THE BIORNA QUANTICS
- The most commonly used method of genetic testing, SNP analysis (used by other genetic testing companies), and a narrative on the proprietary BIORNA QUANTICS FULL SEQUENCE DNA testing method and how it provides the most accurate information available in the market for genetic data analysis and thorough reporting
- A graphic comparison of the two methods of genetic testing and analysis
- A generalized outline of the proprietary BIORNA QUANTICS quantification and normalization of the genomic DNA from each client, which widely differentiates BIORNA QUANTICS from the competition
The process to obtain SNP data has several stages, including collection of a sample, purification of DNA, amplification of the target loci, sequence analysis, and data management
Each of these steps is subject to error associated with the cloning process, sequence reads, mapping of the sequence, and reliability of the reference genome
The highest sources of error rate in SNPs are due to erroneous realignment in low complexity regions and the potential of an incomplete reference genome with respect to the sample. Errors were found in the raw genotype call as high as 1 in 10-15 kb and a post-filtered error rate reduced to 1 in 100-200 kb. While a significant improvement, even this error rate can prove significant in a 3 billion base pair genome.
While there are several dozen peer reviewed articles describing the need for improved computer algorithms there still exist other areas that cannot be remedied by software improvement. For example, individual runs can contain unexpectedly high levels of error due to low quality DNA extractions
When conducting genomic studies on a population of even moderate size, these error rates can be overcome by the number of samples in the test group to account for outliers.
However, when conducting genetic tests on individuals, it is of utmost importance that the procedure used be highly accurate and redundancy built into the test parameters to insure as much accuracy as possible.
In our protocols, we use primers we designed specifically to target DNA to amplify and sequence the DNA material being tested. Amplification of DNA using PCR can introduce mismatched base pairs into a DNA sequence leading to erroneous data. At BIORNA QUANTICS, we greatly minimise this event using a high fidelity polymerase with proof-reading ability that has an extremely low error rate; to date, this enzyme has been used to amplify DNA for cloning in hundreds of constructs in SIMPLIFIED GENETICS™ Laboratory led by Dr. Cooper and it has always provided accurate results.
Laboratory trials and research, demonstrate the importance of high quality and quantity of DNA. This DNA concentration between individuals can vary. Using too little or too much DNA can result in poor quality data, so the quantity for each sample is determined and, if needed, normalized to fall within a range we have determined optimal for the tests conducted in our laboratory.
At BIORNA QUANTICS we recognize the extremely useful data that can be obtained through SNP analysis but we also understand its limitations, which is why we developed our proprietary approach using Sanger sequencing on all of our samples. Like SNP analysis, Sanger sequencing could produce a false positive or negative if only one DNA strand were sequenced. Therefore, we sequence both DNA to insure the base-calling on each strand is in agreement; by sequencing each strand there is an internal control for each sample.
The measures described above add cost to the BIORNA QUANTICS testing methods. However, the extra steps taken in testing the DNA from each patient provides a high level of confidence in every report we send out. There is currently no concussion precursor company in the market that can provide the same level of quality, accuracy and price as SIMPLY SAFE™ and SIMPLY FIT™.
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2. Bioinformatics at COMAV. The COMAV Institute. 2015. https://bioinf.comav.upv.es/courses/sequence_analysis/snp_calling.html
3. Li, H. 2014. Towards Better Understanding of Artifacts in Variant Calling from High-Coverage Samples. Bioinformatics 30:2843-51
4. Farrer, R.A., D.A. Henk, D. macLean, D.J. Studholme, and M.C. Fisher. 2013. Using false discovery rates to benchmark SNP-callers in next-generation sequencing projects. Nature Scientific Reports 3:1512
Brown TA (2002). “Section 2, Chapter 6: 6.1. The Methodology for DNA Sequencing”. Genomes 2 (2nd ed.). Oxford: Bios. ISBN 1-859a96-228-9.