Editage (www.editage.com) for English
language editing.

Matsuo et al. BMC Microbiology(2021) 21:35

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Authors’ contributions
YM1, KK, AF, YM2, YN, SN and KH designed and supervised the study. SK, AF,
YM2, and HO contributed to sample collection. KK and SN built thebioinformatics pipeline. YM1, KM, and TT conducted the experiments. YM1,
YY1, YY2, and SN analyzed the data. YM1 wrote the manuscript. YY1, KK, HB,
SN, and KH contributed to editing the manuscript. All authors read andapproved the final manuscript.

8.

Funding
This work was supported by Japan Society for the Promotion of Science
KAKENHI Grant Number JP19K09339 to YM1 and the branding program as aworld-leading research university on intractable immune and allergic diseases supported by the Ministry of Education, Culture, Sports, Science andTechnology of Japan. The funding bodies had no role in the design of thestudy and collection, analysis, interpretation of data, or manuscript
preparation.

11.

Availability of data and materials
The sequence datasets supporting the conclusions of this article are available
in the DDBJ DRA database (https://www.ddbj.nig.ac.jp/dra/index-e.html)
under accession numbers DRR225043 to DRR225065.

9.
10.

12.

13.

14.

15.
Ethics approval and consent to participate
This study was approved by the Sunkaky Institutional Review Board (No.
2017–27). All participants provided written informed consent.
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.

16.
17.
18.
19.

20.

Author details
1
Department of Human Stress Response Science, Institute of Biomedical
Science, Kansai Medical University, 2-5-1 Shin-machi, Hirakata, Osaka573-1010, Japan. 2HORAC Grand Front Osaka Clinic, Osaka, Japan. 3Obstetrics
and Gynecology, Kansai Medical University Graduate School of Medicine,
Hirakata, Japan. 4Department of Experimental Immunology, Immunology
Frontier Research Center, Osaka University, Osaka, Japan. 5Faculty of
Medicine, Osaka University, Osaka, Japan. 6Molecular Gastroenterology andHepatology, Kyoto Prefectural University of Medicine, Kyoto, Japan.
7
Department of Molecular Life Science, Tokai University School of Medicine,
Isehara, Japan. 8Department of Genomics and Evolutionary Biology, National
Institute of Genetics, Mishima, Japan. 9IVF Osaka Clinic, Osaka, Japan.
10
Database Center for Life Science (DBCLS), Research Organization ofInformation and Systems, Mishima, Japan. 11Program of Biomedical Science,
Graduate School of Integrated Sciences for Life, Hiroshima University,
Higashi-Hiroshima, Japan.

26.

Received: 19 May 2020 Accepted: 18 January 2021

27.

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