Feature-based classifiers for somatic mutation detection in tumour-normal paired sequencing data.

ABSTRACT:

Motivation

The study of cancer genomes now routinely involves using next-generation sequencing technology (NGS) to profile tumours for single nucleotide variant (SNV) somatic mutations. However, surprisingly few published bioinformatics methods exist for the specific purpose of identifying somatic mutations from NGS data and existing tools are often inaccurate, yielding intolerably high false prediction rates. As such, the computational problem of accurately inferring somatic mutations from paired tumour/normal NGS data remains an unsolved challenge.

Results

We present the comparison of four standard supervised machine learning algorithms for the purpose of somatic SNV prediction in tumour/normal NGS experiments. To evaluate these approaches (random forest, Bayesian additive

SUBMITTER: Ding J 

PROVIDER: S-EPMC3259434 | biostudies-literature | 2012 Jan

REPOSITORIES: biostudies-literature