High throughput rapid amplicon sequencing for multilocus sequence typing of M. ovipneumoniae using DNA obtained from clinical samples

High throughput rapid amplicon sequencing for multilocus sequence typing of M. ovipneumoniae using DNA obtained from clinical samples | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article High throughput rapid amplicon sequencing for multilocus sequence typing of M. ovipneumoniae using DNA obtained from clinical samples Isaac Framst, Rebecca M. Wolking, Justin Schonfeld, Nicole Ricker, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4151642/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Spillover events of Mycoplasma ovipneumoniae have devastating effects on wild bighorn sheep populations. Multilocus sequence typing (MLST), a common method for tracking bacterial lineages, is used to monitor spillover events and the spread of M. ovipneumoniae between populations. Most work involving M. ovipneumoniae typing has used Sanger sequencing, however, this technology is time consuming, expensive, and is not well suited to efficient batch sample processing. Our study aimed to develop and validate a workflow for multilocus sequence typing of M. ovipneumoniae using Nanopore Rapid Barcoding sequencing and multiplex PCR. We compare the workflow with Nanopore Native Barcoding library preparation and Illumina MiSeq amplicon protocols to determine the most accurate and cost-effective method for sequencing multiplex amplicons. Results A multiplex PCR was optimized for four housekeeping genes of M. ovipneumoniae using archived DNA samples from wild sheep. Sequences recovered from Nanopore Rapid Barcoding correctly identified all MLST types with the shortest total workflow time, and lowest cost per sample when compared to Nanopore Native Barcoding, and Illumina MiSeq methods. Conclusion Our proposed workflow serves as a convenient and effective diagnostic method for strain typing of M. ovipneumoniae , and could be applied to other bacterial MLST schemes. The workflow is suitable for diagnostic settings where reduced hands-on time, cost and multiplexing capabilities are important. Figures Figure 1 Figure 2 Figure 3 Background Bronchopneumonia is a population-limiting disease of bighorn sheep (BHS), Ovis canadensis , across western North America. M. ovipneumoniae , the primary etiologic agent of this disease, is transmitted to BHS through contact with domestic sheep and goat herds which are reservoirs for the pathogen ( 1 , 2 ). M. ovipneumoniae demonstrates a high degree of genetic and phenotypic diversity across its host range ( 1 , 3 ). The genetic diversity of M. ovipneumoniae is high in domestic sheep, indicating their role as a significant reservoir and source of infection, while in BHS, it is low, suggesting spillover as the primary source of transmission. Indeed, ancestral state reconstruction from MLST sequences confirmed domestic sheep as the primary source of infection for BHS, emphasizing the importance of strain typing to map transmission dynamics ( 4 ). In BHS, an initial outbreak of fatal bronchopneumonia is often followed by recurring fatal outbreaks in lambs. Recurrent outbreaks have been observed from 2 to 15 years after the initial spillover ( 2 , 5 – 7 ). Recent evidence suggests there may be no cross-strain immunity, leaving surviving animals susceptible to infection ( 4 , 8 ). To reduce the likelihood of spillover events, federal and state agencies have implemented policies focused on spatial separation of domestic and wild sheep ( 9 ). Increased sampling efforts in the western US and Canada have recently been undertaken to find the wider prevalence of M. ovipneumoniae in over ten states and three provinces ( 10 ). DNA-based strain typing is used to document the invasion, persistence, and transmission of M. ovipneumoniae in these populations ( 7 ). A previously developed multilocus sequence typing (MLST) scheme targeting four elements, the 16-23S intergenic spacer region (IGS), 16S rRNA region (LM), RNA polymerase β-subunit gene ( rpoB ), and DNA gyrase subunit-β gene ( gyrB ), has demonstrated strong differential typing capability in over 600 samples and 270 strain types ( 1 , 4 ). Owing to the rapid emergence of new strains and the extensive diversity of novel types, creating a conventional database of alternative alleles is impractical ( 1 , 8 , 10 ). In the current Sanger workflow, the four gene fragments are concatenated and then compared pairwise with previously stored type sequences. The definition of a strain is established based on its similarity to stored types, using a specific threshold of four base pairs ( 1 ). In cases where rpoB and gyrB do not amplify or are unavailable, strains are denoted by their IGS length, consistent with historical typing methods in use prior to the current MLST scheme (4, 7). The current MLST laboratory process uses a nested singleplex PCR, and Sanger sequencing applied to each locus. This method is laborious, and expensive if processing large numbers of samples ( 11 ). Oxford Nanopore Technologies (ONT) sequencing has recently been used for singleplex and multiplex MLST ( 12 – 16 ). This method uses a small, low-cost sequencing device which delivers result in real-time capable of multiplexing and high-throughput sequencing. Amplicon sequencing using ONT has been previously validated for Neisseria gonorrhoeae antimicrobial resistance genotyping ( 14 ). This and other similar workflows were estimated to cost approximately 100 times less than Sanger sequencing for large sample sets ( 14 , 17 ). ONT’s Native Barcoding library preparation is recommended for amplicon sequencing due to its higher read accuracy, and preservation of the full-length amplicon (ONT, 2023). Alternatively, Rapid Barcoding library preparation is faster and less expensive, however throughput and raw read accuracy are reported to be reduced ( 18 ). An amplicon-specific protocol for Rapid Barcoding is not provided by ONT, though several studies have used that kit for sequencing multiplexed amplicons with a high degree of accuracy ( 12 – 15 ). Based on these successes, Rapid Barcoding library preparation is expected to be well suited for diagnostic settings because of the short library preparation time, flexible multiplexing options, accuracy, and low cost. Our objective was to develop a sequencing workflow using multiplex PCR followed by Rapid Barcoding Nanopore sequencing using archived DNA from clinical samples. We also compared the speed and accuracy of the optimized Rapid Barcoding workflow with other Nanopore library preparation methods, as well as Sanger, and Illumina sequencing. Methods Samples Archived DNA samples ( n = 88) were provided by the Washington Animal Disease Diagnostic Lab (WADDL) at Washington State University (Pullman WA, USA) (Supplementary 1). The DNA samples originated from bighorn sheep field samples submitted to WADDL between 2011 and 2016 for diagnostic testing as part of a previously published study ( 1 , 4 ). The presence of M. ovipneumoniae DNA was determined by qPCR at WADDL ( 19 ). DNA samples were stored at -20°C in a non-defrosting freezer until processing. Storage time was between 3 and 12 years. M. ovipneumoniae strain Y98 was purchased from the American Type Culture Collection (ATCC 2941 – Y98, domestic sheep, 1976, NCBI BioProject PRJNA253514) for use as a control sample. Bacterial culture and DNA extraction of the reference strain was performed as previously described ( 20 ). Sequence typing of samples was previously determined using nested singleplex PCR assay and Sanger sequencing by WADDL ( 4 ). Since current M. ovipneumoniae strain typing workflows use Sanger sequencing, new methods were compared to results obtained by Sanger sequencing. For initial NGS sequencing, a random subset of 68 samples was chosen. A subsubset of 24 samples was randomly selected for additional sequencing runs to control for sample variation. PCR assays Singleplex PCR. Nested singleplex PCR was performed using primers targeting LM, IGS, rpoB and gyrB loci (Supplementary 2A) ( 4 ). Cycling conditions were modified for Phusion Flash HiFi all-in-one master mix (Thermofisher, Waltham, MA, USA (Supplementary 2B). External nested PCR reactions were performed for IGS, rpoB and gyrB targets, then one microlitre was carried forward to the inner nested reaction (Supplementary 2C). A single PCR reaction was used for LM. A sterile nuclease-free water sample was included in each PCR run as a negative control. Detailed singleplex PCR cycling conditions are described in Supplementary 2. Multiplex PCR. The internal and external primers were pooled in equimolar concentrations of 0.2 µM for a 50 µl PCR reaction (Supplementary 3A) ( 4 ). A 3-step PCR protocol was then optimized using a series of 2-fold serial dilutions of Y98 pure isolate DNA (ATCC 2941 – Y98 strain) and a subset of 5 samples. Optimal annealing temperature and primer concentration were determined experimentally (Supplementary 3B and 3C). Singleplex and multiplex PCR products were stored at -20°C until sequencing library preparation. Gel electrophoresis. For singleplex and multiplex PCR products, 1.5% and 2% agarose gels were used respectively. Gels were prepared in-house using a 1X lithium acetate borate buffer solution (Sigma-Aldrich, Burlington, MA, USA), SYBR safe DNA gel stain (Invitrogen, Waltham, MA, USA) and a 100 to 1000 bp DNA marker (Invitrogen, Waltham, MA, USA), and all samples were loaded using TriTrack loading dye (ThermoFisher Scientific, Waltham, MA, USA). Gels were run in the buffer solution at 120 V, then imaged using a GelDoc Go (Bio-Rad, Hercules CA, USA). Illumina Sequencing Twenty-four samples of four nested singleplex PCR products were submitted to the Advanced Analysis Centre (AAC) Genomics facility (University of Guelph, Guelph, ON, CA) for sequencing on an Illumina MiSeq platform (Illumina Technologies, San Diago, CA, USA). The facility used a modified 16S amplicon protocol (Illumina Part # 15044223 Rev. A) with custom primers, and a maximum insert size of 550 bp (Supplementary 5). Sequences were returned as demultiplexed FASTQ files. ONT sequencing Three ONT sequencing experiments were conducted to determine the optimal library preparation method and flow cell configuration (Table 1 ). Prior to library preparation, PCR products were quantified by a Qubit Fluorometer using the dsDNA broad range kit (Invitrogen, Waltham, MA, USA) and diluted without purification according to the ONT library preparation protocol. Table 1 Description of Oxford Nanopore sequencing experiment conditions. Experiment No. Flow cell Chemistry a Library Preparation b Flow cell c Library sample size d Runtime (h) Experiment 1 R9.4.1 Rapid New 68 16 Washed 24 16 Experiment 2 Experiment 3 R10.4.1 Native New 24 16 Washed 24 16 All sequencing runs were conducted using a minION Mk1B device: Oxford Nanopore Technologies (ONT). a Specific flowcell chemistry used. b Library preparation method. Rapid: Nanopore Rapid Barcoding (SQK-RBK110/114.96); Native: Nanopore Native Barcoding (SQK-NBD114.96). c New: Unused flowcell from ONT, Washed: Washed flow cell reused from previous sequencing run using EXP-WSH004 wash kit. d Number of samples barcoded in the prepared library. The same 24 samples were included in all libraries. ONT Experiment One determined the viability of ONT Rapid Barcoding library preparation for multiplexed amplicons using ONT’s R9.4.1 flow cell, Rapid Barcoding library preparation kit, and flow cell wash kit (EXP-WSH004) (Oxford Nanopore Technologies, Oxford, UK). PCR products were quantified by a Qubit Fluorometer using the dsDNA broad range kit (Invitrogen, Waltham, MA, USA) and diluted without purification according to the ONT library preparation protocol (RBK_9126_v110_revO). Then, 68 multiplex PCR products were barcoded according to the SQK-RBK110.96 protocol. An R9.4.1 flow cell was loaded with 400 ng of DNA library. Following a 16-hour runtime, the flow cell was washed using the EXP-WSH004 kit and immediately loaded with a second library containing a subset of 24 barcoded samples from the preceding run. The second library (washed flow cell) was sequenced for 16 hours. ONT Experiment Two evaluated the Native Barcoding library preparation kit, with the most current ONT R10.4.1 flow cell as recommended by ONT, for comparison with ONT Experiment One. Twenty-four multiplex PCR products were barcoded according to the SQK-NBD114.96 protocol (NBA_9170_v114_revF, ONT, 2023). Following a 16-hour runtime, the flow cell was washed using the EXP-WSH004 kit and immediately loaded with a second library of the same 24 samples. Libraries were differentially barcoded to avoid carryover between runs. The second library was prepared using the same method as the first run and sequenced for 16 hours. ONT Experiment Three was performed identically to ONT Experiment Two by another technician to control for human error. ONT sequencing run parameters. All sequencing runs used a minION Mk1B instrument and minKNOW v23.7.15 (ONT) operating software. The minimum read length was set to 20 bp, real-time basecalling was turned off, data output was set to “.POD5” to collect raw signal data, active channel selection was turned on, and "reserve pores” was turned off to maximize initial throughput. Runtime was set to 16 hours in all experiments. For new and washed flow cells, a flow cell check was conducted immediately prior to loading the library using the “flow cell check” option in the minKNOW software homepage. Flow cells with fewer than 800 new, or 400 washed active pores were not used. Data analysis ONT analysis. Basecalling was performed with Dorado v0.3.0 ( https://github.com/nanoporetech/dorado ) using the “fast” model with demultiplexing. Then, the demultiplexed fastq files were submitted to a custom analysis pipeline (Fig. 2 ). Briefly, basecalled reads were demultiplexed and trimmed using GUPPY v.6.4.8 (ONT), reads below quality score 8 were removed using Chopper v0.5.0 ( 21 ), then reads were aligned to the four reference genes, i.e., a deconvolution step, using Minimap2 v2.24 ( 22 ). The resultant alignments were sorted and indexed, then alignment statistics were generated, including depth and number of reads mapped using samtools v.1.17 ( 23 ). Consensus sequences were “called” using samtools consensus with default calling (Bayseian mode with quality-aware mapping). Draft consensus sequences were polished using Medaka v1.8.1 (ONT) to produce final output sequences. If the average depth of one or more loci in a sample was < 50x, the sample was excluded from downstream analyses. Homopolymer errors in IGS were manually corrected post-polishing by adding a T at position 113 to correct the sequence to 8 Ts. A shell script for this pipeline is provided (Supplementary 4). Sequences were imported into Geneious prime for final typing (Supplementary 6). Illumina analysis. Forward and reverse reads were imported into Geneious Prime v2023.2.1 (Dotmatics). The average read quality and number of reads were recorded using the Geneious statistics panel view for each read group. Reads were paired by name and trimmed using BBDuk v1.0 (BBMap – Bushnell B. - https://jgi.doe.gov/data-and-tools/software-tools/bbtools/ ) via the Geneious plugin, then aligned to the reference sequence within Geneious. Consensus sequences were generated from each alignment and compared to the corresponding reference Sanger sequence using the Geneious local alignment tool. The pairwise identity and number of mismatches were recorded. Finally, LM, IGS and gyrB were concatenated for typing (Supplementary 6). Consensus sequences quality and accuracy determination . Consensus sequences, which are representative sequences of each amplicon, were generated by Bayesian estimation of the true base at each position of the alignment using the Samtools consensus module with quality aware mapping. The accuracy and quality of the consensus sequences from each method were characterized by i) sequencing coverage of each target locus, ii) the number of reads aligned to each reference gene sequence, iii) the percent identity consensus sequence and corresponding Sanger sequence, and iv) the number of mismatches between the consensus and the Sanger sequence. Gaps in the consensus sequence were replaced with Ns and treated as mismatches. The Q-score average read quality for Illumina and ONT runs were recorded using phred-33 encoding. Statistical analyses Linear regression model was constructed to determine the relationship between coverage and mismatches. The “lm” function in R was used with the number of mismatches as the response variable and coverage as the predictor variable ( 24 ).The F-test statistic with associated p-value was used to determine significance of the relationship from the model summary. Tukey’s honestly significant difference test was used to determine the level of independence of errors between genes and runs. Statistical significance was set at p ≤ 0.05. Results Singleplex and multiplex PCR . A three-step singleplex nested PCR protocol was used to amplify the four MLST genes for Illumina sequencing. After gel electrophoresis, distinct bands of the expected sizes were visible for all targets (Supplementary 3D). The multiplex PCR assay was optimized for the amplification of the four MLST loci in a single 50 µl reaction. After electrophoresis, the optimized multiplex PCR produced four distinct bands of 360 bp, 470–510 bp, 547 bp and 680 bp corresponding to LM, IGS, rpoB , and gyrB , respectively (Fig. 3 ). Fragments from external nested primers were not visible. Illumina sequencing Illumina sequencing generated full-length read pairs for IGS, LM and gyrB within 48 hours. Raw read and consensus quality for Illumina was higher than all ONT methods (Tables 2 and 3 ). Illumina consensus sequences were obtained for LM, IGS and gyrB loci, however rpoB (680 bp) consensus sequences could not be resolved because there was no overlap for pairing the forward and reverse reads due to the maximum insert size of 550 bp. Resultant pairings were missing the middle ~ 130 bp. Therefore, downstream analyses omit rpoB for Illumina data. Table 2 Oxford Nanopore and Illumina sequencing run metrics. Experiment Flow cell chemistry a Library preparation b Flow cell c Number of reads (M) Read quality g % Reads > Q20 h Active pores d Pre-filtering e Post-filtering f Pre-filtering e Post-filtering f Experiment 1 R9.4.1 Rapid New 1426 5.66 2.41 12.0 16.6 38.3% Washed 935 2.95 1.45 12.5 16.3 38.5% Experiment 2 R10.4.1 Native New 1646 6.53 4.00 13.6 17.6 38.3% Experiment 3 R10.4.1 Native New 1092 7.87 5.01 12.9 15.7 30.6% Washed 491 3.40 1.99 12.5 16.0 31.4% Illumina i Illumina Modified 16S i N/A N/A 8.32 8.00 30.4 34.2 81.2% a Nanopore flow cell chemistry used. b Type of kit used for library preparation. Rapid: Rapid Barcoding 96 (SQK-RBK110-96 for R9.4.1; SQK-RBK114.96 for R10.4.1); Native: Native Barcoding kit (SQK-NBD114.96). c New: New flow cell from ONT, washed: washed flow cell reused from previous sequencing run using EXP-WSH004 wash kit. d Number of active pores reported by minKNOW at the beginning of the sequencing run following flow cell loading. e Metric calculated immediately post-basecalling before demultiplexing or read filtering. f Metric calculated post-filtering, reads Q8.0 and below removed before calculation. g Read quality reported as an average Q-score (phred33). h Percent of total reads post-filtering with quality score > 20. f Illumina sequencing performed using modified 16S rDNA library preparation with custom primers (Advanced Analysis Center, Guelph, Canada). Table 3 Quality of Oxford Nanopore and Illumina consensus sequences post-alignment for samples with > 50x coverage. Averages reported are aggregate for gyrB , IGS, LM and rpoB for 24 samples, ± standard deviation. Experiment No. a Flow cell Chemistry Library Preparation b Flow cell c No. of samples d Average Coverage e No. Reads Mapping to Reference f Percent Identity to Sanger g Number of Mismatches h % Types Correctly Identified Experiment 1 R9.4.1 Rapid New 60/68 2037x ± 1998 3956 ± 3833 100% ± 0.001% 0.0 2 ± 0.19 100% Washed 21/24 4803x ± 6530 8753 ± 11343 100% 0 100% Experiment 2 R10.4.1 Native New 23/24 13058x ± 13470 13525 ± 14253 99.1% ± 1.3% 3.48 ± 5.59 62% Experiment 3 R10.4.1 Native New 24/24 38293x ± 35904 39017 ± 37751 99.0% ± 0.1% 3.64 ± 3.63 71% Washed 18/24 13972x ± 15120 14195 ± 15963 99.1% ± 0.001% 2.59 ± 5.24 83% Illumina i Illumina Modified 16S rRNA New 20/24 130674x ± 57640 130674 ± 57640 100% ± 0.001% 0.05 ± 0.37 N/A a Experiment 1: Nanopore Rapid Barcoding library preparation, 16 h runtime. Experiment 2 and 3: Nanopore Native Barcoding library preparation, 16 h runtime. b Nanopore library preparation method: Rapid = Rapid Barcoding 96 kit (SQK-RBK110.96), Native = Native Barcoding 96 kit (SQK-RBK114.96). c New: New flowcell from ONT, washed: washed flow cell reused from previous sequencing run using EXP-WSH004 wash kit. d Number of samples over 50x coverage included in analysis / number of samples sequenced. e Coverage of target loci, averaged for all samples over 50x coverage. f Number of reads aligned to the corresponding Sanger sequence reference, reported as an average for all loci and samples in the run. g Percent identity to the Sanger sequence reference of the same sample. h Number of bases that do not match the same position in the Sanger sequence. i Illumina sequencing conducted by third party (Advanced Analysis Center, Guelph, Canada). N/A No data for rpoB (680 bp) presented because of 550bp limit. Therefore, strain type is indeterminate. ONT sequencing Run quality. Run quality and raw read statistics are reported in Table 2 . Average read quality for all ONT runs were similar pre- and post-filtering. The highest run yield, 7.87 Gb, was from the R10.4.1 new flow cell (ONT Experiment Two). The number of active pores decreased by approximately 700 after 16-hour runtime and a washing step. The throughput of washed flow cells from ONT Experiments One and Three were reduced by approximately half for the same 16-hour runtime; however, read quality was similar to the first run. In ONT Experiment Two, flow cell washing was unsuccessful due to the formation of air bubbles over the sensor array which irreversibly damaged the pores leaving fewer than 100 active pores. ONT bioinformatics pipeline. A custom bioinformatic pipeline was constructed using open-source tools (Fig. 2 ). The bioinformatic analysis of the 24 samples took 30 to 50 minutes from read filtering to final output depending on the number of total reads (10 CPU cores, 32Gb memory). Polishing of consensus sequences using Medaka increased agreement with Sanger sequences, especially for lower coverage samples. Comparison of alignment and consensus sequence statistics Consensus sequences were called using alignments generated for each gene and assessed for quality and accuracy (Table 3 ). The depth of coverage varied across Illumina and ONT runs (130674x ± 57640 and 16699x ± 24438, respectively); the highest coverage, 38293x ± 35904x, was observed in ONT Experiment Three (Native Barcoding with R10.4.1 new flow cell, n = 24) and the lowest coverage, 2037x ± 1998x, was observed in ONT Experiment One (Rapid Barcoding, R9.4.1 new flow cell, n = 68). Some samples were excluded from downstream analysis in ONT Experiment Three (washed) (n = 6 removed), due to < 50x coverage even though the average depth for the run was high (1397x ± 15120x). The number of reads aligned to each gene closely correlated with coverage (Table 3 ), where Experiment One had the smallest average number of reads per sample and Experiment Three had the largest. For ONT Experiment One (Rapid Barcoding, R9.4.1), the number of reads was approximately double the average coverage while in ONT Experiments Two and Three (Native Barcoding, R10.4.1 flow cell) the number of reads was equal to the coverage. This highlights the technical differences in library preparation methods and effect of read length. Consensus sequences with coverage greater than 50x were compared with the corresponding Sanger sequence (Table 3 ). Consensus sequences in ONT Experiment One were identical to the corresponding Sanger sequence of the same samples with 100% identity, which resulted in all strain types being identified. ONT Experiments Two and Three consistently shared 99% identity with corresponding Sanger sequences, and there were more mismatches to Sanger sequences than ONT Experiment One. A linear regression analysis of mismatches for ONT Experiments 2 and 3 did not show a relationship between the coverage and the number of mismatches to the corresponding Sanger sequence (R 2 = 0.0001648, F 1,286 = 0.04713, p = 0.8283). Mismatches in Experiments Two and Three occurred more often in gyrB and rpoB targets, and samples with mismatches in one target were more likely to have mismatches in other targets (Tukey’s HSD, p < 0.05) with a bimodal distribution of mismatches. Only 72% (n = 47 of 65) of samples were correctly typed for ONT Experiments Two and Three (Table 3 ). Turnaround time . The time to obtain MLST sequences from DNA samples was determined as the time from PCR preparation to final typing output (Fig. 1 ). Multiplex PCR with ONT Rapid Barcoding library preparation (ONT Experiment One) had the shortest turnaround time of 19.5 hours of which 1 hour 45 minutes were hands-on time. The Illumina sequencing workflow with singleplex PCR could be completed in 52 hours with 8 hours hands-on time, however the turnaround time for Illumina sequencing depended on third-party services which took four to eight weeks for us to receive the sequencing results. Comparison of ONT with Illumina. ONT sequencing generated less reads and coverage overall than Illumina sequencing (Table 3 ). Sequences obtained from ONT Experiment One (Rapid Barcoding, R9.4.1 flow cell) and from Illumina had 100% identity with Sanger sequences. ONT Experiments Two and Three (Native Barcoding, R10.4.1 flow cell) showed lower percent identity with Sanger sequences than the other experiments (Table 3 ). ONT Experiment One (Rapid Barcoding, R9.4.1 flow cell new and washed) was the only method that correctly identified all strain types. Illumina consensus sequences were identical to Sanger sequences for 23 of the 24 samples for LM, gyrB and IGS with three mismatches occurring in a single sequence (IGS, WADDL #00126). Illumina sequencing failed to generate rpoB sequences. Since rpoB could not be recovered fully with the Illumina method, strain types could not be determined. Discussion In this study, we compared the efficiency of different sequencing approaches for strain typing of M. ovipneumoniae , including ONT, Illumina, and Sanger sequencing. For that, we optimized and validated a workflow using multiplex PCR and Rapid ONT sequencing for strain typing of M. ovipneumoniae from DNA samples. Further, we developed a custom bioinformatic pipeline to deconvolute and align reads, generate a consensus, and error-correct the final consensus sequences. Illumina sequences had the highest quality read and consensus Q-scores, however, due to the maximum insert size of 550 bp, the full length rpoB , at 680 bp, could neither be paired nor aligned. To maintain backwards compatibility with the Sanger scheme, the Illumina method we followed was insufficient for all loci and it was more costly and time consuming. However, in a similar study, multiplex PCR of four genes for MSLT of M. genitalium decreased the cost of Illumina library preparation, and all target fragments were under 500 bp in length (Plummer et al. 2020). This approach could be useful for M. ovipneumoniae MLST in diagnostic laboratories which already use Illumina but would require a re-design of the rpoB primer set to reduce amplicon length, which risks removal of relevant bases. The Rapid and Native Barcoding library preparations from ONT were compared to determine their suitability for multiplex amplicon sequencing. The Rapid Barcoding library approach identified 100% of strain types despite having lower total yield and lower per-loci depth than Native Barcoding (72% identified). This suggests that mismatches were not a result of low sequencing depth but might have arisen because of cross-barcoding. A previous study comparing library preparation methods from ONT found that Native Barcoding library preparation delivered the highest total number of reads followed closely by Rapid Barcoding, which is consistent with our findings ( 18 ). However, the same study also showed that even low levels of cross-barcoding during library preparation led to “barcode leakage” during demultiplexing, which increased misidentified single nucleotide variants compared to non-barcoded runs. The updated kit 14 chemistry (Native Barcoding 114.96 vs. previous kit 10 Native Barcoding 110.96) used in our study eliminated thermal inactivation of the barcode ligation enzymes, which could increase the chance of cross-barcoding. Rapid Barcoding uses a heat-activated transposase, which is inactive at room temperature, so there is little risk of cross-barcoding. Thus, we suspect that cross-barcoding during Native Barcoding library preparation contributed to a low proportion of correctly identified strain types. The shortest turnaround time was achieved with the ONT Rapid Barcoding workflow (ONT Experiment One), which was under 20 hours. This optimal workflow takes one-hour for multiplex PCR, 1.5 hours for Rapid Barcoding library preparation, 16 hours for sequencing runtime, and one hour for data analysis. This is promising for diagnostic applications, such as outbreak scenarios, where timely strain identification is critical ( 25 ). The ONT Rapid workflow delivered the strain type in less than 20 hours, while Illumina took more than 50 hours and failed to capture the full length of the rpoB target. A comparison of ONT and Illumina sequencing methods for diagnostic purposes found that shorter turnaround time of ONT sequencing was of significant clinical value and was more important to clinical outcome than the relatively insignificant difference in accuracy between ONT and Illumina sequences ( 25 ). We designed the bioinformatic analysis pipeline to be run using a laptop computer (10 CPUs, 32Gb memory), and to be user-friendly for professionals without a bioinformatics background, or minimally equipped laboratories. The per-sample cost of library preparation for ONT sequencing varies by method. Ligation-based library preparation kits, such as the Native Barcoding kit used in Experiments Two and Three, require costly third-party reagents for end repair, dA-tailing, and adapter ligation. For Experiment Two and Three (Native Barcoding, R10.4.1 flow cell), the library preparation cost was approximately $ 7.30 USD per sample for 12 or more samples. In contrast, the Rapid Barcoding kit used in Experiment One did not require extra reagents and was approximately $ 3.49 USD per sample for 12 or more. We also washed and reused minION flow cells to decrease costs and found that the read quality was not impacted. As shown in Table 3 , a subset of samples from ONT Experiment One (new flow cell) were sequenced in a second run after washing the flow cell (Experiment One/washed). The sequence types obtained from the washed and reused flow cell were identical to those of the corresponding samples in the first run using a new flow cell. Compared to another approach ( 13 ), wherein a single flow cell was successfully used five times, our results also indicate that the effects of the flow cell reuse are marginal, and sequence quality is not influenced by the preceding run. Decreased pore counts following each wash should be accounted for, and we suggest adjusting runtime to reach minimum 50x coverage for all loci in each sample. The Rapid ONT workflow developed in this study generated highly accurate sequences, however, some inherent errors may still exist due to error prone ONT reads. A recent proof of concept for ONT amplicon sequencing called 97% of expected variants and noted a high error rate, especially for homopolymer and homopolymer-adjacent regions ( 17 ). We corrected similar homopolymer errors by using Medaka polishing. Alignment of IGS showing misidentified bases almost always resulted from T8 homopolymers called as T7 at position 113. These were manually corrected since no strain types carry a T7 homopolymer region at that position. This manual correction of homopolymers decreases automation of the method, and therefore more hands-on time is required to check for homopolymer errors. It was anticipated that pooling equimolar quantities for each PCR amplicon would result in comparable average depth for each product when aligned to the respective reference. However, the average depth for each amplicon varied widely between 32 and 16776x (mean = 16230 std err = 24249) across ONT Experiments One, Two, and Three. This result is comparable to another group which noted a range of 127 to 19,626-fold coverage (mean = 8320.69, std err = 452.99) for ONT amplicon sequencing, and a minimum of 100x coverage was required for typing ( 17 ). Similarly, we found that 50x coverage of each amplicon in the multiplex was required for the optimized workflow. Setting a minimum coverage per amplicon ensures all loci in the sample have adequate sequence information for a high-quality consensus sequence. We also found a high standard deviation of coverage between barcodes for all ONT runs (Table 3 ). This suggests that the sequencing run parameters can be better optimized to reduce unnecessary sequencing time by normalizing the coverage across barcodes. Barcode balancing in minKNOW provides this in real-time and could be used for future runs. The lowest per-sample coverage was observed for ONT Experiment One new flow cell with 68 samples. This outcome was consistent with the logical implications of the experimental design in which 68 samples were sequenced for the same amount of time as subsequent runs with only 24 samples. We recommend the use of multiplex PCR and ONT Rapid Barcoding library preparation for M. ovipneumoniae typing due to the high accuracy of the consensus sequences, lowest cost, and shortest turnaround time. These benefits are compounded when multiplexing many samples, making the workflow ideal for outbreak scenarios or population surveys ( 14 , 26 ). The workflow can be implemented in-house with no initial capital, lower per-sample cost than Sanger or Illumina sequencing, and less technician hands-on time. In contrast, the initial capital cost for Illumina sequencing is often prohibitive; laboratories instead rely on off-site commercial facilities which may take upwards of two weeks for results. A unique challenge of this study is the diversity of M. ovipneumoniae strain types. Only a subset of archived samples from bighorn sheep strain types were selected for this study, and we therefore assume the selected samples are representative of all strain types. Furthermore, detection of multiple strain types in one sample was not assessed in this study, although the presence of multiple strain types has previously been observed in wild and domestic sheep ( 27 ). Minimal modifications to our workflow would be needed to add loci or changed for any MLST. Modification of the multiplex PCR and modification of the reference allele text file in the pipeline are the only modifications required to customize this pipeline for adding more loci or substituting other MLST schemes. A limitation of the comparison of the ONT library preparation methods is the difference in technology revisions. ONT Experiment One, used the R9.4.1 flow cell and ONT Experiments Two and Three used the R10.4.1 flow cells. There are few other studies on the performance of R10.4.1/kit14 for amplicon sequencing. One group compared R9.4.1 chemistry with R10.4.0, and reported that although R10.4.0 reads were more accurate, R9.4.1 flow cells were more reliable ( 28 ). The discrepancies we noted between ONT Experiment One and ONT Experiments Two and Three could be explained by the differing flow cell and sequencing chemistry changes and not the library preparation method. Further investigation of R10.4.1 and Rapid Barcoding kit for multiplex amplicon sequencing could eliminate the need for manual homopolymer correction, as claimed by ONT. In this study we used a set runtime of 16 hours for ONT sequencing; however, a similar workflow for MLST of S. aureus stopped sequencing once there were 4000 reads per sample to ensure adequate coverage without oversampling ( 13 ). In that protocol, the authors used a single flow cell five times (467 samples total), and fewer than 4 hours were required for adequate sequence data for the first three runs, with successive run requiring 6–15 hours until the flow cell was depleted ( 13 ). This approach could decrease turnaround time and cost for our workflow. Conclusion We developed and validated a workflow for multilocus sequence typing of M. ovipneumoniae from DNA samples using multiplex PCR and Nanopore Rapid Barcoding sequencing. This method was compared to Nanopore Native Barcoding library preparation and Illumina MiSeq modified amplicon protocols to determine the most accurate and cost-effective method for sequencing multiplex amplicons. Nanopore Rapid Barcoding sequencing produced the most accurate consensus sequences with shortest workflow time. The difficulty of obtaining highly accurate consensus sequences from error prone Nanopore reads was mitigated through high coverage and consensus polishing. Therefore, the workflow is suitable for diagnostic settings where reduced hands-on time, cost and multiplexing capabilities are important. To our knowledge, this is the first Rapid Barcoding ONT workflow developed for Mycoplasma , a method that could be applied to type other Mycoplasma species or other fastidious bacteria. Declarations Ethics approval and consent to participate – Not applicable Consent for publication – Not applicable Availability of data and materials GenBank accession numbers for Sanger sequences of all samples used in this study are available in supplementary 1. Raw sequence reads for all runs conducted in this study, and polished consensus sequences are available at https://doi.org/10.6084/m9.figshare.25395310 Competing interests - The authors declare that they have no competing interests. Funding Grant funding for this study was provided by the Wild Sheep Foundation. In kind contributions from The Ontario Veterinary College Department of Pathobiology Authors contributions IF: prepared samples, performed multiplex PCR and Oxford Nanopore sequencing, and wrote the manuscript. G.C. conducted ONT sequencing. T.E.B., P.L.K., J.B-M., J.S., and N.R. advised methods development, data analysis and revised the manuscript. GM: was responsible for conceptual design of the project, funding acquisition, and supervised the work. Acknowledgements The authors thank the Washington Animal Disease Diagnostic Laboratory for maintaining a collection of bighorn sheep DNA samples, and Dr. Thomas Besser for his mentorship and guidance. References Kamath PL, Manlove K, Cassirer EF, Cross PC, Besser TE. Genetic structure of Mycoplasma ovipneumoniae informs pathogen spillover dynamics between domestic and wild Caprinae in the western United States. Sci Rep. 2019;9:15318. Besser TE, Cassirer EF, Potter KA, Foreyt WJ. Exposure of bighorn sheep to domestic goats colonized with Mycoplasma ovipneumoniae induces sub-lethal pneumonia. PLoS ONE. 2017;12:e0178707–0178707. Maksimovic Z, De la Fe C, Amores J, Gomez-Martin A, Rifatbegovic M. Comparison of phenotypic and genotypic profiles among caprine and ovine Mycoplasma ovipneumoniae strains. Vet Rec. 2017;180:180. Cassirer EF, Manlove KR, Plowright RK, Besser TE. 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Development of a long-read next generation sequencing workflow for improved characterization of fastidious respiratory mycoplasmas. Microbiol (Reading) 168. De Coster W, Rademakers R. 2023. NanoPack2: population-scale evaluation of long-read sequencing data. Bioinf (Oxford England) 39. Li H. New strategies to improve minimap2 alignment accuracy. Bioinformatics. 2021;37:4572–4. Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R. The Sequence Alignment/Map format and SAMtools. Bioinformatics. 2009;25:2078–9. R Core Team. 2023. R: A language and environment for statistical computing, R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/ . Zhang J, Gao L, Zhu C, Jin J, Song C, Dong H, Li Z, Wang Z, Chen Y, Yang Z, Tan Y, Wang L. Clinical value of metagenomic next-generation sequencing by Illumina and Nanopore for the detection of pathogens in bronchoalveolar lavage fluid in suspected community-acquired pneumonia patients. Front Cell Infect Microbiol. 2022;12:1021320–1021320. Wang Y, Zhao Y, Bollas A, Wang Y, Au KF. Nanopore sequencing technology, bioinformatics and applications. Nat Biotechnol. 2021;39:1348–65. Lonas G, Clarke JK, Marshall RB. The isolation of multiple strains of Mycoplasma ovipneumoniae from individual pneumonic sheep lungs. Vet Microbiol. 1991;29:349–60. Sanderson ND, Kapel N, Rodger G, Webster H, Lipworth S, Street TL, Peto T, Crook D, Stoesser N. 2023. Comparison of R9.4.1/Kit10 and R10/Kit12 Oxford Nanopore flowcells and chemistries in bacterial genome reconstruction. Microbial genomics 9. Stamatakis A. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics. 2014;30:1312–3. Tables Table 1. Description of Oxford Nanopore sequencing experiment conditions. Experiment No. Flow cell Chemistry a Library Preparation b Flow cell c Library sample size d Runtime (h) Experiment 1 R9.4.1 Rapid New 68 16 Washed 24 16 Experiment 2 Experiment 3 R10.4.1 Native New 24 16 Washed 24 16 All sequencing runs were conducted using a minION Mk1B device: Oxford Nanopore Technologies (ONT). a Specific flowcell chemistry used. b Library preparation method. Rapid: Nanopore Rapid Barcoding (SQK-RBK110/114.96); Native: Nanopore Native Barcoding (SQK-NBD114.96). c New: Unused flowcell from ONT, Washed: Washed flow cell reused from previous sequencing run using EXP-WSH004 wash kit. d Number of samples barcoded in the prepared library. The same 24 samples were included in all libraries. Table 2. Oxford Nanopore and Illumina sequencing run metrics. Experiment Flow cell chemistry a Library preparation b Flow cell c Number of reads (M) Read quality g % Reads >Q20 h Active pores d Pre-filtering e Post-filtering f Pre-filtering e Post-filtering f Experiment 1 R9.4.1 Rapid New 1426 5.66 2.41 12.0 16.6 38.3% Washed 935 2.95 1.45 12.5 16.3 38.5% Experiment 2 R10.4.1 Native New 1646 6.53 4.00 13.6 17.6 38.3% Experiment 3 R10.4.1 Native New 1092 7.87 5.01 12.9 15.7 30.6% Washed 491 3.40 1.99 12.5 16.0 31.4% Illumina i Illumina Modified 16S i N/A N/A 8.32 8.00 30.4 34.2 81.2% a Nanopore flow cell chemistry used. b Type of kit used for library preparation. Rapid: Rapid Barcoding 96 (SQK-RBK110-96 for R9.4.1; SQK-RBK114.96 for R10.4.1); Native: Native Barcoding kit (SQK-NBD114.96). c New: New flow cell from ONT, washed: washed flow cell reused from previous sequencing run using EXP-WSH004 wash kit. d Number of active pores reported by minKNOW at the beginning of the sequencing run following flow cell loading. e Metric calculated immediately post-basecalling before demultiplexing or read filtering. f Metric calculated post-filtering, reads Q8.0 and below removed before calculation. g Read quality reported as an average Q-score (phred33). h Percent of total reads post-filtering with quality score >20. f Illumina sequencing performed using modified 16S rDNA library preparation with custom primers (Advanced Analysis Center, Guelph, Canada). Table 3. Quality of Oxford Nanopore and Illumina consensus sequences post-alignment for samples with >50x coverage. Averages reported are aggregate for gyrB , IGS, LM and rpoB for 24 samples, ± standard deviation. Experiment No. a Flow cell Chemistry Library Preparation b Flow cell c No. of samples d Average Coverage e No. Reads Mapping to Reference f Percent Identity to Sanger g Number of Mismatches h % Types Correctly Identified Experiment 1 R9.4.1 Rapid New 60/68 2037x ± 1998 3956 ± 3833 100% ± 0.001% 0.0 2± 0.19 100% Washed 21/24 4803x ± 6530 8753 ± 11343 100% 0 100% Experiment 2 R10.4.1 Native New 23/24 13058x ± 13470 13525 ± 14253 99.1% ± 1.3% 3.48 ± 5.59 62% Experiment 3 R10.4.1 Native New 24/24 38293x ± 35904 39017 ± 37751 99.0% ± 0.1% 3.64 ± 3.63 71% Washed 18/24 13972x ± 15120 14195 ± 15963 99.1% ± 0.001% 2.59 ± 5.24 83% Illumina i Illumina Modified 16S rRNA New 20/24 130674x ± 57640 130674 ± 57640 100% ± 0.001% 0.05 ± 0.37 N/A a Experiment 1: Nanopore Rapid Barcoding library preparation, 16 h runtime. Experiment 2 and 3: Nanopore Native Barcoding library preparation, 16 h runtime. b Nanopore library preparation method: Rapid = Rapid Barcoding 96 kit (SQK-RBK110.96), Native = Native Barcoding 96 kit (SQK-RBK114.96). c New: New flowcell from ONT, washed: washed flow cell reused from previous sequencing run using EXP-WSH004 wash kit. d Number of samples over 50x coverage included in analysis / number of samples sequenced. e Coverage of target loci, averaged for all samples over 50x coverage. f Number of reads aligned to the corresponding Sanger sequence reference, reported as an average for all loci and samples in the run. g Percent identity to the Sanger sequence reference of the same sample. h Number of bases that do not match the same position in the Sanger sequence. i Illumina sequencing conducted by third party (Advanced Analysis Center, Guelph, Canada). N/A No data for rpoB (680 bp) presented because of 550bp limit. Therefore, strain type is indeterminate. Additional Declarations No competing interests reported. Supplementary Files Supplementary1samples.xlsx Supplementary2SingleplexPCR.pdf Supplementary3MultiplexPCR.docx Supplementary4ONTpipeline.txt Supplementary5Illuminaprimers.docx Supplementary6Geneiousampliconanalysisinsructions.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4151642","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":283398750,"identity":"a8711f06-9748-4075-997b-af4a70df6086","order_by":0,"name":"Isaac Framst","email":"","orcid":"","institution":"University of Guelph","correspondingAuthor":false,"prefix":"","firstName":"Isaac","middleName":"","lastName":"Framst","suffix":""},{"id":283398751,"identity":"28bf5946-383b-467c-8a24-a4216dfc5973","order_by":1,"name":"Rebecca M. Wolking","email":"","orcid":"","institution":"Washington State University","correspondingAuthor":false,"prefix":"","firstName":"Rebecca","middleName":"M.","lastName":"Wolking","suffix":""},{"id":283398752,"identity":"53a24dc4-58e7-4476-9934-1384b5a39ac0","order_by":2,"name":"Justin Schonfeld","email":"","orcid":"","institution":"Public Health Agency of Canada","correspondingAuthor":false,"prefix":"","firstName":"Justin","middleName":"","lastName":"Schonfeld","suffix":""},{"id":283398753,"identity":"6b90a290-450e-4979-9b1e-712dfc0a1f71","order_by":3,"name":"Nicole Ricker","email":"","orcid":"","institution":"University of Guelph","correspondingAuthor":false,"prefix":"","firstName":"Nicole","middleName":"","lastName":"Ricker","suffix":""},{"id":283398754,"identity":"567f10c7-9b95-4034-a0e1-d7510833595d","order_by":4,"name":"Janet Beeler-Marfisi","email":"","orcid":"","institution":"University of Guelph","correspondingAuthor":false,"prefix":"","firstName":"Janet","middleName":"","lastName":"Beeler-Marfisi","suffix":""},{"id":283398755,"identity":"aed0a859-89ef-4b17-b527-98b97901a3a2","order_by":5,"name":"Gabhan Chalmers","email":"","orcid":"","institution":"University of Guelph","correspondingAuthor":false,"prefix":"","firstName":"Gabhan","middleName":"","lastName":"Chalmers","suffix":""},{"id":283398756,"identity":"dd45b477-aabf-4fd5-9ebf-7662e3666051","order_by":6,"name":"Pauline L. Kamath","email":"","orcid":"","institution":"University of Maine","correspondingAuthor":false,"prefix":"","firstName":"Pauline","middleName":"L.","lastName":"Kamath","suffix":""},{"id":283398757,"identity":"2c70f8d1-eeb8-4d46-8205-6418781bfbee","order_by":7,"name":"Grazieli Maboni","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAr0lEQVRIiWNgGAWjYLCCBAYbCIOHBC1ppGphYDhMghb56MOPPzzMOZ843/0A44O3bURoMTyXZmCQuO124sYzCcyGc4nS0sNgkADW0pDAJs1LnBb2DwcSt51L3Nj/gP03UVrkeXgMGxK3HUicL5HAxkyUFgMenmKGxG3JxhskHjZLzjlHjC097Js//txmJzu/P/nghzdlxNhyAM5gbCBCPciWBnTGKBgFo2AUjAJ0AAABrDj+iCVE6wAAAABJRU5ErkJggg==","orcid":"","institution":"University of Georgia","correspondingAuthor":true,"prefix":"","firstName":"Grazieli","middleName":"","lastName":"Maboni","suffix":""}],"badges":[],"createdAt":"2024-03-22 19:14:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4151642/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4151642/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":53549140,"identity":"f5cfa7b1-0547-498e-b55e-ba3b72eef30b","added_by":"auto","created_at":"2024-03-27 10:55:20","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1112430,"visible":true,"origin":"","legend":"\u003cp\u003eTime to complete \u003cem\u003eMycoplasma ovipneumoniae\u003c/em\u003e MLST workflows from PCR to final typing of sequences. To emphasize differences in hands-on time between methods timeline steps are not drawn to scale. (\u003cstrong\u003eA\u003c/strong\u003e) Four genes are amplified using a single multiplex PCR and barcoded using Nanopore Rapid Barcoding library preparation. The subsequent library is sequenced using the minION device with a new or washed flow cell. (\u003cstrong\u003eB\u003c/strong\u003e) Four genes are amplified using a single multiplex PCR and barcoded using the Nanopore Native Barcoding library preparation. The subsequent library is sequenced using the minION device with a new or washed flow cell. All Nanopore reads from were trimmed and filtered to remove adapters and low-quality regions, then reads were sorted by MLST loci. The resultant alignment was used to call a draft consensus, then polished to correct potential errors. (\u003cstrong\u003eC\u003c/strong\u003e) Four genes are amplified separately using a nested singleplex PCR assay with seven total reactions and prepared for sequencing using the Illumina 16S metagenomic sequencing library preparation with primers modified for the \u003cem\u003eM. ovipneumoniae\u003c/em\u003e MLST scheme. The subsequent library was sequenced with an illumina MiSeq, 600 cycle flow cell by a third-party laboratory. Illumina reads were trimmed and filtered, then aligned to the respective reference gene for consensus calling\u003c/p\u003e","description":"","filename":"Figure1Timeline.png","url":"https://assets-eu.researchsquare.com/files/rs-4151642/v1/034b6a6f5661e632d15e4b8a.png"},{"id":53549174,"identity":"2f6e25bf-896e-46c3-9723-f035cbce9e70","added_by":"auto","created_at":"2024-03-27 10:55:20","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1043019,"visible":true,"origin":"","legend":"\u003cp\u003eBioinformatic workflow for obtaining \u003cem\u003eMycoplasma ovipneumoniae\u003c/em\u003e MLST sequences from multiplexed Oxford Nanopore reads. Squiggle data (fast5/pod5) are basecalled in real-time or post-run using Dorado basecaller. The resultant sequence reads then undergo two deconvolution steps: separation of reads according to barcode using Dorado demux, followed by alignment of reads to the target reference sequences with minimap2. Multiple loci are present in one barcoded sample (multiplex PCR product) and must be binned by target. Consensuses are called from each alignment using Samtools, then concatenated for typing in Geneious Prime. Phylogenies were built using concatenated sequences in RAxML (29). Strain types are determined by the concatenated sequence’s pairwise identity to other archived types.\u003c/p\u003e","description":"","filename":"Figure2bioinformaticworkflow.png","url":"https://assets-eu.researchsquare.com/files/rs-4151642/v1/b3289e2d68a84376067410ef.png"},{"id":53549175,"identity":"8cceaa69-9618-45cd-9f06-8b2abd6c3776","added_by":"auto","created_at":"2024-03-27 10:55:20","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":135001,"visible":true,"origin":"","legend":"\u003cp\u003eGel electrophoresis of the optimized multiplex PCR assay showing amplification of four gene fragments from \u003cem\u003eMycoplasma ovipneumoniae \u003c/em\u003efrom DNA samples (throat washes, lung tissue swab, or nasal swab). DNA marker 100-1000 bp in 100 bp increments. IGS: 16-23S intergenic spacer region; rpoB: RNA polymerase beta subunit gene; gyrB: DNA gyrase beta subunit gene; LM= 16s rDNA. Gel electrophoresis conducted using 2.0% agarose in a 1X lithium acetate borate buffer solution. Gel was run for 1.5 hours at 120V. Gel Imaged using a GelDoc Go system (BioRad), using faint bands protocol.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4151642/v1/26bcf5a1961e56bf49881fab.png"},{"id":53586968,"identity":"9ab475e0-287f-4cd5-a5d0-c7017ef996ca","added_by":"auto","created_at":"2024-03-27 18:46:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1198028,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4151642/v1/6c59cf00-7788-44ee-9519-b7f73cbc9af1.pdf"},{"id":53549171,"identity":"935a885d-b4a1-4416-bf88-d53d7bdcc111","added_by":"auto","created_at":"2024-03-27 10:55:20","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":33643,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementary1samples.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4151642/v1/331e997a18e1473f6e662904.xlsx"},{"id":53549173,"identity":"8b027073-929b-421d-80e2-94275a34a366","added_by":"auto","created_at":"2024-03-27 10:55:20","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":227563,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementary2SingleplexPCR.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4151642/v1/d6b6cb87802e1af00a923870.pdf"},{"id":53549825,"identity":"9728e737-ef8e-40c8-b0e6-6489e52888b8","added_by":"auto","created_at":"2024-03-27 11:03:20","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":18899,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementary3MultiplexPCR.docx","url":"https://assets-eu.researchsquare.com/files/rs-4151642/v1/c2dfaaa5b187fc0c5d047256.docx"},{"id":53549176,"identity":"cff62eec-363c-4c6c-8de7-fc3a07fea036","added_by":"auto","created_at":"2024-03-27 10:55:20","extension":"txt","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":5552,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementary4ONTpipeline.txt","url":"https://assets-eu.researchsquare.com/files/rs-4151642/v1/a73785bade1bad76b6ed1d14.txt"},{"id":53549129,"identity":"3ac7024d-0f6a-4bc3-bb8e-7aa0be38fc44","added_by":"auto","created_at":"2024-03-27 10:55:20","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":15745,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementary5Illuminaprimers.docx","url":"https://assets-eu.researchsquare.com/files/rs-4151642/v1/f00be5c6ae4da38cfa1c535a.docx"},{"id":53549178,"identity":"d451b59d-1752-4f54-9da0-879dee28bae0","added_by":"auto","created_at":"2024-03-27 10:55:20","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":1133274,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementary6Geneiousampliconanalysisinsructions.docx","url":"https://assets-eu.researchsquare.com/files/rs-4151642/v1/3bfc8988cd5a7b229a31080d.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"High throughput rapid amplicon sequencing for multilocus sequence typing of M. ovipneumoniae using DNA obtained from clinical samples","fulltext":[{"header":"Background","content":"\u003cp\u003eBronchopneumonia is a population-limiting disease of bighorn sheep (BHS), \u003cem\u003eOvis canadensis\u003c/em\u003e, across western North America. \u003cem\u003eM. ovipneumoniae\u003c/em\u003e, the primary etiologic agent of this disease, is transmitted to BHS through contact with domestic sheep and goat herds which are reservoirs for the pathogen (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). \u003cem\u003eM. ovipneumoniae\u003c/em\u003e demonstrates a high degree of genetic and phenotypic diversity across its host range (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). The genetic diversity of \u003cem\u003eM. ovipneumoniae\u003c/em\u003e is high in domestic sheep, indicating their role as a significant reservoir and source of infection, while in BHS, it is low, suggesting spillover as the primary source of transmission. Indeed, ancestral state reconstruction from MLST sequences confirmed domestic sheep as the primary source of infection for BHS, emphasizing the importance of strain typing to map transmission dynamics (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). In BHS, an initial outbreak of fatal bronchopneumonia is often followed by recurring fatal outbreaks in lambs. Recurrent outbreaks have been observed from 2 to 15 years after the initial spillover (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Recent evidence suggests there may be no cross-strain immunity, leaving surviving animals susceptible to infection (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). To reduce the likelihood of spillover events, federal and state agencies have implemented policies focused on spatial separation of domestic and wild sheep (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Increased sampling efforts in the western US and Canada have recently been undertaken to find the wider prevalence of \u003cem\u003eM. ovipneumoniae\u003c/em\u003e in over ten states and three provinces (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDNA-based strain typing is used to document the invasion, persistence, and transmission of \u003cem\u003eM. ovipneumoniae\u003c/em\u003e in these populations (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). A previously developed multilocus sequence typing (MLST) scheme targeting four elements, the 16-23S intergenic spacer region (IGS), 16S rRNA region (LM), RNA polymerase β-subunit gene (\u003cem\u003erpoB\u003c/em\u003e), and DNA gyrase subunit-β gene (\u003cem\u003egyrB\u003c/em\u003e), has demonstrated strong differential typing capability in over 600 samples and 270 strain types (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Owing to the rapid emergence of new strains and the extensive diversity of novel types, creating a conventional database of alternative alleles is impractical (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). In the current Sanger workflow, the four gene fragments are concatenated and then compared pairwise with previously stored type sequences. The definition of a strain is established based on its similarity to stored types, using a specific threshold of four base pairs (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). In cases where \u003cem\u003erpoB\u003c/em\u003e and \u003cem\u003egyrB\u003c/em\u003e do not amplify or are unavailable, strains are denoted by their IGS length, consistent with historical typing methods in use prior to the current MLST scheme (4, 7).\u003c/p\u003e \u003cp\u003eThe current MLST laboratory process uses a nested singleplex PCR, and Sanger sequencing applied to each locus. This method is laborious, and expensive if processing large numbers of samples (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Oxford Nanopore Technologies (ONT) sequencing has recently been used for singleplex and multiplex MLST (\u003cspan additionalcitationids=\"CR13 CR14 CR15\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). This method uses a small, low-cost sequencing device which delivers result in real-time capable of multiplexing and high-throughput sequencing. Amplicon sequencing using ONT has been previously validated for \u003cem\u003eNeisseria gonorrhoeae\u003c/em\u003e antimicrobial resistance genotyping (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). This and other similar workflows were estimated to cost approximately 100 times less than Sanger sequencing for large sample sets (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eONT\u0026rsquo;s Native Barcoding library preparation is recommended for amplicon sequencing due to its higher read accuracy, and preservation of the full-length amplicon (ONT, 2023). Alternatively, Rapid Barcoding library preparation is faster and less expensive, however throughput and raw read accuracy are reported to be reduced (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). An amplicon-specific protocol for Rapid Barcoding is not provided by ONT, though several studies have used that kit for sequencing multiplexed amplicons with a high degree of accuracy (\u003cspan additionalcitationids=\"CR13 CR14\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Based on these successes, Rapid Barcoding library preparation is expected to be well suited for diagnostic settings because of the short library preparation time, flexible multiplexing options, accuracy, and low cost.\u003c/p\u003e \u003cp\u003eOur objective was to develop a sequencing workflow using multiplex PCR followed by Rapid Barcoding Nanopore sequencing using archived DNA from clinical samples. We also compared the speed and accuracy of the optimized Rapid Barcoding workflow with other Nanopore library preparation methods, as well as Sanger, and Illumina sequencing.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSamples\u003c/h2\u003e \u003cp\u003eArchived DNA samples (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;88) were provided by the Washington Animal Disease Diagnostic Lab (WADDL) at Washington State University (Pullman WA, USA) (Supplementary 1). The DNA samples originated from bighorn sheep field samples submitted to WADDL between 2011 and 2016 for diagnostic testing as part of a previously published study (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). The presence of \u003cem\u003eM. ovipneumoniae\u003c/em\u003e DNA was determined by qPCR at WADDL (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). DNA samples were stored at -20\u0026deg;C in a non-defrosting freezer until processing. Storage time was between 3 and 12 years. \u003cem\u003eM. ovipneumoniae\u003c/em\u003e strain Y98 was purchased from the American Type Culture Collection (ATCC 2941 \u0026ndash; Y98, domestic sheep, 1976, NCBI BioProject PRJNA253514) for use as a control sample. Bacterial culture and DNA extraction of the reference strain was performed as previously described (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Sequence typing of samples was previously determined using nested singleplex PCR assay and Sanger sequencing by WADDL (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Since current \u003cem\u003eM. ovipneumoniae\u003c/em\u003e strain typing workflows use Sanger sequencing, new methods were compared to results obtained by Sanger sequencing. For initial NGS sequencing, a random subset of 68 samples was chosen. A subsubset of 24 samples was randomly selected for additional sequencing runs to control for sample variation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003ePCR assays\u003c/h2\u003e \u003cp\u003e \u003cem\u003eSingleplex PCR.\u003c/em\u003e Nested singleplex PCR was performed using primers targeting LM, IGS, \u003cem\u003erpoB\u003c/em\u003e and \u003cem\u003egyrB\u003c/em\u003e loci (Supplementary 2A) (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Cycling conditions were modified for Phusion Flash HiFi all-in-one master mix (Thermofisher, Waltham, MA, USA (Supplementary 2B). External nested PCR reactions were performed for IGS, \u003cem\u003erpoB\u003c/em\u003e and \u003cem\u003egyrB\u003c/em\u003e targets, then one microlitre was carried forward to the inner nested reaction (Supplementary 2C). A single PCR reaction was used for LM. A sterile nuclease-free water sample was included in each PCR run as a negative control. Detailed singleplex PCR cycling conditions are described in Supplementary 2.\u003c/p\u003e \u003cp\u003e \u003cem\u003eMultiplex PCR.\u003c/em\u003e The internal and external primers were pooled in equimolar concentrations of 0.2 \u0026micro;M for a 50 \u0026micro;l PCR reaction (Supplementary 3A) (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). A 3-step PCR protocol was then optimized using a series of 2-fold serial dilutions of Y98 pure isolate DNA (ATCC 2941 \u0026ndash; Y98 strain) and a subset of 5 samples. Optimal annealing temperature and primer concentration were determined experimentally (Supplementary 3B and 3C). Singleplex and multiplex PCR products were stored at -20\u0026deg;C until sequencing library preparation.\u003c/p\u003e \u003cp\u003e \u003cem\u003eGel electrophoresis.\u003c/em\u003e For singleplex and multiplex PCR products, 1.5% and 2% agarose gels were used respectively. Gels were prepared in-house using a 1X lithium acetate borate buffer solution (Sigma-Aldrich, Burlington, MA, USA), SYBR safe DNA gel stain (Invitrogen, Waltham, MA, USA) and a 100 to 1000 bp DNA marker (Invitrogen, Waltham, MA, USA), and all samples were loaded using TriTrack loading dye (ThermoFisher Scientific, Waltham, MA, USA). Gels were run in the buffer solution at 120 V, then imaged using a GelDoc Go (Bio-Rad, Hercules CA, USA).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eIllumina Sequencing\u003c/h2\u003e \u003cp\u003eTwenty-four samples of four nested singleplex PCR products were submitted to the Advanced Analysis Centre (AAC) Genomics facility (University of Guelph, Guelph, ON, CA) for sequencing on an Illumina MiSeq platform (Illumina Technologies, San Diago, CA, USA). The facility used a modified 16S amplicon protocol (Illumina Part # 15044223 Rev. A) with custom primers, and a maximum insert size of 550 bp (Supplementary 5). Sequences were returned as demultiplexed FASTQ files.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eONT sequencing\u003c/h2\u003e \u003cp\u003eThree ONT sequencing experiments were conducted to determine the optimal library preparation method and flow cell configuration (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Prior to library preparation, PCR products were quantified by a Qubit Fluorometer using the dsDNA broad range kit (Invitrogen, Waltham, MA, USA) and diluted without purification according to the ONT library preparation protocol.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescription of Oxford Nanopore sequencing experiment conditions.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExperiment No.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFlow cell Chemistry \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLibrary Preparation \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFlow cell \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLibrary sample size \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRuntime (h)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eExperiment 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eR9.4.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRapid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNew\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWashed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eExperiment 2\u003c/p\u003e \u003cp\u003eExperiment 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eR10.4.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNew\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWashed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eAll sequencing runs were conducted using a minION Mk1B device: Oxford Nanopore Technologies (ONT).\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e Specific flowcell chemistry used.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003csup\u003e\u003cb\u003eb\u003c/b\u003e\u003c/sup\u003e Library preparation method. Rapid: Nanopore Rapid Barcoding (SQK-RBK110/114.96); Native: Nanopore Native Barcoding (SQK-NBD114.96).\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003csup\u003e\u003cb\u003ec\u003c/b\u003e\u003c/sup\u003e New: Unused flowcell from ONT, Washed: Washed flow cell reused from previous sequencing run using EXP-WSH004 wash kit.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003csup\u003e\u003cb\u003ed\u003c/b\u003e\u003c/sup\u003e Number of samples barcoded in the prepared library. The same 24 samples were included in all libraries.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eONT Experiment One\u003c/em\u003e determined the viability of ONT Rapid Barcoding library preparation for multiplexed amplicons using ONT\u0026rsquo;s R9.4.1 flow cell, Rapid Barcoding library preparation kit, and flow cell wash kit (EXP-WSH004) (Oxford Nanopore Technologies, Oxford, UK). PCR products were quantified by a Qubit Fluorometer using the dsDNA broad range kit (Invitrogen, Waltham, MA, USA) and diluted without purification according to the ONT library preparation protocol (RBK_9126_v110_revO). Then, 68 multiplex PCR products were barcoded according to the SQK-RBK110.96 protocol. An R9.4.1 flow cell was loaded with 400 ng of DNA library. Following a 16-hour runtime, the flow cell was washed using the EXP-WSH004 kit and immediately loaded with a second library containing a subset of 24 barcoded samples from the preceding run. The second library (washed flow cell) was sequenced for 16 hours.\u003c/p\u003e \u003cp\u003e \u003cem\u003eONT Experiment Two\u003c/em\u003e evaluated the Native Barcoding library preparation kit, with the most current ONT R10.4.1 flow cell as recommended by ONT, for comparison with ONT Experiment One. Twenty-four multiplex PCR products were barcoded according to the SQK-NBD114.96 protocol (NBA_9170_v114_revF, ONT, 2023). Following a 16-hour runtime, the flow cell was washed using the EXP-WSH004 kit and immediately loaded with a second library of the same 24 samples. Libraries were differentially barcoded to avoid carryover between runs. The second library was prepared using the same method as the first run and sequenced for 16 hours.\u003c/p\u003e \u003cp\u003e \u003cem\u003eONT Experiment Three\u003c/em\u003e was performed identically to ONT Experiment Two by another technician to control for human error.\u003c/p\u003e \u003cp\u003e \u003cem\u003eONT sequencing run parameters.\u003c/em\u003e All sequencing runs used a minION Mk1B instrument and minKNOW v23.7.15 (ONT) operating software. The minimum read length was set to 20 bp, real-time basecalling was turned off, data output was set to \u0026ldquo;.POD5\u0026rdquo; to collect raw signal data, active channel selection was turned on, and \"reserve pores\u0026rdquo; was turned off to maximize initial throughput. Runtime was set to 16 hours in all experiments. For new and washed flow cells, a flow cell check was conducted immediately prior to loading the library using the \u0026ldquo;flow cell check\u0026rdquo; option in the minKNOW software homepage. Flow cells with fewer than 800 new, or 400 washed active pores were not used.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eData analysis\u003c/h2\u003e \u003cp\u003e \u003cem\u003eONT analysis.\u003c/em\u003e Basecalling was performed with Dorado v0.3.0 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/nanoporetech/dorado\u003c/span\u003e\u003cspan address=\"https://github.com/nanoporetech/dorado\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) using the \u0026ldquo;fast\u0026rdquo; model with demultiplexing. Then, the demultiplexed fastq files were submitted to a custom analysis pipeline (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Briefly, basecalled reads were demultiplexed and trimmed using GUPPY v.6.4.8 (ONT), reads below quality score 8 were removed using Chopper v0.5.0 (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e), then reads were aligned to the four reference genes, i.e., a deconvolution step, using Minimap2 v2.24 (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). The resultant alignments were sorted and indexed, then alignment statistics were generated, including depth and number of reads mapped using samtools v.1.17 (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Consensus sequences were \u0026ldquo;called\u0026rdquo; using samtools consensus with default calling (Bayseian mode with quality-aware mapping). Draft consensus sequences were polished using Medaka v1.8.1 (ONT) to produce final output sequences. If the average depth of one or more loci in a sample was \u0026lt;\u0026thinsp;50x, the sample was excluded from downstream analyses. Homopolymer errors in IGS were manually corrected post-polishing by adding a T at position 113 to correct the sequence to 8 Ts. A shell script for this pipeline is provided (Supplementary 4). Sequences were imported into Geneious prime for final typing (Supplementary 6).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eIllumina analysis.\u003c/em\u003e Forward and reverse reads were imported into Geneious Prime v2023.2.1 (Dotmatics). The average read quality and number of reads were recorded using the Geneious statistics panel view for each read group. Reads were paired by name and trimmed using BBDuk v1.0 (BBMap \u0026ndash; Bushnell B. - \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://jgi.doe.gov/data-and-tools/software-tools/bbtools/\u003c/span\u003e\u003cspan address=\"https://jgi.doe.gov/data-and-tools/software-tools/bbtools/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) via the Geneious plugin, then aligned to the reference sequence within Geneious. Consensus sequences were generated from each alignment and compared to the corresponding reference Sanger sequence using the Geneious local alignment tool. The pairwise identity and number of mismatches were recorded. Finally, LM, IGS and \u003cem\u003egyrB\u003c/em\u003e were concatenated for typing (Supplementary 6).\u003c/p\u003e \u003cp\u003e \u003cem\u003eConsensus sequences quality and accuracy determination\u003c/em\u003e. Consensus sequences, which are representative sequences of each amplicon, were generated by Bayesian estimation of the true base at each position of the alignment using the Samtools consensus module with quality aware mapping. The accuracy and quality of the consensus sequences from each method were characterized by i) sequencing coverage of each target locus, ii) the number of reads aligned to each reference gene sequence, iii) the percent identity consensus sequence and corresponding Sanger sequence, and iv) the number of mismatches between the consensus and the Sanger sequence. Gaps in the consensus sequence were replaced with Ns and treated as mismatches. The Q-score average read quality for Illumina and ONT runs were recorded using phred-33 encoding.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003eStatistical analyses\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eLinear regression model was constructed to determine the relationship between coverage and mismatches. The \u0026ldquo;lm\u0026rdquo; function in R was used with the number of mismatches as the response variable and coverage as the predictor variable (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e).The F-test statistic with associated p-value was used to determine significance of the relationship from the model summary. Tukey\u0026rsquo;s honestly significant difference test was used to determine the level of independence of errors between genes and runs. Statistical significance was set at p \u0026le; 0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e \u003cem\u003eSingleplex and multiplex PCR\u003c/em\u003e. A three-step singleplex nested PCR protocol was used to amplify the four MLST genes for Illumina sequencing. After gel electrophoresis, distinct bands of the expected sizes were visible for all targets (Supplementary 3D). The multiplex PCR assay was optimized for the amplification of the four MLST loci in a single 50 \u0026micro;l reaction. After electrophoresis, the optimized multiplex PCR produced four distinct bands of 360 bp, 470\u0026ndash;510 bp, 547 bp and 680 bp corresponding to LM, IGS, \u003cem\u003erpoB\u003c/em\u003e, and \u003cem\u003egyrB\u003c/em\u003e, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Fragments from external nested primers were not visible.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eIllumina sequencing\u003c/h2\u003e \u003cp\u003eIllumina sequencing generated full-length read pairs for IGS, LM and \u003cem\u003egyrB\u003c/em\u003e within 48 hours. Raw read and consensus quality for Illumina was higher than all ONT methods (Tables\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Illumina consensus sequences were obtained for LM, IGS and \u003cem\u003egyrB\u003c/em\u003e loci, however \u003cem\u003erpoB\u003c/em\u003e (680 bp) consensus sequences could not be resolved because there was no overlap for pairing the forward and reverse reads due to the maximum insert size of 550 bp. Resultant pairings were missing the middle\u0026thinsp;~\u0026thinsp;130 bp. Therefore, downstream analyses omit \u003cem\u003erpoB\u003c/em\u003e for Illumina data.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOxford Nanopore and Illumina sequencing run metrics.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eExperiment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFlow cell chemistry\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLibrary preparation\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFlow cell\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eNumber of reads (M)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eRead quality \u003csup\u003eg\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e% Reads\u0026thinsp;\u0026gt;\u0026thinsp;Q20 \u003csup\u003eh\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eActive pores \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePre-filtering \u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePost-filtering \u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePre-filtering \u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003ePost-filtering \u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eExperiment 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eR9.4.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRapid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNew\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1426\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e12.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e16.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e38.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWashed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e935\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e12.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e16.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e38.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExperiment 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR10.4.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNew\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1646\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e13.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e17.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e38.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eExperiment 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eR10.4.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNew\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e12.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e15.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e30.6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWashed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e491\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e12.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e16.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e31.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIllumina\u003csup\u003ei\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIllumina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModified 16S\u003csup\u003ei\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e8.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e30.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e34.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e81.2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e\u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e Nanopore flow cell chemistry used.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e\u003csup\u003e\u003cb\u003eb\u003c/b\u003e\u003c/sup\u003e Type of kit used for library preparation. Rapid: Rapid Barcoding 96 (SQK-RBK110-96 for R9.4.1; SQK-RBK114.96 for R10.4.1); Native: Native Barcoding kit (SQK-NBD114.96).\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e\u003csup\u003e\u003cb\u003ec\u003c/b\u003e\u003c/sup\u003e New: New flow cell from ONT, washed: washed flow cell reused from previous sequencing run using EXP-WSH004 wash kit.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e\u003csup\u003e\u003cb\u003ed\u003c/b\u003e\u003c/sup\u003e Number of active pores reported by minKNOW at the beginning of the sequencing run following flow cell loading.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e\u003csup\u003e\u003cb\u003ee\u003c/b\u003e\u003c/sup\u003e Metric calculated immediately post-basecalling before demultiplexing or read filtering.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e\u003csup\u003e\u003cb\u003ef\u003c/b\u003e\u003c/sup\u003e Metric calculated post-filtering, reads Q8.0 and below removed before calculation.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e\u003csup\u003e\u003cb\u003eg\u003c/b\u003e\u003c/sup\u003e Read quality reported as an average Q-score (phred33).\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e\u003csup\u003e\u003cb\u003eh\u003c/b\u003e\u003c/sup\u003e Percent of total reads post-filtering with quality score\u0026thinsp;\u0026gt;\u0026thinsp;20.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e\u003csup\u003e\u003cb\u003ef\u003c/b\u003e\u003c/sup\u003e Illumina sequencing performed using modified 16S rDNA library preparation with custom primers (Advanced Analysis Center, Guelph, Canada).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eQuality of Oxford Nanopore and Illumina consensus sequences post-alignment for samples with \u0026gt;\u0026thinsp;50x coverage. Averages reported are aggregate for \u003cem\u003egyrB\u003c/em\u003e, IGS, LM and \u003cem\u003erpoB\u003c/em\u003e for 24 samples, \u0026plusmn; standard deviation.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExperiment No.\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFlow cell Chemistry\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLibrary Preparation\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFlow cell\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo. of samples \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAverage Coverage \u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNo. Reads Mapping to Reference \u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePercent Identity to Sanger \u003csup\u003eg\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNumber of Mismatches \u003csup\u003eh\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e% Types Correctly Identified\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eExperiment 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eR9.4.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRapid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNew\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e60/68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e2037x\u003c/b\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;1998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e3956\u003c/b\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;3833\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e100%\u003c/b\u003e \u0026plusmn; 0.001%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0 2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e100%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWashed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21/24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e4803x\u003c/b\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;6530\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e8753\u003c/b\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;11343\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e100%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e100%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExperiment 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR10.4.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNew\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23/24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e13058x\u003c/b\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;13470\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e13525\u003c/b\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;14253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e99.1%\u003c/b\u003e \u0026plusmn; 1.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.48\u0026thinsp;\u0026plusmn;\u0026thinsp;5.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e62%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eExperiment 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eR10.4.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNew\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24/24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e38293x\u003c/b\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;35904\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e39017\u003c/b\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;37751\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e99.0%\u003c/b\u003e \u0026plusmn; 0.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.64\u0026thinsp;\u0026plusmn;\u0026thinsp;3.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e71%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWashed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18/24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e13972x\u003c/b\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;15120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e14195\u003c/b\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;15963\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e99.1%\u003c/b\u003e \u0026plusmn; 0.001%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.59\u0026thinsp;\u0026plusmn;\u0026thinsp;5.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e83%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIllumina \u003csup\u003ei\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIllumina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModified 16S rRNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNew\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20/24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e130674x\u003c/b\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;57640\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e130674\u003c/b\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;57640\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e100%\u003c/b\u003e \u0026plusmn; 0.001%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e\u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e Experiment 1: Nanopore Rapid Barcoding library preparation, 16 h runtime. Experiment 2 and 3: Nanopore Native Barcoding library preparation, 16 h runtime.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e\u003csup\u003e\u003cb\u003eb\u003c/b\u003e\u003c/sup\u003e Nanopore library preparation method: Rapid\u0026thinsp;=\u0026thinsp;Rapid Barcoding 96 kit (SQK-RBK110.96), Native\u0026thinsp;=\u0026thinsp;Native Barcoding 96 kit (SQK-RBK114.96).\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e\u003csup\u003e\u003cb\u003ec\u003c/b\u003e\u003c/sup\u003e New: New flowcell from ONT, washed: washed flow cell reused from previous sequencing run using EXP-WSH004 wash kit.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e\u003csup\u003e\u003cb\u003ed\u003c/b\u003e\u003c/sup\u003e Number of samples over 50x coverage included in analysis \u003cb\u003e/\u003c/b\u003e number of samples sequenced.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e\u003csup\u003e\u003cb\u003ee\u003c/b\u003e\u003c/sup\u003e Coverage of target loci, averaged for all samples over 50x coverage.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e\u003csup\u003e\u003cb\u003ef\u003c/b\u003e\u003c/sup\u003e Number of reads aligned to the corresponding Sanger sequence reference, reported as an average for all loci and samples in the run.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e\u003csup\u003e\u003cb\u003eg\u003c/b\u003e\u003c/sup\u003e Percent identity to the Sanger sequence reference of the same sample.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e\u003csup\u003e\u003cb\u003eh\u003c/b\u003e\u003c/sup\u003e Number of bases that do not match the same position in the Sanger sequence.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e\u003csup\u003e\u003cb\u003ei\u003c/b\u003e\u003c/sup\u003e Illumina sequencing conducted by third party (Advanced Analysis Center, Guelph, Canada).\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003eN/A No data for \u003cem\u003erpoB\u003c/em\u003e (680 bp) presented because of 550bp limit. Therefore, strain type is indeterminate.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eONT sequencing\u003c/h2\u003e \u003cp\u003e \u003cem\u003eRun quality.\u003c/em\u003e Run quality and raw read statistics are reported in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Average read quality for all ONT runs were similar pre- and post-filtering. The highest run yield, 7.87 Gb, was from the R10.4.1 new flow cell (ONT Experiment Two). The number of active pores decreased by approximately 700 after 16-hour runtime and a washing step. The throughput of washed flow cells from ONT Experiments One and Three were reduced by approximately half for the same 16-hour runtime; however, read quality was similar to the first run. In ONT Experiment Two, flow cell washing was unsuccessful due to the formation of air bubbles over the sensor array which irreversibly damaged the pores leaving fewer than 100 active pores.\u003c/p\u003e \u003cp\u003e \u003cem\u003eONT bioinformatics pipeline.\u003c/em\u003e A custom bioinformatic pipeline was constructed using open-source tools (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The bioinformatic analysis of the 24 samples took 30 to 50 minutes from read filtering to final output depending on the number of total reads (10 CPU cores, 32Gb memory). Polishing of consensus sequences using Medaka increased agreement with Sanger sequences, especially for lower coverage samples.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eComparison of alignment and consensus sequence statistics\u003c/h2\u003e \u003cp\u003eConsensus sequences were called using alignments generated for each gene and assessed for quality and accuracy (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The depth of coverage varied across Illumina and ONT runs (130674x\u0026thinsp;\u0026plusmn;\u0026thinsp;57640 and 16699x \u0026plusmn; 24438, respectively); the highest coverage, 38293x\u0026thinsp;\u0026plusmn;\u0026thinsp;35904x, was observed in ONT Experiment Three (Native Barcoding with R10.4.1 new flow cell, n\u0026thinsp;=\u0026thinsp;24) and the lowest coverage, 2037x\u0026thinsp;\u0026plusmn;\u0026thinsp;1998x, was observed in ONT Experiment One (Rapid Barcoding, R9.4.1 new flow cell, n\u0026thinsp;=\u0026thinsp;68). Some samples were excluded from downstream analysis in ONT Experiment Three (washed) (n\u0026thinsp;\u003cem\u003e=\u003c/em\u003e\u0026thinsp;6 removed), due to \u0026lt;\u0026thinsp;50x coverage even though the average depth for the run was high (1397x\u0026thinsp;\u0026plusmn;\u0026thinsp;15120x). The number of reads aligned to each gene closely correlated with coverage (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), where Experiment One had the smallest average number of reads per sample and Experiment Three had the largest. For ONT Experiment One (Rapid Barcoding, R9.4.1), the number of reads was approximately double the average coverage while in ONT Experiments Two and Three (Native Barcoding, R10.4.1 flow cell) the number of reads was equal to the coverage. This highlights the technical differences in library preparation methods and effect of read length.\u003c/p\u003e \u003cp\u003eConsensus sequences with coverage greater than 50x were compared with the corresponding Sanger sequence (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Consensus sequences in ONT Experiment One were identical to the corresponding Sanger sequence of the same samples with 100% identity, which resulted in all strain types being identified. ONT Experiments Two and Three consistently shared 99% identity with corresponding Sanger sequences, and there were more mismatches to Sanger sequences than ONT Experiment One. A linear regression analysis of mismatches for ONT Experiments 2 and 3 did not show a relationship between the coverage and the number of mismatches to the corresponding Sanger sequence (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.0001648, F\u003csub\u003e1,286\u003c/sub\u003e = 0.04713, p\u0026thinsp;=\u0026thinsp;0.8283). Mismatches in Experiments Two and Three occurred more often in \u003cem\u003egyrB\u003c/em\u003e and \u003cem\u003erpoB\u003c/em\u003e targets, and samples with mismatches in one target were more likely to have mismatches in other targets (Tukey\u0026rsquo;s HSD, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) with a bimodal distribution of mismatches. Only 72% (n\u0026thinsp;=\u0026thinsp;47 of 65) of samples were correctly typed for ONT Experiments Two and Three (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cem\u003eTurnaround time\u003c/em\u003e. The time to obtain MLST sequences from DNA samples was determined as the time from PCR preparation to final typing output (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Multiplex PCR with ONT Rapid Barcoding library preparation (ONT Experiment One) had the shortest turnaround time of 19.5 hours of which 1 hour 45 minutes were hands-on time. The Illumina sequencing workflow with singleplex PCR could be completed in 52 hours with 8 hours hands-on time, however the turnaround time for Illumina sequencing depended on third-party services which took four to eight weeks for us to receive the sequencing results.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eComparison of ONT with Illumina.\u003c/em\u003e ONT sequencing generated less reads and coverage overall than Illumina sequencing (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Sequences obtained from ONT Experiment One (Rapid Barcoding, R9.4.1 flow cell) and from Illumina had 100% identity with Sanger sequences. ONT Experiments Two and Three (Native Barcoding, R10.4.1 flow cell) showed lower percent identity with Sanger sequences than the other experiments (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). ONT Experiment One (Rapid Barcoding, R9.4.1 flow cell new and washed) was the only method that correctly identified all strain types. Illumina consensus sequences were identical to Sanger sequences for 23 of the 24 samples for LM, \u003cem\u003egyrB\u003c/em\u003e and IGS with three mismatches occurring in a single sequence (IGS, WADDL #00126). Illumina sequencing failed to generate \u003cem\u003erpoB\u003c/em\u003e sequences. Since \u003cem\u003erpoB\u003c/em\u003e could not be recovered fully with the Illumina method, strain types could not be determined.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we compared the efficiency of different sequencing approaches for strain typing of \u003cem\u003eM. ovipneumoniae\u003c/em\u003e, including ONT, Illumina, and Sanger sequencing. For that, we optimized and validated a workflow using multiplex PCR and Rapid ONT sequencing for strain typing of \u003cem\u003eM. ovipneumoniae\u003c/em\u003e from DNA samples. Further, we developed a custom bioinformatic pipeline to deconvolute and align reads, generate a consensus, and error-correct the final consensus sequences.\u003c/p\u003e \u003cp\u003eIllumina sequences had the highest quality read and consensus Q-scores, however, due to the maximum insert size of 550 bp, the full length \u003cem\u003erpoB\u003c/em\u003e, at 680 bp, could neither be paired nor aligned. To maintain backwards compatibility with the Sanger scheme, the Illumina method we followed was insufficient for all loci and it was more costly and time consuming. However, in a similar study, multiplex PCR of four genes for MSLT of \u003cem\u003eM. genitalium\u003c/em\u003e decreased the cost of Illumina library preparation, and all target fragments were under 500 bp in length (Plummer et al. 2020). This approach could be useful for \u003cem\u003eM. ovipneumoniae\u003c/em\u003e MLST in diagnostic laboratories which already use Illumina but would require a re-design of the \u003cem\u003erpoB\u003c/em\u003e primer set to reduce amplicon length, which risks removal of relevant bases.\u003c/p\u003e \u003cp\u003eThe Rapid and Native Barcoding library preparations from ONT were compared to determine their suitability for multiplex amplicon sequencing. The Rapid Barcoding library approach identified 100% of strain types despite having lower total yield and lower per-loci depth than Native Barcoding (72% identified). This suggests that mismatches were not a result of low sequencing depth but might have arisen because of cross-barcoding. A previous study comparing library preparation methods from ONT found that Native Barcoding library preparation delivered the highest total number of reads followed closely by Rapid Barcoding, which is consistent with our findings (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). However, the same study also showed that even low levels of cross-barcoding during library preparation led to \u0026ldquo;barcode leakage\u0026rdquo; during demultiplexing, which increased misidentified single nucleotide variants compared to non-barcoded runs. The updated kit 14 chemistry (Native Barcoding 114.96 vs. previous kit 10 Native Barcoding 110.96) used in our study eliminated thermal inactivation of the barcode ligation enzymes, which could increase the chance of cross-barcoding. Rapid Barcoding uses a heat-activated transposase, which is inactive at room temperature, so there is little risk of cross-barcoding. Thus, we suspect that cross-barcoding during Native Barcoding library preparation contributed to a low proportion of correctly identified strain types.\u003c/p\u003e \u003cp\u003eThe shortest turnaround time was achieved with the ONT Rapid Barcoding workflow (ONT Experiment One), which was under 20 hours. This optimal workflow takes one-hour for multiplex PCR, 1.5 hours for Rapid Barcoding library preparation, 16 hours for sequencing runtime, and one hour for data analysis. This is promising for diagnostic applications, such as outbreak scenarios, where timely strain identification is critical (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). The ONT Rapid workflow delivered the strain type in less than 20 hours, while Illumina took more than 50 hours and failed to capture the full length of the \u003cem\u003erpoB\u003c/em\u003e target. A comparison of ONT and Illumina sequencing methods for diagnostic purposes found that shorter turnaround time of ONT sequencing was of significant clinical value and was more important to clinical outcome than the relatively insignificant difference in accuracy between ONT and Illumina sequences (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). We designed the bioinformatic analysis pipeline to be run using a laptop computer (10 CPUs, 32Gb memory), and to be user-friendly for professionals without a bioinformatics background, or minimally equipped laboratories.\u003c/p\u003e \u003cp\u003eThe per-sample cost of library preparation for ONT sequencing varies by method. Ligation-based library preparation kits, such as the Native Barcoding kit used in Experiments Two and Three, require costly third-party reagents for end repair, dA-tailing, and adapter ligation. For Experiment Two and Three (Native Barcoding, R10.4.1 flow cell), the library preparation cost was approximately \u003cspan\u003e$\u003c/span\u003e7.30 USD per sample for 12 or more samples. In contrast, the Rapid Barcoding kit used in Experiment One did not require extra reagents and was approximately \u003cspan\u003e$\u003c/span\u003e3.49 USD per sample for 12 or more.\u003c/p\u003e \u003cp\u003eWe also washed and reused minION flow cells to decrease costs and found that the read quality was not impacted. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, a subset of samples from ONT Experiment One (new flow cell) were sequenced in a second run after washing the flow cell (Experiment One/washed). The sequence types obtained from the washed and reused flow cell were identical to those of the corresponding samples in the first run using a new flow cell. Compared to another approach (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e), wherein a single flow cell was successfully used five times, our results also indicate that the effects of the flow cell reuse are marginal, and sequence quality is not influenced by the preceding run. Decreased pore counts following each wash should be accounted for, and we suggest adjusting runtime to reach minimum 50x coverage for all loci in each sample.\u003c/p\u003e \u003cp\u003eThe Rapid ONT workflow developed in this study generated highly accurate sequences, however, some inherent errors may still exist due to error prone ONT reads. A recent proof of concept for ONT amplicon sequencing called 97% of expected variants and noted a high error rate, especially for homopolymer and homopolymer-adjacent regions (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). We corrected similar homopolymer errors by using Medaka polishing. Alignment of IGS showing misidentified bases almost always resulted from T8 homopolymers called as T7 at position 113. These were manually corrected since no strain types carry a T7 homopolymer region at that position. This manual correction of homopolymers decreases automation of the method, and therefore more hands-on time is required to check for homopolymer errors.\u003c/p\u003e \u003cp\u003eIt was anticipated that pooling equimolar quantities for each PCR amplicon would result in comparable average depth for each product when aligned to the respective reference. However, the average depth for each amplicon varied widely between 32 and 16776x (mean\u0026thinsp;=\u0026thinsp;16230 std err\u0026thinsp;=\u0026thinsp;24249) across ONT Experiments One, Two, and Three. This result is comparable to another group which noted a range of 127 to 19,626-fold coverage (mean\u0026thinsp;=\u0026thinsp;8320.69, std err\u0026thinsp;=\u0026thinsp;452.99) for ONT amplicon sequencing, and a minimum of 100x coverage was required for typing (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Similarly, we found that 50x coverage of each amplicon in the multiplex was required for the optimized workflow. Setting a minimum coverage per amplicon ensures all loci in the sample have adequate sequence information for a high-quality consensus sequence. We also found a high standard deviation of coverage between barcodes for all ONT runs (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). This suggests that the sequencing run parameters can be better optimized to reduce unnecessary sequencing time by normalizing the coverage across barcodes. Barcode balancing in minKNOW provides this in real-time and could be used for future runs. The lowest per-sample coverage was observed for ONT Experiment One new flow cell with 68 samples. This outcome was consistent with the logical implications of the experimental design in which 68 samples were sequenced for the same amount of time as subsequent runs with only 24 samples.\u003c/p\u003e \u003cp\u003eWe recommend the use of multiplex PCR and ONT Rapid Barcoding library preparation for \u003cem\u003eM. ovipneumoniae\u003c/em\u003e typing due to the high accuracy of the consensus sequences, lowest cost, and shortest turnaround time. These benefits are compounded when multiplexing many samples, making the workflow ideal for outbreak scenarios or population surveys (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). The workflow can be implemented in-house with no initial capital, lower per-sample cost than Sanger or Illumina sequencing, and less technician hands-on time. In contrast, the initial capital cost for Illumina sequencing is often prohibitive; laboratories instead rely on off-site commercial facilities which may take upwards of two weeks for results.\u003c/p\u003e \u003cp\u003eA unique challenge of this study is the diversity of \u003cem\u003eM. ovipneumoniae\u003c/em\u003e strain types. Only a subset of archived samples from bighorn sheep strain types were selected for this study, and we therefore assume the selected samples are representative of all strain types. Furthermore, detection of multiple strain types in one sample was not assessed in this study, although the presence of multiple strain types has previously been observed in wild and domestic sheep (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Minimal modifications to our workflow would be needed to add loci or changed for any MLST. Modification of the multiplex PCR and modification of the reference allele text file in the pipeline are the only modifications required to customize this pipeline for adding more loci or substituting other MLST schemes. A limitation of the comparison of the ONT library preparation methods is the difference in technology revisions. ONT Experiment One, used the R9.4.1 flow cell and ONT Experiments Two and Three used the R10.4.1 flow cells. There are few other studies on the performance of R10.4.1/kit14 for amplicon sequencing. One group compared R9.4.1 chemistry with R10.4.0, and reported that although R10.4.0 reads were more accurate, R9.4.1 flow cells were more reliable (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). The discrepancies we noted between ONT Experiment One and ONT Experiments Two and Three could be explained by the differing flow cell and sequencing chemistry changes and not the library preparation method. Further investigation of R10.4.1 and Rapid Barcoding kit for multiplex amplicon sequencing could eliminate the need for manual homopolymer correction, as claimed by ONT. In this study we used a set runtime of 16 hours for ONT sequencing; however, a similar workflow for MLST of \u003cem\u003eS. aureus\u003c/em\u003e stopped sequencing once there were 4000 reads per sample to ensure adequate coverage without oversampling (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). In that protocol, the authors used a single flow cell five times (467 samples total), and fewer than 4 hours were required for adequate sequence data for the first three runs, with successive run requiring 6\u0026ndash;15 hours until the flow cell was depleted (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). This approach could decrease turnaround time and cost for our workflow.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eWe developed and validated a workflow for multilocus sequence typing of \u003cem\u003eM. ovipneumoniae\u003c/em\u003e from DNA samples using multiplex PCR and Nanopore Rapid Barcoding sequencing. This method was compared to Nanopore Native Barcoding library preparation and Illumina MiSeq modified amplicon protocols to determine the most accurate and cost-effective method for sequencing multiplex amplicons. Nanopore Rapid Barcoding sequencing produced the most accurate consensus sequences with shortest workflow time. The difficulty of obtaining highly accurate consensus sequences from error prone Nanopore reads was mitigated through high coverage and consensus polishing. Therefore, the workflow is suitable for diagnostic settings where reduced hands-on time, cost and multiplexing capabilities are important. To our knowledge, this is the first Rapid Barcoding ONT workflow developed for \u003cem\u003eMycoplasma\u003c/em\u003e, a method that could be applied to type other \u003cem\u003eMycoplasma\u003c/em\u003e species or other fastidious bacteria.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u0026ndash; Not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e \u0026ndash; Not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGenBank accession numbers for Sanger sequences of all samples used in this study are available in supplementary 1. Raw sequence reads for all runs conducted in this study, and polished consensus sequences are available at https://doi.org/10.6084/m9.figshare.25395310\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCompeting interests - The authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGrant funding for this study was provided by the Wild Sheep Foundation. In kind contributions from The Ontario Veterinary College Department of Pathobiology\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIF: prepared samples, performed multiplex PCR and Oxford Nanopore sequencing, and wrote the manuscript. G.C. conducted ONT sequencing. T.E.B., P.L.K., J.B-M., J.S., and N.R. advised methods development, data analysis and revised the manuscript. GM: was responsible for conceptual design of the project, funding acquisition, and supervised the work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank the Washington Animal Disease Diagnostic Laboratory for maintaining a collection of bighorn sheep DNA samples, and Dr. Thomas Besser for his mentorship and guidance.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKamath PL, Manlove K, Cassirer EF, Cross PC, Besser TE. Genetic structure of Mycoplasma ovipneumoniae informs pathogen spillover dynamics between domestic and wild Caprinae in the western United States. Sci Rep. 2019;9:15318.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBesser TE, Cassirer EF, Potter KA, Foreyt WJ. Exposure of bighorn sheep to domestic goats colonized with Mycoplasma ovipneumoniae induces sub-lethal pneumonia. PLoS ONE. 2017;12:e0178707\u0026ndash;0178707.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaksimovic Z, De la Fe C, Amores J, Gomez-Martin A, Rifatbegovic M. Comparison of phenotypic and genotypic profiles among caprine and ovine Mycoplasma ovipneumoniae strains. Vet Rec. 2017;180:180.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCassirer EF, Manlove KR, Plowright RK, Besser TE. Evidence for strain-specific immunity to pneumonia in bighorn sheep. J Wildl Manag. 2017;81:133\u0026ndash;43.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePlowright RK, Manlove KR, Besser TE, P\u0026aacute;ez DJ, Andrews KR, Matthews PE, Waits LP, Hudson PJ, Cassirer EF, Leo GD. Age-specific infectious period shapes dynamics of pneumonia in bighorn sheep. Ecol Lett. 2017;20:1325\u0026ndash;36.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBesser TE, Cassirer EF, Potter KA, VanderSchalie J, Fischer A, Knowles DP, Herndon DR, Rurangirwa FR, Weiser GC, Srikumaran S. Association of Mycoplasma ovipneumoniae infection with population-limiting respiratory disease in free-ranging Rocky Mountain bighorn sheep (Ovis canadensis canadensis). J Clin Microbiol. 2008;46:423\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBesser TE, Highland MA, Baker K, Cassirer EF, Anderson NJ, Ramsey JM, Mansfield K, Bruning DL, Wolff P, Smith JB, Jenks JA. Causes of pneumonia epizootics among bighorn sheep, Western United States, 2008\u0026ndash;2010. Emerg Infect Dis. 2012;18:406\u0026ndash;14.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWalsh DP, Felts BL, Cassirer EF, Besser TE, Jenks JA. 2023. Host vs. pathogen evolutionary arms race: Effects of exposure history on individual response to a genetically diverse pathogen. Frontiers in ecology and evolution 10.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrewer C, Hurley K, Schwanke B, Allen J, Jorgenson J, Henry B, Schwantje H, Stephenson T, Bleich V, George J, Miller M, Toweill D, Carlsen T, Nordeen T, Trindle B, Cox M, Rominger E, Wiedmann B, Whittaker D, Coggins V, Kanta J, Benzon T, Hernandez F, Aoude A, Karpowitz J, Martorello D, McWhirter D, Hegel T, Carey J, Woolever M, Rinkes T, Krause A. 2012. Recommendations for Domestic Sheep and Goat Management in Wild Sheep Habitat. USDA.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMartin AM, Cassirer EF, Waits LP, Plowright RK, Cross PC, Andrews KR. Genomic association with pathogen carriage in bighorn sheep (Ovis canadensis). 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Development of a long-read next generation sequencing workflow for improved characterization of fastidious respiratory mycoplasmas. Microbiol (Reading) 168.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDe Coster W, Rademakers R. 2023. NanoPack2: population-scale evaluation of long-read sequencing data. Bioinf (Oxford England) 39.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi H. New strategies to improve minimap2 alignment accuracy. Bioinformatics. 2021;37:4572\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R. The Sequence Alignment/Map format and SAMtools. Bioinformatics. 2009;25:2078\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eR Core Team. 2023. R: A language and environment for statistical computing, R Foundation for Statistical Computing, Vienna, Austria. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.R-project.org/\u003c/span\u003e\u003cspan address=\"https://www.R-project.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang J, Gao L, Zhu C, Jin J, Song C, Dong H, Li Z, Wang Z, Chen Y, Yang Z, Tan Y, Wang L. Clinical value of metagenomic next-generation sequencing by Illumina and Nanopore for the detection of pathogens in bronchoalveolar lavage fluid in suspected community-acquired pneumonia patients. Front Cell Infect Microbiol. 2022;12:1021320\u0026ndash;1021320.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang Y, Zhao Y, Bollas A, Wang Y, Au KF. Nanopore sequencing technology, bioinformatics and applications. Nat Biotechnol. 2021;39:1348\u0026ndash;65.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLonas G, Clarke JK, Marshall RB. The isolation of multiple strains of Mycoplasma ovipneumoniae from individual pneumonic sheep lungs. Vet Microbiol. 1991;29:349\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSanderson ND, Kapel N, Rodger G, Webster H, Lipworth S, Street TL, Peto T, Crook D, Stoesser N. 2023. Comparison of R9.4.1/Kit10 and R10/Kit12 Oxford Nanopore flowcells and chemistries in bacterial genome reconstruction. Microbial genomics 9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStamatakis A. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics. 2014;30:1312\u0026ndash;3.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e Description of Oxford Nanopore sequencing experiment conditions.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.8045515394913%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eExperiment No.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.946452476572958%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFlow cell Chemistry \u003csup\u003ea\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.34805890227577%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eLibrary Preparation \u003csup\u003eb\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.056224899598394%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFlow cell \u003csup\u003ec\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.92235609103079%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eLibrary sample size \u003csup\u003ed\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.92235609103079%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eRuntime (h)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.8045515394913%\" rowspan=\"2\"\u003e\n \u003cp\u003eExperiment 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.946452476572958%\" rowspan=\"2\"\u003e\n \u003cp\u003eR9.4.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.34805890227577%\" rowspan=\"2\"\u003e\n \u003cp\u003eRapid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.056224899598394%\"\u003e\n \u003cp\u003eNew\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.92235609103079%\"\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.92235609103079%\" valign=\"top\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.546325878594246%\"\u003e\n \u003cp\u003eWashed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.22683706070288%\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.22683706070288%\" valign=\"top\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.8045515394913%\" rowspan=\"2\"\u003e\n \u003cp\u003eExperiment 2\u003c/p\u003e\n \u003cp\u003eExperiment 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.946452476572958%\" rowspan=\"2\"\u003e\n \u003cp\u003eR10.4.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.34805890227577%\" rowspan=\"2\"\u003e\n \u003cp\u003eNative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.056224899598394%\"\u003e\n \u003cp\u003eNew\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.92235609103079%\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.92235609103079%\" valign=\"top\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.546325878594246%\"\u003e\n \u003cp\u003eWashed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.22683706070288%\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.22683706070288%\" valign=\"top\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAll sequencing runs were conducted using a minION Mk1B device: Oxford Nanopore Technologies (ONT).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003csup\u003ea\u0026nbsp;\u003c/sup\u003e\u003c/strong\u003eSpecific flowcell chemistry used.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003csup\u003eb\u0026nbsp;\u003c/sup\u003e\u003c/strong\u003eLibrary preparation method. Rapid: Nanopore Rapid Barcoding (SQK-RBK110/114.96); Native: Nanopore Native Barcoding (SQK-NBD114.96).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003csup\u003ec\u0026nbsp;\u003c/sup\u003e\u003c/strong\u003eNew: Unused flowcell from ONT, Washed: Washed flow cell reused from previous sequencing run using EXP-WSH004 wash kit.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003csup\u003ed\u0026nbsp;\u003c/sup\u003e\u003c/strong\u003eNumber of samples barcoded in the prepared library. The same 24 samples were included in all libraries.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u003c/strong\u003e Oxford Nanopore and Illumina sequencing run metrics. \u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" align=\"left\" width=\"992\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.362173038229376%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eExperiment\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.959758551307846%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eFlow cell chemistry\u003csup\u003ea\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.066398390342052%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eLibrary preparation\u003csup\u003eb\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.853118712273641%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eFlow cell\u003csup\u003ec\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.438631790744467%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.14486921529175%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of reads (M)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.52917505030181%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eRead quality \u003csup\u003eg\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.645875251509055%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e% Reads \u0026gt;Q20 \u003csup\u003eh\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.379110251450676%\"\u003e\n \u003cp\u003e\u003cstrong\u003eActive pores \u003csup\u003ed\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.21083172147002%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePre-filtering \u003csup\u003ee\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.21083172147002%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePost-filtering \u003csup\u003ef\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.729206963249517%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePre-filtering \u003csup\u003ee\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.47001934235977%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePost-filtering \u003csup\u003ef\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.372608257804632%\" rowspan=\"2\"\u003e\n \u003cp\u003eExperiment 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.969788519637461%\" rowspan=\"2\"\u003e\n \u003cp\u003eR9.4.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.07754279959718%\" rowspan=\"2\"\u003e\n \u003cp\u003eRapid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.862034239677744%\"\u003e\n \u003cp\u003eNew\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.445115810674723%\"\u003e\n \u003cp\u003e1426\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.084592145015106%\"\u003e\n \u003cp\u003e5.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.084592145015106%\"\u003e\n \u003cp\u003e2.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.27190332326284%\"\u003e\n \u003cp\u003e12.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.178247734138973%\"\u003e\n \u003cp\u003e16.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.653575025176234%\"\u003e\n \u003cp\u003e38.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.922173274596181%\"\u003e\n \u003cp\u003eWashed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.397944199706314%\"\u003e\n \u003cp\u003e935\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.621145374449338%\"\u003e\n \u003cp\u003e2.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.621145374449338%\"\u003e\n \u003cp\u003e1.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.977973568281937%\"\u003e\n \u003cp\u003e12.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.29955947136564%\"\u003e\n \u003cp\u003e16.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.160058737151248%\"\u003e\n \u003cp\u003e38.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.372608257804632%\"\u003e\n \u003cp\u003eExperiment 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.969788519637461%\"\u003e\n \u003cp\u003eR10.4.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.07754279959718%\"\u003e\n \u003cp\u003eNative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.862034239677744%\"\u003e\n \u003cp\u003eNew\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.445115810674723%\"\u003e\n \u003cp\u003e1646\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.084592145015106%\"\u003e\n \u003cp\u003e6.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.084592145015106%\"\u003e\n \u003cp\u003e4.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.27190332326284%\"\u003e\n \u003cp\u003e13.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.178247734138973%\"\u003e\n \u003cp\u003e17.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.653575025176234%\"\u003e\n \u003cp\u003e38.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.372608257804632%\" rowspan=\"2\"\u003e\n \u003cp\u003eExperiment 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.969788519637461%\" rowspan=\"2\"\u003e\n \u003cp\u003eR10.4.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.07754279959718%\" rowspan=\"2\"\u003e\n \u003cp\u003eNative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.862034239677744%\"\u003e\n \u003cp\u003eNew\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.445115810674723%\"\u003e\n \u003cp\u003e1092\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.084592145015106%\"\u003e\n \u003cp\u003e7.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.084592145015106%\"\u003e\n \u003cp\u003e5.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.27190332326284%\"\u003e\n \u003cp\u003e12.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.178247734138973%\"\u003e\n \u003cp\u003e15.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.653575025176234%\"\u003e\n \u003cp\u003e30.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.922173274596181%\"\u003e\n \u003cp\u003eWashed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.397944199706314%\"\u003e\n \u003cp\u003e491\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.621145374449338%\"\u003e\n \u003cp\u003e3.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.621145374449338%\"\u003e\n \u003cp\u003e1.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.977973568281937%\"\u003e\n \u003cp\u003e12.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.29955947136564%\"\u003e\n \u003cp\u003e16.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.160058737151248%\"\u003e\n \u003cp\u003e31.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.372608257804632%\"\u003e\n \u003cp\u003eIllumina\u003csup\u003ei\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.969788519637461%\"\u003e\n \u003cp\u003eIllumina\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.07754279959718%\"\u003e\n \u003cp\u003eModified 16S\u003csup\u003ei\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.862034239677744%\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.445115810674723%\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.084592145015106%\"\u003e\n \u003cp\u003e8.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.084592145015106%\"\u003e\n \u003cp\u003e8.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.27190332326284%\"\u003e\n \u003cp\u003e30.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.178247734138973%\"\u003e\n \u003cp\u003e34.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.653575025176234%\"\u003e\n \u003cp\u003e81.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u003csup\u003ea\u0026nbsp;\u003c/sup\u003e\u003c/strong\u003eNanopore flow cell chemistry used.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003csup\u003eb\u0026nbsp;\u003c/sup\u003e\u003c/strong\u003eType of kit used for library preparation. Rapid: Rapid Barcoding 96 (SQK-RBK110-96 for R9.4.1; SQK-RBK114.96 for R10.4.1); Native: Native Barcoding kit (SQK-NBD114.96).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003csup\u003ec\u0026nbsp;\u003c/sup\u003e\u003c/strong\u003eNew: New flow cell from ONT, washed: washed flow cell reused from previous sequencing run using EXP-WSH004 wash kit.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003csup\u003ed\u0026nbsp;\u003c/sup\u003e\u003c/strong\u003eNumber of active pores reported by minKNOW at the beginning of the sequencing run following flow cell loading.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003csup\u003ee\u0026nbsp;\u003c/sup\u003e\u003c/strong\u003eMetric calculated immediately post-basecalling before demultiplexing or read filtering.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003csup\u003ef\u0026nbsp;\u003c/sup\u003e\u003c/strong\u003eMetric calculated post-filtering, reads Q8.0 and below removed before calculation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003csup\u003eg\u0026nbsp;\u003c/sup\u003e\u003c/strong\u003eRead quality reported as an average Q-score (phred33).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003csup\u003eh\u0026nbsp;\u003c/sup\u003e\u003c/strong\u003ePercent of total reads post-filtering with quality score \u0026gt;20.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003csup\u003ef\u0026nbsp;\u003c/sup\u003e\u003c/strong\u003eIllumina sequencing performed using modified 16S rDNA library preparation with custom primers (Advanced Analysis Center, Guelph, Canada).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3.\u0026nbsp;\u003c/strong\u003eQuality of Oxford Nanopore and Illumina consensus sequences post-alignment for samples with \u0026gt;50x coverage. Averages reported are aggregate for \u003cem\u003egyrB\u003c/em\u003e, IGS, LM and \u003cem\u003erpoB\u003c/em\u003e for 24 samples,\u0026nbsp;\u0026plusmn; standard deviation.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"879\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.090909090909092%\"\u003e\n \u003cp\u003eExperiment No.\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.7272727272727275%\"\u003e\n \u003cp\u003eFlow cell Chemistry\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.409090909090908%\"\u003e\n \u003cp\u003eLibrary Preparation\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.522727272727273%\"\u003e\n \u003cp\u003eFlow cell\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.159090909090909%\"\u003e\n \u003cp\u003eNo. of samples \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.840909090909092%\"\u003e\n \u003cp\u003eAverage Coverage \u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.818181818181818%\"\u003e\n \u003cp\u003eNo. Reads Mapping to Reference \u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.818181818181818%\"\u003e\n \u003cp\u003ePercent Identity to Sanger \u003csup\u003eg\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.818181818181818%\"\u003e\n \u003cp\u003eNumber of Mismatches \u003csup\u003eh\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.795454545454545%\"\u003e\n \u003cp\u003e% Types Correctly Identified\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.090909090909092%\" rowspan=\"2\"\u003e\n \u003cp\u003eExperiment 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.7272727272727275%\" rowspan=\"2\"\u003e\n \u003cp\u003eR9.4.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.409090909090908%\" rowspan=\"2\"\u003e\n \u003cp\u003eRapid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.522727272727273%\"\u003e\n \u003cp\u003eNew\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.159090909090909%\"\u003e\n \u003cp\u003e60/68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.840909090909092%\"\u003e\n \u003cp\u003e\u003cstrong\u003e2037x\u003c/strong\u003e \u0026plusmn; 1998\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.818181818181818%\"\u003e\n \u003cp\u003e\u003cstrong\u003e3956\u0026nbsp;\u003c/strong\u003e\u0026plusmn; 3833\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.818181818181818%\"\u003e\n \u003cp\u003e\u003cstrong\u003e100%\u0026nbsp;\u003c/strong\u003e\u0026plusmn; 0.001%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.818181818181818%\"\u003e\n \u003cp\u003e0.0 2\u0026plusmn; 0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.795454545454545%\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.398176291793312%\"\u003e\n \u003cp\u003eWashed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.574468085106384%\"\u003e\n \u003cp\u003e21/24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.173252279635257%\"\u003e\n \u003cp\u003e\u003cstrong\u003e4803x\u003c/strong\u003e \u0026plusmn; 6530\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.80547112462006%\"\u003e\n \u003cp\u003e\u003cstrong\u003e8753\u0026nbsp;\u003c/strong\u003e\u0026plusmn; 11343\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.80547112462006%\"\u003e\n \u003cp\u003e\u003cstrong\u003e100%\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.80547112462006%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.437689969604863%\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.090909090909092%\"\u003e\n \u003cp\u003eExperiment 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.7272727272727275%\"\u003e\n \u003cp\u003eR10.4.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.409090909090908%\"\u003e\n \u003cp\u003eNative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.522727272727273%\"\u003e\n \u003cp\u003eNew\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.159090909090909%\"\u003e\n \u003cp\u003e23/24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.840909090909092%\"\u003e\n \u003cp\u003e\u003cstrong\u003e13058x\u003c/strong\u003e \u0026plusmn; 13470\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.818181818181818%\"\u003e\n \u003cp\u003e\u003cstrong\u003e13525\u003c/strong\u003e \u0026plusmn; 14253\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.818181818181818%\"\u003e\n \u003cp\u003e\u003cstrong\u003e99.1%\u003c/strong\u003e \u0026plusmn; 1.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.818181818181818%\"\u003e\n \u003cp\u003e3.48 \u0026plusmn; 5.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.795454545454545%\"\u003e\n \u003cp\u003e62%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.090909090909092%\" rowspan=\"2\"\u003e\n \u003cp\u003eExperiment 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.7272727272727275%\" rowspan=\"2\"\u003e\n \u003cp\u003eR10.4.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.409090909090908%\" rowspan=\"2\"\u003e\n \u003cp\u003eNative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.522727272727273%\"\u003e\n \u003cp\u003eNew\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.159090909090909%\"\u003e\n \u003cp\u003e24/24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.840909090909092%\"\u003e\n \u003cp\u003e\u003cstrong\u003e38293x\u003c/strong\u003e \u0026plusmn; 35904\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.818181818181818%\"\u003e\n \u003cp\u003e\u003cstrong\u003e39017\u003c/strong\u003e \u0026plusmn; 37751\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.818181818181818%\"\u003e\n \u003cp\u003e\u003cstrong\u003e99.0%\u003c/strong\u003e \u0026plusmn; 0.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.818181818181818%\"\u003e\n \u003cp\u003e3.64 \u0026plusmn; 3.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.795454545454545%\"\u003e\n \u003cp\u003e71%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.398176291793312%\"\u003e\n \u003cp\u003eWashed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.574468085106384%\"\u003e\n \u003cp\u003e18/24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.173252279635257%\"\u003e\n \u003cp\u003e\u003cstrong\u003e13972x\u003c/strong\u003e \u0026plusmn; 15120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.80547112462006%\"\u003e\n \u003cp\u003e\u003cstrong\u003e14195\u003c/strong\u003e \u0026plusmn; 15963\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.80547112462006%\"\u003e\n \u003cp\u003e\u003cstrong\u003e99.1%\u003c/strong\u003e \u0026plusmn; 0.001%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.80547112462006%\"\u003e\n \u003cp\u003e2.59 \u0026plusmn; 5.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.437689969604863%\"\u003e\n \u003cp\u003e83%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.090909090909092%\"\u003e\n \u003cp\u003eIllumina \u003csup\u003ei\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.7272727272727275%\"\u003e\n \u003cp\u003eIllumina\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.409090909090908%\"\u003e\n \u003cp\u003eModified 16S rRNA\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.522727272727273%\"\u003e\n \u003cp\u003eNew\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.159090909090909%\"\u003e\n \u003cp\u003e20/24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.840909090909092%\"\u003e\n \u003cp\u003e\u003cstrong\u003e130674x\u003c/strong\u003e \u0026plusmn; 57640\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.818181818181818%\"\u003e\n \u003cp\u003e\u003cstrong\u003e130674\u003c/strong\u003e \u0026plusmn; 57640\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.818181818181818%\"\u003e\n \u003cp\u003e\u003cstrong\u003e100%\u003c/strong\u003e \u0026plusmn; 0.001%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.818181818181818%\"\u003e\n \u003cp\u003e0.05 \u0026plusmn; 0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.795454545454545%\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u003csup\u003ea\u003c/sup\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eExperiment 1: Nanopore Rapid Barcoding library preparation, 16 h runtime. Experiment 2 and 3: Nanopore Native Barcoding library preparation, 16 h runtime.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003csup\u003eb\u003c/sup\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eNanopore library preparation method: Rapid = Rapid Barcoding 96 kit (SQK-RBK110.96), Native = Native Barcoding 96 kit (SQK-RBK114.96).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003csup\u003ec\u003c/sup\u003e\u003c/strong\u003e New: New flowcell from ONT, washed: washed flow cell reused from previous sequencing run using EXP-WSH004 wash kit.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003csup\u003ed\u003c/sup\u003e\u003c/strong\u003e Number of samples over 50x coverage included in analysis \u003cstrong\u003e/\u003c/strong\u003e number of samples sequenced.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003csup\u003ee\u003c/sup\u003e\u003c/strong\u003e Coverage of target loci, averaged for all samples over 50x coverage.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003csup\u003ef\u003c/sup\u003e\u003c/strong\u003e Number of reads aligned to the corresponding Sanger sequence reference, reported as an average for all loci and samples in the run.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003csup\u003eg\u003c/sup\u003e\u003c/strong\u003e Percent identity to the Sanger sequence reference of the same sample.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003csup\u003eh\u003c/sup\u003e\u003c/strong\u003e Number of bases that do not match the same position in the Sanger sequence.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003csup\u003ei\u003c/sup\u003e\u003c/strong\u003e Illumina sequencing conducted by third party (Advanced Analysis Center, Guelph, Canada).\u003c/p\u003e\n\u003cp\u003eN/A No data for \u003cem\u003erpoB\u003c/em\u003e (680 bp) presented because of 550bp limit. Therefore, strain type is indeterminate.\u0026nbsp;\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-4151642/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4151642/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eSpillover events of \u003cem\u003eMycoplasma ovipneumoniae\u003c/em\u003e have devastating effects on wild bighorn sheep populations. Multilocus sequence typing (MLST), a common method for tracking bacterial lineages, is used to monitor spillover events and the spread of \u003cem\u003eM. ovipneumoniae\u003c/em\u003e between populations. Most work involving \u003cem\u003eM. ovipneumoniae\u003c/em\u003e typing has used Sanger sequencing, however, this technology is time consuming, expensive, and is not well suited to efficient batch sample processing. Our study aimed to develop and validate a workflow for multilocus sequence typing of \u003cem\u003eM. ovipneumoniae\u003c/em\u003e using Nanopore Rapid Barcoding sequencing and multiplex PCR. We compare the workflow with Nanopore Native Barcoding library preparation and Illumina MiSeq amplicon protocols to determine the most accurate and cost-effective method for sequencing multiplex amplicons.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA multiplex PCR was optimized for four housekeeping genes of \u003cem\u003eM. ovipneumoniae\u003c/em\u003e using archived DNA samples from wild sheep. Sequences recovered from Nanopore Rapid Barcoding correctly identified all MLST types with the shortest total workflow time, and lowest cost per sample when compared to Nanopore Native Barcoding, and Illumina MiSeq methods.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eOur proposed workflow serves as a convenient and effective diagnostic method for strain typing of \u003cem\u003eM. ovipneumoniae\u003c/em\u003e, and could be applied to other bacterial MLST schemes. The workflow is suitable for diagnostic settings where reduced hands-on time, cost and multiplexing capabilities are important.\u003c/p\u003e","manuscriptTitle":"High throughput rapid amplicon sequencing for multilocus sequence typing of M. ovipneumoniae using DNA obtained from clinical samples","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-27 10:55:14","doi":"10.21203/rs.3.rs-4151642/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"774a7937-8c57-4ee8-827e-5a55bb39b08a","owner":[],"postedDate":"March 27th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-03-27T18:38:36+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-27 10:55:14","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4151642","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4151642","identity":"rs-4151642","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}