Journal article
forestSV: structural variant discovery through statistical learning
Nature methods, Vol.9(8), pp.819-821
08/2012
DOI: 10.1038/nmeth.2085
PMCID: PMC3427657
PMID: 22751202
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, which 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.
Details
- Title: Subtitle
- forestSV: structural variant discovery through statistical learning
- Creators
- Jacob J Michaelson - Beyster Center for Molecular Genomics of Neuropsychiatric Diseases, University of California, San Diego, La Jolla, California, USAJonathan Sebat - Beyster Center for Molecular Genomics of Neuropsychiatric Diseases, University of California, San Diego, La Jolla, California, USA
- Resource Type
- Journal article
- Publication Details
- Nature methods, Vol.9(8), pp.819-821
- DOI
- 10.1038/nmeth.2085
- PMID
- 22751202
- PMCID
- PMC3427657
- ISSN
- 1548-7091
- eISSN
- 1548-7105
- Language
- English
- Date published
- 08/2012
- Academic Unit
- Roy J. Carver Department of Biomedical Engineering; Communication Sciences and Disorders; Psychiatry; Iowa Neuroscience Institute
- Record Identifier
- 9984070344802771
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