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Applications in:
Non-Coding Genomics

Over 95% of disease and trait related variants are found in the non-coding genome. However, to identify the important causal variants amongst the hundreds of non-functional ones is a major challenge because for non-coding variants we cannot deduce functionality from sequence alone.  Using our SuRE™ screens we obtain a functional read-out for millions of non-coding variants in parallel.

Assay the functional impact of non-coding mutations for entire genomes or specific mutation panels

 

Most studies investigating the relationship between genetics and traits or diseases are based on correlation between genotypes and phenotypes over many individuals (e.g. GWAS studies). Typically the resolution of such studies is in the order tens of kilobases and rather than identifying single causal variants, groups of ~100 variants are found amongst which the causal variant needs to be identified. While for variants in the coding part of the genome we can predict the functional outcome, we cannot make such predictions for non-coding variants based on sequence alone. Using SuRE™ we can functionally asses millions of variants for their impact on promoter or enhancer activity. Essentially this provides a filter to go from thousands of significant variants to a handful that can then be extensively studied.

Examples of projects in this space are:

  1. Large-scale screens on entire genomes. This quickly generates a large database with millions of functionally annotated variants and could lead to the identification of variants that are important for these particular individuals. 
     

  2. Focused screens where we analyze variants (100-100,000) that are of specific interest to the customer.
     

  3. Saturating mutagenesis screens in which we focus on several regulatory elements and study the impact of thousands of variants in these elements (see figure above). 

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To get a feel of the type of datasets we generate, feel free to explore the beta-version of our public browser: please click here.

The Value of Functional Genome Annotation

Having genome-specific regulatory maps can help identify functional mutations among millions of non-functional ones.

95%

More than 95% of variants associated to traits and diseases are found in the non-coding genome.

Typically the resolution of studies trying to associate genotype with phenotype is in the order of tens of kilobases – far to low to pinpoint the causal variant or mutation.

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SuRE can screen entire genomes at a resolution smaller than 300bp for mutations that affect the activity of the 2 main types of regulatory elements: promoters and enhancers