OncoGenomics

Background

Structural variants (SVs) & Single nucleotide polymorphisms (SNPs) are a hallmark of the genomic instability that underlies cancer and other diseases, include translocations, large deletions, amplifications, and inversions. The accurate and timely detection of cancer associated mutations, including structural variants, is important for patient management, from early detection to monitoring for cellular relapse, as well as predicting outcome of treatment. Detection of all cancer associated mutations is complicated by the low tumor cellularity often present in tumor samples and biopsies due to contaminating normal cells.

What we do?

Tumor-associated structural variants are complicated when they occur in repetitive regions, which account for over half of the human genome. The ability of the sequencing technologies we use, to read through repetitive regions could make it an ideal tool for detecting tumor-associated structural variants. The longread nature (up to 20 kb) of third generation sequencing allows us to read through repetitive regions. Even with long mate-pair sequencing at deep coverage, 2nd generation methods’ shortread sequences prohibit accurate and efficient mapping of repetitive regions, which often house SVs. After sequencing, we perform AI-enhanced analysis to give you an insight towards all mutations present in the targeted gene, we generate an AI-enhanced report with full list of benign, potentially pathogenic, and pathogenic mutations, along with mutations that can be targeted by genomic drugs. We also provide the possibility to store mutations linked to individuals, in order to provide better genetic counselling for offspring and map the mutations throughout the entire family tree, and also map mutations throughout the entire country to provide basis for GWAS studies.
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