Microbiota profiling with long amplicons using Nanopore sequencing: full-length 16S rRNA gene and the 16S-ITS-23S of the rrn operon

Background: Profiling the microbiome of low-biomass samples is challenging for metagenomics since these samples are prone to contain DNA from other sources (e.g. host or environment). The usual approach is sequencing short regions of the 16S rRNA gene, which fails to assign taxonomy to genus and species level. To achieve an increased taxonomic resolution, we aim to develop long-amplicon PCR-based approaches using Nanopore sequencing. We assessed two different genetic markers: the full-length 16S rRNA (~1,500 bp) and the 16S-ITS-23S region from the rrn operon (4,300 bp). Methods: We sequenced a clinical isolate of Staphylococcus pseudintermedius, two mock communities and two pools of low-biomass samples (dog skin). Nanopore sequencing was performed on MinION™ using the 1D PCR barcoding kit. Sequences were pre-processed, and data were analyzed using EPI2ME or Minimap2 with rrn database. Consensus sequences of the 16S-ITS-23S genetic marker were obtained using canu. Results: The full-length 16S rRNA and the 16S-ITS-23S region of the rrn operon were used to retrieve the microbiota composition of the samples at the genus and species level. For the Staphylococcus pseudintermedius isolate, the amplicons were assigned to the correct bacterial species in ~98% of the cases with the16S-ITS-23S genetic marker, and in ~68%, with the 16S rRNA gene when using EPI2ME. Using mock communities, we found that the full-length 16S rRNA gene represented better the abundances of a microbial community; whereas, 16S-ITS-23S obtained better resolution at the species level. Finally, we characterized low-biomass skin microbiota samples and detected species with an environmental origin. Conclusions: Both full-length 16S rRNA and the 16S-ITS-23S of the rrn operon retrieved the microbiota composition of simple and complex microbial communities, even from the low-biomass samples such as dog skin. For an increased resolution at the species level, targeting the 16S-ITS-23S of the rrn operon would be the best choice.


Introduction
The microbiota profile of low-biomass samples such as skin is challenging for metagenomics. These samples are prone to contain DNA contamination from the host or exogenous sources, which can overcome the DNA of interest 1,2 . Thus, the usual approach is amplifying and sequencing certain genetic markers that are ubiquitously found within the studied kingdom rather than performing metagenomics. Ribosomal marker genes are a common choice: 16S rRNA and 23S rRNA genes to taxonomically classify bacteria 3,4 ; and ITS1 and ITS2 regions for fungi 5,6 .
Until now, most studies of microbiota rely on massive parallel sequencing, and target a short fragment of the 16S rRNA gene, which presents nine hypervariable regions (V1-V9) that are used to infer taxonomy 7,8 . The most common choices for host-associated microbiota are V4 or V1-V2 regions, which present different taxonomic coverage and resolution depending on the taxa 9,10 .
Apart from the biases derived from the primer choice, short fragment strategies usually fail to assign taxonomy reliably at the genus and species level. This taxonomic resolution is particularly useful when associating microbiota to clinics such as in characterizing disease status or when developing microbiotabased products, such as pre-or pro-biotics 11 . For example, in human atopic dermatitis (AD) the signature for AD-prone skin when compared to healthy skin was enriched for Streptococcus and Gemella, but depleted in Dermacoccus. Moreover, nine different bacterial species were identified to have significant AD-associated microbiome differences 12 . In canine atopic dermatitis, Staphylococcus pseudintermedius has been classically associated with the disease. Microbiota studies of canine atopic dermatitis presented an overrepresentation of Staphylococcus genus 13,14 , but the species was only confirmed when complementing the studies using directed qPCRs for the species of interest 13 or using a Staphylococcus-specific database and V1-V3 region amplification 14 .
With the launching of single-molecule technology sequencers (e.g. PacBio or Oxford Nanopore Technologies), these shortlength associated issues can be overcome by sequencing the full-length 16S rRNA gene (~1,500 bp) or even the nearlycomplete rrn operon (~4,300 bp), which includes the 16S rRNA gene, ITS region, and 23S rRNA gene.
Several studies assessing the full-length 16S rRNA gene have already been performed using Nanopore sequencing to: i) characterize artificial bacterial communities (mock community) 15-17 ; ii) complex microbiota samples, from the mouse gut 18 , wastewater 19 , microalgae 20 and dog skin 21 ; and iii) the pathogenic agent in a clinical sample [22][23][24] . Some studies have been performed using the nearly-complete rrn operon to characterize mock communities 25 and complex natural communities 26 .
Here, we aim to assess these two long-amplicon approaches using MinION TM (Oxford Nanopore Technologies), a singlemolecule sequencer that is portable, affordable with a small budget and offers long-read output. Its main limitation is a higher error rate than massive sequencing. We will test our approaches by sequencing several samples with different degrees of complexity: i) a clinical isolate of Staphylococcus pseudintermedius, ii) two bacterial mock communities; and iii) two complex skin microbiota samples.

Samples and DNA extraction
We first sequenced a pure bacterial isolate of S. pseudintermedius obtained from the ear of a dog affected by otitis.
Then, we used two DNA mock communities as simple and well-defined microbiota samples: • HM-783D, kindly donated by BEI resources, containing genomic DNA from 20 bacterial strains with staggered ribosomal RNA operon counts (between 10 3 and 10 6 copies per organism per μl).
• ZymoBIOMICS™ Microbial Community DNA standard that contained a mixture of genomic DNA extracted from pure cultures of eight bacterial strains.
As a complex microbial community, we used two DNA sample pools from the skin microbiota of healthy dogs targeting two different skin sites: i) dorsal back (DNA from two dorsal samples from Beagle dogs); and ii) chin (DNA from five chin samples from Golden Retriever/Labrador crossed dogs). Skin microbiota samples were collected using Sterile Catch-All™ Sample Collection Swabs (Epicentre Biotechnologies) soaked in sterile SCF-1 solution (50 mM Tris buffer (pH 8), 1 mM

Amendments from Version 1
We hereby present a revised version of our manuscript, based on the comments made by the referees and more results since version 1 was published.
The main changes of the manuscript when compared to version 1 are those stated here: -We have assessed the performance of the mapping approach with the uncorrected long-amplicons strategy presented here.
-We have expanded the results adding a section of the de novo assembly of the 16S-ITS-23S genetic marker.
-We have provided more detail on the methodology used from the lab bench to the bioinformatics part, with supplementary file 1 providing the commands used for the mapping approach.
-The mapping results have been updated since in version 1 chimeras were computed differently for each amplicon and led to confusing results (more chimera for 16S rRNA than for 16S-ITS-23S). Now for both amplicons, we have performed base-level alignment using Minimap2 as the first step before yacrd (detailed in Supplementary File 1).
-The figures and tables have been simplified, re-ordered and completed to be more informative and clear. We got rid of the graphic bars and kept the heatmaps. Finally, we want to thank all the referees for their time to evaluate our work and their invaluable feedback.

REVISED
EDTA, and 0.5% Tween-20). DNA was extracted from the swabs using the PowerSoil™ DNA isolation kit (MO BIO) and blank samples were processed simultaneously (for further details on sample collection and DNA extraction see 27).

PCR amplification of ribosomal markers
We evaluated two ribosomal markers in this study: the fulllength 16S rRNA gene (~1,500 bp) and the 16S-ITS-23S region of the ribosomal operon (rrn) (~4,300 bp). Before sequencing, bacterial DNA was amplified using a nested PCR, with a first PCR to add the specific primer sets tagged with the Oxford Nanopore universal tag and a second PCR to add the barcodes from the PCR barcoding kit (EXP-PBC001) (Supplementary Table 1). Each PCR reaction included a no-template control sample to assess possible reagent contamination.
For the first PCR, we targeted: i) the full-length 16S rRNA gene using 16S-27F 28 and 16S-1492R 29 primer set and ii) the 16S-ITS-23S of the rrn operon using 16S-27F and 23S-2241R 28 primer set (Supplementary Table 1). All the three primers contained the Oxford Nanopore tag, which is an overhang that allows barcoding the samples during the second PCR. Again, the amplicons were cleaned-up with the AMPure XP beads (Beckman Coulter) using a 0.5X and 0.45X ratio for the 16S rRNA gene and the whole rrn operon, respectively. For each sample, quality and quantity were assessed using Nanodrop and Qubit™ fluorometer (Life Technologies, Carlsbad, CA), respectively. The samples with higher DNA concentrations were checked by agarose gel to see the size profile of the PCR products (Supplementary Figure 1).
The different barcoded samples were pooled in equimolar ratio to obtain a final pool (1,000-1,500 ng in 45 μl) to do the sequencing library. In few cases, 16S-ITS-23S amplicons did not reach the initial amount of required DNA and we proceeded with lower input material.

Nanopore sequencing library preparation
The Ligation Sequencing Kit 1D (SQK-LSK108; Oxford Nanopore Technologies) was used to prepare the amplicon library to load into the MinION TM (Oxford Nanopore Technologies), following the manufacturer's protocol. Input DNA samples were composed of 1-1.5 μg of the barcoded DNA pool in a volume of 45 μl and 5 μl of DNA CS (DNA from lambda phage, used as a positive control in the sequencing). The DNA was processed for end repair and dA-tailing using the NEBNext End Repair/dA-tailing Module (New England Biolabs). A purification step using 1X Agencourt AMPure XP beads (Beckman Coulter) was performed.
For the adapter ligation step, a total of 0.2 pmol of the endprepped DNA were added in a mix containing 50 μl of Blunt/TA ligase master mix (New England Biolabs) and 20 μl of adapter mix and then incubated at room temperature for 10 min. We performed a purification step using Adapter Bead Binding buffer (provided in the SQK-LSK108 kit) and 0.5X Agencourt AMPure XP beads (Beckman Coulter) to finally obtain the DNA library.
We prepared the pre-sequencing mix (14 μl of DNA library) to be loaded by mixing it with Library Loading beads (25.5 μl) and Running Buffer with fuel mix (35.5 μl). We used two SpotON Flow Cells Mk I (R9.4.1) (FLO-MIN106). After the quality control, we primed the flowcell with a mixture of Running Buffer with fuel mix (RBF from SQK-LSK108) and Nuclease-free water (575 μl + 625 μl). Immediately after priming, the nanopore sequencing library was loaded in a dropwise fashion using the SpotON port.
Once the library was loaded, we initiated a standard 48 h sequencing protocol using the MinKNOW™ software v1.15.

Data analysis workflow
The samples were run using the MinKNOW software. After the run, fast5 files were base-called and de-multiplexed using Albacore v2.3.1. A second de-multiplexing round was performed with Porechop v0.2.3 30 , where only the barcodes that agreed with Albacore were kept. Porechop was also used to trim the barcodes and the adapters from the sequences, as well as 45 extra base pairs from each end that correspond to the length of the universal tags and custom primers (See Supplementary Figure 2 for a schematic overview of the process and Supplementary File 1 for the bioinformatics workflow of the mapping approach).
After the trimming, reads were selected by size: 1,200 bp to 1,800 bp for 16S rRNA gene; and 3,500 to 5,000 bp for the 16S-ITS-23S of the rrn operon. Afterwards, we removed chimeras with the following approach: i) we mapped each mock community to its mock database and the complex samples to the complete rrn database using Minimap2 v2.16 (with base-level alignment and z-score set to 70) 31 ; ii) chimeras were detected and removed using yacrd v0.5 32 .
To assign taxonomy to the trimmed and filtered reads we used to strategies: 1) a mapping-based strategy using Minimap2 v2.16 31 (with base-level alignment and z-score set to 70); or 2) a taxonomic classifier using What's in my Pot (WIMP) 33 , a workflow from EPI2ME in the Oxford Nanopore Technologies cloud (based on Centrifuge software 34 ).
For the mapping-based strategy, we performed Minimap2 again with the non-chimeric sequences. We applied extra filtering steps to retain the final results: we kept only those reads that aligned to the reference with a block equal or larger than 1,000 bp (for 16S rRNA gene) and 3,000 bp (for the 16S-ITS-23S of the rrn operon). For reads that hit two or more references, only the alignments with the highest Smith-Waterman alignment score (AS score) were kept.
The reference databases used in this study were: • Mock DB: a collection of the complete genomes that were included in each mock community, as described by the manufacturer. The HM-783D database was retrieved from NCBI using the reference accession numbers, while Zymobiomics mock community has already its database online on the Amazon AWS server.
• rrn DB: sequences from the whole ribosomal operon from 22,351 different bacterial species retrieved from Genbank by Benitez-Paez et al. 25 . We have manually added a sequence of the rrn operon from S. pseudintermedius.
For assessing the mapping-based strategy, we have made a subset with the rrn DB to exclude all the operons that were representatives of Gammaproteobacteria class as an example to see how the alignment-based approach performs when missing main references within the database. These operons were identified by introducing a list of all the genera of the rrn DB as a batch in NCBI Taxonomy browser. The sequences belonging to Gammaproteobacteria (code 1236) were removed from the rrn DB.
For the taxonomic classification using the WIMP workflow, which uses the NCBI database, only those hits with a classification score >300 were kept 34 .
Ampvis2 package in R was used to plot the heatmaps 35 and the Phyloseq package, to plot the alpha rarefaction curves 36 .
An earlier version of this article can be found on bioRxiv (doi: https://doi.org/10.1101/450734)

Results
We have assessed the performance of the full-length 16S rRNA and the 16S-ITS-23S rRNA region of the ribosomal (rrn) operon to profile the microbial composition of several samples: a bacterial isolate, two mock communities and two complex skin samples (chin and dorsal back).
The samples amplified using the full-length 16S rRNA gene recovered a higher percentage of reads after the quality control when compared to 16S-ITS-23S of the rrn operon: 73-95% vs. 30-79%. For the 16S-ITS-23S of the rrn operon, the largest percentage of reads was lost during the length trimming step since some of the reads presented lengths that were shorter than expected (Supplementary Table 2).

Bacterial isolate analysis
We first sequenced an isolate of S. pseudintermedius obtained from a canine otitis. When using WIMP approach with the 16S-ITS-23S of the rrn operon, 97.5% of the sequences were correctly assigned at the species level as S. pseudintermedius. However, with the full-length 16S rRNA gene, 68% of the sequences were correctly assigned at the species level as S. pseudintermedius, while 13% at the genus one and ~20% were wrongly assigned, either by not reaching the species level or by giving an incorrect species (Table 1).
When using the mapping approach with the rrn DB, we obtained no hit to S. pseudintermedius. Instead, they were hitting mostly to Staphylococcus schleiferi, which is a closely related species; there were also few hits to Staphylococcus hyicus and Staphylococcus agnetis. This result was due to the rrn DB did not contain any representative of S. pseudintermedius. When including S. pseudintermedius sequence to the rrn DB (rrn + S. pseudintermedius) both markers retrieved the correct result with more than 97% of the assignments hitting the correct reference.
When comparing the alignment results obtained with Minimap2 and rrn DB vs rrn DB + S. pseudinteremedius, we found that the Smith-Waterman alignment score (AS) presented higher values in the correct alignments, especially when using 16S-ITS-23S marker gene ( Figure 1). Thus, the AS score could  be a filter to identify a wrong taxonomic assignment due to the lack of a reference in the database.
Assessment of the mapping-based strategy As we have already seen for S. pseudintermedius, in the mapping strategy a lack of a reference in the database led to an incorrect taxonomic assignment. Since both marker genes chosen for the microbiota profiling are highly conserved among bacteria, the mapping strategy (through Minimap2) will always align to some reference.
To check the behavior of the mapping approach when using an incomplete database, we have performed an example test using the mock communities. We have mapped the mock communities both against the complete rrn DB and against a subset of the rrn DB without any representative of the Gammaproteobacteria class. The rrn DB 25 contains 22,351 different bacterial species, including representatives of the species in both mock communities. We have chosen Gammaproteobacteria because each mock community contains three Gammaproteobacteria species, representing around 24% of the total microbial composition.
We checked the alignment score values and the alignment block length to detect any differences on the alignment performance when using complete or an incomplete database. We plotted two histograms: i) read counts distributed by the alignment block length; and ii) read counts distributed by the alignment score (AS) (Figure 2). For the 16S-ITS-23S genetic marker, we detected a clear pattern: when aligning to the rrn DB without Gammaproteobacteria, both histograms changed from a left-skewed distribution to a bimodal distribution with two peaks ( Figure 2). A new peak appeared at the lower values that included the wrong taxonomic assignments, which are species not present in the mock community or the non-concordant hits when compared to the complete rrn DB results. Thus, for the 16S-ITS-23S marker, the initial filtering step by alignment block length will get rid of most of the incorrect taxonomic assignments. However, this pattern was not observed with the full-length 16S rRNA gene ( Figure 2) or when closely-related references were present in the database, as seen above for the S. pseudintermedius isolate. So, to further confirm taxonomic results (especially for the 16S rRNA gene), we assigned taxonomy using two different bioinformatics approaches that work with different databases.

Mock community analyses
We analyzed two microbial mock communities to validate the ability of the presented approach: i) to quantify what is expected and detect biases of the technique; and ii) to reach a reliable taxonomic assignment at the species level.
For the first aim, we used the HM-783D mock community that contained genomic DNA from 20 bacterial strains with staggered ribosomal RNA operon counts (from 10 3 to 10 6 copies per organism per μl). This mock community would allow us determining if our approach reliably represents the actual bacterial composition of the community, especially considering the low-abundant species. We assigned taxonomy with Minimap2 and a database containing only the 20 representative bacterial genomes in the HM-783D mock community (Minimap2-mock DB).
On the one hand, using the full-length 16S rRNA gene we detected all the bacterial species present in the mock community, even the low-abundant ones. On the other hand, using the 16S-ITS-23S of the rrn operon we detected only the most abundant species (at least 10 4 operon copies) ( Figure 3A). This could be due to the lower sequencing depth obtained with 16S-ITS-23S when compared with 16S rRNA (Supplementary Table 2). Moreover, the relative abundances of 16S-ITS-23S sequences were more biased than those obtained from 16S rRNA gene sequencing, which confirmed that the primers for 16S-ITS-23S of the rrn operon need to be improved for a better representation of the actual abundances ( Figure 3B and 3C).
To assess if the technique and the analyses would give a reliable taxonomy at the species level we used Zymobiomics mock community, which contains equal quantities of 8 bacterial species. The expected 16S rRNA gene content for each representative is also known, so we were able to determine if the different analysis approaches reliably represented the actual bacterial composition of the community. We sequenced the Zymobiomics mock community twice per marker gene and found that the replicates presented equivalent results.
We assigned taxonomy with three different approaches: i) Minimap2 and a database containing only the 8 bacterial species of the correspondent mock community (mock DB); ii) Minimap2 and a database containing sequences for the rrn operon of 22,351 different bacterial species (rrn DB 25 ) and iii) WIMP from EPI2ME and NCBI RefSeq database.
Similarly to what we have seen for the HM-783D mock community, when using the mapping strategy with Minimap2 and the mock database, we detected that the full-length 16S rRNA gene retrieved better the actual abundances of the mock community. The 16S-ITS-23S genetic marker over-and underrepresented most of the bacterial species in the mock community. When using larger databases such as rrn DB and NCBI RefSeq, both the full-length 16S rRNA gene and the 16S-ITS-23S were able to detect 8 out of 8 bacterial species of the Zymobiomics mock community ( Figure 4A). However, we also detected other taxa that included mostly higher taxonomic rank taxa (sequences not assigned to species level), but also not expected taxa (wrongly-assigned species), especially with WIMP and NCBI RefSeq database (see Supplementary  Table 3 for complete taxonomic assignments).
On one hand, the mapping approach using the rrn DB provided highly similar results to the reference, despite the larger size of this database ( Figure 4A), especially with 16S-ITS-23S marker (99% of the reads were correctly assigned at the species level with 16S-ITS-23S; near 90% for 16S). On the other hand, with the WIMP workflow and NCBI RefSeq database, a larger number of sequences are classified as "Other taxa". Again, this is especially remarkable when using the full-length 16S rRNA gene, with > 50% of the taxonomic assignments not hitting the expected bacterial species. The results were also confirmed by alpha diversity analyses: WIMP strategy overestimated the actual bacterial diversity, when compared to rrn DB and the reference ( Figure 4B).

Complex microbial community analyses
We profiled two complex and uncharacterized microbial communities from dog skin (chin and dorsal). We used both long-amplicon markers and the two bioinformatics approaches -Minimap2 and rrn DB and WIMP with NCBI RefSeq databaseto corroborate the results.
For chin samples of healthy dogs, we found a high abundance of Pseudomonas species (>40% of total relative abundance using 16S rRNA and >60% using 16S-ITS-23S) followed by other genus with lower abundances such as Erwinia and Pantoea. Focusing on Pseudomonas, at the species level we were able to detect that the most abundant species was Pseudomonas koreensis, followed by Pseudomonas putida and Pseudomonas fluorescens ( Figure 5A and Supplementary Table 3). On the other hand, dorsal skin samples were dominated by bacteria from the genera Stenotrophomonas, Sanguibacter, and Bacillus. We reached species level for Stenotrophomonas rhizophila and Sanguibacter keddieii. It should be noted that Glutamicibacter arilaitensis is the same species as Arthrobacter arilaitensis, with newer nomenclature ( Figure 5B and Supplementary  Table 3). For both skin sample replicates, the results of the most abundant species converged using the two different methods and allowed for characterizing this complex low-biomass microbial community at the species level.
Finally, analyzing the dorsal skin samples, we also detected the presence of contamination from the previous nanopore run (Supplementary Table 2). We sequenced dorsal skin samples twice: one with a barcode previously used for sequencing the HM-783D mock community and another one with a new barcode. We were able to detect mock community representatives within the re-used barcode ( Figure 5B). Some of them were found only in the sample that was using the re-used barcode (Sample_1); others were also present in the skin sample, such as Bacillus cereus or Staphylococcus aureus. In total, this contamination from the previous run was representing ~6% of the sample composition.
De novo assembly of the 16S-ITS-23S genetic marker To further confirm the results obtained with the complex samples using directly the raw reads, we performed the assembly and consensus of the 16S-ITS-23S genetic marker using canu and we assigned taxonomy of the consensus sequences using BLAST.
For the HM-783D mock community, we were able to retrieve some of the most abundant bacterial species blasting with >99% of identity to their reference (Escherichia coli, Staphylococcus

epidermidis, Streptococcus mutans and Clostridium beijerinnkii).
For the Zymobiomics mock community, we found a consensus sequence for all the bacterial species with >99% of identity. The only two exceptions were Salmonella enterica that presented a consensus sequence with an identity of 98.8% and Escherichia coli that presented no consensus sequence (Table 2  and Supplementary Table 4).
For the complex microbial communities, the de novo consensus sequences usually presented lower identity than those of the mock community. For the dorsal skin, the ones with higher consensus accuracy were Stenotrophomonas rhizophila (99.3 %), Streptococcus mutans (99.1 %) and Arthrobacter arilaitensis (98.6 %), thus confirming the results retrieved using directly the raw reads. For the chin, the three contigs with higher consensus accuracy hit Pseudomonas fluorescens (>98.5%). We detected also other contigs with lower consensus accuracy values, with previously not seen taxonomic assignments (Supplementary Table 4).

Discussion
The full-length 16S rRNA and the 16S-ITS-23S of the rrn operon identified the bacterial isolate and revealed the microbiota composition of the mock communities and the complex skin samples, even at the genus and the species level. So, we present this long-amplicon approach as a method to profile the microbiota of low-biomass samples at deeper taxonomic levels.
The long amplicons were analyzed as raw reads (uncorrected), using both a mapping-based approach through Minimap2 and the "What's in My Pot" workflow by Oxford Nanopore to confirm results using a double approach. Although Nanopore sequencing has a high error rate (average accuracy for the S. pseudintermedius isolate: 89%), we compensated this low accuracy with longer fragments to assess the taxonomy of several bacterial communities. In general, the longer the marker, the higher the taxonomical resolution with both analyses performed. In this case, the longer 16S-ITS-23S marker remarkably improved the taxonomy assignment at the species level.
Moreover, we also performed de novo assembly of the 16S-ITS-23S amplicons obtaining consensus sequences that allowed us to validate some of the taxonomy retrieved with the longamplicon raw reads. A de novo approach allowed retrieving consensus sequences with high accuracy (>99% of identity) for simple microbial communities. However, when working with more complex communities this consensus accuracy was generally lower. These lower accuracies found in complex microbial communities could be due to the lower sequencing depth, an uneven distribution of the bacterial species, and a mix of some closely similar species within the same contig.
Mock communities' analyses allowed us assessing the performance and the biases of the methodology, from the lab bench to the bioinformatics analyses and final results. In general, we found that the full-length 16S rRNA gene represents better the abundances of a microbial community; whereas, 16S-ITS-23S obtains better resolution at the species level.
So, do the long-amplicon approaches represent the actual bacterial composition? On one hand, we detected biases of our primer sets for both genetic markers, since some of the species of the mock communities were over-and under-represented. For example, Actinomyces odontolyticus and Rhodobacter sphaeroides seem to not amplify properly, neither with 16S rRNA gene, nor the 16S-ITS-23S of the rrn operon. Previous studies also detected the same pattern for these specific bacteria even when using or comparing different primer sets 16,21 .
Overall, the 16S rRNA primer set seemed less biased than the 16S-ITS-23S of the rrn operon, which over-and underrepresented most of the bacterial species, suggesting that the 16S-ITS-23S primers should be improved for unbiased representation of the community.
On the other hand, with the HM-783D staggered mock community -with some low-abundant species-we aimed to assess the sensitivity of both approaches. With the 16S rRNA marker gene, we detected all bacterial members of both mock communities. However, when using the 16S-ITS-23S of the rrn operon, some of the low-abundant species in the HM-783D mock community were not detected. This was probably due to the fact that we obtained a lower number of reads -up to one magnitude less than with the 16S rRNA gene. Since we combined 16S rRNA and 16S-ITS-23S amplicons in the same run, this led to an underrepresentation of the 16S-ITS-23S amplicons and consequently a lower sequencing depth. This was probably due to the combination of various issues: i) not enough DNA mass to begin with the indicated number of molecules; ii) reads with shorter size than expected (~1,500 bp); iii) shorter fragments tend to be sequenced preferentially with Nanopore sequencing. Thus, for future studies our recommendation would be multiplexing samples with the same amplicon size to avoid underrepresentation of the longest one and improving PCR parameters or adding more PCR cycles to the longer amplicons to get more input DNA mass.
In the bioinformatics analyses, our aim was to confirm the results with two independent workflows and different databases rather than comparing them. We saw that the most abundant species were usually concordant with both strategies at a qualitative level. Some exceptions were due to the lack of that species in the rrn database, such as that seen for S. pseudintermedius. With the WIMP workflow and the 16S rRNA gene, many sequences did not reach species level. Previous studies analyzing the microbiota obtained with Nanopore reads have compared the performance of several databases using the 16S-ITS-23S 26 and software for the 16S rRNA gene 22 . They also found similar results as reported here: some false positives associated to specific software 22 , as well as a high impact on the unclassified reads depending on the size of the database used 26 .
When using EPI2ME (WIMP with NCBI Ref database), the amplicons from the S. pseudintermedius isolate were assigned to the correct bacterial species in ~98% and ~68% of the cases, using the 16S-ITS-23S of the rrn operon and 16S rRNA gene, respectively. In a previous study, Moon and collaborators used the full-length 16S rRNA gene for characterizing an isolate of Campylobacter fetus and the marker correctly assigned the species for ~89% of the sequences using EPI2ME 23 . The ratio of success on the correct assignment at the species level depends on the species itself and its degree of sequence similarity in the selected genetic marker. Within the Staphylococcus genus, the 16S rRNA gene presents the highest similarity (around ~97%) when compared to other genetic markers 37 .
On the other hand, we observed that the mapping strategy (through Minimap2) could lead to a wrong assigned species if the interrogated bacterium is not represented on the chosen database. Minimap2 provides faster results than EPI2ME, but it needs an accurate comprehensive and representative database. Extra filtering steps using the alignment block length or Smith-Waterman alignment score could potentially be used to discard a wrong taxonomic assignment.
Switching to complex microbial communities, we found that dog chin was colonized by different Pseudomonas species.
Recently, Meason-Smith and collaborators found Pseudomonas species associated with malodor in bloodhound dogs 38 . However, these were not the main bacteria found within the skin site tested, but were in low abundance, differing from what we have found here. On the other hand, Riggio and collaborators detected Pseudomonas as one of the main genera in canine oral microbiota in the normal, gingivitis and periodontitis groups 39 . However, the Pseudomonas species were not the same that we have detected here. It should be noted that we had characterized these chin samples with 16S V1-V2 amplicons in a previous study 27 , where we found some mutual exclusion patterns for Pseudomonadaceae family. This taxon showed an apparent "invasive pattern", which could be mainly explained for the recent contact of the dog with an environmental source that contained larger bacterial loads before sampling 27 . Thus, our main hypothesis is that the Pseudomonas species detected on dog chin came from the environment, since they have been previously isolated from environments such as soil or water sources 40,41 .
None of the most abundant species in dog dorsal skin had previously been associated with healthy skin microbiota either in human or in dogs. Some of them have an environmental origin, such as Stenotrophomonas rhizophila, which is mainly associated with plants 42 ; or Sporosarcina psychrophila, which is widely distributed in terrestrial and aquatic environments 43 . The Bacillus cereus main reservoir is also the soil, although it can be a commensal of root plants and guts of insects, and can also be a pathogen for insects and mammals 44 . Overall, environmental-associated bacteria have already been associated with dog skin microbiota and are to be expected, since dogs constantly interact with the environment 27 .
Regarding Stenotrophomonas in human microbiota studies, Flores et al. found that this genus was enriched in atopic dermatitis patients that were responders to emollient treatment 45 . However, previous studies on this skin disease found Stenotrophomonas maltophila associated to the disease rather than Stenotrophomonas rhizophila 46 . Achromobacter xylosoxidans has been mainly associated with different kind of infections, as well as skin and soft tissue infections in humans 47 . However, both dogs included in this pool were healthy and with representatives of both genus/species, a fact that reinforces the need to study the healthy skin microbiome at the species level before considering some species pathogenic. The other abundant bacteria detected on dog skin have been isolated in very different scenarios: Sanguibacter keddieii from cow milk and blood 48,49 ; and Glutamicibacter arilaitensis (formerly Arthrobacter arilaitensis) is commonly isolated in cheese surfaces 50,51 .
In general, we obtained taxonomy assignment down to species level with both the full-length 16S rRNA gene and the 16S-ITS-23S of the rrn operon, although it was not always feasible due to: i) high similarity of the marker chosen within some genera, especially for the 16S rRNA gene; ii) an incomplete database; and iii) sequencing errors. In the light of these results, for an increased resolution at the species level, the 16S-ITS-23S of the rrn operon would be the best choice. At the expenses of an increased taxonomic resolution, we could have missed few bacterial species due to unlinked rrn genes. While in hostassociated environments (e.g. gut) bacterial species with unlinked rrn genes are rare, if profiling natural environments (e.g. soil) this approach may be missing a significant proportion of the diversity 52 . So far, this genetic marker does not have as many complete and curated databases as 16S rRNA gene. If choosing to use 16S-ITS-23S genetic marker, we could add some filtering steps to filter out the "wrongly assigned taxonomy" and have more reliable taxonomic results, both using the alignment block length and the AS score using Minimap2.
Other gene-marker strategies have been further described to profile the microbiota with Nanopore sequencing. For example, sequencing 16S rRNA genes by Intramolecular-ligated Nanopore Consensus (INC-seq) 15,53 that allowed retrieving corrected full-length 16S rRNA genes. Another approach would be sequencing the cDNA from size selected small subunit rRNAs that allows retrieving many 16S rRNA genes using a primer-free approach 54 . However, these alternative strategies have been applied to the 16S rRNA gene that has a limited taxonomic resolution within some genera. Recently an approach using unique molecular identifiers (UMIs) for obtaining corrected full-length rrn operon has been applied to characterize a mock community 55 . The characterization of full-length ribosomal operons by the UMI approach has the potential to expand databases to make them more comprehensive with higher taxonomic resolution.
Further studies should be aiming to obtain reads with higher accuracy, either using consensus methods or applying new developments (new techniques, new basecallers or new R10 pores, etc.). Studies comparing marker-based strategies with metagenomics will determine the most accurate marker for microbiota studies in low-biomass samples.  The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The authors have begun to put their findings into context with prior studies in the literature using MinION and rRNA genes or operons. However, I disagree with their conclusions that the primers used for rRNA operon amplification need improvement rather than the choice of software and database. Specifically, Cuscó report that mock databases containing only those targets in the mock community can et al. correctly assign OTUs using WIMP and MiniMap2 (Fig 3 and 4). While larger databases such as a rRNA operon database or the NCBI Refseq database incorrectly assign taxa (from 12-55% of reads). This may be true for the software they employed. However, other studies have used QIIME, BLASTN, Centrifuge, and Discontinuous MegaBLAST to identify OTUs for the MinION platform for 16S rRNA genes or ribosomal operons. I think an acknowledgement that these various software packages have also been employed to analyze the MinION reads in the scientific literature would benefit the F1000 readership.

Open Peer Review
Especially since the authors indicate that our study (Kerkhof ., 2017 ) supports their findings which is et al incorrect. We actually found the databases which provided the most number of OTU calls for our raw reads were the NCBI 16S rRNA database (>18K entries) and the ARB/Silva NR99 database (>645K entries). The intermediate size databases (Greengenes and RDP) did not work as well using Discontinuous Megablast. I suspect the issue with incorrect OTU calling stems more from the software used to screen the database than from the primers or the size of the database being used.
A brief discussion of variability resulting from different PCR amplification reagents and what might be considered a best practice for data analysis for future studies would be helpful.
Other Specific Comments: Figure 3 has the calculated target gene concentration "Reference" on the right side of the heat map while Figure 4 has the "Reference" on the left side. This makes it hard for the reader to figure out what should be for the WIMP or MiniMap2 results as a basis for comparison.

Comments to the authors:
The manuscript describes a study assaying 2 mock bacterial communities or 2 complex skin microbiome samples from dogs (chin or dorsal back) using both near-full length 16S rRNA genes and near-full length rRNA operons with the Oxford Nanopore MinION. The authors employ a library preparation method generating either 16S amplicons (1400 bp) or rRNA operons (4500 bp) including barcoding with a 1D ligation/sequencing kit and FLO-MIN 106 cells. The data analysis pipeline utilized Albacore basecalling, near-full length amplicon size selection, and screening by What's in my Pot (WIMP) and Minimap2 against both NCBI and rrn databases. The authors demonstrate increased resolution at the species level with longer reads, that there can be large losses of raw sequence reads by size selection for rrn amplicons in their hands, and that the data analysis software and database can influence the results of MinION bacterial community analysis. 2. 3.
in the supplementary figures of Kerkhof (2017 ). Likewise, an acknowledgement of the various et al.
software packages that has been employed to analyze the MinION reads in the scientific literature would benefit the readership. It appears that QIIME, BLASTN, Centrifuge, LAST aligner, Discontinuous MegaBLAST, WIMP, and MiniMap2 have all been used to identify OTUs for the MinION platform for 16S rRNA genes or rrn operons. As the authors have shown, the software/database being used can be very influential in the results of MinION screens and a synopsis of what they have found in context with other investigators (% bacterial assignment vs. % error) may point to a best practice for future studies.

Other Specific Comments:
Page 3: I find it awkward/confusing to indicate the number of operons per microorganism per microliter here for the mock communities. Bacteria generally have 1-15 ribosomal operons in their genomes. I think it is clearer to just indicate the number of target rRNA operons is 10^3-10^6 for this particular DNA mixture.
Page 3: The barcoding expansion pack (EXP-PBC001) requires that the primers contain overhangs attached to the rRNA primers. This is not mentioned by the authors. Did they put overhangs on 27F/1492R/2241R? If so, the first round of target amplification may be affected by the presence of these overhangs. This should be indicated.

Page 4:
The authors clearly show the danger of performing PCR and only characterizing the amplification product by Qubit fluorescence. If they had done agarose gels on the PCR reactions, they may have detected the short amplification products in their initial rrn operon reactions. Furthermore, these short reads are preferentially ligated using the SQK-LSK108 sequencing kit since there are more picomole ends. This best practice of visualizing PCR amplifications for size determinations before sequencing should be explicitly stated.
Page 4: I am a little confused by the 0.5 nM notation for PCR product in the barcoding reaction. If the authors used 50 microliter reactions, did they put 25 ng of 1st round PCR product in their barcoding reactions for a 15 cycle amplification? Can the authors just state the mass of DNA used to barcode? Secondly, Table 2 indicates BC1, BC2, and BC3 were not used. Was there a reason these barcodes were not utilized?
Page 6: Stating that the rrn operon profiling was more biased probably because of the lower sequencing depth does not recognize that others have not reported comparable bias or that it is probably a reflection of their potentially compromised amplification efficiencies. This conclusion should be viewed with caution, given the amplification issues noted above.
through the MinION™ portable nanopore sequencer.

If applicable, is the statistical analysis and its interpretation appropriate? Not applicable
Are all the source data underlying the results available to ensure full reproducibility? Yes

Are the conclusions drawn adequately supported by the results? Partly
No competing interests were disclosed.

Competing Interests:
Reviewer Expertise: Molecular ecology of microbial systems I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.

5.
minor points to the manuscript: Figure 2: according to the text, was detected using the 16S rRNA gene, Actinomyces odontolyticus however, '0' in the figure can create confusion. It would be better to represent the number of copies of using more decimal places or adding a caption for this species. Actinomyces odontolyticus Figure 3a: -It would be better to change the figure (e.g. heatmap) to make it easier for readers to recognize under-represented and over-represented bacteria.
also seems Listeria monocytogenes under-represented in the analyses using the mock database and rrn database, and the corresponding sentence in Page 7 may be changed.
-Include the classification method (WIMP, minimap2) along with the name of the database, as in figure 4, to allow general readers to more easily match the methods and the database.
In the last paragraph of page 7, it seems that the criteria of the percentage of wrongly assigned species for the rrn operon are different from that for the 16S rRNA gene. If the authors would like to insist on better resolution by using the rrn operon, they need to demonstrate the data of the analysis using multiple species including species that tend to be under-represented or over-represented.

If applicable, is the statistical analysis and its interpretation appropriate? Yes
Are all the source data underlying the results available to ensure full reproducibility? Yes

Are the conclusions drawn adequately supported by the results? Yes
No competing interests were disclosed. Competing Interests: 1.

Reviewer Expertise: neuroinfection, encephalitis
I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.

Amanda Warr
Roslin Institute, Royal (Dick) School of Veterinary Studies, University of Edinburgh, Edinburgh, UK Cusco evaluate methods for long read sequencing and classification of marker genes from microbial et al. communities, both for mock communities of known microbial composition and complex communities from dog skin from two anatomical locations, chin and back. They find that long read sequencing of 16S and the operon is sufficiently accurate to classify microbes and that is more sensitive at the species rrn rrn level.
This work demonstrates a valuable option for species identification from microbial samples using long reads, overcoming the current high error rate through covering a larger region. The work also highlights some of the issues that can arise from multiplexing amplicons of differing lengths when using Nanopore sequencing.
Overall the paper is well written and detailed, however there are a few details I feel could be addressed: 16S length reads in barcodes: rrn Do you expect this to be entirely from barcode misassignment or were these shorter fragments produced during PCR? You state that the loss of amplicons during the length trimming step was probably due to over-representation of 16S rrn amplicon on the flow cell, and most of the reads lost were roughly 16S amplicon sized -are you suggesting that there are large numbers of 16S reads that are assigned to barcodes after 2 rrn rounds of demultiplexing? Are these shorter reads actually whole 16S amplicons or fragments of ? rrn Expected sensitivity given read count: The authors state that failure to detect the less abundant species from the mock community in the dataset was "probably" due to their being fewer reads. rrn As the proportions of the species in the mock samples are known, theoretically what total number of reads would be necessary to detect the less abundant species? Given the number of rrn reads obtained, did the authors detect as many species as they would expect to detect and what is the minimum total number of reads they would need to be likely to detect the lowest abundance species?
Differences in classification methods: Differences in classifications between the mock community database/rrn database and the NCBI database may be attributable to differences in the tools, with minimap2 being used for the mock and databases and WIMP (based on centrifuge) rrn being used for the NCBI database. My understanding is that the authors are mainly interested in 3.

4.
tools, with minimap2 being used for the mock and databases and WIMP (based on centrifuge) rrn being used for the NCBI database. My understanding is that the authors are mainly interested in classifications from different databases rather than differences between methods. While the authors do not directly compare the classification results between these different methods in text, some of the figures appear to imply that these results are directly comparable (e.g. Figure 3a). It would be useful if either all three databases were used with a single method (for example, using centrifuge with all three databases) or if these were at least more obviously separated or marked as coming from different classification methods in the figures.
Classification rates against NCBI: The authors should further discuss ways to improve the classification rates, will the biggest improvements come from reduced error rate, better classification tools, improving species representation in databases? The authors conclude that in the future we should aim to improve accuracy, but one of the main results here is that sequencing the full 16S/ overcomes the problem of the current error rate -perhaps highlight benefits such as rrn improved barcode assignment and emphasise that while this works well classification against a large database would likely improve with increased accuracy. The authors also conclude that rrn offers higher resolution at species level, however I suspect that currently more species have 16S sequences in databases than .
rrn Additionally, I have a few minor corrections mainly around small grammatical errors and figure/table modifications: Page 5: Paragraph beginning "To assign taxonomy...", change "to strategies" to "two strategies". Also I would change the last sentence on the page to say "some of the reads excluded were the expected length of the 16S rRNA gene rather than the operon". Figure 1 should also be rrn labelling Albacore as the basecaller.
Page 6: change "would allow us determining" to "would allow us to determine".
Page 11, column 2, line 2: change "associated to" to "associated with".    In the conclusion the authors have suggested ways to improve accuracy of this method in the future, I would add the R2C2 method (Volden , 2018 ) as an option to improve consensus accuracy here et al. also, while designed for cDNA it could be applied to fragments of genomic DNA.
The study demonstrates that nanopore sequencing can give accurate classifications even at the current level of error rate if the reference database contains the right sequences. The study also shows how sequencing the longer fragment spanning both the 16S rRNA and 23S rRNA genes improves the taxonomic classification when the database contains a matching sequence.

Is the work clearly and accurately presented and does it cite the current literature?
The study mentions that the classification methods rely heavily on reference databases so it would be relevant to include citations for papers with methods for producing new reference sequences for both 16S rRNA and the longer fragment in the discussion (metagenomics, artificial long reads, primer free methods). Methods for improving read accuracy are also mentioned as important but the only methods mentioned are future upgrades from the company, relevant existing literature is not included (INC-seq, UMIs etc.). The study concludes that sequencing the entire "rrn operon" would be the best choice but it would be relevant to compare the size of current databases for the 16S rRNA gene versus the rrn operon. The presence of conserved sites for designing better primers is also extremely important but not discussed. Furthermore, there is evidence that quite a few organisms have unlinked rRNA genes, which will thus be missed by a full operon approach.
Citations are also needed for bioinformatics tools for both processing and visualisation of the data.

Is the study design appropriate and is the work technically sound?
The study uses mapping to a reference database to point out that the sequences can get genus-and species-level classification. However, the method will always report a genus and a species even in the absence of the correct sequence in the reference database as indicated from the sequencing of the S. pure culture with the "rrn" method. It will be important to simulate the impact on the pseudintermedius results when there is no closely related sequences in the database. This could be done by removing all reference sequences within the Gammaproteobacteria and mapping the HM-783D to the modified database and monitor where the reads end up. It would also be helpful if there was a way to distinguish between reads that have the "correct" match and reads that just happen to map because the 16S rRNA gene is extremely conserved. Something similar would be relevant for the EPI2ME workflow but as the authors cannot control the reference database, it is probably not feasible. One of the advantages of the mock communities should be information about the copy numbers for the rRNA genes but there is no information on this included in the study and how it affects the results.

Are sufficient details of methods and analysis provided to allow replication by others?
The methods section lacks information about what happens after mapping the reads. How are the figures generated, what software is used, etc.? It would also be helpful if the specific scripts/commands used to run the bioinformatics analysis were available. The figure gives a decent overview of the bioinformatics processing but seems to miss the visualisation tools used. The main role of Albacore is basecalling the raw data not just demultiplexing. The figure could be improved further if you include the wet lab part of the work, so it becomes clear why the demultiplexing step is included and where the raw data comes from. A mapping step is integrated in the chimera detection (removal?) workflow but it might be better to omit mentioning mapping in that step as it can be confusing that the figure has two mapping steps in a row.

Figure 2: heatmap mock community:
The caption needs to explain what the numbers represent e.g. percentage of sequenced reads/mapped reads. It would be great if the heatmap included the "true" composition of the mock community for comparison. Copy number for each organism in the mock would also be relevant to include in the figure.
Since there are only two columns, it would be better to have the sample labels at the top and with horizontal text preferably with a name that makes it easier to interpret the figure.     Getting rid of the bar charts would allow for making a big heatmap with the data from Figure 4A and 4B combined. This way the reader can also compare the results from the two different sample sites. A naming system that makes it clearer that "_1" and "_2" are replicates would also help the reader interpret the figure. Presenting results aggregated at different levels, which could be included in one another is a bit confusing e.g., " " could be included in " " which again could be included in " Bacillus cereus Bacillus ". Bacillales Tables: Table 1: Primer sequences: Fine but could be moved to supplementary. Make headers easier to understand e.g. "% seq 1st QC" could be "% of reads passing QC", "Albacore pass" could just be "# reads after basecalling" etc. Where is the number after chimera detection? pass" could just be "# reads after basecalling" etc. Where is the number after chimera detection? Add a column with data accession ID and move the table to supplementary then the sample names can also be expanded so the reader does not have to look to the bottom for an explanation of abbreviations. I suggest adding a column at the end with the number of reads mapping/classified for each sample so the reader know what fraction is included.  the table that is missing from the "rrn" Staphylococcus pseudintermedius database. As the paper mentions genus-and species-level classification as the target you may benefit from aggregating the values for and HKU10-03 as splitting this S. pseudintermedius S. pseudintermedius into strains makes it more confusing as your numbers in the text do not match the ones in the table. 1.

© 2018 Benítez-Páez A.
This is an open access peer review report distributed under the terms of the Creative Commons , which permits unrestricted use, distribution, and reproduction in any medium, provided the original Attribution License work is properly cited.

Alfonso Benítez-Páez
Microbial Ecology, Nutrition and Health Research Unit, Institute of Agrochemistry and Food Technology-Spanish National Research Council (IATA-CSIC), Valencia, Spain Cusco and co-workers present an evaluation of both a mock community and the dog skin associated microbiota. The authors made use of the single-molecule Nanopore DNA sequencing technology and compared two different technical approaches by studying the nearly-full 16S rRNA bacterial gene and the nearly-full bacterial rRNA operon.
In my opinion, this work represents an important advance regarding the application of nanopore technology in the field of microbiome research.
The main strength of the work is its detailed technical description regarding the protocols for library preparation, sequencing and basecalling, that altogether facilitate the reproducibility. Moreover, the genetic data generated was properly deposited in a specialized database for public accession to whomever may want to replicate the analysis of long reads by similar approaches or new ones.
The figure quality is good and the information disclosed by them is well accompanied with appropriate captions.
Notwithstanding, I have some minor concerns about the work that should be clarified, at least for me: The last paragraph of page 7 describes the level of reads correctly assigned to species level for the microbial isolate . However, some of the values cited in the text Staphylococcus pseudintermedius do not match, at least, explicitly in Table 3. So, the authors should revise this issue or better describe the information obtained.
The authors found that the study of a nearly-full 16S rRNA gene reflects in a better way the expected abundances of microbial species present in the mock community tested. This comparative analysis with regard to the results obtained by using the rrn operon should be accompanied by a linear regression analysis, declaring respective Pearson's "r" coefficients, that can measure more accurately the efficiency of both methods and better support the authors' observations and conclusions.
Additionally to the observed richness (observed species) and Shannon diversity, the authors could also include a microbial community evenness evaluation of reference and observed microbiome data from the different approaches evaluated in the study, so that additional conclusions could be addressed.
Given the issues with underrepresentation of "rrn" data as a consequence of mixing this type of synthetic DNA with nearly-full 16S rRNA amplicons, the authors should highlight this observation as a major issue of this approach and state a clear recommendation to avoid this type of multiplexing for future studies.
It is necessary to better describe the contamination issues described in the last paragraph of the 5. It is necessary to better describe the contamination issues described in the last paragraph of the results (page 9). I'm not sure if this cross-contamination came from re-utilization of a flowcell or if this came from contamination of the barcoded-primer, used during nested PCR, with amplicons/DNA from the mock community. In a similar manner, the estimation of 6% of contamination has to be explained in detail (species/proportions discarded or having been taken into account to calculate this percentage).

Is the work clearly and accurately presented and does it cite the current literature? Yes
Is the study design appropriate and is the work technically sound? Yes

If applicable, is the statistical analysis and its interpretation appropriate? Partly
Are all the source data underlying the results available to ensure full reproducibility? Yes

Are the conclusions drawn adequately supported by the results? Yes
No competing interests were disclosed.

Competing Interests:
Reviewer Expertise: Human microbiome, Microbial genomics, Nanopore sequencing, Computational biology, Metagenomics I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.