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RAPTR-SV: a hybrid method for the detection of structural variants

Derek M. Bickhart, Jana L. Hutchison, Lingyang Xu, Robert D. Schnabel, Jeremy F. Taylor, James M. Reecy, Steven Schroeder, Curt P. Van Tassell, Tad S. Sonstegard, George E. Liu
Bioinformatics 2015 v.31 no.13 pp. 2084-2090
algorithms, computer analysis, computer software, data collection, genetic techniques and protocols, genome, prediction
Motivation: Identification of structural variants (SVs) in sequence data results in a large number of false positive calls using existing software, which overburdens subsequent validation. Results: 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 with 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 2-fold more duplications than Delly, while making ∼85% fewer duplication predictions. Availability and implementation: 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.