Plasmids are self-replicating DNA circles of various sizes that can mediate genetic exchanges between bacteria, a process known as horizontal gene transfer. Therefore, sequencing plasmids is of interest for basic and clinical microbiology research. However, given that plasmids are rich in repetitive sequences, obtaining a fully assembled sequence typically requires long-read sequencing reads. Here, we describe protocols for the sequencing of plasmid samples using Nanopore, an affordable long-read sequencing method that has become popular in recent years. These protocols have been optimized for small- and medium-sized plasmids from laboratory and clinical samples. Relative to generic Nanopore sequencing protocols, our plasmid-customized protocols eliminate the need for an additional genomic extraction step, tolerate a range of DNA sizes, and maximize the number of samples that can be sequenced using a single flow cell. In addition, we provide up-to-date bioinformatic steps to assemble the sequence produced.

We are very grateful to Steve Salipante (University of Washington) for generously sharing a set of clinical E. coli strains published in45. This work was partially supported by research support funds for a Directorship in Innovation and Entrepreneurial Development Appointment from UCSC to MC and by the University of California Alianza MX grant &#8220;Genomic profiling of Tuberculosis isolates Among High-Burden and Vulnerable Populations in Mexico: insights into transmission and drug resistance&#8221; to MC.

The authors have nothing to disclose.

The identification and characterization of plasmids is of high interest for public health, clinical microbiology, and microbial ecology, as they facilitate the maintenance and dissemination of adaptive genes across ecological settings1,5 and serve as platforms for accelerated evolution4. The introduction of NGS technologies allowed the high-throughput sequencing of genomes of microbial isolates and of populations, although the post-assembly identification of complete plasmids or of plasmid-derived contigs generally requires long-read sequences because short-read sequencing data is unable to resolve repeat regions larger than the size of the reads. ONT has become a very popular long-read sequencing approach, with applications that go beyond whole genome sequencing and metagenomics and that include plasmid sequence verification, detection and characterization of epigenetic modifications, RNA sequencing, and the evaluation of the intrinsic instability of tandem gene arrays (reviewed in12,49).
Here, the researchers present a workflow for ONT sequencing of plasmids designed for situations where the chromosomal sequence is already known or of no interest. The reason is that plasmid extraction is labor-intensive, costly, and time-consuming (since the host population needs to be grown in culture and processed), and sequencing the whole genome using nanopore technology is often sufficient to obtain complete plasmid assemblies while providing their genomic context. Examples where the protocol is helpful include the verification of a plasmid sequence in the context of plasmid genetic engineering50,51, improving the accuracy of plasmid sequences already obtained by WGS24, or the characterization of plasmids that have been captured from clinical or environmental samples by conjugation.
DNA extraction from clinical and environmental samples may need some customization. Here, the authors report that some of the clinical strains they used produced a biofilm matrix that clogged the filters, requiring multiple pelleting steps and sometimes necessitating the addition of two filters. Also, to sequence conjugative plasmids, a larger volume of culture is needed relative to sequencing multicopy plasmids, as naturally-occurring medium- and large-sized plasmids tend to have a low plasmid copy number2.
The protocol has allowed the sequencing of plasmids between 4 kb and 173 kb in size. This range falls squarely within the manufacturer&#8217;s specifications (between 2 and 200 kb). Recently, a study looked at 23,000 plasmid sequences deposited in NCBI and found significant size differences depending on the source, with plasmids of human origin having the smallest median size (76 kb), and soil and plant plasmids the largest (215 and 427 kb, respectively); plasmids from all other sources had a median size between 79 and 147 kb, so this protocol should cover most plasmids, which the possible exception of megaplasmids52 and plasmids of soil or plant origin3.
The authors include a plasmid DNA enrichment step to improve the accuracy of plasmid DNA assemblies. The presence of large amounts of chromosomal DNA floods cell pores on the sequencing flow cell with non-target reads, substantially reducing plasmid DNA depth. A plasmid DNA enrichment step was already implemented to attempt the complete assembly of plasmid DNA in clinical samples for the detection of ARGs24. This showed substantial improvements in the effectiveness of completing plasmid assembly with nanopore-only sequence (78% complete assemblies relative to benchmark). Other examples followed50,53,54.
To improve read accuracy, two of these studies used a technique developed by nanopore called &#8220;paired basecalling on pseudopaired reads&#8221;50,53. This approach involves aligning the raw electrical signals from sense and antisense strands of the same DNA molecule, resulting in an improvement in accuracy of about an order of magnitude. Paired basecalling on pseudopaired reads is compatible with multiplexing, so long as the strategy allows for the identification of the forward and reverse strands of the same molecule. Thus, it cannot be used in plasmids purified from samples of unknown plasmid complement or containing a mix of plasmids. Note that the number of plasmids in a genome typically ranges between 0 and 7 plasmids (particularly in clinical isolates)2,55.
The researchers noticed a high level of chromosomal contamination in the samples (Table 10). The level of contamination of 10 samples analyzed ranged between 28 and 72% based on read mapping. However, when the researchers normalized this sequence to the size of the chromosome relative to that of the plasmid or plasmids present, it was found that plasmid sequences were on average 144&#8211;fold more abundant than chromosomal ones. Translating this number to the level of plasmid DNA enrichment would require knowing the plasmid copy number of the plasmids in each sample.
MinION flow cells typically produce a sequencing output of ~30 GB, theoretically enabling the sequencing of well over 96 plasmids per run. In practice, multiplexing is also constrained by the 96-barcode limit, making 96 plasmids the practical upper bound per flow cell. However, chromosomal DNA contamination and natural barcode imbalance reduce the effective read depth per sample. To ensure sufficient read depth for each sample, a more conservative approach is to multiplex approximately 24 plasmids per flow cell. The recommended depth for the accurate assembly of the plasmid sequence is between 50 and 70x32,56. In the illustrative examples presented in this article, the authors ran 21 multiplexed samples in one flow cell and obtained a mean depth (109x), which aligns well with the recommended sample optimum per flow cell.
Note that, with excess read depth, pass/fail read partitions are less informative for assessing overall read quality. Instead, metrics derived from the length distribution provide a clearer representation of dataset quality. One such metric is the N50, defined as the read length at which reads of that length or longer account for 50% of the total bases. This value is highly sensitive to quality-control steps. For example, filtering low-quality or short reads with tools such as Filtlong removes a substantial fraction of the shorter tail of the distribution, thereby inflating the N50. This effect is evident in Table 9.
The authors used ONT&#8217;s Rapid Barcode kit, which uses a transposome to fragment plasmid DNA, and thus it does not require prior knowledge of the plasmid&#8217;s restriction pattern. An earlier study found that for small plasmids (&#60;20 kb), enzymatic fragmentation is necessary because small plasmids remain circularized during most DNA extractions, and therefore contain no free ends for adapter ligation54. That study did not use barcodes, which generate additional free ends through PCR amplification, so in this case, this is likely a moot point. OnRamp, one of the advanced procedures for routine plasmid validation, avoids the use of barcodes by leveraging full-length plasmid reads for assembly, simplifying sample preparation54. In this case, the authors preferred barcodes for three reasons. The first one is to build an assembly regardless of the plasmid length. The second is to tolerate chromosomal DNA contamination. The third one is that this opens the possibility of multiplexing several samples, as stated above.
An earlier study noted that the aggregation of different assemblers improved the final outcome by cancelling out biases built into individual assembly platforms24. This meta-analysis is a feature that has been incorporated into the assembly program that the authors use, Autocycler34. Relative to its earlier version, named Trycycler57, Autocycler is a bit more automated; it uses several subsets of the reads, and (as mentioned before) it performs assemblies with multiple assemblers to create a more accurate consensus assembly. However, most assemblers, even the best, such as Flye28, Canu29, and Raven30, have difficulty with the presence of plasmids with extensive sequence homology, erroneously merging plasmid sequences, or generating multiple copies of a single plasmid,&#160;and are prone to missing small plasmids58,59 (see also Figure 4C). When this occurs, cutting out lower-quality reads and removing short reads can lead to solving chimeric contigs.
To establish the overall accuracy of the sequencing protocols, the authors compared the ONT-only assemblies with assemblies polished by combining long-read and short-reads, which (short of PacBio data) is the current gold standard. The authors also had gel data showing the number of plasmids present and their estimated sizes (Table 8,&#160;third column). The researchers successfully assembled all 13 plasmids found in the 10 samples and obtained sizes consistent with the assembled sequences. This is quite a success, as LR-sequence assemblers often fail to assemble plasmids with structurally complex sequences. At a more granular level, pairwise comparisons between the two sets of sequences indicated a variable level of accuracy (Table 8). Five of the plasmid assemblies had areas without coverage in the ONT assemblies, presumably the result of sequence artifactually added during the ONT-only sequence assembly. Of the other seven, three showed single-nucleotide polymorphisms, with pairwise differences ranging between 0.0023 and 0.05%, and four showed perfect concordance. The level of accuracy does not seem to correlate with the level of plasmid DNA enrichment or sequencing depth, but the presence of more than one plasmid in a given sample does, possibly because having more than one plasmid tends to amplify the level of sequence complexity (Table 8). In sum, the workflow is designed to optimize plasmid sequence yield and accuracy. While it is able to assemble plasmid sequences, it produces a variable level of accuracy at the nucleotide level. The protocol can be used to sequence plasmids of a wide range of sizes, tolerates at least 72% chromosomal DNA contamination, and can accommodate (depending on plasmid size and flow cell state) at least 24 plasmids in a single flow cell.

Plasmids are self-replicating pieces of DNA found ubiquitously in bacteria. They are highly variable in size, ranging from &lt;800 bp to 2.5 Mbp, and they typically follow a bimodal distribution, with a smaller peak corresponding to multicopy, non-conjugative plasmids and a larger peak corresponding to low-copy, often conjugative plasmids1,2. Plasmids play a key role in microbial ecology, carrying genes that help their hosts adapt to environmental challenges or access new ecological niches. Plasmids also serve as platforms for accelerated evolution, providing increased genetic plasticity and mediating horizontal gene transfer across strains, species, genera, and even families3,4. Network and comparative genomic analyses have confirmed the role of the environmental resistome as a persistent reservoir for clinically relevant antibiotic resistance genes (ARGs), thereby facilitating their maintenance and dissemination across ecological settings5. Tracking plasmids and their cargo of antibiotic resistance and virulence genes helps identify transmission routes6. Therefore, the identification and characterization of plasmids is of high interest for a variety of public health, clinical microbiology, and biotechnological applications.
The introduction of next-generation sequencing (NGS) technologies, which enable the high-throughput sequencing of unknown DNA templates, has opened the door to the genomic characterization of microbial isolates. Whole genome sequencing (WGS) enables tracking the emergence, spread, and transmission of new pathogens, drug-resistant strains, and vaccine-evading variants. It is also increasingly being used to help identify transmission routes during clinical outbreaks, potentially informing interventions for future outbreak prevention7. Finally, WGS allows the profiling of ARGs, which can inform therapeutic decisions8, although linking ARGs to qualitative and quantitative antibiotic resistance phenotypes is still a work in progress9.
However, the short-read (SR) sequencing data typically produced by WGS at scale is generally in the 400–500 bp range. This size is shorter than the size of most Mobile Genetic Elements (MGEs) and cannot therefore resolve them10. The in silico identification of plasmid sequences in WGS data is further complicated by incomplete coverage, and because of shared sequences between plasmids and between bacterial chromosomes and other plasmids10. As a consequence, assemblies are often extensively fragmented and incomplete, making their subgenomic location (chromosomal or plasmid) impossible to ascribe10.
Long-read (LR) sequencing technology generates highly contiguous assemblies spanning across more repeats, greatly facilitating the production of complete plasmid assemblies. There are essentially two types of long-read, single-molecule technologies available11,12. One of these approaches, nanopore sequencing, is becoming popular in microbial genomics due to its low cost, fast turnaround times, and suitability for resource-limited environments13,14.
Nanopore sequencing devices use flow cells containing arrays of thousands of nanopores. The DNA is loaded into the flow cell, opened up by a motor protein with helicase activity, and it goes through the nanopore driven by a current differential. As nucleotides in a given single strand pass through individual nanopores, the current is disrupted to produce a characteristic signal that is measured by an electronic sensor connected to each pore. This signal is decoded using basecalling algorithms, and the resulting data is assembled and analyzed further downstream using various bioinformatics tools.
Compared to SR-sequencing approaches, nanopore sequencing tends to produce lower read quality (lower Q-scores) and to generate insertions/deletions in homopolymer sequences during the data analysis of electrical signals (basecalling)15. It has also been noted that nearby methylation in native DNA can cause systematic base calling errors16. A commonly adopted solution has been to include short-read data for post-assembly error correction. However, this process (known as “polishing”) increases the cost and complexity overhead17. Increasing sequencing coverage can also improve accuracy, but it is not always technically achievable.
In 2024, it was shown that Oxford Nanopore Sequencing (ONT) R10.4.1 flow cells and V14 chemistry yield high-quality and contiguous assemblies for G- bacteria, while finer-scale analyses on single-nucleotide levels still benefit from SR-sequencing data18. However, the gap between LR- and SR-sequencing accuracy is gradually being closed. Various recent enhancements include a more precise data sampling process at an increased sequencing speed, the introduction of a novel basecaller tailored for native bacterial DNA and methylation, yielding increased base call accuracies19, and updated analysis tools such as new algorithms for phasing, variant calling, and star-allele calling. Note also that the latest Dorado algorithm V.0.9.0 ONT (2025) can be used for rebase calling of raw squiggle data. Indeed, some WGS studies are already reporting the generation of microbial genomes by nanopore sequencing of a quality for which short-read polishing is not expected to significantly improve the consensus sequence20,21.
Plasmids often contain the bulk of acquired resistance genes, particularly in Enterobacteriaceae isolates22. Therefore, resolving plasmid sequences allows the contextualization of AR genes, which is key for tracking AMR transmission23,24. Nanopore sequencing has already been used to examine the microevolution of blaKPC harboring plasmids25. MinION devices have also been used for real-time benchtop sequencing of plasmids and resistance gene detection in clinical isolates in hospital settings26. Having said that, plasmid interrogation is not yet part of routine infection control practice.
Despite LR-sequencing’s substantial improvement over SR-sequencing in the ability to rescue plasmids from genomic sequence27, some problems remain. Long read assemblers such as Flye28, Canu29 and Raven30 produce chromosomal assemblies of high quality but struggle to assemble plasmid sequences. This is in part due to insufficient depth of coverage because plasmid DNA is underrepresented in WGS sequencing data31. Note that due to their modular structure, abundance of secondary structures, and frequent presence of repeats, the high quality assembly for plasmid DNA sequence requires higher coverage than chromosomal DNA (50x read depth32).
Enriching the DNA samples with plasmid sequences is an effective approach to increase the depth of coverage, but isolation or enrichment of plasmid DNA before DNA sequencing is too expensive or laborious for clinical diagnostic applications and might introduce bias by enriching selected DNA sequences. In 2017, plasmid-enriched DNA was used to attempt the complete assembly of plasmid DNA in clinical samples for the detection of ARGs24. Eight isolates from six different Enterobacteriaceae species were sequenced to maximize population and plasmid structural diversity. After trying several assemblers, it was demonstrated that Canu/Canu+Pilon clearly outperformed others; this research group also tried a meta-assembly approach that achieved the resolution of additional plasmid structures, resulting in the complete assembly of 78% of all known plasmids in the sequenced samples. However, the presence of large-repeat structures and (to a lesser degree) problems with DNA extraction still posed significant problems for the detection of all single-contig plasmid assemblies24.
More recently, adaptive sampling as a complementary approach for the enrichment of low-abundance plasmid sequences has been examined by rejecting chromosomal sequences in bacterial isolate samples, although this method requires known reference sequences33.
Here, the authors describe protocols for ONT sequencing that are customized for samples enriched in plasmid DNA. Combining increased representation of plasmid DNA with up-to-date library preparation protocols, flow cell technology, and sequence assembly tools, the workflow produced assemblies that matched sizes inferred from gel mobility and, in 2/3 of the cases, either matched SR-sequencing polishing or were within 0.05% pairwise sequence identity. The enrichment of plasmid DNA sequences also maximizes the efficiency of plasmid sequencing, allowing parallel sequencing of up to 24 samples with optimal coverage. The protocols are not meant to replace standard nanopore WGS approaches, but to maximize the accuracy and efficiency of plasmid sequencing in situations where the chromosomal sequence is already known or not of interest.
The workflow includes plasmid DNA extraction, library preparation with barcoding, adapter ligation to a motor protein, checking the flow cell, loading the library onto a flow cell for sequencing, data analysis of the electrical signals (in real time or post hoc), sequence assembly using Autocycler34, and various downstream analyses (Figure 1).
<img alt="Plasmid sequencing workflow diagram; DNA extraction, library preparation, sequencing, annotation." class="xfigimg" src="/files/ftp_upload/70202/70202fig1.jpg" data-altupdatedat="1785816036000" data-alt="Figure 1"> 
Figure 1: Plasmid sequencing workflow using Oxford Nanopore Technologies. Step 1: DNA extraction; Step 2: Library preparation; Step 3: Sequencing; Step 4: Assembly and annotation. Created in BioRender. Cortés, G. (2026) https://BioRender.com/u3b15st. <a href="https://www.jove.com/files/ftp_upload/70202/70202fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

<table><tbody><tr><td>1.5 mL Eppendorf DNA LoBind tube</td><td>Sigma Aldrich</td><td>EP022431081</td><td>It can be replaced for another brand</td></tr><tr><td>1.5 mL tube racks</td><td>Thermo Fisher Scientific</td><td>22-313630</td><td>It can be replaced for another brand</td></tr><tr><td>15 mL conical tubes</td><td>FALCON</td><td>352096</td><td>It can be replaced for another brand</td></tr><tr><td>15 mL tube racks</td><td>Thermo Fisher Scientific</td><td>8850</td><td>It can be replaced for another brand</td></tr><tr><td>2.0 mL microcentrifuge tubes</td><td>Fisherbrand</td><td>05-408-138</td><td>It can be replaced for another brand</td></tr><tr><td>250 mL sterile flasks&amp;nbsp;</td><td>PYREX</td><td>5320</td><td>It can be replaced for another brand</td></tr><tr><td>Carbenicillin</td><td>GOLD BIOTECHNOLOGY</td><td>C-103-50</td><td>It can be replaced for another brand</td></tr><tr><td>Centrifuge</td><td>Eppendorf&amp;nbsp;</td><td>5424R-PR-R</td><td>It can be replaced for another brand</td></tr><tr><td>Centrifuge&amp;nbsp;</td><td>Eppendorf&amp;nbsp;</td><td>5810R-P-R</td><td>It can be replaced for another brand</td></tr><tr><td>Centrifuge bottles</td><td>Sigma Aldrich</td><td>B1283-4EA</td><td>It can be replaced for another brand</td></tr><tr><td>Disposable Inoculating Loops: 1 &amp;micro;L</td><td>Fisherbrand</td><td>22-363-595</td><td>It can be replaced for another brand</td></tr><tr><td>Electronic Digital Scale</td><td>Denver Instrument</td><td>APX-2001</td><td>It can be replaced for another brand</td></tr><tr><td>Ethanol</td><td>Fisher Scientific&amp;nbsp;</td><td>04-355-720</td><td>It can be replaced for another brand</td></tr><tr><td>Ethanol-resistant markers</td><td>Sharpie</td><td>37001; 37002; 37003</td><td>It can be replaced for another brand</td></tr><tr><td>Flow cell (R10.4.1)</td><td>Oxford Nanopore Technologies</td><td>FLO-MIN114</td><td></td></tr><tr><td>Flow Cell Wash Kit&amp;nbsp;</td><td>Oxford Nanopore Technologies</td><td>EXP-WSH004</td><td></td></tr><tr><td>Gloves</td><td>X-GEN</td><td>44-100M</td><td>Any nitrile gloves brand works</td></tr><tr><td>Ice bucket</td><td>Thermo Fisher Scientific</td><td>432128</td><td>It can be replaced for another brand</td></tr><tr><td>Isopropanol</td><td>Fisher Scientific&amp;nbsp;</td><td>BP26184</td><td>It can be replaced for another brand</td></tr><tr><td>MinION Sequencing Device&amp;nbsp;</td><td>Oxford Nanopore Technologies</td><td>MIN-101B</td><td></td></tr><tr><td>Native Barcoding Kit 96 V14</td><td>Oxford Nanopore Technologies</td><td>SQK-NBD114.96</td><td></td></tr><tr><td>NucleoBond Xtra MIDI Plasmid DNA Extraction Kit</td><td>Takara</td><td>740410.5</td><td></td></tr><tr><td>PCR tube racks</td><td>AXYGEN&amp;nbsp;</td><td>&amp;nbsp;R96PCRFSP</td><td>It can be replaced for another brand</td></tr><tr><td>PCR tubes: (0.2 mL)</td><td>AXYGEN&amp;nbsp;</td><td>PCR-02-C</td><td>It can be replaced for another brand</td></tr><tr><td>Program autocycler v0.5.2</td><td>Ryan Wick / University of Melbourne</td><td></td><td></td></tr><tr><td>Program bcftools v1.22</td><td>Wellcome Sanger Institute (HTSlib / Samtools team)</td><td></td><td></td></tr><tr><td>Program bwa v0.7.19</td><td>Heng Li, originally at the Broad Institute</td><td></td><td></td></tr><tr><td>Program canu v2.3</td><td>University of California, Davis &amp;amp; Pacific Biosciences collaborators</td><td></td><td></td></tr><tr><td>Program flye v2.9.6</td><td>University of California, San Diego (Pavel Pevzner lab)</td><td></td><td></td></tr><tr><td>Program medaka v2.1.1</td><td>Oxford Nanopore Technologies</td><td></td><td></td></tr><tr><td>Program metamdbg&amp;nbsp;v1.2</td><td>INRIA</td><td></td><td></td></tr><tr><td>Program miniasm v0.3</td><td>Heng Li</td><td></td><td></td></tr><tr><td>Program minimap2 v2.28</td><td>Heng Li</td><td></td><td></td></tr><tr><td>Program minipolish&amp;nbsp; v0.2.0&amp;nbsp; &amp;nbsp; &amp;nbsp; &amp;nbsp; &amp;nbsp; &amp;nbsp;&amp;nbsp;</td><td>Ryan Wick / University of Melbourne</td><td></td><td></td></tr><tr><td>Program porechop v0.2.4</td><td>Ryan Wick / University of Melbourne</td><td></td><td></td></tr><tr><td>Program racon v1.5.0</td><td>Genome Institute of Singapore</td><td></td><td></td></tr><tr><td>Program samtools v1.22.1</td><td>Wellcome Sanger Institute (HTSlib / Samtools project)</td><td></td><td></td></tr><tr><td>Qubit&amp;trade; 4 Fluorometer</td><td>Invitrogen&amp;nbsp;</td><td>Q33226</td><td></td></tr><tr><td>Qubit&amp;trade; Assay Tubes</td><td>Invitrogen&amp;nbsp;</td><td>Q32856</td><td></td></tr><tr><td>Rapid Barcoding Kit 24 and 96 V14&amp;nbsp;</td><td>Oxford Nanopore Technologies</td><td>SQK-RBK114.96</td><td></td></tr><tr><td>Set of micropipettes that dispense between 1 &amp;ndash; 10 &amp;mu;L (P10), 2 &amp;ndash; 20 &amp;mu;L (P20), 20 &amp;ndash; 200 &amp;mu;L (P200) and 200 &amp;ndash; 1000 &amp;mu;L (P1000)</td><td>RAININ</td><td>17008648; 17008650; 17008652; 17008653</td><td>Micropipettes should be calibrated</td></tr><tr><td>Spectrophotometer</td><td>Thermo Scientific</td><td>335905P</td><td>It can be replaced for another brand</td></tr><tr><td>Sterilized tips for 10 &amp;mu;L, 200 &amp;mu;L and 1000 &amp;mu;L&amp;nbsp;</td><td>Eclipse</td><td>1011-260-000-9; 1018-260-000; 1019-260-000-9</td><td></td></tr><tr><td>Thermal cycler C1000 Touch&amp;nbsp;</td><td>Bio-Rad</td><td>1851196</td><td>It can be replaced for another brand</td></tr><tr><td>Water bath</td><td>Thermo Scientific</td><td>51221052</td><td>It can be replaced for another brand</td></tr></tbody></table>

The present study describes a workflow for nanopore sequencing designed to maximize plasmid DNA yield and sequencing accuracy. Researchers cover each step of the process, which includes plasmid DNA extraction, library preparation with rapid barcoding and adapter ligation, loading of the library onto a flow cell for sequencing, base calling (in real time or post-hoc), and sequence assembly using Autocycler. The authors also specify the computer requirements, which largely depend on whether basecalling is done in real time. The authors sequenced plasmids purified from ten clinical strains using this workflow. To remove host-cell strain differences as a variable, the authors&#160; also conjugated these clinical strains with a common recipient strain and sequenced the transconjugants. These experiments produced plasmid assemblies that match plasmid sizes inferred from gel mobility and that, in 2/3 of cases, also match short-read polished assemblies or are within 0.05% pairwise sequence identity. The enrichment of plasmid DNA sequences also maximizes the efficiency of plasmid sequencing, allowing parallel sequencing of up to 24 samples with optimal coverage. This protocol is adequate&#160;for plasmids ranging from 4 to 174 kb and tolerates up to 72% chromosomal contamination without affecting accuracy. This protocol is ideal for situations where the chromosomal sequence is already known or of no interest, such as for plasmid sequence verification, improving the accuracy of plasmid sequences already obtained by WGS, or sequencing plasmids captured by conjugation. The organism used to illustrate this work is Escherichia coli, which is representative of hosts used for recombinant gene expression and for plasmid capture through conjugation.

The current study describes a workflow for nanopore sequencing of plasmids when the chromosomal sequence is already known or of no interest. An initial plasmid DNA enrichment step ensures optimal plasmid sequence depth and maximizes the number of samples that can be sequenced per flow cell.

long-read-plasmid-sequencing-assembly-using-nanopore-sequencing-based

Long-Read Plasmid Sequencing and Assembly Using Nanopore Sequencing-Based Workflows

The present study describes a workflow for nanopore sequencing designed to maximize plasmid DNA yield and sequencing accuracy. Researchers cover each step ...

We investigate the replication, maintenance, and dissemination of self-replicating circular DNA elements, known as plasmids, which necessitates the determination of their sequences. This protocol addresses coverage and accuracy issues associated with long-read sequencing by enriching plasmid DNA and optimizing assembly methods. Our protocol is designed to sequence plasmids in situations where the chromosomal sequence is either known or irrelevant.To begin, streak the samples onto LB auger plates supplemented with the appropriate antibiotic to select for the respective plasmid-borne resistance marker. Incubate the plates at 37 degrees Celsius for 24 hours. The next day, inoculate three to five milliliters of LB medium supplemented with the appropriate antibiotic with a single isolated colony.Incubate the culture with shaking at 37 degrees Celsius for approximately eight hours or until the optical density at 600 nanometers reaches one to two. Then inoculate 400 milliliters of LB medium supplemented with the appropriate antibiotic with 400 microliters of the starter culture and incubate as demonstrated earlier. Then transfer the culture into centrifuge bottles appropriate for the culture volume.Centrifuge at 4, 200 G for 10 minutes and resuspend the pellet using the reagent volumes recommended by the manufacturer. Following cell lysis and neutralization, centrifuge the lysate at 4, 200 G for 30 minutes. Transfer the supernatant to a new tube and centrifuge it again.Perform equilibration, washing, and DNA elution according to the manufacturer's instructions. Then resuspend the precipitated DNA in 100 microliters of nuclease-free water to maximize concentration for nanopore library preparation. Next, to quantify the DNA, prepare the assay tubes as per the presented table.After calibrating the fluorometer, insert the sample tubes into the fluorometer and record the readings according to the instrument manual. Program the thermal cycler to 30 degrees Celsius for two minutes, followed by 80 degrees Celsius for two minutes. Spin and mix the thawed kit components as per the presented table.Incubate the PCR tubes prepared for barcoding using the thermal cycler program. Then place the tubes on ice to cool. Pool the barcoded samples in a clean 1.5 milliliter Eppendorf tube, ensuring the total volume does not exceed 1, 000 microliters.Once the beads are vortexed, add an equal volume of beads to the pooled barcoded samples. Mix by flicking the tube, followed by a 10-minute incubation. Then wash the beads with one milliliter of 80%ethanol while on the magnet.Remove the ethanol and repeat once. Remove the tube from the magnet. Resuspend the beads in elution buffer using the appropriate volume based on the number of barcodes in use and incubate for 10 minutes at room temperature.Quantify the DNA concentration using one microliter of the eluded sample on the fluorometer to confirm successful library preparation. For adapter ligation, transfer 11 microliters of the DNA library into a new tube and label it. Then mix the presented components in a fresh tube to dilute the thawed rapid adapter.Next, add one microliter of diluted rapid adapter to the 11 microliters of DNA library and flick the tube to mix the contents. Spin down the tube briefly to ensure the complete volume collects at the bottom and incubate the tube on the bench for five minutes. Prepare the flow cell priming mix in a fresh DNA tube and mix by inversion and pipetting.Lift the lid of the device and insert the flow cell into it while applying gentle pressure to ensure proper placement. After checking for air bubbles within or adjacent to the priming port, draw back 20 to 30 microliters of fluid to remove bubbles. Then draw up 800 microliters of priming mix and dispense it into the priming port while avoiding air bubbles and let the device sit for five minutes.Mix the DNA library with the appropriate volume of library beads in a new tube as presented here. Load 200 microliters of priming mix into the flow cell priming port. Adjust the sample port cover to reveal the port.Dispense 75 microliters of the DNA library with beads into the sample port one drop at a time, allowing each drop to absorb before adding the next. Then reapply the sample port cover and close the priming port. Once the device lid is closed, begin the sequencing run.Set the runtime limit based on the number of plasmids being multiplexed with a default of 48 hours and a maximum of 72 hours. Select the super accurate base calling mode and the appropriate rapid barcode kit option. Then select the raw FAST5 or POD5 data and start base calling.To generate a filtered FASTQ file for downstream assembly, trim long reads using Filtlong to remove approximately 5-10%of the lowest quality reads while retaining approximately 90%of the total yield. Subsample reads using Autocycler according to recommended settings. Assemble each read subset using Raven, Flye, Kanu, Miniasm, Myloasm, NECAT, Plassembler, and other selected assemblers.Visualize each assembly graph file using bandage. Evaluate contigs for circularity, size consistency across assemblers, and evidence of chimeric joints. Remove linear contigs lacking support across multiple assemblers and retain circular contigs with consistent size estimates.Remove minor unsupported contigs before consensus generation when major plasmids are circularized. Proceed to consensus generation after manual curation. Generate the consensus sequence using Autocycler with the curated assemblies.Execute the remaining Autocycler commands to produce the final consensus plasmid sequence. Lastly, polish the final assembly using trimmed long reads with Medaka to improve consensus accuracy. All conjugation assays produced transconjugants, confirming the presence of a conjugative plasmid in the donors.The sizes of assembled plasmid sequences were congruent with estimates based on pulse field gel electrophoresis mobility. Nanopore sequencing produced sufficient read output and quality for all samples analyzed. Chromosomal contamination rates ranged between 72.1%and 28.0%with an average of 46.9%Pulse field gel electrophoresis analysis of strain plasmid UPEC_271 revealed a band corresponding to a plasmid of approximately 145 kilobase pairs.DNA from one of the transconjugants showed a sequence identical to the 145 kilobases. Pulse field gel electrophoresis analysis of strain blood 100913 identified two plasmids of approximately 70 and 110 kilobase pairs. Nanopore sequencing confirmed two plasmid assemblies of 71, 182 base pairs and 111, 226 base pairs.The most important consideration is whether chromosomal sequence is of interest. If so, standard whole genome sequencing protocols are better. Our protocol's increased coverage opens the door to the study of heteroplasmy, i.e.plasmid sequence variation within a given population.

Comprehensive Protocols for Nanopore Sequencing of Plasmid DNA, from DNA Extraction to Sequence Assembly

Bui Hoang Phuc

The rapid rise of multidrug-resistant (MDR) bacteria has become a significant global public health issue, prompting the scientific community to deepen the understanding of resistance mechanisms through microbial genomics. This Methods Collection aims to provide standardized protocols for isolating, sequencing, analyzing, and interpreting microbial genomes from clinical and environmental sources. It will feature reproducible workflows for identifying antibiotic resistance genes, conducting comparative genomics of pathogenic strains, and utilizing bioinformatics pipelines in microbial research. By showcasing established methods, this collection will support new and experienced researchers in adopting genomic approaches for antimicrobial resistance surveillance, molecular diagnostics, and evolutionary studies.

JoVE Immunology and Infection includes content from a variety of fields working toward a shared initiative of improving global health. Techniques evaluating the biological response to pathogens from the molecular to organismal level are presented as well as therapeutic agents and their efficacy in treating disease.

Immunology and Infection

Standardized Methods in Microbial Genomics and Antimicrobial Resistance Research

The rapid rise of multidrug-resistant (MDR) bacteria has become a significant global public health issue, prompting the scientific community to deepen the understanding of resistance mechanisms through microbial genomics. Curated by Bui Hoang Phuc. Explore Immunology and Infection techniques on JoVE.

Guest Editor

Collection Overview

Articles

Long-Read Plasmid Sequencing and Assembly Using Nanopore Sequencing-Based Workflows