RAPTR-SV: A Hybrid Method for the Detection of Structural Variants

Date
2015-01-01
Authors
Bickhart, Derek
Reecy, James
Hutchison, Jana
Xu, Lingyang
Schnabel, Robert
Taylor, Jeremy
Reecy, James
Schroeder, Steven
Van Tassell, Curt
Sonstegard, Tad
Liu, George
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Animal Science
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Animal Science
Abstract

Identification of Structural Variants (SV) in sequence data results in a large number of false positive calls using existing software, which overburdens subsequent validation. Simulations using RAPTR-SV and other, similar algorithms for SV detection revealed that RAPTR-SV had superior sensitivity and precision, as it recovered 66.4% of simulated tandem duplications with a precision of 99.2%. When compared to calls made by Delly and LUMPY on available datasets from the 1000 genomes project, RAPTR-SV showed superior sensitivity for tandem duplications, as it identified two-fold more duplications than Delly, while making approximately 85% fewer duplication predictions. RAPTR-SV is written in Java and uses new features in the collections framework in the latest release of the Java version 8 language specifications. A compiled version of the software, instructions for usage and test results files are available on the GitHub repository page: https://github.com/njdbickhart/RAPTR-SV.

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This article is from Bioinformatics 31 (2015): 2084, doi:10.1093/bioinformatics/btv086.

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