First let’s define what a Structural Variation is. A structural variation (SV) is generally defined as a region of DNA that shows a change in copy number (deletions, insertions and duplications), orientation (inversions) or chromosomal location (translocations) between individuals. They can be balanced where there is no gain or loss of genetics materials (inversions or translocations) between chromosomes. They can also be unbalanced where a piece of the genome can be lost or duplicated, termed as CNV (copy number variation). Extreme cases of these unbalanced SV’s as an example of this would be if they are missing or have extra chromosomes often called aneuploidy.
Genomics researchers rely on a reference genome assembly to assign variants to their proper genomic context. A reference genome aka a reference assembly is a digital nucleic acid sequence database, assembled by scientists as a representative example of the set of genes in one idealized individual organism of a species. Genome assembly refers to the process of taking a large number of short DNA sequences and putting them back together to create a representation of the original chromosomes from which the DNA originated]. De novo genome assemblies assume no prior knowledge of the source DNA sequence length, layout or composition.
There are 25 different human populations across the globe, the reference dataset includes 64 assembled genomes from these populations. This is important because it captures the genetic differences based on these different populations. One would assume that many of these differences come from diet, climates and environments, also hereditary genetics based on their regional location.
This advancement has fueled the study of the genome and the structural variations within genetically-driven health conditions. That’s why certain populations may have a predisposition to diabetes, high blood pressure and heart disease. Diets or circumstances that may have for thousands of years impacted the genetics of that population, become known through these genomic studies. What these assembled genomes have taught us is monumental. And through this discovery a spin-off of genomic technology companies, cell and gene therapy/editing companies are racing to find the therapies and treatments to fight these rare and undiagnosed genetic diseases. These structural variations can be of key importance to detect mutations, prior to a company developing and marketing therapies for rare and undiagnosed diseases. Before any company can put a drug into a human body a series of steps, research, and documented works MUST be done. Sometimes the SV’s (structural variations) are missed prior to an IND filing that can make our break FDA approval. If they don’t make FDA approval years of scientific research can end up tossed into a bin.
KromaTiD analyzes structural variations and mutations with superiority, using a technology called dGH (directional Genome Hybridization). KromaTiD’s directional Genomic Hybridization™ (dGH) technology combines bioinformatics driven design of unique single-stranded synthetic probes with strand-specific hybridization strategies and is the only genomics approach capable of detecting DNA sequence, location and orientation in a single test.
dGH begins with bioinformatic analysis to identify probe target sequences. Next, chromosomal directionality and repeat regions within the target sequence are identified. The result is probes that are designed to hybridize solely to repeat free regions of the sequence of interest. With the ability to resolve very small inversions and translocations, dGH technology can be employed in both directed and de novo discovery studies, quickly providing data on new or variable rearrangements for your patient screening or target validation needs. If a complete genetic profile is required, pair dGH with NGS on the same samples to build a comprehensive mutation profile.
For inversions or translocations with known breakpoints, targeted dGH assays are an efficient method of screening large populations or libraries and can be multiplexed and automated to provide data on multiple aberrations in the same test.
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