A Software Platform for Somatic Variant Calling using Next-generation Sequencing Data

Abstract

Recent advances in genome sequencing technologies provide unprecedented opportunities to characterize individual genomic landscapes and identify mutations relevant for diagnosis and therapy. Accurate detection of somatic mutation is an essential part of cancer genome analysis, and plays an important role in oncotarget identifications. Next generation sequencing (NGS) holds the promise to revolutionize somatic mutation detection. A lot of computational methods are developed for cancer sequencing data processing and analysis. However, few tools are specialized for cancer genome and sample characteristics because most methods initially focus on normal genome sequencing data. Here, we surveyed computational methods for detecting mutations for whole-genome/whole-exome cancer sequencing data analysis supporting four distinct mutation types: single nucleotide variants (SNVs), small insertions or deletions (Indels), copy number variations (CNVs), and large structural variants (SVs). We discuss the problems and challenges of current methods and also present a software platform for somatic variant calling that may improve calling accuracy and computational power. ⓒ 2015 DGIST