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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.

SUBMITTER: Michaelson JJ

PROVIDER: S-EPMC3427657 | biostudies-literature | 2012 Jul

REPOSITORIES: biostudies-literature

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forestSV: structural variant discovery through statistical learning.

Michaelson Jacob J JJ Sebat Jonathan J

Nature methods 20120701 8

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. ...[more]

PMID: 22751202

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