Clinical Somatic Mutant Caller: detection of clinically relevant somatic mutations in NGS data.

Since the emergence of high-throughput genomic sequencing technologies, a huge volume of data has been accumulated on somatic mutations in cancer. Only limited number of mutations have been associated with a known clinical prognosis and can be used to determine accurate diagnosis and targeted treatment. These mutations are located in cancer gene “hot spots” and usually interfere with oncogene functionality. There are a number of tools available designated for somatic variation detection in NGS data, however most of the tools are quite complex: they require preprocessing of NGS data (alignment and quality check) and substantial computational resources. Despite the popularity of the available approaches, most of them have disadvantages that limit their scope of applicability: these tools detect wide variety of somatic mutations neglecting their clinical relevance. It makes it difficult to implement these approaches in clinics.

Here we developed a tool (CSMC) that detects a given list of interest of clinically relevant somatic mutations in NGS data from cancer sample in a simple and efficient way. CSMC does not require data preprocessing and can work with limited computational resources. It quantifies number of mutant reads and implements Poisson statistics to determine if the mutation is present in the sample. Тo test the applicability of our method, several datasets consisting of simulated and real NGS data from cancer samples were used. Comparison analysis showed that CSMC successfully detects all given somatic mutations, that are present in datasets.


   Полина Козюлина
   Максим Иванов
Время выполнения проекта: Feb 2017 — Jun 2017