Frequently Asked Questions

• Scientific rigor and peace of mind.
• E. coli and other hosts will go to great lengths to avoid expressing your leaky toxic gene, including modifying your plasmid in unexpected ways that are invisible to targeted Sanger sequencing.
• Plasmid inserts are getting longer and more complex. Instead of multiple Sanger runs or synthesizing a sequencing primer or doing primer walking, sequence the whole plasmid.
• Long reads are ideal for resolving repetitive regions that stymie Sanger sequencing.
• Are you sure your plasmid isn't a dimer? Are you sure there aren't multiple plasmids in your strain? Sanger sequencing won't tell you, and we see it all the time.
• It's neither much more expensive nor slower.

In the vast majority of cases, we deliver plasmid sequencing results within one business day of receipt of your samples.

Our service for plasmids is intended for a clonal population of molecules. You can send mixtures of molecular species, but since we can't predict the analysis outcome, it's at your own risk.

• If your species are very similar (e.g. differ by only a few nucleotides), the pipeline will most likely create a single .fasta consensus file with low confidence positions at SNP/indel locations. You can view those locations in your provided stats.csv and .fastq files.
• If your species are sufficiently distinct (e.g. vastly different in size or sequence), the pipeline will first attempt to make a .fasta consensus file from the highest abundance species. It will also attempt to make a consensus of other species with read counts that are >20% of that of the most abundant species. Ultimately, which species end up producing a consensus will vary depending on overall sample quality, coverage, and relative abundance/degradation of each species.

Sequencing is considered successful if the pipeline is able to generate any consensus, even if it is not your target. Re-sequencing mixtures won't change the relative proportions of the species, but you can submit multiple aliquots if you need higher total coverage.

If the pipeline does not produce a consensus for your target, you can download the raw reads from your Dashboard.

If you are interested in sequencing a known mixture (e.g. barcode or variant libraries), please refer to the section below on Custom Sequencing Service.

As per Oxford Nanopore’s specs for the chemistry and flowcells we currently use for plasmid sequencing, the raw read accuracy is 99.6%.
Deeper coverage — meaning more reads from which to build a consensus — generally increases the accuracy of results.

The most common error modes for Oxford Nanopore are deletions in homopolymer stretches and errors at the middle position of the Dcm methylation site CCTGG and CCAGG.
This limitation is expected to improve with future updates to their sequencing chemistry and basecalling software.

We do not guarantee any specific level of coverage. The number of raw reads generated can vary substantially depending on sample quality.
Successful samples sent at the required concentration typically yield in the high dozens to hundreds (or thousands!) of raw sequencing reads. Final coverage of the consensus depends on how many of the raw sequencing reads are full-length plasmids and whether any degraded plasmid reads can be aligned to the full-length consensus.
Average coverage is reported in the header line of the fasta file. Coverage over ~20x indicates a very accurate consensus.

• .fasta file (for consensus data): We return a polished consensus sequence of each plasmid in fasta format.
• [OPTIONAL] .fastq file (for raw reads): If you like, you can elect during the order process to receive the raw fastq sequencing reads. You can also download the raw reads anytime from your Dashboard.
• .svg file (for raw reads): We return raw read length histograms for each plasmid, which provide unique insight into the contents of your samples. See details on interpreting your histograms.
• .html pLannotate map (for consensus data): We return a plasmid map for each sample, generated with the excellent pLannotate tool from the Barrick Lab.
• .gbk GenBank file (for consensus data): We return the same pLannotate map in GenBank file format.

Finally, we return two files that show how confident we are in our basecall at each position of our consensus sequence:

• .csv file (comparing raw reads to consensus data): We align the raw reads to the consensus results and return a stats.csv file that lists how well the raw reads agree at each position.
• .fastq file (comparing raw reads to consensus data): We create this .fastq file from the stats.csv file to visualize our consensus confidence. It contains the consensus base call and a “consensus score” (an internal metric of our confidence for the consensus basecall) for each position. This .fastq file can be viewed in software such as SnapGene Viewer to quickly identify low-confidence positions.

  

  • IMPORTANT: Our "consensus score" is measured on the same Phred scale as standard Q-scores and is reported in the standard range of 1 to 42. This score does not correlate linearly with the percent of raw reads agreeing with the consensus. It is weighted by the number of raw reads available, ONT's machine learning algorithms for basecalling, and other statistical considerations. For example, a "consensus score" lower than 20 corresponds to a low-confidence position where under 70% of the raw reads match the consensus.
  • IMPORTANT: This .fastq file for the consensus is DISTINCT from the .fastq files for the raw read data. This consensus .fastq file will be delivered with your standard sequencing results, whereas the .fastq file for the raw read data must be requested during order submission or downloaded separately from the Dashboard.

Note we do not return chromatograms (.ab1 files) because we do not do Sanger sequencing. The confidence files described above provide similar information.

The histogram displays raw read lengths between 1-25kb (for standard plasmids) and is subdivided into 100 bins. Before sequencing your plasmids, we linearize them so that we get mostly full-length sequence reads. As a result, the lengths of our raw sequencing reads reflect the lengths of the molecular species in your sample.

Ideally, your target plasmid will be the only species in the sample, and we will see one dominant peak in the read length histogram:

If your raw reads contain varying numbers of indels (common for noisy raw reads), this may sometimes cause the read lengths to straddle a bin boundary and artifactually create an appearance of two separate peaks:

More often than you would expect, though, we see multiple peaks corresponding to multiple plasmids, or a peak of a different size than the customer expected:

(The analysis pipeline will provide consensus sequences for any plasmids in the mixture that obtain enough coverage and are at least 20% the height of the max peak in the sample. If you observe a histogram peak here that does not have an accompanying consensus delivered in your data, then coverage of that species was too low to assemble or that peak was actually a mixture of plasmid species. If sequencing fails entirely due to low coverage for all species in the mixture, you will not receive any consensus sequences, but you may find some useful information in the raw reads.)

Occasionally we see a sample with a dominant peak in addition to an abundance of degraded DNA (genomic and/or plasmid). In some cases the dominant peak may still produce a consensus, if read coverage and accuracy are sufficient:

Sometimes we see a decent number of reads for the sample but there is NO dominant peak, indicating an abundance of degraded DNA (genomic and/or plasmid) from a poor plasmid prep, or that the strain contains no plasmids:

Often, the read count is too low to distinguish any peaks or to generate any consensus:

I think @plasmidsaurus is just trolling me 💀"Propagate a plasmid in MG1655, what's the worst that can happen?" pic.twitter.com/IGzXa5jpeL

— Sean Leonard (@spleonard1) April 22, 2022

It is relatively rare that we cannot return a plasmid sequence, but some rate of failure is unavoidable. We may attempt to re-sequence failed samples if your sample quality and quantity permits (with follow-up results delivered in 2-3 business days). If the sample fails a second time, we will conclude that something about the sample makes it unsequenceable. Check out our Sequencing Workflow. Unfortunately we must still charge for failed samples, since we spend more time and resources on them than we do on successes.

In the vast majority of cases, we deliver linear/amplicon sequencing results within one business day of receipt of your samples.

Our service for linear/amplicons is intended for a clonal population of molecules. You can send mixtures of molecular species, but since we can't predict the analysis outcome, it's at your own risk.

• If your species are very similar (e.g. differ by only a few nucleotides), the pipeline will most likely create a single .fasta consensus file with low confidence positions at SNP/indel locations. You can view those locations in your provided stats.csv and .fastq files.
• If your species are sufficiently distinct (e.g. vastly different in size or sequence), the pipeline will first attempt to make a .fasta consensus file from the highest abundance species. It will also attempt to make a consensus of other species with read counts that are >20% of that of the most abundant species. Ultimately, which species end up producing a consensus will vary depending on overall sample quality, coverage, and relative abundance/degradation of each species.

Sequencing is considered successful if the pipeline is able to generate any consensus, even if it is not your target. Re-sequencing mixtures won't change the relative proportions of the species, but you can submit multiple aliquots if you need higher total coverage.

If the pipeline does not produce a consensus for your target, you can download the raw reads from your Dashboard and bin them yourself, but please note that raw reads are much more noisy and error-prone (~98.3% accurate) than consensus reads.

If you are interested in sequencing a known mixture (e.g. barcode or variant libraries), please refer to the section below on our Custom Sequencing Service.

As per Oxford Nanopore’s specs for the chemistry and flowcells we currently use for linear/amplicon sequencing, the raw read accuracy is 99.6%.
Deeper coverage — meaning more reads from which to build a consensus — generally increases the accuracy of results.

Depending on the sequence of your sample, the assembler does sometimes have difficulty reconstructing the terminal ends of linear DNA, which may result in up to ~25 nucleotides missing from the 3’ and/or 5’ ends of your insert. If you observe this happening with your samples, you may download the raw reads from your Dashboard and reconstruct the ends with your preferred method.

The most common error modes for Oxford Nanopore are deletions in homopolymer stretches and errors at the middle position of the Dcm methylation site CCTGG or CCAGG.
This limitation is expected to improve with future updates to their sequencing chemistry and basecalling software.

We do not guarantee any specific level of coverage. Number of raw reads generated can vary substantially depending on sample quality.
Successful samples sent at the required concentration typically yield in the high dozens to hundreds (or thousands!) of raw sequencing reads.
Average coverage is reported in the header line of the fasta file. Coverage over ~20x indicates a very accurate consensus.

• .fasta file (for consensus data): We return a polished consensus sequence in fasta format.
• [OPTIONAL] .fastq file (for raw reads): If you like, you can now also elect during the order process to receive the raw fastq sequencing reads. You can also download the raw reads anytime from your Dashboard.

Finally, we return two files that show how confident we are in our basecall at each position of our consensus sequence:

• .csv file (comparing raw reads to consensus data): We align the raw reads to the consensus results and return a stats.csv file that lists how well the raw reads agree at each position.
• .fastq file (comparing raw reads to consensus data): We create this .fastq file from the stats.csv file to visualize our consensus confidence. It contains the consensus base call and a “consensus score” (an internal metric of our confidence for the consensus basecall) for each position. This .fastq file can be viewed in software such as SnapGene Viewer to quickly identify low-confidence positions.

  

  • IMPORTANT: Our "consensus score" is measured on the same Phred scale as standard Q-scores and is reported in the standard range of 1 to 42. This score does not correlate linearly with the percent of raw reads agreeing with the consensus. It is weighted by the number of raw reads available, ONT's machine learning algorithms for basecalling, and other statistical considerations. For example, a "consensus score" lower than 20 corresponds to a low-confidence position where under 70% of the raw reads match the consensus.
  • IMPORTANT: This .fastq file for the consensus is DISTINCT from the .fastq files for the raw read data. This consensus .fastq file will be delivered with your standard sequencing results, whereas the .fastq file for the raw read data must be requested during order submission or downloaded separetely from the Dashboard.

We do not return chromatograms (.ab1 files) because we do not do Sanger sequencing. The confidence files described above provide similar information.

We do not return histograms for linear samples because these samples are fragmented during library prep, thus a clonal peak is not produced on the histogram and it is typically not informative.

It is relatively rare that we cannot return a linear/amplicon sequence, but some rate of failure is unavoidable. We may attempt to re-sequence failed samples if your sample quality and quantity permits (with follow-up results delivered in 2-3 business days). If the sample fails a second time, we will conclude that something about the sample makes it unsequenceable. Check out our Sequencing Workflow. Unfortunately we must still charge for failed samples, since we spend more time and resources on them than we do on successes.

In the vast majority of cases, we deliver bacterial genomes sequencing results within 3-5 business days of receipt of your samples.

As per Oxford Nanopore’s specs for the chemistry and flowcells we currently use for bacterial genome sequencing, the raw read accuracy is 99.6%.
The accuracy of the final assembly varies depending on coverage and data quality.
Deeper coverage — meaning more reads from which to build a consensus — generally increases the accuracy of results.

We target 210 Mb (standard Bacteria service) or 360 Mb (Big Bacteria service) of raw data, and typcially exceed this. If this target cannot be achieved, our failure policy applies.

• .fasta file: We return a polished consensus sequence of the genome in fasta format.
• .fastq file: The raw fastq sequencing reads.
• Various other files: Files containing assembly statistics and genome annotations.

For bacterial genome sequencing, “failure” refers to the failure of your sample to produce at least 210 Mb (standard Bacteria service) or 360 Mb (Big Bacteria service) of raw data.
Our low sequencing prices and fast turnaround times do not include extensive QC to determine why bacterial genome samples fail.
Although we do not provide definitive reasons for failure, by far the most common reasons are:

• Samples are not prepared at required DNA concentration.
  Concentration of samples for bacterial genome sequencing should be at 50 ng/µL in >20 µL of elution buffer.
  The most common cause of this is using a Nanodrop to quantify DNA concentration. We strongly recommend using a Qubit or equivalent.
• Samples contain contaminants.
  Bacterial samples should have 260/280 greater than 1.8, 260/230 2.0-2.2, no RNA, no denaturants, no detergents, no insoluble/colored/cloudy material, and no residual contaminants from the organism/tissue.
• Fragmented/degraded gDNA
  At least 50% of the DNA should be more than 15kb in length for the best bacterial genome sequencing outcome. Samples should be handled with utmost care (pipetting with wide-bore tips, minimal freeze/thaw cycles, no vortexing, no extreme temperature/pH, no intercalating dyes, no UV radiation, not over-dried).

To achieve optimal sequencing results, please follow our recommended bacterial sample prep and QC steps.

We will evaluate the results of the initial sequencing attempt to determine whether additional sequencing may produce a successful outcome, and if so we will perform additional sequencing at no additional charge. If we determine that additional sequencing would not produce a successful outcome (e.g. the input gDNA is too degraded, contains too much contaminating RNA or other compounds, etc.), then no repeat sequencing will be performed; in this case, if you would like more sequencing data, you will need to submit a new sequencing request and ship new samples.

This service is intended for a clonal population (single species) of bacteria. You can send mixtures of different bacterial species for sequencing, but since we can't predict the assembly outcome, it's at your own risk.

The total amount of raw data obtained for your sample will be divided up between however many species are present in your sample, thereby reducing each species’ own genome coverage and possibly inhibiting assembly of particular species in the sample. Re-sequencing mixtures won't change the relative proportions of the species, but you can submit multiple aliquots if you need higher total coverage. Ultimately, which species end up producing an assembly will vary depending on overall sample quality, coverage, and relative abundance/degradation of each species.

Sequencing is considered successful if we obtain either 210 Mb (standard service) or 360 Mb (big service) of raw data from your sample, even if it does not produce an assembly for your target species. If the pipeline does not produce an assembly for your target, you can attempt to bin the raw reads (~99.6% accurate) by species yourself prior to running your own assembly pipeline.

If your gDNA extract also contains native plasmid DNA, then yes, you will probably receive some sequencing reads for those plasmids. Input DNA fragments < 3kb are typically omitted during the sequencing process, but otherwise we do not filter out small plasmid-sized reads during assembly, so it is likely that they will also produce their own plasmid assemblies in addition to the gDNA assembly. Ultimately, which types of DNA in your sample end up producing an assembly will vary depending on overall sample quality, coverage, and relative abundance/degradation of each type of DNA.

Yes, any species can technically be sequenced and assembled with this method, but submitting samples for non-bacterial applications is at your own risk since we have not optimized the amount of data required for each specimen type and our assembly/annotation pipeline is targeted for bacteria. You may need to submit multiple aliquots of each sample in order to get enough genome coverage for these larger, more complex eukaryotic genomes (and note that you would need to combine data from all your aliquots prior to running your own assembly pipeline). If you are planning to submit a large number of samples for such an off-label application, contact us prior to submission to discuss options.

We are planning to add a yeast genome sequencing service in the near future that is specifically tailored to the data requirements and annotations for yeast. Stay tuned!

If you are interested in sequencing a known mixture (e.g. barcode or variant libraries), please send us an email at plasmids@snpsaurus.com to set up a custom project so that we can collect the amount of data you require.

Since our standard pipeline won't be able to generate a consensus from your mixture, we will send you the raw reads for you to analyze yourself; however we are able to use the newest ONT chemistry for these custom preps, which now delivers ~99.6% accurate raw reads!

Probably! Email us at plasmids@snpsaurus.com to discuss your project details.

In the vast majority of cases, we deliver custom sequencing results within 5-7 business days of receipt of your samples.

As per Oxford Nanopore’s specs for the chemistry and flowcells we currently use for custom project sequencing, the raw read accuracy is 99.6%. We do not provide a consensus or assembly for custom projects; we provide the raw reads and you need perform your own analyses, which will dictate your final data accuracy.

Custom projects require us tailoring our sequencing service to meet each customer's experimental needs. Please email us at plasmids@snpsaurus.com to discuss and initiate your project.

The cost for each custom sequencing project is calculated as follows:

Total data required = Number of samples x Insert length x Number of species/barcodes/variants x Coverage required per variant

Custom projects start at $750 for up to 2 Gb of total raw data and add $100 for each additional 1 Gb. There will also be a $25 per-sample barcoding surcharge added for projects with more than 1 sample. We will provide you a price estimate (and custom quote if you need it) when you email us to discuss your project.

We provide only the raw reads (~99.6% accurate) for custom projects. You would need to perform analyses (demultiplexing barcodes, generating consensus, aligning variants, etc.) yourself.