title: forestSV: structural variant discovery through statistical learning.
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.
authors: Beyster Center for Molecular Genomics of Neuropsychiatric Diseases, University of California, San Diego, La Jolla, California, USA.
authors: Jacob J
authors: JJ
authors: Michaelson
authors: Jonathan
authors: J
authors: Sebat
mesh_terms: D004252:DNA Mutational Analysis; D003627:Data Interpretation, Statistical; D056914:Genomic Structural Variation / Q000235:genetics*; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods*; D006801:Humans; D012680:Sensitivity and Specificity