Create an account (if needed) and log in to complete the online order form or import the order information from an Excel file. Prepare your sample according to the Sample Preparation Guidelines. Take advantage of our local pick-up options, or ship to 5350 Partners Court, Suite C
Frederick, MD 21703.
OD260/280 = 1.80 – 1.90; Concentration: 30-100 ng/µl, 10 µl • For custom service sequencing of very large circular plasmids, ≥100 ng (30-150 kb) /µl, 10 µl, or ≥200 ng (150-300 kb) /µl, 10 µl.
Scientific rigor and peace of mind.
Identify any possible mutation sites outside of the gene analyzed by Sanger sequencing which could be occurred during cloning.
For target DNA insertion longer than 1 kb, instead of multiple Sanger runs or synthesizing a sequencing primer or doing primer walking, sequence the whole plasmid not only save time but also save cost.
Our service is intended for purified plasmids prepared from a clonal population of molecules. You can send mixtures of molecular species, but we can’t predict the analysis outcome, so it’s at your own risk. There two most common outcomes:
If your species are very similar (e.g. differ by only a few nucleotides), the pipeline will most likely create a single SampleName_contig.fas file (one contig) with low confidence positions at SNP/in/del locations. You can view those locations in your provided sampleName_contig_chrom.csv and sampleName_contig_lowc.csv.
If your species are sufficiently distinct (e.g. vastly different in size or sequence), the pipeline will most likely create more than one contigs, SampleName_contig001.fas, SampleName_contig002.fas etc. 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 contigs, 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 analyze the raw reads from your provided SampleName_reads.fastq.gz by your own tools.
: Six data files will be generated for each plasmid sample.
A Summary as “.pdf” – A report with the summary of results and data
One or more than one contigs as “contig.fas” – A continuous sequence with annotations resulting from the reassembly of the passed reads generated by the Nanopore sequencing analysis [Note: You may use SnapGene viewer (free download: https://www.snapgene.com/snapgene-viewer), or other software to visualize the annotation as a plasmid circle map or linear map to analyze the sequence]
One excel file as “contig._chrom.csv” – List of the whole sequence determined (open via Microsoft Excel)
Another excel file as “contig_low.csv” –List of nucleotides with low score/confidence (open via Microsoft Excel)
“Reads.fastq.gz” – fastq raw data file
“Sample_status.txt” – Data statistics and barcode number.
For plasmids, “failure” refers to the failure of your sample to produce a consensus sequence with at least 10x coverage. Our low sequencing prices and fast turnaround times do not include extensive QC to determine why plasmid samples fail. Although we do not provide definitive reasons for failure, by far the most common reasons are:
Samples are not prepared to meet with the required DNA concentration and quality, concentration ≥ 30 ng/µl, OD260/280 = 1.80 -1.90.
Samples contain a mixture of plasmid species and/or fragmented genomic DNA or fragmented plasmids. You may see evidence of this failure mode in a wide range of read lengths reported in the raw read length histogram.
It is relatively rare that we cannot return a plasmid sequence, but some rate of failure is unavoidable. We may atempt 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 un-sequenceable. Unfortunately, we must still charge for failed samples, since we spend more time and resources on them than we do on successes