ISOWN: accurate somatic mutation identification in the absence of normal tissue controls.

ABSTRACT:

Background

A key step in cancer genome analysis is the identification of somatic mutations in the tumor. This is typically done by comparing the genome of the tumor to the reference genome sequence derived from a normal tissue taken from the same donor. However, there are a variety of common scenarios in which matched normal tissue is not available for comparison.

Results

In this work, we describe an algorithm to distinguish somatic single nucleotide variants (SNVs) in next-generation sequencing data from germline polymorphisms in the absence of normal samples using a machine learning approach. Our algorithm was evaluated using a family of supervised learning classifications across six different cancer types and ~1600 samples, including cell lines, fresh frozen tissues, and

SUBMITTER: Kalatskaya I 

PROVIDER: S-EPMC5490163 | biostudies-literature | 2017 Jun

REPOSITORIES: biostudies-literature