Why Ranomics

Not all clinically actionable information is readily available.

The rapid acceleration of genetic testing has been accompanied by the exponential growth in the number of novel genetic variants-- or to be more precise, the number of variants with no publicly available evidence of their clinical consequence or impact on protein function.

Public disease-gene databases are wholly dependent on individual labs volunteering their own data. Unfortunately, these databases are infrequently updated and don’t represent the full collective knowledge on genetic variants.This means that most labs are left to perform time-consuming and ultimately unsuccessful searches for information on seemingly “novel” variants.

At Ranomics, we know that you need information on every variant, especially when it’s never been previously seen in a clinical case. That’s why our knowledgebase provides functional evidence on every missense variant in commonly tested for genes like BRCA1 and TP53.

Reliable functional information has been hard to come by.

The prevalence of dissimilar, unvalidated research methodologies used across academic research institutions has created a bottleneck between research utility and clinical adoption. As a result, very little of the functional data published on rare variants can be relied on for clinical application.

Only by standardizing functional assay data acquisition, processing, and reporting can that bottleneck be removed.

Using our high-throughput variant analysis technology, we synthesize every missense variant of an indicated gene, then test each variant’s impact on protein function in cellular models specific to an indicated disease or biological activity.

Because the impact of every variant in the Ranomics knowledgebase is benchmarked against clinically-validated missense variants of the same gene, you can reliably apply our functional designations when assessing pathogenicity.

Predictive algorithms actually aren’t all that predictive.

In the face of disparate clinical information and unverified research studies, even algorithmic methods of predicting a variant’s functional impact have fallen short.

In silico tools like PolyPhen and SIFT are often inaccurate and contradictory, and are widely considered low-weight evidence in various classification systems.

Observing a variant’s impact in representative in vitro models provides a clearer, more accurate picture of its role in disease pathogenesis.

Ranomics provides real-world functional impact information on thousands of high, medium, and low-penetrance missense variants. When validated against established benign and pathogenic variants, Ranomics’ functional evidence proves over 95% accurate, compared to 60% for PolyPhen.

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