forestSV: structural variant discovery through statistical learning
Related Experiment Video
Updated: May 20, 2026

Following the Dynamics of Structural Variants in Experimentally Evolved Populations
Published on: February 3, 2023
Jacob J Michaelson1, Jonathan Sebat
1Beyster Center for Molecular Genomics of Neuropsychiatric Diseases, University of California, San Diego, La Jolla, California, USA.
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.
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