SVM²: An Improved Paired-End-Based Tool for the Detection of Small Genomic Structural Variations Using High-Throughput Single-Genome Resequencing Data

Nucleic Acids Res. 2012 Oct;40(18):e145. doi: 10.1093/nar/gks606. Epub 2012 Jun 25.

Abstract

Several bioinformatics methods have been proposed for the detection and characterization of genomic structural variation (SV) from ultra high-throughput genome resequencing data. Recent surveys show that comprehensive detection of SV events of different types between an individual resequenced genome and a reference sequence is best achieved through the combination of methods based on different principles (split mapping, reassembly, read depth, insert size, etc.). The improvement of individual predictors is thus an important objective. In this study, we propose a new method that combines deviations from expected library insert sizes and additional information from local patterns of read mapping and uses supervised learning to predict the position and nature of structural variants. We show that our approach provides greatly increased sensitivity with respect to other tools based on paired end read mapping at no cost in specificity, and it makes reliable predictions of very short insertions and deletions in repetitive and low-complexity genomic contexts that can confound tools based on split mapping of reads.

Publication types

  • Evaluation Study
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Genome, Human
  • Genomic Structural Variation*
  • Heterozygote
  • High-Throughput Nucleotide Sequencing*
  • Humans
  • Sequence Analysis, DNA*
  • Software*
  • Support Vector Machine