Can you perform CNV analysis?

Yes, our system can detect CNV on the same data. 

Which algorithm do you use to detect CNV?

  • OncoDNA uses an internal proprietary algorithm to detect CNV (i.e. OncoCNV).
  • This generally works by comparing your data to a baseline model (which is created using negative CNV samples).

What is a baseline and why do you need it to analyze CNV?

  • A baseline provides a reference model to the normal sample. The baseline is created from multiple samples with no known CNV using the same panel
  • CNV is then analyzed by comparing your dataset to a known dataset (baseline).

How many samples do you need to build the baseline for CNV analysis?

10-15 samples (~5 positive CNV and ~10 negative CNVs), is needed to build the baseline. The more samples, the better.

What is Gene "Uniformity"? 

  • Uniformity refers to how evenly distributed is the coverage for the gene.
  • The average number of bases that spans the coding exon is calculated for the base on the gene and the percentage of bases greater than 20% of the average is defined as the “Gene Uniformity”.

For example :

  • The beginning of the target might have high coverage with 1000x, but the end might only have 40x coverage = low uniformity
  • Uniformity of 90% means 90% of the bases (within a target) is within 20% of the average.

If you need any further information, please email us at: commercial_group@oncodna.com 

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