A novel machine learning approach (svmSomatic) to distinguish somatic and germline mutations using next-generation sequencing data.

ABSTRACT: Somatic mutations are a large category of genetic variations, which play an essential role in tumorigenesis. Detection of somatic single nucleotide variants (SNVs) could facilitate downstream analysis of tumorigenesis. Many computational methods have been developed to detect SNVs, but most require normal matched samples to differentiate somatic SNVs from the normal state, which can be difficult to obtain. Therefore, developing new approaches for detecting somatic SNVs without matched samples are crucial. In this work, we detected somatic mutations from individual tumor samples based on a novel machine learning approach, svmSomatic, using next-generation sequencing (NGS) data. In addition, as somatic SNV detection can be impacted by multiple mutations, with germline mutations and co-occurre

SUBMITTER: Mao YF 

PROVIDER: S-EPMC7995270 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature