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PMID: 22751202 Published · epublish English Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't

forestSV: structural variant discovery through statistical learning.

Nature methods ·Vol. 9 ·No. 8 ·2012-07-01 ·Pages 819-21

Michaelson JJ, Sebat J

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.

MeSH Terms
DNA Mutational Analysis Data Interpretation, Statistical Genomic Structural Variation/genetics High-Throughput Nucleotide Sequencing/methods Humans Sensitivity and Specificity
Authors & Affiliations
2 authors, click to expand affiliations / ORCID
Michaelson Jacob J
Beyster Center for Molecular Genomics of Neuropsychiatric Diseases, University of California, San Diego, La Jolla, California, USA.
Sebat Jonathan
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Article Info
Journal
Nature methods
Abbr.
Nat Methods
ISSN
1548-7105
DOI
10.1038/nmeth.2085
Published
2012-07-01
Epub
2012-00-01
Pages
819-21
Language
English
Region
United States
NLM ID
101215604
PMCID
PMC3427657
Subset
IM
Grants
NIMH NIH HHS · R01 MH076431 · United States
NHGRI NIH HHS · U01 HG005725 · United States
NIMH NIH HHS · MH076431 · United States
NHGRI NIH HHS · HG005725 · United States
External Links
PubMed source DOI: 10.1038/nmeth.2085 PMC Full Text View journal details
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