3Cs: Clinical Data, Common Variants,
Conservation Data
Why do we need a pathogenicity prediction tool?
When a patient’s genetic information is analyzed using whole exome sequencing (WES), about 100,000 genetic variants are found. Approximately 5 million genetic variants are found with whole genome sequencing (WGS). However, for the majority of detected variants, there is insufficient evidence to determine whether the variant is disease-causing.
Pathogenicity prediction provides additional evidence for variant interpretation. To streamline this process, several prediction tools using AI technology have been developed and are utilized in the market.
3billion has developed 3Cnet, which shows more robust variant pathogenicity prediction and classification performance compared to currently available tools on the market. 3billion applies this powerful prediction tool to streamline accelerate diagnosis.
3Cnet, which has recently been updated to version 2, has expanded the types of variants that can be evaluated, including start-loss, stop-gain, stop-loss, in-frame deletion, frameshift, in-frame insertion, delins, duplication, 5' extension, and 3' extension. It is now possible to predict the pathogenicity of 99.99% of variants.
What makes 3Cnet different?
- 1. Clinical Data
- Information on pathogenic and benign variants from ClinVar database
- 2. Common Variants
- Information on common variants from GnomAD database
- 3. Conservation Data
- Information on variants reflecting evolutionary
3Cnet learns from three types of data to minimize bias: clinical data, common variant data, and conservation data. 3Cnet’s sensitivity is 2.2 times higher than that of other variant pathogenicity prediction tools.
＊Top-k recall shows each prediction tool’s probability of determining the correct disease-causing variant among the top-ranked variants.