Journal
NATURE METHODS
Volume 9, Issue 8, Pages 819-+Publisher
NATURE PORTFOLIO
DOI: 10.1038/NMETH.2085
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Funding
- US National Institutes of Health [HG005725, MH076431]
- Beyster Family Foundation
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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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