forestSV: structural variant discovery through statistical learning

forestSV: structural variant discovery through statistical learning by Jacob J Michaelson & Jonathan Sebat published in Nature Methods 9, 819–821 (2012).

Abstract

Detecting genomic structural variants from high-throughput sequencing data is a complex and unresolved challenge. We have developed a statistical learning approach, based on Random Forests, that integrates prior knowledge about the characteristics of structural variants and leads to improved discovery in high-throughput sequencing data. The implementation of this technique, forestSV, offers high sensitivity and specificity coupled with the flexibility of a data-driven approach.