MultiGeMS: detection of SNVs from multiple samples using model selection on high-throughput sequencing data.

ABSTRACT:

Motivation

Single nucleotide variant (SNV) detection procedures are being utilized as never before to analyze the recent abundance of high-throughput DNA sequencing data, both on single and multiple sample datasets. Building on previously published work with the single sample SNV caller genotype model selection (GeMS), a multiple sample version of GeMS (MultiGeMS) is introduced. Unlike other popular multiple sample SNV callers, the MultiGeMS statistical model accounts for enzymatic substitution sequencing errors. It also addresses the multiple testing problem endemic to multiple sample SNV calling and utilizes high performance computing (HPC) techniques.

Results

A simulation study demonstrates that MultiGeMS ranks highest in precision among a selection of popular multiple sa

SUBMITTER: Murillo GH 

PROVIDER: S-EPMC6280882 | biostudies-literature | 2016 May

REPOSITORIES: biostudies-literature