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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.