A Classification Approach for Genome Structural Variations Detection

Abstract

Background: Finding accurate genome structural variations (SVs) is important for understanding phenotype diversity and complex diseases. Limited research using classification to find SVs from next-generation sequencing is available. Additionally, the existing algorithms are mainly dependent on an analysis of the alignment signatures of paired-end reads for the prediction of different types of variations. Here, the candidate SV regions and their features are computed using single reads only. Classification is used to predict the variation types of these regions.