Journal or Book Title
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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Bickhart, Derek M.; Hutchison, Jana L.; Xu, Lingyang; Schnabel, Robert; Taylor, Jeremy F.; Reecy, James M.; Schroeder, Steven; Van Tassell, Curt P.; Sonstegard, Tad S.; and Liu, George E., "RAPTR-SV: A Hybrid Method for the Detection of Structural Variants" (2015). Animal Science Publications. 157.